From 7b83b7900f26620238d0fc9d66423ac76a178469 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 10:55:50 -0700 Subject: [PATCH 01/16] Add harness-engineering-bench (benchmarks at top level) Bring the benchmark harness with each benchmark as a top-level sibling of gaia/ (ale-bench, officeqa, swe-atlas-qna, tau3), flattening the former candidates/ grouping. --- harness-engineering-bench/README.md | 43 + .../ale-bench/.gitignore | 2 + .../ale-bench/environment/Dockerfile | 19 + .../environment/agent-baseline/solution.cpp | 8 + .../environment/agent-seed/solution.cpp | 8 + .../ale-bench/environment/docker-compose.yaml | 60 + .../ale-bench/environment/main/seed.sh | 12 + .../ale-bench/environment/sidecar/Dockerfile | 42 + .../environment/sidecar/ale_factory.py | 153 + .../environment/sidecar/dind-entrypoint.sh | 27 + .../environment/sidecar/harness/config.json | 6 + .../environment/sidecar/harness/score.py | 144 + .../ale-bench/environment/sidecar/serve.json | 1 + .../ale-bench/instruction.md | 498 +++ .../ale-bench/solution/solve.sh | 6 + harness-engineering-bench/ale-bench/task.toml | 15 + .../ale-bench/tests/test.sh | 5 + harness-engineering-bench/gaia/README.md | 39 + .../gaia/baseline/.gitignore | 4 + .../gaia/baseline/README.md | 82 + .../gaia/baseline/build.yaml | 89 + .../gaia/baseline/secrets.env.example | 15 + .../gaia/baseline/target/pyproject.toml | 25 + .../target/src/gaia_agent/__init__.py | 5 + .../baseline/target/src/gaia_agent/agent.py | 275 ++ .../gaia/baseline/target/tests/test_agent.py | 72 + .../gaia/baseline/target/uv.lock | 2127 ++++++++++++ .../gaia/partitions/development.json | 35 + .../gaia/partitions/manifest.json | 1187 +++++++ .../gaia/partitions/test.json | 68 + .../gaia/partitions/validation.json | 68 + .../gaia/scripts/partition_gaia.py | 263 ++ harness-engineering-bench/officeqa/.gitignore | 7 + .../officeqa/baseline/build.yaml | 84 + .../officeqa/baseline/target/pyproject.toml | 25 + .../target/src/officeqa_agent/__init__.py | 5 + .../target/src/officeqa_agent/agent.py | 279 ++ .../baseline/target/tests/test_agent.py | 72 + .../officeqa/baseline/target/uv.lock | 2129 ++++++++++++ .../officeqa/partitions/development.json | 51 + .../officeqa/partitions/manifest.json | 264 ++ .../officeqa/partitions/test.json | 101 + .../officeqa/partitions/validation.json | 100 + .../officeqa/scripts/vendor_tasks.sh | 56 + .../scripts/partition_dataset.py | 313 ++ .../swe-atlas-qna/README.md | 26 + .../swe-atlas-qna/baseline/.gitignore | 2 + .../swe-atlas-qna/baseline/README.md | 24 + .../swe-atlas-qna/baseline/build.yaml | 78 + .../baseline/target/pyproject.toml | 25 + .../target/src/atlas_agent/__init__.py | 5 + .../baseline/target/src/atlas_agent/agent.py | 215 ++ .../baseline/target/tests/test_agent.py | 63 + .../swe-atlas-qna/baseline/target/uv.lock | 2127 ++++++++++++ .../swe-atlas-qna/partitions/development.json | 27 + .../swe-atlas-qna/partitions/manifest.json | 1152 +++++++ .../swe-atlas-qna/partitions/test.json | 52 + .../swe-atlas-qna/partitions/validation.json | 51 + harness-engineering-bench/tau3/README.md | 22 + .../tau3/baseline/.gitignore | 2 + .../tau3/baseline/README.md | 25 + .../tau3/baseline/build.yaml | 90 + .../tau3/baseline/target/pyproject.toml | 25 + .../target/src/tau3_agent/__init__.py | 5 + .../baseline/target/src/tau3_agent/agent.py | 382 +++ .../tau3/baseline/target/tests/test_agent.py | 236 ++ .../tau3/baseline/target/uv.lock | 2127 ++++++++++++ .../tau3/partitions/development.json | 77 + .../tau3/partitions/manifest.json | 3029 +++++++++++++++++ .../tau3/partitions/test.json | 152 + .../tau3/partitions/validation.json | 152 + 71 files changed, 19060 insertions(+) create mode 100644 harness-engineering-bench/README.md create mode 100644 harness-engineering-bench/ale-bench/.gitignore create mode 100644 harness-engineering-bench/ale-bench/environment/Dockerfile create mode 100644 harness-engineering-bench/ale-bench/environment/agent-baseline/solution.cpp create mode 100644 harness-engineering-bench/ale-bench/environment/agent-seed/solution.cpp create mode 100644 harness-engineering-bench/ale-bench/environment/docker-compose.yaml create mode 100755 harness-engineering-bench/ale-bench/environment/main/seed.sh create mode 100644 harness-engineering-bench/ale-bench/environment/sidecar/Dockerfile create mode 100644 harness-engineering-bench/ale-bench/environment/sidecar/ale_factory.py create mode 100644 harness-engineering-bench/ale-bench/environment/sidecar/dind-entrypoint.sh create mode 100644 harness-engineering-bench/ale-bench/environment/sidecar/harness/config.json create mode 100644 harness-engineering-bench/ale-bench/environment/sidecar/harness/score.py create mode 100644 harness-engineering-bench/ale-bench/environment/sidecar/serve.json create mode 100644 harness-engineering-bench/ale-bench/instruction.md create mode 100755 harness-engineering-bench/ale-bench/solution/solve.sh create mode 100644 harness-engineering-bench/ale-bench/task.toml create mode 100755 harness-engineering-bench/ale-bench/tests/test.sh create mode 100644 harness-engineering-bench/gaia/README.md create mode 100644 harness-engineering-bench/gaia/baseline/.gitignore create mode 100644 harness-engineering-bench/gaia/baseline/README.md create mode 100644 harness-engineering-bench/gaia/baseline/build.yaml create mode 100644 harness-engineering-bench/gaia/baseline/secrets.env.example create mode 100644 harness-engineering-bench/gaia/baseline/target/pyproject.toml create mode 100644 harness-engineering-bench/gaia/baseline/target/src/gaia_agent/__init__.py create mode 100644 harness-engineering-bench/gaia/baseline/target/src/gaia_agent/agent.py create mode 100644 harness-engineering-bench/gaia/baseline/target/tests/test_agent.py create mode 100644 harness-engineering-bench/gaia/baseline/target/uv.lock create mode 100644 harness-engineering-bench/gaia/partitions/development.json create mode 100644 harness-engineering-bench/gaia/partitions/manifest.json create mode 100644 harness-engineering-bench/gaia/partitions/test.json create mode 100644 harness-engineering-bench/gaia/partitions/validation.json create mode 100644 harness-engineering-bench/gaia/scripts/partition_gaia.py create mode 100644 harness-engineering-bench/officeqa/.gitignore create mode 100644 harness-engineering-bench/officeqa/baseline/build.yaml create mode 100644 harness-engineering-bench/officeqa/baseline/target/pyproject.toml create mode 100644 harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/__init__.py create mode 100644 harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/agent.py create mode 100644 harness-engineering-bench/officeqa/baseline/target/tests/test_agent.py create mode 100644 harness-engineering-bench/officeqa/baseline/target/uv.lock create mode 100644 harness-engineering-bench/officeqa/partitions/development.json create mode 100644 harness-engineering-bench/officeqa/partitions/manifest.json create mode 100644 harness-engineering-bench/officeqa/partitions/test.json create mode 100644 harness-engineering-bench/officeqa/partitions/validation.json create mode 100755 harness-engineering-bench/officeqa/scripts/vendor_tasks.sh create mode 100644 harness-engineering-bench/scripts/partition_dataset.py create mode 100644 harness-engineering-bench/swe-atlas-qna/README.md create mode 100644 harness-engineering-bench/swe-atlas-qna/baseline/.gitignore create mode 100644 harness-engineering-bench/swe-atlas-qna/baseline/README.md create mode 100644 harness-engineering-bench/swe-atlas-qna/baseline/build.yaml create mode 100644 harness-engineering-bench/swe-atlas-qna/baseline/target/pyproject.toml create mode 100644 harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/__init__.py create mode 100644 harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py create mode 100644 harness-engineering-bench/swe-atlas-qna/baseline/target/tests/test_agent.py create mode 100644 harness-engineering-bench/swe-atlas-qna/baseline/target/uv.lock create mode 100644 harness-engineering-bench/swe-atlas-qna/partitions/development.json create mode 100644 harness-engineering-bench/swe-atlas-qna/partitions/manifest.json create mode 100644 harness-engineering-bench/swe-atlas-qna/partitions/test.json create mode 100644 harness-engineering-bench/swe-atlas-qna/partitions/validation.json create mode 100644 harness-engineering-bench/tau3/README.md create mode 100644 harness-engineering-bench/tau3/baseline/.gitignore create mode 100644 harness-engineering-bench/tau3/baseline/README.md create mode 100644 harness-engineering-bench/tau3/baseline/build.yaml create mode 100644 harness-engineering-bench/tau3/baseline/target/pyproject.toml create mode 100644 harness-engineering-bench/tau3/baseline/target/src/tau3_agent/__init__.py create mode 100644 harness-engineering-bench/tau3/baseline/target/src/tau3_agent/agent.py create mode 100644 harness-engineering-bench/tau3/baseline/target/tests/test_agent.py create mode 100644 harness-engineering-bench/tau3/baseline/target/uv.lock create mode 100644 harness-engineering-bench/tau3/partitions/development.json create mode 100644 harness-engineering-bench/tau3/partitions/manifest.json create mode 100644 harness-engineering-bench/tau3/partitions/test.json create mode 100644 harness-engineering-bench/tau3/partitions/validation.json diff --git a/harness-engineering-bench/README.md b/harness-engineering-bench/README.md new file mode 100644 index 00000000..91bae5dd --- /dev/null +++ b/harness-engineering-bench/README.md @@ -0,0 +1,43 @@ +# Harness engineering benchmarks + +`harness-engineering-bench` contains end-to-end benchmarks for automatically +improving the harness of an agent or the code of a component used to build +agents. Each leaf directory pairs one editable target program with one immutable +Harbor dataset and compiles them into an outer Harbor optimization task. + +The benchmark definitions intentionally keep three boundaries visible: + +- `target/` is the program the optimization agent may edit. +- `partitions/` pins the cases and the development/validation/test split. +- `build.yaml` is trusted configuration: model, evaluator, access policy, + budgets, and final scoring. + +In each benchmark, the complete development tasks and attachments are mounted +read-only for the optimization agent. Development evaluations expose per-case +results and complete Harbor trial records, including exact failures and +target-agent logs. Validation remains aggregate-only, and +test is reachable only by the trusted final verifier. + +The paper-era benchmark stack remains available on the `paper/v1` branch and +the `paper-v1` tag. New benchmarks should use this Harbor-native layout. + +## Benchmarks + +Promoted benchmarks live at the top level. Task sets still under review live in +`candidates/`; we work through the list in the paper's `benchmark-scoping.md` and +promote a task set to the top level once it is ready. + +### Promoted + +| Benchmark | Editable target | Dataset | Split | +| --- | --- | --- | --- | +| [GAIA baseline](gaia/baseline/) | Tool-using Responses API agent | Harbor `gaia/gaia` | 20% / 40% / 40% | + +### Candidates + +| Benchmark | Editable target | Dataset | Split | +| --- | --- | --- | --- | +| [SWE-Atlas-QnA baseline](candidates/swe-atlas-qna/baseline/) | Codebase investigation agent | Harbor `scale-ai/swe-atlas-qna` | 20% / 40% / 40% | +| [tau3 baseline](candidates/tau3/baseline/) | MCP customer-service agent | Harbor `sierra-research/tau3-bench` | 20% / 40% / 40% | +| [OfficeQA baseline](candidates/officeqa/baseline/) | Grounded document-QA agent | Treasury Bulletin corpus | 20% / 40% / 40% | +| [ALE-Bench (ahc011)](candidates/ale-bench/) | C++ solver program (component, not an agent) | ALE-Bench AHC problem | public / private seeds | diff --git a/harness-engineering-bench/ale-bench/.gitignore b/harness-engineering-bench/ale-bench/.gitignore new file mode 100644 index 00000000..9c185ae7 --- /dev/null +++ b/harness-engineering-bench/ale-bench/.gitignore @@ -0,0 +1,2 @@ +environment/vero/ +runs/ diff --git a/harness-engineering-bench/ale-bench/environment/Dockerfile b/harness-engineering-bench/ale-bench/environment/Dockerfile new file mode 100644 index 00000000..9f2e4e6f --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/Dockerfile @@ -0,0 +1,19 @@ +# Main service: the optimizer workbench a coding agent works in. +# Holds the editable target (solution.cpp) plus the VeRO CLI so the agent can +# call `vero harbor eval` / `vero harbor status` against the trusted sidecar. +FROM ghcr.io/astral-sh/uv:python3.12-bookworm + +RUN apt-get update \ + && apt-get install -y --no-install-recommends git ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +# VeRO is not on public PyPI; vendor it into the build context first +# (`cp -r /vero environment/vero`) then install it. +COPY vero /opt/vero +RUN uv pip install --system "/opt/vero[harbor]" + +COPY agent-seed /opt/agent-seed +COPY main/seed.sh /opt/seed.sh +RUN chmod +x /opt/seed.sh && useradd -m -u 1001 agent + +WORKDIR /work/agent diff --git a/harness-engineering-bench/ale-bench/environment/agent-baseline/solution.cpp b/harness-engineering-bench/ale-bench/environment/agent-baseline/solution.cpp new file mode 100644 index 00000000..3ba34998 --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/agent-baseline/solution.cpp @@ -0,0 +1,8 @@ +#include +int main(){ + std::ios::sync_with_stdio(false); + std::string line; + while (std::getline(std::cin, line)) {} + std::cout << "\n"; + return 0; +} diff --git a/harness-engineering-bench/ale-bench/environment/agent-seed/solution.cpp b/harness-engineering-bench/ale-bench/environment/agent-seed/solution.cpp new file mode 100644 index 00000000..3ba34998 --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/agent-seed/solution.cpp @@ -0,0 +1,8 @@ +#include +int main(){ + std::ios::sync_with_stdio(false); + std::string line; + while (std::getline(std::cin, line)) {} + std::cout << "\n"; + return 0; +} diff --git a/harness-engineering-bench/ale-bench/environment/docker-compose.yaml b/harness-engineering-bench/ale-bench/environment/docker-compose.yaml new file mode 100644 index 00000000..704a03fa --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/docker-compose.yaml @@ -0,0 +1,60 @@ +# Harbor merges this after its generated main-service configuration. +services: + main: + command: ["/opt/seed.sh"] + environment: + VERO_EVAL_URL: "http://eval-sidecar:8000" + OPENAI_API_KEY: "${OPENAI_API_KEY:?OPENAI_API_KEY must be set}" + OPENAI_BASE_URL: "${OPENAI_BASE_URL:?OPENAI_BASE_URL must be set}" + volumes: + - agent_repo:/work/agent + - token_state:/state/token:ro + depends_on: + eval-sidecar: + condition: service_healthy + + eval-sidecar: + build: + context: . + dockerfile: sidecar/Dockerfile + # Runs its own Docker daemon (DinD) so ALE-Bench's sibling judge/tool + # containers share this container's filesystem — required for their bind + # mounts to resolve. Needs privileged + host cgroup ns (nested cgroup v2 + # otherwise fails: "cannot enter cgroupv2 ... it is in threaded mode"). + privileged: true + cgroup: host + command: + - "vero" + - "harbor" + - "serve" + - "--factory" + - "ale_factory:build" + - "--config" + - "/opt/serve.json" + - "--admin-token" + - "/state/token/admin.token" + environment: + # ALE-Bench talks to the in-container dockerd started by the entrypoint. + DOCKER_HOST: "unix:///var/run/docker.sock" + ALE_BENCH_DATA: "/state/ale-data" + volumes: + - agent_repo:/work/agent:ro + - admin_state:/state/admin + - token_state:/state/token + - ale_data:/state/ale-data + # DinD storage (avoids overlay-on-overlay issues for the inner daemon). + - docker_storage:/var/lib/docker + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"] + interval: 5s + timeout: 10s + # The entrypoint starts dockerd + pulls the ~4GB judge image before serving. + retries: 40 + start_period: 180s + +volumes: + agent_repo: + admin_state: + token_state: + ale_data: + docker_storage: diff --git a/harness-engineering-bench/ale-bench/environment/main/seed.sh b/harness-engineering-bench/ale-bench/environment/main/seed.sh new file mode 100755 index 00000000..3264b668 --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/main/seed.sh @@ -0,0 +1,12 @@ +#!/bin/sh +set -eu +if [ ! -d /work/agent/.git ]; then + cp -a /opt/agent-seed/. /work/agent/ + cd /work/agent + git init -q + git add -A + git -c user.email=seed@vero.test -c user.name=seed commit -qm "baseline" +fi +find /work/agent -exec chown agent:agent {} + +git config --system --add safe.directory /work/agent +exec sleep infinity diff --git a/harness-engineering-bench/ale-bench/environment/sidecar/Dockerfile b/harness-engineering-bench/ale-bench/environment/sidecar/Dockerfile new file mode 100644 index 00000000..177b0351 --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/sidecar/Dockerfile @@ -0,0 +1,42 @@ +# Trusted evaluation sidecar. It owns scoring, budget, disclosure, and final +# selection. The inner loop is a plain CommandBackend (harness/score.py), which +# calls ALE-Bench's deterministic judge. ALE-Bench spawns the C++ judge + its +# Rust-tool builder as sibling Docker containers with bind mounts, so this +# container runs its OWN Docker daemon (DinD) — the sibling containers must share +# this container's filesystem for their mounts to resolve. Requires privileged. +FROM ghcr.io/astral-sh/uv:python3.12-bookworm + +RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + git ca-certificates curl \ + docker.io iptables \ + libcairo2 libffi8 \ + && rm -rf /var/lib/apt/lists/* + +# VeRO (not on public PyPI): vendor into the build context, then install. +COPY vero /opt/vero +RUN uv pip install --system "/opt/vero[harbor]" + +# ALE-Bench toolkit (the judge orchestration + scoring). +RUN uv pip install --system "git+https://github.com/SakanaAI/ALE-Bench.git" + +COPY agent-baseline /opt/agent-baseline +COPY sidecar/ale_factory.py /opt/ale_factory.py +COPY sidecar/harness /opt/harness +COPY sidecar/serve.json /opt/serve.json +COPY sidecar/dind-entrypoint.sh /opt/dind-entrypoint.sh +RUN chmod +x /opt/dind-entrypoint.sh + +RUN cd /opt/agent-baseline \ + && git init -q \ + && git add -A \ + && git -c user.email=baseline@vero.test -c user.name=baseline commit -qm "baseline" \ + && git config --system --add safe.directory /opt/agent-baseline \ + && git config --system --add safe.directory /work/agent \ + && git config --system --add safe.directory /work/agent/.git + +ENV PYTHONPATH=/opt +WORKDIR /opt +# Start the in-container Docker daemon + bootstrap the judge image, then exec the +# serve command passed by docker-compose. +ENTRYPOINT ["/opt/dind-entrypoint.sh"] diff --git a/harness-engineering-bench/ale-bench/environment/sidecar/ale_factory.py b/harness-engineering-bench/ale-bench/environment/sidecar/ale_factory.py new file mode 100644 index 00000000..7cffd178 --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/sidecar/ale_factory.py @@ -0,0 +1,153 @@ +"""Custom Harbor sidecar factory for ALE-Bench (program optimization). + +vero drives the OUTER loop: a coding agent edits /work/agent/solution.cpp to +maximize the ALE-Bench score for a fixed AHC problem. Each candidate is scored by +a plain CommandBackend (harness/score.py -> ALE-Bench's deterministic judge), +NOT a nested `harbor run`. Disclosure maps onto ALE-Bench's public/private split: +development+validation are scored on public seeds, test on private seeds. +""" + +from __future__ import annotations + +from pathlib import Path + +from vero.candidate_repository import GitCandidateRepository +from vero.evaluation import ( + BackendRegistry, + BudgetLedger, + CaseRange, + EvaluationBudget, + EvaluationDatabase, + EvaluationSet, + Evaluator, + MetricSelector, + ObjectiveSpec, +) +from vero.evaluation.command import CommandBackend, CommandBackendConfig +from vero.evaluation.engine import EvaluationEngine +from vero.harbor.serve import SidecarComponents +from vero.harbor.sidecar import EvaluationSidecar, SidecarEvaluationPolicy +from vero.harbor.transport import GitCandidateTransport +from vero.harbor.verifier import CanonicalVerifier, VerificationSelection, VerificationTarget +from vero.sandbox import LocalSandbox +from vero.workspace import GitWorkspace + +SET = "ale-bench" +# ahc011 is a maximization problem; ALE-Bench zeroes invalid/CE/TLE submissions. +OBJ = ObjectiveSpec( + selector=MetricSelector(metric="score"), + direction="maximize", + failure_value=0.0, +) + + +def _backend(harness_root: str) -> CommandBackend: + return CommandBackend( + CommandBackendConfig( + harness_root=harness_root, + command=[ + "python", "{harness}/score.py", + "--workspace", "{workspace}", + "--request", "{request}", + "--report", "{report}", + "--artifacts", "{artifacts}", + ], + # The judge runs the submission in Docker; the scorer needs daemon access. + passthrough_environment=["PATH", "HOME", "DOCKER_HOST", "HF_TOKEN"], + ) + ) + + +async def build(config: dict) -> SidecarComponents: + repo_path = config["repo_path"] + agent_repo_path = config["agent_repo_path"] + session_dir = Path(config["session_dir"]) + harness_root = config["harness_root"] + session_dir.mkdir(parents=True, exist_ok=True) + + sandbox = await LocalSandbox.create(root=Path(repo_path).parent) + workspace = await GitWorkspace.from_path(sandbox, repo_path) + candidate_repository = await GitCandidateRepository.create( + session_dir / "candidates", workspace=workspace + ) + database = EvaluationDatabase.load_reconciled( + database_path=session_dir / "database.json", + evaluations_dir=session_dir / "evaluations", + database_id="ale", + ) + ledger = BudgetLedger( + [ + EvaluationBudget( + backend_id="cmd", + evaluation_set_key=f"cmd:{SET}:development", + total_runs=40, + ), + EvaluationBudget( + backend_id="cmd", + evaluation_set_key=f"cmd:{SET}:validation", + total_runs=40, + ), + ], + path=session_dir / "budgets.json", + ) + ledger.save() + + engine = EvaluationEngine( + evaluator=Evaluator( + candidate_repository=candidate_repository, + sandbox=workspace.sandbox, + session_dir=session_dir, + session_id="ale", + ), + backends=BackendRegistry({"cmd": _backend(harness_root)}), + database=database, + database_path=session_dir / "database.json", + budget_ledger=ledger, + ) + transport = GitCandidateTransport( + workspace=workspace, + candidate_repository=candidate_repository, + agent_repo_path=agent_repo_path, + ) + baseline = await transport.trusted_candidate("HEAD") + + selection = VerificationSelection( + mode="auto_best", + backend_id="cmd", + evaluation_set=EvaluationSet( + name=SET, partition="validation", selection=CaseRange(start=0, stop=1) + ), + objective=OBJ, + baseline_candidate=baseline, + ) + sidecar = EvaluationSidecar( + engine=engine, + candidate_transport=transport, + access_policies=[ + SidecarEvaluationPolicy( + backend_id="cmd", evaluation_set_name=SET, partition="development", + disclosure="full", expose_case_resources=True, objective=OBJ, + ), + SidecarEvaluationPolicy( + backend_id="cmd", evaluation_set_name=SET, partition="validation", + disclosure="aggregate", min_aggregate_cases=1, objective=OBJ, + ), + ], + admin_volume=session_dir / "admin", + ) + await sidecar.initialize_context() + + verifier = CanonicalVerifier( + engine=engine, + selection=selection, + targets=[ + VerificationTarget( + reward_key="score", backend_id="cmd", + evaluation_set=EvaluationSet(name=SET, partition="test"), + objective=OBJ, max_attempts=1, + ) + ], + admin_volume=session_dir / "admin", + score_baseline=True, + ) + return SidecarComponents(sidecar=sidecar, verifier=verifier) diff --git a/harness-engineering-bench/ale-bench/environment/sidecar/dind-entrypoint.sh b/harness-engineering-bench/ale-bench/environment/sidecar/dind-entrypoint.sh new file mode 100644 index 00000000..34332361 --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/sidecar/dind-entrypoint.sh @@ -0,0 +1,27 @@ +#!/bin/sh +# Start an in-container Docker daemon (DinD) so ALE-Bench's judge + Rust-tool +# containers are SIBLINGS on a daemon whose filesystem IS this container's — the +# only way their bind mounts resolve. (A host/VM socket mount fails: the sibling +# containers resolve mount sources against the daemon host, not this container.) +# Requires the container to run privileged. Then serve (the passed command). +set -eu + +dockerd >/var/log/dockerd.log 2>&1 & +for i in $(seq 1 60); do + docker info >/dev/null 2>&1 && break + sleep 1 +done +if ! docker info >/dev/null 2>&1; then + echo "dockerd failed to start" >&2 + cat /var/log/dockerd.log >&2 || true + exit 1 +fi + +# Bootstrap the ALE-Bench C++ judge image. ALE-Bench expects the tag +# ale-bench:cpp20-202301; upstream builds it as `FROM yimjk/ale-bench:cpp20-202301` +# plus `chown UID:GID /workdir` — a no-op as root — so a pull + retag suffices. +docker pull yimjk/ale-bench:cpp20-202301 +docker tag yimjk/ale-bench:cpp20-202301 ale-bench:cpp20-202301 +# rust:1.79.0-buster (tool build) and httpd (vis) are pulled on demand by ale_bench. + +exec "$@" diff --git a/harness-engineering-bench/ale-bench/environment/sidecar/harness/config.json b/harness-engineering-bench/ale-bench/environment/sidecar/harness/config.json new file mode 100644 index 00000000..b9c49353 --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/sidecar/harness/config.json @@ -0,0 +1,6 @@ +{ + "problem_id": "ahc011", + "code_language": "cpp20", + "num_workers": 2, + "lite_version": true +} diff --git a/harness-engineering-bench/ale-bench/environment/sidecar/harness/score.py b/harness-engineering-bench/ale-bench/environment/sidecar/harness/score.py new file mode 100644 index 00000000..d3fba1f0 --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/sidecar/harness/score.py @@ -0,0 +1,144 @@ +"""Trusted ALE-Bench scorer for a CommandBackend. + +vero drives the OUTER loop (a coding agent edits /work/agent/solution.cpp); each +candidate is scored here by ALE-Bench's deterministic judge. Disclosure maps onto +ALE-Bench's public/private split: + development / validation -> session.public_eval (public seeds, disclosed feedback) + test -> session.private_eval (private seeds, held-out) + +The judge runs the submission in Docker, so this process needs Docker daemon +access (DOCKER_HOST) and the language image (ale-bench:-) present. +Config (problem id, language, workers) is read from harness/config.json. +""" + +from __future__ import annotations + +import argparse +import json +import time +import traceback +from pathlib import Path + + +def _load_config(harness_dir: Path) -> dict: + cfg = json.loads((harness_dir / "config.json").read_text(encoding="utf-8")) + cfg.setdefault("code_language", "cpp20") + cfg.setdefault("num_workers", 2) + cfg.setdefault("lite_version", True) + return cfg + + +def _partition(command_input: dict) -> str: + request = command_input.get("request") or {} + evaluation_set = request.get("evaluation_set") or {} + return evaluation_set.get("partition") or "development" + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--request", type=Path, required=True) + parser.add_argument("--report", type=Path, required=True) + parser.add_argument("--artifacts", type=Path, required=True) + args = parser.parse_args() + + harness_dir = Path(__file__).resolve().parent + config = _load_config(harness_dir) + command_input = json.loads(args.request.read_text(encoding="utf-8")) + partition = _partition(command_input) + held_out = partition == "test" + + artifact_dir = args.artifacts / "ale-bench" + artifact_dir.mkdir(parents=True, exist_ok=True) + started = time.perf_counter() + + try: + code = (args.workspace / "solution.cpp").read_text(encoding="utf-8") + + import ale_bench + + session = ale_bench.start( + problem_id=config["problem_id"], + lite_version=config["lite_version"], + num_workers=int(config["num_workers"]), + run_visualization_server=False, + ) + try: + if held_out: + result, rank, performance = session.private_eval( + code, code_language=config["code_language"] + ) + extra = {"rank": rank, "performance": performance} + else: + result = session.public_eval( + code, code_language=config["code_language"] + ) + extra = {} + finally: + close = getattr(session, "close", None) + if callable(close): + close() + + judge = str(getattr(result, "overall_judge_result", "")) + accepted = 1.0 if judge.upper().endswith("ACCEPTED") else 0.0 + score = float(getattr(result, "overall_absolute_score", 0.0) or 0.0) + runtime_ms = (time.perf_counter() - started) * 1000.0 + + (artifact_dir / "result.json").write_text( + json.dumps( + {"partition": partition, "judge": judge, "score": score, **extra}, + indent=2, + default=str, + ) + + "\n", + encoding="utf-8", + ) + report = { + "schema_version": 1, + "status": "success", + "metrics": { + "score": score, + "accepted": accepted, + "runtime_ms": runtime_ms, + }, + "artifacts": [ + { + "path": "ale-bench/result.json", + "media_type": "application/json", + "description": f"ALE-Bench {partition} judge result", + } + ], + } + if accepted == 0.0: + report["diagnostics"] = [ + { + "code": "not_accepted", + "message": f"judge result: {judge}", + "severity": "warning", + "phase": "judge", + } + ] + except BaseException as error: # noqa: BLE001 - report failure as a scored 0 + runtime_ms = (time.perf_counter() - started) * 1000.0 + (artifact_dir / "failure.log").write_text( + "".join(traceback.format_exception(error)), encoding="utf-8" + ) + report = { + "schema_version": 1, + "status": "failed", + "metrics": {"score": 0.0, "accepted": 0.0, "runtime_ms": runtime_ms}, + "diagnostics": [ + { + "code": "scorer_error", + "message": f"{type(error).__name__}: {error}", + "severity": "error", + "phase": "scoring", + } + ], + } + + args.report.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") + + +if __name__ == "__main__": + main() diff --git a/harness-engineering-bench/ale-bench/environment/sidecar/serve.json b/harness-engineering-bench/ale-bench/environment/sidecar/serve.json new file mode 100644 index 00000000..06ae9f4a --- /dev/null +++ b/harness-engineering-bench/ale-bench/environment/sidecar/serve.json @@ -0,0 +1 @@ +{ "repo_path": "/opt/agent-baseline", "agent_repo_path": "/work/agent", "session_dir": "/state/admin/session", "harness_root": "/opt/harness" } diff --git a/harness-engineering-bench/ale-bench/instruction.md b/harness-engineering-bench/ale-bench/instruction.md new file mode 100644 index 00000000..f26c762c --- /dev/null +++ b/harness-engineering-bench/ale-bench/instruction.md @@ -0,0 +1,498 @@ +# Optimize the ALE-Bench solution (AHC problem: ahc011) + +Improve `solution.cpp` in `/work/agent` to **maximize the ALE-Bench score** on the +sliding-tile problem below. The current baseline is a trivial C++ program that +reads the input and outputs zero moves; it scores ~299686. Write a real heuristic +(read the input, plan tile slides, print the move string) to score higher. + +`solution.cpp` must be a self-contained C++ program (compiled as `cpp20`) that +reads the problem input from stdin and writes the answer to stdout in the format +the statement specifies. + +## Workflow +1. Edit `/work/agent/solution.cpp`. +2. Commit your change with Git. +3. Score the current commit on the validation set (public seeds): + + ```bash + vero harbor eval --backend cmd --evaluation-set ale-bench \ + --partition validation --start 0 --stop 1 + ``` + Then `vero harbor eval-status JOB_ID` / `vero harbor eval-result JOB_ID`. +4. Use `vero harbor status` to see evaluation access. VeRO pools your measurements, + re-scores the best on the held-out private seeds, and ships the best candidate + (the unmodified baseline is a floor, so a regression is never shipped). + +The score is the ALE-Bench absolute score (higher is better); an invalid / +compile-error / TLE submission scores 0. + +--- + +## Problem statement + +Story +-------- +Takahashi loves puzzles and is playing with the following famous sliding puzzle. +> There are $N^2-1$ tiles on an $N \times N$ board. +> There is a single empty square, and you can slide an adjacent tile in any of the four directions into the empty square. +> Some picture is divided into each tile. By repeatedly sliding the tiles, please align the picture. + +The trouble is, Takahashi had thrown away the instruction manual, so he lost the target picture. +According to his memory, the target picture was a tree. +By repeating the sliding operation, please complete a tree. + +![example](./images/example.gif) + +Problem Statement +-------- +There are $N^2-1$ tiles on an $N \times N$ board. +Let $(i, j)$ denote the coordinates of row $i$ $(0\leq i \leq N-1)$ from the top and column $j$ $(0\leq j\leq N-1)$ from the left. +Each tile contains a figure with lines from its center towards one or more of four directions: up, down, left, and right. +We represent each tile using a bitmask with 1 for left, 2 for up, 4 for right, and 8 for down, as follows. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Tile + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Binary + +0000 + +0001 + +0010 + +0011 + +0100 + +0101 + +0110 + +0111 + +1000 + +1001 + +1010 + +1011 + +1100 + +1101 + +1110 + +1111 +
+Hex + +0 + +1 + +2 + +3 + +4 + +5 + +6 + +7 + +8 + +9 + +a + +b + +c + +d + +e + +f +
+ +The number 0 represents an empty square, and there is exactly one empty square. +With a single operation, you can slide one of the tiles adjacent to the empty square in the four directions to the location of the empty square. After the move, the square from which the tile was moved becomes an empty square. +You can repeat the sliding operation at most $T=2\times N^3$ times. + +After finishing the operations, consider a graph with $N^2-1$ squares other than the empty square as vertices and the following edges. + +- For each $(i, j)$ $(0\leq i\leq N-2, 0\leq j\leq N-1)$, if $(i,j)$ is a tile with a downward line and $(i+1,j)$ is a tile with an upward line, then construct an edge between $(i,j)$ and $(i+1,j)$. +- For each $(i, j)$ $(0\leq i\leq N-1, 0\leq j\leq N-2)$, if $(i,j)$ is a tile with a rightward line and $(i,j+1)$ is a tile with a leftward line, then construct an edge between $(i,j)$ and $(i,j+1)$. + +Your task is to find a short sequence of operations such that the size of the largest tree in this graph, i.e., the number of vertices of the largest connected component without cycles, is as large as possible. +It is guaranteed that within $T$ operations you can construct a tree of size $N^2-1$ with the empty square in $(N-1,N-1)$. +Note that the final position of the empty square is arbitrary and you do not have to move it to $(N-1,N-1)$. + +Scoring +-------- +Let $K$ be the number of operations and $S$ be the size of the largest tree painted on the board after applying the sequence of operations. +Then, you will get the following score. + +- If $SWA . + +#### Number of test cases +- Provisional test: 50 +- System test: 3000. We will publish seeds.txt (sha256=041256f962c6ba1a60294ad7a575684d6e401163cba316cf978f2e66a4f7b0e3) after the contest is over. +- Both provisional and system tests contain the same number of inputs for each $N=6,7,8,9,10$. + +The score of a submission is the total scores for each test case. +In the provisional test, if your submission produces illegal output or exceeds the time limit for some test cases, the submission itself will be judged as WA or TLE , and the score of the submission will be zero. +In the system test, if your submission produces illegal output or exceeds the time limit for some test cases, only the score for those test cases will be zero. + +#### About execution time +Execution time may vary slightly from run to run. +In addition, since system tests simultaneously perform a large number of executions, it has been observed that execution time increases by several percent compared to provisional tests. +For these reasons, submissions that are very close to the time limit may result in TLE in the s +ystem test. +Please measure the execution time in your program to terminate the process, or have enough margin in the execution time. + + +Input +-------- +Input is given from Standard Input in the following format: + +~~~ +$N$ $T$ +$t_{0,0}$ $\cdots$ $t_{0,N-1}$ +$\vdots$ +$t_{N-1,0}$ $\cdots$ $t_{N-1,N-1}$ +~~~ + +$N$ is an integer representing the height and width of the board, satisfying $6\leq N\leq 10$. +In all test cases, $T=2\times N^3$. +$t_{i,0}$ $\cdots$ $t_{i,N-1}$ is a string of length $N$. +The $j$-th character $t_{i,j}$ is `0`-`9` or `a`-`f` which is the hexadecimal representation of the figure contained in the tile $(i,j)$. + +Output +-------- + +By representing each operation of sliding the upward, downward, leftward, or rightward adjacent tile into the empty square by a single character `U`, `D`, `L` or `R`, respectively, output the sequence of $K$ operations as a string of length $K$ in one line to Standard Output. + +Show example + + +Input Generation +-------- +
+ +#### Generation of $N$ and $T$ +We generate $N$ as the remainder of the seed value divided by 5 + 6. +Hence, you can generate inputs with a specific $N$ value by adjusting the seed value. +We set $T=2\times N^3$. + +#### Generation of $t_{i,j}$ +Let $[k]=\\{0,1,\cdots,k-1\\}$. +We randomly generate a spanning tree $(V,F)$ with vertices $V=[N]\times [N]\setminus \\{(N-1,N-1)\\}$ as follows. + +1. First, we randomly shuffle edges $\\{\\{(i,j),(i+1,j)\\}\mid (i,j)\in [N-1]\times [N]\setminus \\{(N-2,N-1)\\}\\}\cup\\{\\{(i,j),(i,j+1)\\}\mid (i,j)\in [N]\times [N-1]\setminus \\{(N-1,N-2)\\}\\}$ and obtain an ordered edge list $e_0, e_1, \cdots$. +2. Starting from $F=\emptyset$, for each $e_k=\\{(i,j),(i',j')\\}$, we insert $e_k$ into $F$ if $(i,j)$ and $(i',j')$ are not connected in $(V,F)$. + +From the obtained spanning tree, we construct tiles on which a tree of size $N^2-1$ is drawn, as follows. + +1. For each $(i,j)$, if $\\{(i,j),(i+1,j)\\}\in F$, then draw a downward line on tile $(i, j)$ and an upward line on tile $(i+1,j)$. +2. For each $(i,j)$, if $\\{(i,j),(i,j+1)\\}\in F$, then draw a rightward line on tile $(i, j)$ and a leftward line on tile $(i,j+1)$. + +Finally, starting from the constructed tile layout, randomly perform $T=2\times N^3$ sliding operations, and let $t$ be the tile layout after the operations. +Here, the $k (\geq 2)$-th operation is chosen uniformly at random from at most three directions excluding the direction that reverts the $(k-1)$-th operation. + +
+ +Tools (Input generator and visualizer) +-------- +- Web version: This is more powerful than the local version and can display animations. +- Local version: You need a compilation environment of Rust language. + - Pre-compiled binary for Windows: If you are not familiar with the Rust language environment, please use this instead. + + +You are allowed to share output images (PNG) of the provided visualizer for seed=0 on twitter during the contest. +Note that sharing in video format is prohibited. + +You have to use the specified hashtag and public account. +You can only share visualization results and scores for seed=0. +Do not share GIFs, output itself, scores for other seeds or mention solutions or discussions. + +(Added) The visualizer has a feature to change the value of N, but sharing visualization results for changed inputs is also prohibited. + + +List of shared images + +{sample example} diff --git a/harness-engineering-bench/ale-bench/solution/solve.sh b/harness-engineering-bench/ale-bench/solution/solve.sh new file mode 100755 index 00000000..7bf3ea71 --- /dev/null +++ b/harness-engineering-bench/ale-bench/solution/solve.sh @@ -0,0 +1,6 @@ +#!/bin/sh +set -eu +cd /work/agent +git config user.name optimizer; git config user.email o@v.test +vero harbor eval --backend cmd --evaluation-set ale-bench --partition validation --start 0 --stop 1 +vero harbor status diff --git a/harness-engineering-bench/ale-bench/task.toml b/harness-engineering-bench/ale-bench/task.toml new file mode 100644 index 00000000..c0d5986e --- /dev/null +++ b/harness-engineering-bench/ale-bench/task.toml @@ -0,0 +1,15 @@ +schema_version = "1.3" + +[task] +name = "vero/ale-bench-ahc011" +description = "Optimize solution.cpp to maximize the ALE-Bench score on AHC problem ahc011." + +[agent] +user = "agent" + +[verifier] +environment_mode = "shared" +timeout_sec = 1200 + +[environment] +build_timeout_sec = 3600 diff --git a/harness-engineering-bench/ale-bench/tests/test.sh b/harness-engineering-bench/ale-bench/tests/test.sh new file mode 100755 index 00000000..ba6f13b8 --- /dev/null +++ b/harness-engineering-bench/ale-bench/tests/test.sh @@ -0,0 +1,5 @@ +#!/bin/sh +set -eu +mkdir -p /logs/verifier +vero harbor finalize --token-file /state/token/admin.token --output /logs/verifier/reward.json +cat /logs/verifier/reward.json diff --git a/harness-engineering-bench/gaia/README.md b/harness-engineering-bench/gaia/README.md new file mode 100644 index 00000000..a2d89c8b --- /dev/null +++ b/harness-engineering-bench/gaia/README.md @@ -0,0 +1,39 @@ +# GAIA + +The GAIA benchmark uses the canonical Harbor task packages and verifier. It +does not reproduce the paper's pure-language subset or its original split. + +The committed split is deterministic and stratified by GAIA level and whether +the task has an attached file: + +- development: 33 cases (20%) +- validation: 66 cases (40%) +- test: 66 cases (40%) + +All 165 cases come from the immutable dataset reference recorded in +[`partitions/manifest.json`](partitions/manifest.json). The development set is +available to the optimization agent with full result disclosure. Validation is +aggregate-only and is used to select candidates. Test is held out until Harbor +grades the completed outer task. The complete development task packages and +attachments are mounted read-only under `.vero/cases/`; successful and failed +development evaluations place their complete Harbor trial records—including +exact failures and target-agent logs—under +`.vero/evaluations/`. Neither validation nor test resources are mounted. + +The pinned GAIA tasks declare a 600-second Harbor agent timeout. The build +config sets `case_timeout_seconds: 180` and +`task_agent_timeout_seconds: 600`, so VeRO invokes Harbor with an agent-timeout +multiplier of `0.3`. The 180-second limit reported to the optimizer is therefore +the timeout enforced by the inner Harbor trial. + +To verify or regenerate the split after downloading the pinned Harbor dataset: + +```bash +uv run --python 3.12 scripts/partition_gaia.py \ + --tasks-dir /path/to/downloaded/gaia \ + --check +``` + +Refreshing from a different registry revision is an explicit operation: update +the dataset constant in the script and `build.yaml`, then run with +`--fetch-registry`. Review the manifest diff before committing it. diff --git a/harness-engineering-bench/gaia/baseline/.gitignore b/harness-engineering-bench/gaia/baseline/.gitignore new file mode 100644 index 00000000..fba1daff --- /dev/null +++ b/harness-engineering-bench/gaia/baseline/.gitignore @@ -0,0 +1,4 @@ +compiled/ +target/.venv/ +target/.pytest_cache/ + diff --git a/harness-engineering-bench/gaia/baseline/README.md b/harness-engineering-bench/gaia/baseline/README.md new file mode 100644 index 00000000..d8846ce0 --- /dev/null +++ b/harness-engineering-bench/gaia/baseline/README.md @@ -0,0 +1,82 @@ +# GAIA tool-using baseline + +This leaf benchmark optimizes a small Harbor-native GAIA agent. The editable +program uses the OpenAI Responses API with three capabilities: + +- web search; +- shell commands inside the GAIA task environment; +- image inspection and final-answer submission. + +The trusted build fixes the evaluated model to +`gpt-5.4-mini-2026-03-17`. A candidate may change the agent's prompts, control +flow, tool definitions, file handling, or dependencies, but it cannot change +the dataset, verifier, split, model, access policy, or final test target. + +## Compile the outer Harbor task + +From the repository root: + +```bash +cd vero +VERO_SKIP_SECRET_CHECK=1 uv run vero harbor build \ + --config ../harness-engineering-bench/gaia/baseline/build.yaml \ + --output ../harness-engineering-bench/gaia/baseline/compiled +``` + +Omit `VERO_SKIP_SECRET_CHECK=1` for a real build so VeRO verifies that the +OpenAI and Modal credentials declared in `build.yaml` are present. Set +`OPENAI_BASE_URL` to your OpenAI-compatible endpoint; use +`https://api.openai.com/v1` when calling OpenAI directly. The `compiled/` +directory is generated and intentionally ignored. + +The compiled task does not expose the upstream OpenAI key to either editable +program. A separate inference-gateway container holds it. The outer coding +agent uses a producer-scoped token for `gpt-5.4` or `gpt-5.5`; GAIA candidates use an +evaluation-scoped token restricted to `gpt-5.4-mini-2026-03-17`. Their request +and token budgets are recorded separately and are visible through +`vero harbor status`. + +Run the outer optimization through VeRO so it can give Harbor's coding-agent +adapter the scoped credential while forwarding the upstream credential only to +the gateway. `vero harbor run` is the whole pipeline in one command: it compiles +the `build.yaml` to a temporary task, wires credentials (relocating the upstream +inference key to the gateway and handing the optimizer a scoped producer token), +and invokes Harbor. Put run secrets in a dotenv file rather than exporting them +by hand — copy `secrets.env.example` to `secrets.env` (gitignored) and fill it +in: + +```bash +cp harness-engineering-bench/gaia/baseline/secrets.env.example \ + harness-engineering-bench/gaia/baseline/secrets.env # then edit it + +cd vero +uv run vero harbor run \ + --config ../harness-engineering-bench/gaia/baseline/build.yaml \ + --env-file ../harness-engineering-bench/gaia/baseline/secrets.env \ + --environment docker \ + --agent codex \ + --model gpt-5.6-sol \ + --yes \ + -o ../runs/gaia-full/jobs +``` + +`--env-file` values override the ambient shell and are passed only through the +subprocess environment, never on the command line. Anything after the known +options (here `--yes -o ...`) is forwarded verbatim to `harbor run`. The +`--model` value is fed to `${optimizer_model}` so the producer scope's +allow-list and the optimizer agent's model stay in lockstep (no 403 mismatch). +Use `--environment modal` to run the optimizer itself remotely, or `docker` to +run it locally against Modal eval backends. + +The coding agent edits only `target/`; inner evaluations run the candidate +against the pinned GAIA tasks on Modal. Complete development tasks and +attachments are mounted read-only under `.vero/cases/`. After each development +evaluation, complete Harbor trial records for every case—including exact +failures and target-agent logs—are available under +`.vero/evaluations/`. Validation remains aggregate-only, and test remains +verifier-only. + +Before Modal teardown, the shared verifier exports the complete VeRO session +and a self-contained `experiment.html` into Harbor's verifier artifacts. Keep +the session archive alongside the HTML: it is the canonical candidate and +evaluation history and can be rendered again with `vero report`. diff --git a/harness-engineering-bench/gaia/baseline/build.yaml b/harness-engineering-bench/gaia/baseline/build.yaml new file mode 100644 index 00000000..654ed2a6 --- /dev/null +++ b/harness-engineering-bench/gaia/baseline/build.yaml @@ -0,0 +1,89 @@ +name: vero/optimize-gaia-baseline +description: >- + Improve a tool-using GAIA agent while preserving the Harbor agent interface. + The target must answer canonical GAIA tasks by writing /app/answer.txt. +agent_repo: target +task_source: gaia/gaia@sha256:bbc356f476e0b70ba77da11a9be7d6345918d1e4a2daade0d6dfb82ee6f7b761 +task_manifest: ../partitions/manifest.json +agent_import_path: gaia_agent.agent:GaiaAgent +harbor_requirement: harbor[modal]==0.18.0 + +partition_files: + development: ../partitions/development.json + validation: ../partitions/validation.json + test: ../partitions/test.json + +# total_cases = 4 full passes per partition (dev 33*4, validation 66*4). +agent_access: + - partition: development + disclosure: full + expose_case_resources: true + total_cases: 132 + - partition: validation + disclosure: aggregate + expose_case_resources: false + min_aggregate_cases: 5 + total_cases: 264 + +selection_partition: validation +targets: + - partition: test + reward_key: reward + failure_value: 0.0 + max_attempts: 1 + +evaluation_set_name: gaia +objective: + selector: + metric: score + direction: maximize +reward_mode: submit # agent picks; falls back to auto_best, then current version +baseline_floor: true +score_baseline: true +rescore_top_k: 3 +rescore_attempts: 1 + +model: openai/gpt-5.4-mini-2026-03-17 +environment_name: ${inner_env:-modal} +harbor_python_version: "3.12" +n_attempts: 1 +max_retries: 1 +infrastructure_max_attempts: 3 +infrastructure_retry_delay_seconds: 5 +aggregate_attempts: best +feedback_transcripts: true +feedback_max_bytes: 12000 +expose_attempt_detail: false +timeout_seconds: 3600 +case_timeout_seconds: 180 +task_agent_timeout_seconds: 600 +max_concurrency: 8 +error_rate_threshold: 0.1 +verifier_timeout_seconds: 7200 +secrets: + - MODAL_TOKEN_ID + - MODAL_TOKEN_SECRET + - WANDB_API_KEY + - WANDB_BASE_URL # self-hosted W&B (scaleai.wandb.io) + +# off: breaks the eval's uv resolution of gaia_agent; needs an isolation fix. +harness_user: null + +wandb: + project: vero-gaia + group: gaia + log_traces: true + +inference_gateway: + upstream_api_key_env: OPENAI_API_KEY + upstream_base_url_env: OPENAI_BASE_URL + producer: + allowed_models: ["${optimizer_model:-gpt-5.4}"] + max_concurrency: 8 + evaluation: + allowed_models: [gpt-5.4-mini-2026-03-17] + max_requests: 15000 + max_tokens: 100000000 + max_concurrency: 64 +instruct_multifidelity: true +instruct_exhaust_budget: true diff --git a/harness-engineering-bench/gaia/baseline/secrets.env.example b/harness-engineering-bench/gaia/baseline/secrets.env.example new file mode 100644 index 00000000..33bc5a52 --- /dev/null +++ b/harness-engineering-bench/gaia/baseline/secrets.env.example @@ -0,0 +1,15 @@ +# Run secrets for `vero harbor run --env-file`. +# Copy to `secrets.env` (gitignored) and fill in. File values override the +# ambient shell and never appear on the command line. + +# Upstream inference credentials. VeRO reads these as the real provider key/URL +# (per the compiled gateway's *_source fields), relocates them to the +# VERO_INFERENCE_UPSTREAM_* target vars, and hands the optimizer a scoped +# producer token instead — so the raw key never reaches the coding agent. +OPENAI_API_KEY=sk-your-upstream-inference-key +OPENAI_BASE_URL=https://your-openai-compatible-endpoint/v1 + +# Modal credentials for the eval sidecar backends (declared required by the +# compiled task.toml). Copy from your ~/.modal.toml profile. +MODAL_TOKEN_ID=ak-... +MODAL_TOKEN_SECRET=as-... diff --git a/harness-engineering-bench/gaia/baseline/target/pyproject.toml b/harness-engineering-bench/gaia/baseline/target/pyproject.toml new file mode 100644 index 00000000..23136ade --- /dev/null +++ b/harness-engineering-bench/gaia/baseline/target/pyproject.toml @@ -0,0 +1,25 @@ +[project] +name = "vero-gaia-agent" +version = "0.1.0" +description = "Editable Harbor-native GAIA baseline for VeRO" +requires-python = ">=3.12" +dependencies = [ + "harbor==0.18.0", + "openai==2.46.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/gaia_agent"] + +[dependency-groups] +dev = [ + "pytest>=9.0.2", + "pytest-asyncio>=1.3.0", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] diff --git a/harness-engineering-bench/gaia/baseline/target/src/gaia_agent/__init__.py b/harness-engineering-bench/gaia/baseline/target/src/gaia_agent/__init__.py new file mode 100644 index 00000000..17948c7a --- /dev/null +++ b/harness-engineering-bench/gaia/baseline/target/src/gaia_agent/__init__.py @@ -0,0 +1,5 @@ +"""Harbor-native GAIA target agent.""" + +from gaia_agent.agent import GaiaAgent + +__all__ = ["GaiaAgent"] diff --git a/harness-engineering-bench/gaia/baseline/target/src/gaia_agent/agent.py b/harness-engineering-bench/gaia/baseline/target/src/gaia_agent/agent.py new file mode 100644 index 00000000..e7611b31 --- /dev/null +++ b/harness-engineering-bench/gaia/baseline/target/src/gaia_agent/agent.py @@ -0,0 +1,275 @@ +"""A compact, tool-using GAIA baseline built on the OpenAI Responses API.""" + +from __future__ import annotations + +import base64 +import json +import mimetypes +from pathlib import Path +from typing import Any, override + +from harbor.agents.base import BaseAgent +from harbor.environments.base import BaseEnvironment +from harbor.models.agent.context import AgentContext +from openai import AsyncOpenAI + +MAX_TURNS = 24 +MAX_TOOL_OUTPUT_CHARS = 20_000 +MAX_IMAGE_BYTES = 20 * 1024 * 1024 + +INSTRUCTIONS = """You are a careful general-purpose research agent solving a GAIA task. + +Work until you have a well-supported exact answer. You can search the web, run shell +commands in the task's Linux environment, and inspect attached images. Attached files +named in the task are available under /app/files. Use Python and install focused +packages when they make an analysis more reliable. Cross-check facts and calculations. + +The grader reads /app/answer.txt. When ready, call submit_answer with only the exact +answer requested by the task: no explanation, label, markdown, or surrounding prose. +Never modify benchmark tests, verifier files, or expected answers. +""" + +TOOLS: list[dict[str, Any]] = [ + {"type": "web_search"}, + { + "type": "function", + "name": "run_shell", + "description": ( + "Run a non-interactive shell command inside the GAIA task environment. " + "Use /app as the working directory and inspect /app/files for attachments." + ), + "parameters": { + "type": "object", + "properties": { + "command": {"type": "string", "description": "Shell command to run"} + }, + "required": ["command"], + "additionalProperties": False, + }, + "strict": True, + }, + { + "type": "function", + "name": "read_image", + "description": "Load an image from the task environment for visual inspection.", + "parameters": { + "type": "object", + "properties": { + "path": { + "type": "string", + "description": "Absolute image path, normally under /app/files", + } + }, + "required": ["path"], + "additionalProperties": False, + }, + "strict": True, + }, + { + "type": "function", + "name": "submit_answer", + "description": "Write the exact final answer and finish the task.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "type": "string", + "description": "Exact answer only, with no explanation", + } + }, + "required": ["answer"], + "additionalProperties": False, + }, + "strict": True, + }, +] + + +class GaiaAgent(BaseAgent): + """Research agent whose source is the editable optimization target.""" + + @staticmethod + @override + def name() -> str: + return "gaia-responses-baseline" + + @override + def version(self) -> str: + return "0.1.0" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + if self.model_name is None: + raise ValueError("GAIA agent requires a Harbor model") + self._api_model = self.model_name.removeprefix("openai/") + self._client = AsyncOpenAI() + + @override + async def setup(self, environment: BaseEnvironment) -> None: + result = await environment.exec("mkdir -p /app", timeout_sec=30) + if result.return_code != 0: + raise RuntimeError(result.stderr or "could not prepare /app") + + @staticmethod + def _truncate(value: str) -> str: + if len(value) <= MAX_TOOL_OUTPUT_CHARS: + return value + half = MAX_TOOL_OUTPUT_CHARS // 2 + omitted = len(value) - (2 * half) + return f"{value[:half]}\n...[{omitted} characters omitted]...\n{value[-half:]}" + + def _trace(self, event: dict[str, Any]) -> None: + self.logs_dir.mkdir(parents=True, exist_ok=True) + with (self.logs_dir / "gaia-trace.jsonl").open("a", encoding="utf-8") as file: + file.write(json.dumps(event, ensure_ascii=False, default=str) + "\n") + + async def _run_shell( + self, environment: BaseEnvironment, command: str + ) -> dict[str, Any]: + result = await environment.exec(command, cwd="/app", timeout_sec=120) + return { + "return_code": result.return_code, + "stdout": self._truncate(result.stdout or ""), + "stderr": self._truncate(result.stderr or ""), + } + + async def _read_image( + self, environment: BaseEnvironment, remote_path: str + ) -> tuple[dict[str, Any], str | None]: + if not remote_path.startswith("/app/"): + return {"error": "image path must be under /app"}, None + local_path = self.logs_dir / "images" / Path(remote_path).name + local_path.parent.mkdir(parents=True, exist_ok=True) + await environment.download_file(remote_path, local_path) + if local_path.stat().st_size > MAX_IMAGE_BYTES: + return {"error": "image exceeds the 20 MiB tool limit"}, None + media_type = mimetypes.guess_type(local_path.name)[0] + if media_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}: + return {"error": f"unsupported image media type: {media_type}"}, None + encoded = base64.b64encode(local_path.read_bytes()).decode("ascii") + return { + "loaded": remote_path, + "media_type": media_type, + }, f"data:{media_type};base64,{encoded}" + + async def _submit(self, environment: BaseEnvironment, answer: str) -> None: + normalized = answer.strip() + if not normalized: + raise ValueError("answer must not be empty") + local_path = self.logs_dir / "answer.txt" + local_path.parent.mkdir(parents=True, exist_ok=True) + local_path.write_text(normalized + "\n", encoding="utf-8") + await environment.upload_file(local_path, "/app/answer.txt") + + @staticmethod + def _usage_value(value: Any, name: str) -> int: + result = getattr(value, name, 0) if value is not None else 0 + return int(result or 0) + + @override + async def run( + self, + instruction: str, + environment: BaseEnvironment, + context: AgentContext, + ) -> None: + next_input: Any = instruction + previous_response_id: str | None = None + input_tokens = 0 + output_tokens = 0 + cached_tokens = 0 + + for turn in range(1, MAX_TURNS + 1): + request: dict[str, Any] = { + "model": self._api_model, + "instructions": INSTRUCTIONS, + "input": next_input, + "tools": TOOLS, + "reasoning": {"effort": "medium"}, + "max_output_tokens": 8000, + "parallel_tool_calls": False, + } + if previous_response_id is not None: + request["previous_response_id"] = previous_response_id + response = await self._client.responses.create(**request) + usage = response.usage + input_tokens += self._usage_value(usage, "input_tokens") + output_tokens += self._usage_value(usage, "output_tokens") + cached_tokens += self._usage_value( + getattr(usage, "input_tokens_details", None), "cached_tokens" + ) + + calls = [item for item in response.output if item.type == "function_call"] + self._trace( + { + "turn": turn, + "response_id": response.id, + "output_text": response.output_text, + "function_calls": [ + {"name": call.name, "arguments": call.arguments} + for call in calls + ], + } + ) + if not calls: + if response.output_text.strip(): + await self._submit(environment, response.output_text) + context.metadata = {"turns": turn, "trace": "gaia-trace.jsonl"} + break + raise RuntimeError("model returned neither an answer nor a tool call") + + next_input = [] + submitted = False + for call in calls: + try: + arguments = json.loads(call.arguments) + except json.JSONDecodeError as error: + result: dict[str, Any] = {"error": f"invalid arguments: {error}"} + image_url = None + else: + image_url = None + if call.name == "run_shell": + result = await self._run_shell( + environment, arguments["command"] + ) + elif call.name == "read_image": + result, image_url = await self._read_image( + environment, arguments["path"] + ) + elif call.name == "submit_answer": + await self._submit(environment, arguments["answer"]) + result = {"submitted": True} + submitted = True + else: + result = {"error": f"unknown tool: {call.name}"} + self._trace({"turn": turn, "tool": call.name, "result": result}) + next_input.append( + { + "type": "function_call_output", + "call_id": call.call_id, + "output": json.dumps(result, ensure_ascii=False), + } + ) + if image_url is not None: + next_input.append( + { + "role": "user", + "content": [ + { + "type": "input_text", + "text": f"Image loaded from {arguments['path']}.", + }, + {"type": "input_image", "image_url": image_url}, + ], + } + ) + if submitted: + context.metadata = {"turns": turn, "trace": "gaia-trace.jsonl"} + break + previous_response_id = response.id + else: + raise RuntimeError(f"GAIA agent exceeded {MAX_TURNS} turns") + + context.n_input_tokens = input_tokens + context.n_output_tokens = output_tokens + context.n_cache_tokens = cached_tokens diff --git a/harness-engineering-bench/gaia/baseline/target/tests/test_agent.py b/harness-engineering-bench/gaia/baseline/target/tests/test_agent.py new file mode 100644 index 00000000..714769e4 --- /dev/null +++ b/harness-engineering-bench/gaia/baseline/target/tests/test_agent.py @@ -0,0 +1,72 @@ +from pathlib import Path +from types import SimpleNamespace + +import pytest + +from gaia_agent import GaiaAgent + + +class FakeEnvironment: + def __init__(self): + self.uploads: list[tuple[Path, str]] = [] + + async def exec(self, command, **kwargs): + return SimpleNamespace(return_code=0, stdout="", stderr="") + + async def upload_file(self, source_path, target_path): + self.uploads.append((Path(source_path), target_path)) + + +class FakeResponses: + async def create(self, **kwargs): + return SimpleNamespace( + id="response-1", + output=[ + SimpleNamespace( + type="function_call", + name="submit_answer", + arguments='{"answer":"42"}', + call_id="call-1", + ) + ], + output_text="", + usage=SimpleNamespace( + input_tokens=120, + output_tokens=8, + input_tokens_details=SimpleNamespace(cached_tokens=20), + ), + ) + + +@pytest.mark.asyncio +async def test_agent_submits_answer_and_populates_context(tmp_path, monkeypatch): + monkeypatch.setenv("OPENAI_API_KEY", "test-key") + agent = GaiaAgent( + logs_dir=tmp_path / "logs", + model_name="openai/gpt-5.4-mini-2026-03-17", + ) + agent._client = SimpleNamespace(responses=FakeResponses()) + environment = FakeEnvironment() + context = SimpleNamespace( + metadata=None, + n_input_tokens=None, + n_output_tokens=None, + n_cache_tokens=None, + ) + + await agent.run("What is six times seven?", environment, context) + + assert len(environment.uploads) == 1 + answer_path, remote_path = environment.uploads[0] + assert answer_path.read_text(encoding="utf-8") == "42\n" + assert remote_path == "/app/answer.txt" + assert 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"gaia/076c8171-9b3b-49b9-a477-244d2a532826", + "gaia/08cae58d-4084-4616-b6dd-dd6534e4825b", + "gaia/08f3a05f-5947-4089-a4c4-d4bcfaa6b7a0", + "gaia/0b260a57-3f3a-4405-9f29-6d7a1012dbfb", + "gaia/0bdb7c40-671d-4ad1-9ce3-986b159c0ddc", + "gaia/0ff53813-3367-4f43-bcbd-3fd725c1bf4b", + "gaia/114d5fd0-e2ae-4b6d-a65a-870da2d19c08", + "gaia/14569e28-c88c-43e4-8c32-097d35b9a67d", + "gaia/17b5a6a3-bc87-42e8-b0fb-6ab0781ef2cc", + "gaia/1dcc160f-c187-48c2-b68e-319bd4354f3d", + "gaia/2d83110e-a098-4ebb-9987-066c06fa42d0", + "gaia/2dfc4c37-fec1-4518-84a7-10095d30ad75", + "gaia/3cef3a44-215e-4aed-8e3b-b1e3f08063b7", + "gaia/3da89939-209c-4086-8520-7eb734e6b4ef", + "gaia/3ff6b7a9-a5bd-4412-ad92-0cd0d45c0fee", + "gaia/4d0aa727-86b1-406b-9b33-f870dd14a4a5", + "gaia/50ad0280-0819-4bd9-b275-5de32d3b5bcb", + "gaia/50ec8903-b81f-4257-9450-1085afd2c319", + "gaia/5188369a-3bbe-43d8-8b94-11558f909a08", + "gaia/544b7f0c-173a-4377-8d56-57b36eb26ddf", + "gaia/56137764-b4e0-45b8-9c52-1866420c3df5", + "gaia/56db2318-640f-477a-a82f-bc93ad13e882", + "gaia/5a0c1adf-205e-4841-a666-7c3ef95def9d", + "gaia/5d0080cb-90d7-4712-bc33-848150e917d3", + "gaia/5f982798-16b9-4051-ab57-cfc7ebdb2a91", + "gaia/624cbf11-6a41-4692-af9c-36b3e5ca3130", + "gaia/6359a0b1-8f7b-499b-9336-840f9ab90688", + "gaia/65afbc8a-89ca-4ad5-8d62-355bb401f61d", + "gaia/65da0822-a48a-4a68-bbad-8ed1b835a834", + "gaia/6f37996b-2ac7-44b0-8e68-6d28256631b4", + "gaia/7619a514-5fa8-43ef-9143-83b66a43d7a4", + "gaia/7673d772-ef80-4f0f-a602-1bf4485c9b43", + "gaia/7a4a336d-dcfa-45a0-b014-824c7619e8de", + "gaia/7bd855d8-463d-4ed5-93ca-5fe35145f733", + "gaia/7dd30055-0198-452e-8c25-f73dbe27dcb8", + "gaia/8131e2c0-0083-4265-9ce7-78c2d568425d", + "gaia/853c8244-429e-46ca-89f2-addf40dfb2bd", + "gaia/8f80e01c-1296-4371-9486-bb3d68651a60", + "gaia/9318445f-fe6a-4e1b-acbf-c68228c9906a", + "gaia/9d191bce-651d-4746-be2d-7ef8ecadb9c2", + "gaia/9f41b083-683e-4dcf-9185-ccfeaa88fa45", + "gaia/a0c07678-e491-4bbc-8f0b-07405144218f", + "gaia/a1e91b78-d3d8-4675-bb8d-62741b4b68a6", + "gaia/a26649c6-1cb2-470a-871e-6910c64c3e53", + "gaia/ad2b4d70-9314-4fe6-bfbe-894a45f6055f", + "gaia/b2c257e0-3ad7-4f05-b8e3-d9da973be36e", + "gaia/b415aba4-4b68-4fc6-9b89-2c812e55a3e1", + "gaia/b7f857e4-d8aa-4387-af2a-0e844df5b9d8", + "gaia/b9763138-c053-4832-9f55-86200cb1f99c", + "gaia/bec74516-02fc-48dc-b202-55e78d0e17cf", + "gaia/c61d22de-5f6c-4958-a7f6-5e9707bd3466", + "gaia/c714ab3a-da30-4603-bacd-d008800188b9", + "gaia/d1af70ea-a9a4-421a-b9cc-94b5e02f1788", + "gaia/d700d50d-c707-4dca-90dc-4528cddd0c80", + "gaia/da52d699-e8d2-4dc5-9191-a2199e0b6a9b", + "gaia/dc22a632-937f-4e6a-b72f-ba0ff3f5ff97", + "gaia/ded28325-3447-4c56-860f-e497d6fb3577", + "gaia/e142056d-56ab-4352-b091-b56054bd1359", + "gaia/e1fc63a2-da7a-432f-be78-7c4a95598703", + "gaia/e9a2c537-8232-4c3f-85b0-b52de6bcba99", + "gaia/ebbc1f13-d24d-40df-9068-adcf735b4240", + "gaia/ecbc4f94-95a3-4cc7-b255-6741a458a625", + "gaia/f46b4380-207e-4434-820b-f32ce04ae2a4", + "gaia/f918266a-b3e0-4914-865d-4faa564f1aef" +] diff --git a/harness-engineering-bench/gaia/partitions/validation.json b/harness-engineering-bench/gaia/partitions/validation.json new file mode 100644 index 00000000..bacfad28 --- /dev/null +++ b/harness-engineering-bench/gaia/partitions/validation.json @@ -0,0 +1,68 @@ +[ + "gaia/0383a3ee-47a7-41a4-b493-519bdefe0488", + "gaia/04a04a9b-226c-43fd-b319-d5e89743676f", + "gaia/05407167-39ec-4d3a-a234-73a9120c325d", + "gaia/08c0b6e9-1b43-4c2e-ae55-4e3fce2c2715", + "gaia/0a3cd321-3e76-4622-911b-0fda2e5d6b1a", + "gaia/0bb3b44a-ede5-4db5-a520-4e844b0079c5", + "gaia/0e9e85b8-52b9-4de4-b402-5f635ab9631f", + "gaia/11af4e1a-5f45-467d-9aeb-46f4bb0bf034", + "gaia/1f975693-876d-457b-a649-393859e79bf3", + "gaia/23dd907f-1261-4488-b21c-e9185af91d5e", + "gaia/2a649bb1-795f-4a01-b3be-9a01868dae73", + "gaia/32102e3e-d12a-4209-9163-7b3a104efe5d", + "gaia/33d8ea3b-6c6b-4ff1-803d-7e270dea8a57", + "gaia/366e2f2b-8632-4ef2-81eb-bc3877489217", + "gaia/384d0dd8-e8a4-4cfe-963c-d37f256e7662", + "gaia/3f57289b-8c60-48be-bd80-01f8099ca449", + "gaia/42576abe-0deb-4869-8c63-225c2d75a95a", + "gaia/46719c30-f4c3-4cad-be07-d5cb21eee6bb", + "gaia/48eb8242-1099-4c26-95d4-ef22b002457a", + "gaia/4b650a35-8529-4695-89ed-8dc7a500a498", + "gaia/4b6bb5f7-f634-410e-815d-e673ab7f8632", + "gaia/4d51c4bf-4b0e-4f3d-897b-3f6687a7d9f2", + "gaia/4fc2f1ae-8625-45b5-ab34-ad4433bc21f8", + "gaia/50f58759-7bd6-406f-9b0d-5692beb2a926", + "gaia/54612da3-fd56-4941-80f4-5eb82330de25", + "gaia/5b2a14e8-6e59-479c-80e3-4696e8980152", + "gaia/5cfb274c-0207-4aa7-9575-6ac0bd95d9b2", + "gaia/676e5e31-a554-4acc-9286-b60d90a92d26", + "gaia/67e8878b-5cef-4375-804e-e6291fdbe78a", + "gaia/6b078778-0b90-464d-83f6-59511c811b01", + "gaia/708b99c5-e4a7-49cb-a5cf-933c8d46470d", + "gaia/71345b0a-9c7d-4b50-b2bf-937ec5879845", + "gaia/72c06643-a2fa-4186-aa5c-9ec33ae9b445", + "gaia/72e110e7-464c-453c-a309-90a95aed6538", + "gaia/73c1b9fe-ee1d-4cf4-96ca-35c08f97b054", + "gaia/7cc4acfa-63fd-4acc-a1a1-e8e529e0a97f", + "gaia/87c610df-bef7-4932-b950-1d83ef4e282b", + "gaia/8d46b8d6-b38a-47ff-ac74-cda14cf2d19b", + "gaia/935e2cff-ae78-4218-b3f5-115589b19dae", + "gaia/983bba7c-c092-455f-b6c9-7857003d48fc", + "gaia/9e1fc53b-46ff-49a1-9d05-9e6faac34cc5", + "gaia/a0068077-79f4-461a-adfe-75c1a4148545", + "gaia/a3fbeb63-0e8c-4a11-bff6-0e3b484c3e9c", + "gaia/a56f1527-3abf-41d6-91f8-7296d6336c3f", + "gaia/a7feb290-76bb-4cb7-8800-7edaf7954f2f", + "gaia/ad37a656-079a-49f9-a493-7b739c9167d1", + "gaia/bda648d7-d618-4883-88f4-3466eabd860e", + "gaia/c365c1c7-a3db-4d5e-a9a1-66f56eae7865", + "gaia/c526d8d6-5987-4da9-b24c-83466fa172f3", + "gaia/c8b7e059-c60d-472e-ad64-3b04ae1166dc", + "gaia/cca70ce6-1952-45d2-acd4-80c903b0bc49", + "gaia/cf106601-ab4f-4af9-b045-5295fe67b37d", + "gaia/cffe0e32-c9a6-4c52-9877-78ceb4aaa9fb", + "gaia/d0633230-7067-47a9-9dbf-ee11e0a2cdd6", + "gaia/db4fd70a-2d37-40ea-873f-9433dc5e301f", + "gaia/de9887f5-ead8-4727-876f-5a4078f8598c", + "gaia/df6561b2-7ee5-4540-baab-5095f742716a", + "gaia/e0c10771-d627-4fd7-9694-05348e54ee36", + "gaia/e29834fd-413a-455c-a33e-c3915b07401c", + "gaia/e2d69698-bc99-4e85-9880-67eaccd66e6c", + "gaia/e961a717-6b25-4175-8a68-874d28190ee4", + "gaia/ec09fa32-d03f-4bf8-84b0-1f16922c3ae4", + "gaia/ed58682d-bc52-4baa-9eb0-4eb81e1edacc", + "gaia/f0f46385-fc03-4599-b5d3-f56496c3e69f", + "gaia/f2feb6a4-363c-4c09-a804-0db564eafd68", + "gaia/f3917a3d-1d17-4ee2-90c5-683b072218fe" +] diff --git a/harness-engineering-bench/gaia/scripts/partition_gaia.py b/harness-engineering-bench/gaia/scripts/partition_gaia.py new file mode 100644 index 00000000..a30d06ff --- /dev/null +++ b/harness-engineering-bench/gaia/scripts/partition_gaia.py @@ -0,0 +1,263 @@ +#!/usr/bin/env python3 +"""Create and verify the committed GAIA development/validation/test split.""" + +from __future__ import annotations + +import argparse +import asyncio +import hashlib +import json +import tomllib +from collections import Counter, defaultdict +from fractions import Fraction +from pathlib import Path +from typing import Any + +DATASET_NAME = "gaia/gaia" +DATASET_DIGEST = "bbc356f476e0b70ba77da11a9be7d6345918d1e4a2daade0d6dfb82ee6f7b761" +TASK_SOURCE = f"{DATASET_NAME}@sha256:{DATASET_DIGEST}" +SEED = "vero-gaia-v1" +PARTITIONS = ("development", "validation", "test") +RATIOS = { + "development": Fraction(1, 5), + "validation": Fraction(2, 5), + "test": Fraction(2, 5), +} +TARGET_COUNTS = {"development": 33, "validation": 66, "test": 66} + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument( + "--tasks-dir", + type=Path, + required=True, + help="Directory containing the 165 exported GAIA task directories", + ) + parser.add_argument( + "--output-dir", + type=Path, + default=Path(__file__).parents[1] / "partitions", + ) + parser.add_argument("--fetch-registry", action="store_true") + parser.add_argument("--check", action="store_true") + return parser.parse_args() + + +def _task_root(path: Path) -> Path: + path = path.expanduser().resolve() + if len(list(path.glob("*/task.toml"))) == 165: + return path + nested = path / "gaia" + if len(list(nested.glob("*/task.toml"))) == 165: + return nested + raise ValueError(f"{path} does not contain exactly 165 exported GAIA tasks") + + +def _read_tasks(path: Path) -> list[dict[str, str]]: + tasks: list[dict[str, str]] = [] + for task_dir in sorted(_task_root(path).iterdir()): + if not task_dir.is_dir(): + continue + config = tomllib.loads((task_dir / "task.toml").read_text(encoding="utf-8")) + canonical_name = config.get("task", {}).get("name") + if canonical_name != f"gaia/{task_dir.name}": + raise ValueError( + f"{task_dir.name} has unexpected canonical name {canonical_name!r}" + ) + tags = config.get("metadata", {}).get("tags", []) + levels = [tag for tag in tags if tag in {"level1", "level2", "level3"}] + if len(levels) != 1: + raise ValueError(f"{task_dir.name} does not have exactly one GAIA level") + attachments = sorted((task_dir / "environment" / "workspace").glob("*")) + if len(attachments) > 1: + raise ValueError(f"{task_dir.name} has more than one attached file") + attachment = ( + attachments[0].suffix.lower().removeprefix(".") if attachments else "none" + ) + tasks.append( + { + "name": canonical_name, + "level": levels[0], + "attachment": attachment, + } + ) + if len(tasks) != 165: + raise ValueError(f"expected 165 tasks, found {len(tasks)}") + return tasks + + +async def _fetch_registry_refs() -> tuple[str, dict[str, str]]: + try: + from harbor.registry.client.package import PackageDatasetClient + except ImportError as error: + raise RuntimeError( + "--fetch-registry requires the exactly pinned Harbor package" + ) from error + + metadata = await PackageDatasetClient().get_dataset_metadata(TASK_SOURCE) + refs = {task.get_name(): str(task.ref) for task in metadata.task_ids} + return str(metadata.version), refs + + +def _existing_refs(output_dir: Path) -> tuple[str, dict[str, str]]: + manifest_path = output_dir / "manifest.json" + if not manifest_path.is_file(): + raise ValueError("no existing manifest; rerun with --fetch-registry") + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + refs = {} + for item in manifest["tasks"]: + name = item["name"] + refs[name if "/" in name else f"gaia/{name}"] = item["ref"] + return manifest["dataset_version"], refs + + +def _stable_key(task_name: str) -> str: + return hashlib.sha256(f"{SEED}:{task_name}".encode()).hexdigest() + + +def _allocate(tasks: list[dict[str, str]]) -> dict[str, list[str]]: + strata: dict[tuple[str, str], list[dict[str, str]]] = defaultdict(list) + for task in tasks: + attachment_group = "none" if task["attachment"] == "none" else "attached" + strata[(task["level"], attachment_group)].append(task) + + allocation: dict[tuple[str, str], dict[str, int]] = {} + remaining_by_stratum: dict[tuple[str, str], int] = {} + totals = Counter() + for stratum, members in strata.items(): + row = { + partition: int(len(members) * RATIOS[partition]) for partition in PARTITIONS + } + allocation[stratum] = row + totals.update(row) + remaining_by_stratum[stratum] = len(members) - sum(row.values()) + + deficits = { + partition: TARGET_COUNTS[partition] - totals[partition] + for partition in PARTITIONS + } + while sum(remaining_by_stratum.values()): + choices: list[tuple[Fraction, tuple[str, str], str]] = [] + for stratum, remaining in remaining_by_stratum.items(): + if remaining == 0: + continue + size = len(strata[stratum]) + for partition in PARTITIONS: + if deficits[partition] == 0: + continue + ideal = size * RATIOS[partition] + shortfall = ideal - allocation[stratum][partition] + choices.append((shortfall, stratum, partition)) + if not choices: + raise RuntimeError("could not satisfy exact partition sizes") + _, stratum, partition = max( + choices, + key=lambda item: ( + item[0], + -PARTITIONS.index(item[2]), + item[1], + ), + ) + allocation[stratum][partition] += 1 + remaining_by_stratum[stratum] -= 1 + deficits[partition] -= 1 + + result = {partition: [] for partition in PARTITIONS} + for stratum in sorted(strata): + members = sorted(strata[stratum], key=lambda task: _stable_key(task["name"])) + cursor = 0 + for partition in PARTITIONS: + count = allocation[stratum][partition] + result[partition].extend( + task["name"] for task in members[cursor : cursor + count] + ) + cursor += count + for partition in PARTITIONS: + result[partition].sort() + if len(result[partition]) != TARGET_COUNTS[partition]: + raise RuntimeError(f"wrong {partition} size: {len(result[partition])}") + return result + + +def _render( + tasks: list[dict[str, str]], + partitions: dict[str, list[str]], + dataset_version: str, + refs: dict[str, str], +) -> dict[str, str]: + names = {task["name"] for task in tasks} + if names != set(refs): + raise ValueError( + "downloaded task names do not match the pinned registry dataset" + ) + membership = { + name: partition + for partition, partition_tasks in partitions.items() + for name in partition_tasks + } + partition_digest = hashlib.sha256( + json.dumps(partitions, sort_keys=True, separators=(",", ":")).encode() + ).hexdigest() + stratum_counts = Counter( + f"{task['level']}:{'none' if task['attachment'] == 'none' else 'attached'}" + for task in tasks + ) + manifest: dict[str, Any] = { + "schema_version": 1, + "task_source": TASK_SOURCE, + "dataset_name": DATASET_NAME, + "dataset_version": dataset_version, + "seed": SEED, + "ratios": {partition: float(RATIOS[partition]) for partition in PARTITIONS}, + "stratified_by": ["level", "attachment_presence"], + "partition_counts": TARGET_COUNTS, + "partition_digest": f"sha256:{partition_digest}", + "stratum_counts": dict(sorted(stratum_counts.items())), + "tasks": [ + { + **task, + "ref": refs[task["name"]], + "partition": membership[task["name"]], + } + for task in sorted(tasks, key=lambda item: item["name"]) + ], + } + rendered = { + f"{partition}.json": json.dumps(partitions[partition], indent=2) + "\n" + for partition in PARTITIONS + } + rendered["manifest.json"] = json.dumps(manifest, indent=2) + "\n" + return rendered + + +def main() -> None: + args = _parse_args() + tasks = _read_tasks(args.tasks_dir) + args.output_dir = args.output_dir.expanduser().resolve() + if args.fetch_registry: + dataset_version, refs = asyncio.run(_fetch_registry_refs()) + else: + dataset_version, refs = _existing_refs(args.output_dir) + rendered = _render(tasks, _allocate(tasks), dataset_version, refs) + + if args.check: + changed = [ + filename + for filename, content in rendered.items() + if not (args.output_dir / filename).is_file() + or (args.output_dir / filename).read_text(encoding="utf-8") != content + ] + if changed: + raise SystemExit("partition files are stale: " + ", ".join(changed)) + print("GAIA partitions match the pinned dataset and split algorithm") + return + + args.output_dir.mkdir(parents=True, exist_ok=True) + for filename, content in rendered.items(): + (args.output_dir / filename).write_text(content, encoding="utf-8") + print(f"wrote {len(rendered)} files to {args.output_dir}") + + +if __name__ == "__main__": + main() diff --git a/harness-engineering-bench/officeqa/.gitignore b/harness-engineering-bench/officeqa/.gitignore new file mode 100644 index 00000000..1a540c79 --- /dev/null +++ b/harness-engineering-bench/officeqa/.gitignore @@ -0,0 +1,7 @@ +# Vendored task dirs (246, ~12MB) are fetched locally via scripts/vendor_tasks.sh +# — not committed (they bloat the repo + slow secret scans). Partitions + config +# + agent ARE committed; the tasks are reproducibly re-fetched. +tasks/ +baseline/target/.venv/ +**/__pycache__/ +**/*.pyc diff --git a/harness-engineering-bench/officeqa/baseline/build.yaml b/harness-engineering-bench/officeqa/baseline/build.yaml new file mode 100644 index 00000000..9d84aef7 --- /dev/null +++ b/harness-engineering-bench/officeqa/baseline/build.yaml @@ -0,0 +1,84 @@ +name: vero/optimize-officeqa-baseline +description: >- + Improve a grounded-reasoning agent on OfficeQA (Treasury Bulletin corpus QA). + The agent must search /app/corpus/ and write the exact answer to /app/answer.txt; + scoring is rule-based (numeric/text match with 1% tolerance). +# Local (vendored) dataset — OfficeQA is not in the Harbor hub, only the +# harbor-datasets git repo. task_source points at the vendored task dirs; the +# per-task task.toml carries docker_image=officeqa-corpus (full-corpus mode), so +# extra_harbor_args uses --no-force-build to pull that prebuilt image instead of +# building the guard Dockerfile. +agent_repo: target +task_source: ../tasks +task_manifest: ../partitions/manifest.json +agent_import_path: officeqa_agent.agent:OfficeQaAgent +harbor_requirement: harbor[modal]==0.18.0 +extra_harbor_args: ["--no-force-build"] + +partition_files: + development: ../partitions/development.json + validation: ../partitions/validation.json + test: ../partitions/test.json + +agent_access: + - partition: development + disclosure: full + expose_case_resources: true + total_runs: 6 + total_cases: 98 + - partition: validation + disclosure: aggregate + expose_case_resources: false + min_aggregate_cases: 5 + total_runs: 4 + total_cases: 196 + +selection_partition: validation +targets: + - partition: test + reward_key: reward + failure_value: 0.0 + max_attempts: 1 + +evaluation_set_name: officeqa +objective: + selector: + metric: score + direction: maximize +reward_mode: auto_best +baseline_floor: true +score_baseline: true +rescore_top_k: 3 +rescore_attempts: 1 + +model: openai/gpt-5.4-mini-2026-03-17 +environment_name: modal +harbor_python_version: "3.12" +n_attempts: 1 +max_retries: 1 +infrastructure_max_attempts: 3 +infrastructure_retry_delay_seconds: 5 +aggregate_attempts: best +feedback_transcripts: true +feedback_max_bytes: 12000 +expose_attempt_detail: false +timeout_seconds: 7200 +case_timeout_seconds: 300 +task_agent_timeout_seconds: 1800 +max_concurrency: 8 +error_rate_threshold: 0.1 +verifier_timeout_seconds: 3600 +secrets: + - MODAL_TOKEN_ID + - MODAL_TOKEN_SECRET +inference_gateway: + upstream_api_key_env: OPENAI_API_KEY + upstream_base_url_env: OPENAI_BASE_URL + producer: + allowed_models: ["${optimizer_model:-gpt-5.4}"] + max_concurrency: 8 + evaluation: + allowed_models: [gpt-5.4-mini-2026-03-17] + max_requests: 15000 + max_tokens: 100000000 + max_concurrency: 64 diff --git a/harness-engineering-bench/officeqa/baseline/target/pyproject.toml b/harness-engineering-bench/officeqa/baseline/target/pyproject.toml new file mode 100644 index 00000000..7c845c35 --- /dev/null +++ b/harness-engineering-bench/officeqa/baseline/target/pyproject.toml @@ -0,0 +1,25 @@ +[project] +name = "vero-officeqa-agent" +version = "0.1.0" +description = "Editable Harbor-native GAIA baseline for VeRO" +requires-python = ">=3.12" +dependencies = [ + "harbor==0.18.0", + "openai==2.46.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/officeqa_agent"] + +[dependency-groups] +dev = [ + "pytest>=9.0.2", + "pytest-asyncio>=1.3.0", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] diff --git a/harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/__init__.py b/harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/__init__.py new file mode 100644 index 00000000..82d42f4f --- /dev/null +++ b/harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/__init__.py @@ -0,0 +1,5 @@ +"""Harbor-native GAIA target agent.""" + +from officeqa_agent.agent import OfficeQaAgent + +__all__ = ["OfficeQaAgent"] diff --git a/harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/agent.py b/harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/agent.py new file mode 100644 index 00000000..46bcde33 --- /dev/null +++ b/harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/agent.py @@ -0,0 +1,279 @@ +"""A compact, tool-using OfficeQA baseline built on the OpenAI Responses API.""" + +from __future__ import annotations + +import base64 +import json +import mimetypes +from pathlib import Path +from typing import Any, override + +from harbor.agents.base import BaseAgent +from harbor.environments.base import BaseEnvironment +from harbor.models.agent.context import AgentContext +from openai import AsyncOpenAI + +MAX_TURNS = 24 +MAX_TOOL_OUTPUT_CHARS = 20_000 +MAX_IMAGE_BYTES = 20 * 1024 * 1024 + +INSTRUCTIONS = """You are a careful grounded-reasoning agent answering questions from a +document corpus (OfficeQA). + +Work until you have a well-supported exact answer grounded in the corpus. Run shell +commands in the task's Linux environment to search and read the documents. The corpus +location is given in the task instructions (typically a directory such as /app/corpus/ +with an index file); grep/search it to find the relevant documents, then read them. +Use Python and install focused packages when they make an analysis more reliable. +Cross-check facts and calculations; scoring uses a numeric tolerance, so report precise +numbers in the requested units. + +The grader reads /app/answer.txt. When ready, call submit_answer with only the exact +answer requested by the task: no explanation, label, markdown, or surrounding prose. +Never modify benchmark tests, verifier files, or expected answers. +""" + +TOOLS: list[dict[str, Any]] = [ + {"type": "web_search"}, + { + "type": "function", + "name": "run_shell", + "description": ( + "Run a non-interactive shell command inside the OfficeQA task environment. " + "Use /app as the working directory and inspect the corpus directory named in the task instructions." + ), + "parameters": { + "type": "object", + "properties": { + "command": {"type": "string", "description": "Shell command to run"} + }, + "required": ["command"], + "additionalProperties": False, + }, + "strict": True, + }, + { + "type": "function", + "name": "read_image", + "description": "Load an image from the task environment for visual inspection.", + "parameters": { + "type": "object", + "properties": { + "path": { + "type": "string", + "description": "Absolute image path, normally under the task corpus dir", + } + }, + "required": ["path"], + "additionalProperties": False, + }, + "strict": True, + }, + { + "type": "function", + "name": "submit_answer", + "description": "Write the exact final answer and finish the task.", + "parameters": { + "type": "object", + "properties": { + "answer": { + "type": "string", + "description": "Exact answer only, with no explanation", + } + }, + "required": ["answer"], + "additionalProperties": False, + }, + "strict": True, + }, +] + + +class OfficeQaAgent(BaseAgent): + """Research agent whose source is the editable optimization target.""" + + @staticmethod + @override + def name() -> str: + return "officeqa-responses-baseline" + + @override + def version(self) -> str: + return "0.1.0" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + if self.model_name is None: + raise ValueError("OfficeQA agent requires a Harbor model") + self._api_model = self.model_name.removeprefix("openai/") + self._client = AsyncOpenAI() + + @override + async def setup(self, environment: BaseEnvironment) -> None: + result = await environment.exec("mkdir -p /app", timeout_sec=30) + if result.return_code != 0: + raise RuntimeError(result.stderr or "could not prepare /app") + + @staticmethod + def _truncate(value: str) -> str: + if len(value) <= MAX_TOOL_OUTPUT_CHARS: + return value + half = MAX_TOOL_OUTPUT_CHARS // 2 + omitted = len(value) - (2 * half) + return f"{value[:half]}\n...[{omitted} characters omitted]...\n{value[-half:]}" + + def _trace(self, event: dict[str, Any]) -> None: + self.logs_dir.mkdir(parents=True, exist_ok=True) + with (self.logs_dir / "officeqa-trace.jsonl").open("a", encoding="utf-8") as file: + file.write(json.dumps(event, ensure_ascii=False, default=str) + "\n") + + async def _run_shell( + self, environment: BaseEnvironment, command: str + ) -> dict[str, Any]: + result = await environment.exec(command, cwd="/app", timeout_sec=120) + return { + "return_code": result.return_code, + "stdout": self._truncate(result.stdout or ""), + "stderr": self._truncate(result.stderr or ""), + } + + async def _read_image( + self, environment: BaseEnvironment, remote_path: str + ) -> tuple[dict[str, Any], str | None]: + if not remote_path.startswith("/app/"): + return {"error": "image path must be under /app"}, None + local_path = self.logs_dir / "images" / Path(remote_path).name + local_path.parent.mkdir(parents=True, exist_ok=True) + await environment.download_file(remote_path, local_path) + if local_path.stat().st_size > MAX_IMAGE_BYTES: + return {"error": "image exceeds the 20 MiB tool limit"}, None + media_type = mimetypes.guess_type(local_path.name)[0] + if media_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}: + return {"error": f"unsupported image media type: {media_type}"}, None + encoded = base64.b64encode(local_path.read_bytes()).decode("ascii") + return { + "loaded": remote_path, + "media_type": media_type, + }, f"data:{media_type};base64,{encoded}" + + async def _submit(self, environment: BaseEnvironment, answer: str) -> None: + normalized = answer.strip() + if not normalized: + raise ValueError("answer must not be empty") + local_path = self.logs_dir / "answer.txt" + local_path.parent.mkdir(parents=True, exist_ok=True) + local_path.write_text(normalized + "\n", encoding="utf-8") + await environment.upload_file(local_path, "/app/answer.txt") + + @staticmethod + def _usage_value(value: Any, name: str) -> int: + result = getattr(value, name, 0) if value is not None else 0 + return int(result or 0) + + @override + async def run( + self, + instruction: str, + environment: BaseEnvironment, + context: AgentContext, + ) -> None: + next_input: Any = instruction + previous_response_id: str | None = None + input_tokens = 0 + output_tokens = 0 + cached_tokens = 0 + + for turn in range(1, MAX_TURNS + 1): + request: dict[str, Any] = { + "model": self._api_model, + "instructions": INSTRUCTIONS, + "input": next_input, + "tools": TOOLS, + "reasoning": {"effort": "medium"}, + "max_output_tokens": 8000, + "parallel_tool_calls": False, + } + if previous_response_id is not None: + request["previous_response_id"] = previous_response_id + response = await self._client.responses.create(**request) + usage = response.usage + input_tokens += self._usage_value(usage, "input_tokens") + output_tokens += self._usage_value(usage, "output_tokens") + cached_tokens += self._usage_value( + getattr(usage, "input_tokens_details", None), "cached_tokens" + ) + + calls = [item for item in response.output if item.type == "function_call"] + self._trace( + { + "turn": turn, + "response_id": response.id, + "output_text": response.output_text, + "function_calls": [ + {"name": call.name, "arguments": call.arguments} + for call in calls + ], + } + ) + if not calls: + if response.output_text.strip(): + await self._submit(environment, response.output_text) + context.metadata = {"turns": turn, "trace": "officeqa-trace.jsonl"} + break + raise RuntimeError("model returned neither an answer nor a tool call") + + next_input = [] + submitted = False + for call in calls: + try: + arguments = json.loads(call.arguments) + except json.JSONDecodeError as error: + result: dict[str, Any] = {"error": f"invalid arguments: {error}"} + image_url = None + else: + image_url = None + if call.name == "run_shell": + result = await self._run_shell( + environment, arguments["command"] + ) + elif call.name == "read_image": + result, image_url = await self._read_image( + environment, arguments["path"] + ) + elif call.name == "submit_answer": + await self._submit(environment, arguments["answer"]) + result = {"submitted": True} + submitted = True + else: + result = {"error": f"unknown tool: {call.name}"} + self._trace({"turn": turn, "tool": call.name, "result": result}) + next_input.append( + { + "type": "function_call_output", + "call_id": call.call_id, + "output": json.dumps(result, ensure_ascii=False), + } + ) + if image_url is not None: + next_input.append( + { + "role": "user", + "content": [ + { + "type": "input_text", + "text": f"Image loaded from {arguments['path']}.", + }, + {"type": "input_image", "image_url": image_url}, + ], + } + ) + if submitted: + context.metadata = {"turns": turn, "trace": "officeqa-trace.jsonl"} + break + previous_response_id = response.id + else: + raise RuntimeError(f"OfficeQA agent exceeded {MAX_TURNS} turns") + + context.n_input_tokens = input_tokens + context.n_output_tokens = output_tokens + context.n_cache_tokens = cached_tokens diff --git a/harness-engineering-bench/officeqa/baseline/target/tests/test_agent.py b/harness-engineering-bench/officeqa/baseline/target/tests/test_agent.py new file mode 100644 index 00000000..d24fac10 --- /dev/null +++ b/harness-engineering-bench/officeqa/baseline/target/tests/test_agent.py @@ -0,0 +1,72 @@ +from pathlib import Path +from types import SimpleNamespace + +import pytest + +from officeqa_agent import OfficeQaAgent + + +class FakeEnvironment: + def __init__(self): + self.uploads: list[tuple[Path, str]] = [] + + async def exec(self, command, **kwargs): + return SimpleNamespace(return_code=0, stdout="", stderr="") + + async def upload_file(self, source_path, target_path): + self.uploads.append((Path(source_path), target_path)) + + +class FakeResponses: + async def create(self, **kwargs): + return SimpleNamespace( + id="response-1", + output=[ + SimpleNamespace( + type="function_call", + name="submit_answer", + arguments='{"answer":"42"}', + call_id="call-1", + ) + ], + output_text="", + usage=SimpleNamespace( + input_tokens=120, + output_tokens=8, + input_tokens_details=SimpleNamespace(cached_tokens=20), + ), + ) + + +@pytest.mark.asyncio +async def test_agent_submits_answer_and_populates_context(tmp_path, monkeypatch): + monkeypatch.setenv("OPENAI_API_KEY", "test-key") + agent = OfficeQaAgent( + logs_dir=tmp_path / "logs", + model_name="openai/gpt-5.4-mini-2026-03-17", + ) + agent._client = SimpleNamespace(responses=FakeResponses()) + environment = FakeEnvironment() + context = SimpleNamespace( + metadata=None, + n_input_tokens=None, + n_output_tokens=None, + n_cache_tokens=None, + ) + + await agent.run("What is six times seven?", environment, context) + + assert len(environment.uploads) == 1 + answer_path, remote_path = environment.uploads[0] + assert answer_path.read_text(encoding="utf-8") == "42\n" + assert remote_path == "/app/answer.txt" + assert context.n_input_tokens == 120 + assert context.n_output_tokens == 8 + assert context.n_cache_tokens == 20 + assert context.metadata == {"turns": 1, "trace": "officeqa-trace.jsonl"} + + +def test_agent_requires_model(tmp_path, monkeypatch): + monkeypatch.setenv("OPENAI_API_KEY", "test-key") + with pytest.raises(ValueError, match="requires a Harbor model"): + OfficeQaAgent(logs_dir=tmp_path) diff --git a/harness-engineering-bench/officeqa/baseline/target/uv.lock b/harness-engineering-bench/officeqa/baseline/target/uv.lock new file mode 100644 index 00000000..f5774308 --- /dev/null +++ b/harness-engineering-bench/officeqa/baseline/target/uv.lock @@ -0,0 +1,2129 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" + +[[package]] +name = "aiohappyeyeballs" +version = "2.7.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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build.yaml's +# task_source at them. The repo is large (~4GB), so we sparse-checkout only +# datasets/officeqa. Each task.toml (schema 1.0) is then patched with a canonical +# [task].name, which vero's local task staging requires. +# +# Usage: bash scripts/vendor_tasks.sh +# Result: harness-engineering-bench/candidates/officeqa/tasks/officeqa-uid*/ (246 tasks) +set -euo pipefail + +here="$(cd "$(dirname "$0")/.." && pwd)" # harness-engineering-bench/officeqa +dest="$here/tasks" +work="$(mktemp -d)" +trap 'rm -rf "$work"' EXIT + +echo "Sparse-cloning datasets/officeqa from harbor-datasets ..." +git clone --depth 1 --filter=blob:none --no-checkout \ + https://github.com/harbor-framework/harbor-datasets.git "$work/hd" +git -C "$work/hd" sparse-checkout init --cone +git -C "$work/hd" sparse-checkout set datasets/officeqa +git -C "$work/hd" checkout + +src="$work/hd/datasets/officeqa" +count=$(find "$src" -maxdepth 1 -type d -name 'officeqa-uid*' | wc -l | tr -d ' ') +[ "$count" -gt 0 ] || { echo "ERROR: no tasks fetched" >&2; exit 1; } + +echo "Copying $count task dirs -> $dest ..." +rm -rf "$dest"; mkdir -p "$dest" +cp -R "$src"/officeqa-uid* "$dest/" + +echo "Patching task.toml with canonical [task].name ..." +python3 - "$dest" <<'PY' +import glob, os, re, sys +root = sys.argv[1] +patched = 0 +for d in sorted(glob.glob(os.path.join(root, "officeqa-uid*"))): + uid = os.path.basename(d) + f = os.path.join(d, "task.toml") + txt = open(f).read() + if re.search(r'(?m)^\[task\]', txt): + continue + block = f'[task]\nname = "officeqa/{uid}"\n\n' + lines = txt.splitlines(keepends=True) + if lines and lines[0].lstrip().startswith("version"): + new = lines[0] + "\n" + block + "".join(lines[1:]) + else: + new = block + txt + open(f, "w").write(new) + patched += 1 +print(f" patched {patched} task.toml files") +PY + +echo "Done: $(find "$dest" -maxdepth 1 -type d -name 'officeqa-uid*' | wc -l | tr -d ' ') tasks in $dest" diff --git a/harness-engineering-bench/scripts/partition_dataset.py b/harness-engineering-bench/scripts/partition_dataset.py new file mode 100644 index 00000000..320f972f --- /dev/null +++ b/harness-engineering-bench/scripts/partition_dataset.py @@ -0,0 +1,313 @@ +#!/usr/bin/env python3 +"""Create and verify deterministic splits for Harbor-native benchmarks.""" + +from __future__ import annotations + +import argparse +import asyncio +import hashlib +import json +import tomllib +from collections import Counter, defaultdict +from dataclasses import dataclass +from fractions import Fraction +from pathlib import Path +from typing import Any + +PARTITIONS = ("development", "validation", "test") +RATIOS = { + "development": Fraction(1, 5), + "validation": Fraction(2, 5), + "test": Fraction(2, 5), +} + + +@dataclass(frozen=True) +class BenchmarkSpec: + dataset_name: str + dataset_digest: str + seed: str + task_count: int + target_counts: dict[str, int] + export_dir_name: str + stratified_by: tuple[str, ...] + + @property + def task_source(self) -> str: + return f"{self.dataset_name}@sha256:{self.dataset_digest}" + + +SPECS = { + "swe-atlas-qna": BenchmarkSpec( + dataset_name="scale-ai/swe-atlas-qna", + dataset_digest=( + "0e26bc0313ae2fc6f912b67b928e648c7f20d17d91f765f702a93042ce5be0e4" + ), + seed="vero-swe-atlas-qna-v1", + task_count=124, + target_counts={"development": 25, "validation": 49, "test": 50}, + export_dir_name="swe-atlas-qna", + stratified_by=("repository",), + ), + "tau3": BenchmarkSpec( + dataset_name="sierra-research/tau3-bench", + dataset_digest=( + "a57304f682894ac061090769af771a3617664f3ff6e5417d4eadf8e30433e4d9" + ), + seed="vero-tau3-v1", + task_count=375, + target_counts={"development": 75, "validation": 150, "test": 150}, + export_dir_name="tau3-bench", + stratified_by=("domain",), + ), +} + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("benchmark", choices=sorted(SPECS)) + parser.add_argument( + "--tasks-dir", + type=Path, + required=True, + help="Directory containing an exported copy of the pinned Harbor dataset", + ) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--fetch-registry", action="store_true") + parser.add_argument("--check", action="store_true") + return parser.parse_args() + + +def _task_root(path: Path, spec: BenchmarkSpec) -> Path: + path = path.expanduser().resolve() + candidates = (path, path / spec.export_dir_name) + for candidate in candidates: + if len(list(candidate.glob("*/task.toml"))) == spec.task_count: + return candidate + raise ValueError( + f"{path} does not contain exactly {spec.task_count} exported tasks" + ) + + +def _task_details( + benchmark: str, task_dir: Path, config: dict[str, Any] +) -> dict[str, str]: + metadata = config.get("metadata", {}) + if benchmark == "swe-atlas-qna": + details = { + "repository": str(metadata.get("repository") or ""), + "language": str(metadata.get("language") or ""), + "category": str(metadata.get("category") or ""), + } + if not all(details.values()): + raise ValueError(f"{task_dir.name} has incomplete SWE-Atlas metadata") + return details + + keywords = config.get("task", {}).get("keywords", []) + domains = { + keyword + for keyword in keywords + if keyword in {"airline", "banking_knowledge", "retail", "telecom"} + } + if len(domains) != 1: + raise ValueError(f"{task_dir.name} does not identify one tau3 domain") + return { + "domain": domains.pop(), + "difficulty": str(metadata.get("difficulty") or "unknown"), + } + + +def _read_tasks( + benchmark: str, path: Path, spec: BenchmarkSpec +) -> list[dict[str, str]]: + tasks: list[dict[str, str]] = [] + for task_dir in sorted(_task_root(path, spec).iterdir()): + if not task_dir.is_dir(): + continue + config = tomllib.loads((task_dir / "task.toml").read_text(encoding="utf-8")) + canonical_name = config.get("task", {}).get("name") + if not isinstance(canonical_name, str) or not canonical_name.startswith( + spec.dataset_name.split("/", 1)[0] + "/" + ): + raise ValueError( + f"{task_dir.name} has unexpected canonical name {canonical_name!r}" + ) + details = _task_details(benchmark, task_dir, config) + stratum = ":".join(details[field] for field in spec.stratified_by) + tasks.append({"name": canonical_name, "stratum": stratum, **details}) + if len(tasks) != spec.task_count: + raise ValueError(f"expected {spec.task_count} tasks, found {len(tasks)}") + if len({task["name"] for task in tasks}) != spec.task_count: + raise ValueError("dataset contains duplicate canonical task names") + return tasks + + +async def _fetch_registry_refs( + spec: BenchmarkSpec, +) -> tuple[str, dict[str, str]]: + try: + from harbor.registry.client.package import PackageDatasetClient + except ImportError as error: + raise RuntimeError( + "--fetch-registry requires the exactly pinned Harbor package" + ) from error + + metadata = await PackageDatasetClient().get_dataset_metadata(spec.task_source) + refs = {task.get_name(): str(task.ref) for task in metadata.task_ids} + return str(metadata.version), refs + + +def _existing_refs(output_dir: Path) -> tuple[str, dict[str, str]]: + manifest_path = output_dir / "manifest.json" + if not manifest_path.is_file(): + raise ValueError("no existing manifest; rerun with --fetch-registry") + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + refs = {item["name"]: item["ref"] for item in manifest["tasks"]} + return manifest["dataset_version"], refs + + +def _stable_key(seed: str, task_name: str) -> str: + return hashlib.sha256(f"{seed}:{task_name}".encode()).hexdigest() + + +def _allocate(tasks: list[dict[str, str]], spec: BenchmarkSpec) -> dict[str, list[str]]: + strata: dict[str, list[dict[str, str]]] = defaultdict(list) + for task in tasks: + strata[task["stratum"]].append(task) + + allocation: dict[str, dict[str, int]] = {} + remaining_by_stratum: dict[str, int] = {} + totals = Counter() + for stratum, members in strata.items(): + row = { + partition: int(len(members) * RATIOS[partition]) for partition in PARTITIONS + } + allocation[stratum] = row + totals.update(row) + remaining_by_stratum[stratum] = len(members) - sum(row.values()) + + deficits = { + partition: spec.target_counts[partition] - totals[partition] + for partition in PARTITIONS + } + while sum(remaining_by_stratum.values()): + choices: list[tuple[Fraction, str, str]] = [] + for stratum, remaining in remaining_by_stratum.items(): + if remaining == 0: + continue + size = len(strata[stratum]) + for partition in PARTITIONS: + if deficits[partition] == 0: + continue + ideal = size * RATIOS[partition] + choices.append( + (ideal - allocation[stratum][partition], stratum, partition) + ) + if not choices: + raise RuntimeError("could not satisfy exact partition sizes") + _, stratum, partition = max( + choices, + key=lambda item: (item[0], -PARTITIONS.index(item[2]), item[1]), + ) + allocation[stratum][partition] += 1 + remaining_by_stratum[stratum] -= 1 + deficits[partition] -= 1 + + result = {partition: [] for partition in PARTITIONS} + for stratum in sorted(strata): + members = sorted( + strata[stratum], key=lambda task: _stable_key(spec.seed, task["name"]) + ) + cursor = 0 + for partition in PARTITIONS: + count = allocation[stratum][partition] + result[partition].extend( + task["name"] for task in members[cursor : cursor + count] + ) + cursor += count + for partition in PARTITIONS: + result[partition].sort() + if len(result[partition]) != spec.target_counts[partition]: + raise RuntimeError(f"wrong {partition} size: {len(result[partition])}") + return result + + +def _render( + tasks: list[dict[str, str]], + partitions: dict[str, list[str]], + dataset_version: str, + refs: dict[str, str], + spec: BenchmarkSpec, +) -> dict[str, str]: + names = {task["name"] for task in tasks} + if names != set(refs): + raise ValueError("exported task names do not match the pinned registry dataset") + membership = { + name: partition + for partition, partition_tasks in partitions.items() + for name in partition_tasks + } + partition_digest = hashlib.sha256( + json.dumps(partitions, sort_keys=True, separators=(",", ":")).encode() + ).hexdigest() + manifest: dict[str, Any] = { + "schema_version": 1, + "task_source": spec.task_source, + "dataset_name": spec.dataset_name, + "dataset_version": dataset_version, + "seed": spec.seed, + "ratios": {partition: float(RATIOS[partition]) for partition in PARTITIONS}, + "stratified_by": list(spec.stratified_by), + "partition_counts": spec.target_counts, + "partition_digest": f"sha256:{partition_digest}", + "stratum_counts": dict( + sorted(Counter(task["stratum"] for task in tasks).items()) + ), + "tasks": [ + { + **task, + "ref": refs[task["name"]], + "partition": membership[task["name"]], + } + for task in sorted(tasks, key=lambda item: item["name"]) + ], + } + rendered = { + f"{partition}.json": json.dumps(partitions[partition], indent=2) + "\n" + for partition in PARTITIONS + } + rendered["manifest.json"] = json.dumps(manifest, indent=2) + "\n" + return rendered + + +def main() -> None: + args = _parse_args() + spec = SPECS[args.benchmark] + tasks = _read_tasks(args.benchmark, args.tasks_dir, spec) + output_dir = args.output_dir.expanduser().resolve() + if args.fetch_registry: + dataset_version, refs = asyncio.run(_fetch_registry_refs(spec)) + else: + dataset_version, refs = _existing_refs(output_dir) + rendered = _render(tasks, _allocate(tasks, spec), dataset_version, refs, spec) + + if args.check: + changed = [ + filename + for filename, content in rendered.items() + if not (output_dir / filename).is_file() + or (output_dir / filename).read_text(encoding="utf-8") != content + ] + if changed: + raise SystemExit("partition files are stale: " + ", ".join(changed)) + print(f"{args.benchmark} partitions match the pinned dataset") + return + + output_dir.mkdir(parents=True, exist_ok=True) + for filename, content in rendered.items(): + (output_dir / filename).write_text(content, encoding="utf-8") + print(f"wrote {len(rendered)} files to {output_dir}") + + +if __name__ == "__main__": + main() diff --git a/harness-engineering-bench/swe-atlas-qna/README.md b/harness-engineering-bench/swe-atlas-qna/README.md new file mode 100644 index 00000000..fa125f6f --- /dev/null +++ b/harness-engineering-bench/swe-atlas-qna/README.md @@ -0,0 +1,26 @@ +# SWE-Atlas-QnA + +This benchmark optimizes an agent that investigates a checked-out software +repository and writes an evidence-backed answer to a deep codebase question. +Scoring is the canonical rubric judge shipped in each Harbor task. + +The pinned 124-task dataset is split 25/49/50 for development, validation, and +test. The split is deterministic and stratified by source repository so that +the ten represented codebases occur across the three partitions. + +Regenerate or verify the committed split from an exported dataset: + +```bash +python harness-engineering-bench/scripts/partition_dataset.py swe-atlas-qna \ + --tasks-dir /path/to/exported/dataset \ + --output-dir harness-engineering-bench/candidates/swe-atlas-qna/partitions \ + --fetch-registry + +python harness-engineering-bench/scripts/partition_dataset.py swe-atlas-qna \ + --tasks-dir /path/to/exported/dataset \ + --output-dir harness-engineering-bench/candidates/swe-atlas-qna/partitions \ + --check +``` + +`--fetch-registry` requires the pinned Harbor package and verifies every task +name and content digest against Harbor Hub. diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/.gitignore b/harness-engineering-bench/swe-atlas-qna/baseline/.gitignore new file mode 100644 index 00000000..35e4297b --- /dev/null +++ b/harness-engineering-bench/swe-atlas-qna/baseline/.gitignore @@ -0,0 +1,2 @@ +compiled/ +.vero/ diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/README.md b/harness-engineering-bench/swe-atlas-qna/baseline/README.md new file mode 100644 index 00000000..14b115ee --- /dev/null +++ b/harness-engineering-bench/swe-atlas-qna/baseline/README.md @@ -0,0 +1,24 @@ +# SWE-Atlas-QnA codebase agent + +This leaf benchmark optimizes a small Harbor-native agent that explores the +repository mounted at `/app` and writes its final answer to +`/logs/agent/answer.txt`. The editable program controls its prompt, search +strategy, shell tools, context management, and answer synthesis. + +The trusted build pins the target model to `gpt-5.4-mini-2026-03-17`, the +dataset version, split, budgets, access policy, and final test partition. The +Harbor tasks retain their canonical rubric-based verifier. That verifier needs +`OPENAI_API_BASE`; the target agent uses `OPENAI_BASE_URL`. They may point to +the same OpenAI-compatible endpoint. + +Compile from the repository root: + +```bash +cd vero +VERO_SKIP_SECRET_CHECK=1 uv run vero harbor build \ + --config ../harness-engineering-bench/candidates/swe-atlas-qna/baseline/build.yaml \ + --output ../harness-engineering-bench/candidates/swe-atlas-qna/baseline/compiled +``` + +For a real run, provide `OPENAI_API_KEY`, `OPENAI_BASE_URL`, +`OPENAI_API_BASE`, and the Modal credentials declared in `build.yaml`. diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml new file mode 100644 index 00000000..f8de56df --- /dev/null +++ b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml @@ -0,0 +1,78 @@ +name: vero/optimize-swe-atlas-qna-baseline +description: >- + Improve a codebase investigation agent on canonical SWE-Atlas-QnA tasks. + The target must inspect /app without modifying it and write a comprehensive, + evidence-backed answer to /logs/agent/answer.txt. +agent_repo: target +task_source: scale-ai/swe-atlas-qna@sha256:0e26bc0313ae2fc6f912b67b928e648c7f20d17d91f765f702a93042ce5be0e4 +task_manifest: ../partitions/manifest.json +agent_import_path: atlas_agent.agent:AtlasAgent +harbor_requirement: harbor[modal]==0.18.0 + +partition_files: + development: ../partitions/development.json + validation: ../partitions/validation.json + test: ../partitions/test.json + +agent_access: + - partition: development + disclosure: full + expose_case_resources: true + total_runs: 12 + total_cases: 100 + - partition: validation + disclosure: aggregate + expose_case_resources: false + min_aggregate_cases: 5 + total_runs: 8 + total_cases: 196 + +selection_partition: validation +targets: + - partition: test + reward_key: reward + failure_value: 0.0 + max_attempts: 1 + +evaluation_set_name: swe-atlas-qna +objective: + selector: + metric: score + direction: maximize +reward_mode: auto_best +baseline_floor: true +score_baseline: true +rescore_top_k: 3 +rescore_attempts: 1 + +model: openai/gpt-5.4-mini-2026-03-17 +environment_name: modal +harbor_python_version: "3.12" +n_attempts: 1 +max_retries: 1 +infrastructure_max_attempts: 3 +infrastructure_retry_delay_seconds: 5 +aggregate_attempts: best +feedback_transcripts: true +feedback_max_bytes: 16000 +expose_attempt_detail: false +timeout_seconds: 14400 +max_concurrency: 8 +error_rate_threshold: 0.1 +verifier_timeout_seconds: 14400 +secrets: + - MODAL_TOKEN_ID + - MODAL_TOKEN_SECRET +inference_gateway: + upstream_api_key_env: OPENAI_API_KEY + upstream_base_url_env: OPENAI_BASE_URL + producer: + allowed_models: ["${optimizer_model:-gpt-5.4}"] + max_concurrency: 8 + evaluation: + allowed_models: [gpt-5.4-mini-2026-03-17] + max_requests: 15000 + max_tokens: 100000000 + max_concurrency: 64 +instruct_multifidelity: true +instruct_exhaust_budget: true diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/target/pyproject.toml b/harness-engineering-bench/swe-atlas-qna/baseline/target/pyproject.toml new file mode 100644 index 00000000..a86baeab --- /dev/null +++ b/harness-engineering-bench/swe-atlas-qna/baseline/target/pyproject.toml @@ -0,0 +1,25 @@ +[project] +name = "vero-swe-atlas-qna-agent" +version = "0.1.0" +description = "Editable Harbor-native SWE-Atlas-QnA baseline for VeRO" +requires-python = ">=3.12" +dependencies = [ + "harbor==0.18.0", + "openai==2.46.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/atlas_agent"] + +[dependency-groups] +dev = [ + "pytest>=9.0.2", + "pytest-asyncio>=1.3.0", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/__init__.py b/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/__init__.py new file mode 100644 index 00000000..f952d201 --- /dev/null +++ b/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/__init__.py @@ -0,0 +1,5 @@ +"""Harbor-native SWE-Atlas-QnA target agent.""" + +from atlas_agent.agent import AtlasAgent + +__all__ = ["AtlasAgent"] diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py b/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py new file mode 100644 index 00000000..62d5e12c --- /dev/null +++ b/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py @@ -0,0 +1,215 @@ +"""A compact codebase investigation agent built on the Responses API.""" + +from __future__ import annotations + +import json +from typing import Any, override + +from harbor.agents.base import BaseAgent +from harbor.environments.base import BaseEnvironment +from harbor.models.agent.context import AgentContext +from openai import AsyncOpenAI + +MAX_TURNS = 30 +MAX_TOOL_OUTPUT_CHARS = 30_000 + +INSTRUCTIONS = """You investigate software repositories and answer deep codebase questions. + +The task repository is checked out at /app. Gather concrete evidence before answering: +map the relevant subsystem, search definitions and call sites, read tests and history when +useful, and run focused read-only experiments if the question asks for observed behavior. +Do not modify source files. Distinguish what the code proves from your own inferences. + +Your final answer should be comprehensive and precise. Cite repository-relative paths, +symbols, and line numbers wherever they support a claim. When finished, call +submit_answer with the complete answer; do not merely describe what you would inspect. +""" + +TOOLS: list[dict[str, Any]] = [ + { + "type": "function", + "name": "run_shell", + "description": ( + "Run a non-interactive command in the repository at /app. Use this for " + "read-only exploration and temporary experiments; never edit repository files." + ), + "parameters": { + "type": "object", + "properties": { + "command": {"type": "string", "description": "Shell command to run"} + }, + "required": ["command"], + "additionalProperties": False, + }, + "strict": True, + }, + { + "type": "function", + "name": "submit_answer", + "description": "Write the complete evidence-backed answer and finish the task.", + "parameters": { + "type": "object", + "properties": { + "answer": {"type": "string", "description": "Complete final answer"} + }, + "required": ["answer"], + "additionalProperties": False, + }, + "strict": True, + }, +] + + +class AtlasAgent(BaseAgent): + """Repository Q&A agent whose source is the editable optimization target.""" + + @staticmethod + @override + def name() -> str: + return "swe-atlas-responses-baseline" + + @override + def version(self) -> str: + return "0.1.0" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + if self.model_name is None: + raise ValueError("SWE-Atlas agent requires a Harbor model") + self._api_model = self.model_name.removeprefix("openai/") + self._client = AsyncOpenAI() + + @override + async def setup(self, environment: BaseEnvironment) -> None: + result = await environment.exec("test -d /app", timeout_sec=30) + if result.return_code != 0: + raise RuntimeError(result.stderr or "task repository is missing at /app") + + @staticmethod + def _truncate(value: str) -> str: + if len(value) <= MAX_TOOL_OUTPUT_CHARS: + return value + half = MAX_TOOL_OUTPUT_CHARS // 2 + omitted = len(value) - (2 * half) + return f"{value[:half]}\n...[{omitted} characters omitted]...\n{value[-half:]}" + + def _trace(self, event: dict[str, Any]) -> None: + self.logs_dir.mkdir(parents=True, exist_ok=True) + with (self.logs_dir / "atlas-trace.jsonl").open("a", encoding="utf-8") as file: + file.write(json.dumps(event, ensure_ascii=False, default=str) + "\n") + + async def _run_shell( + self, environment: BaseEnvironment, command: str + ) -> dict[str, Any]: + result = await environment.exec(command, cwd="/app", timeout_sec=180) + return { + "return_code": result.return_code, + "stdout": self._truncate(result.stdout or ""), + "stderr": self._truncate(result.stderr or ""), + } + + def _submit(self, answer: str) -> None: + normalized = answer.strip() + if not normalized: + raise ValueError("answer must not be empty") + self.logs_dir.mkdir(parents=True, exist_ok=True) + (self.logs_dir / "answer.txt").write_text( + f"<>\n{normalized}\n<>\n", + encoding="utf-8", + ) + + @staticmethod + def _usage_value(value: Any, name: str) -> int: + result = getattr(value, name, 0) if value is not None else 0 + return int(result or 0) + + @override + async def run( + self, + instruction: str, + environment: BaseEnvironment, + context: AgentContext, + ) -> None: + next_input: Any = instruction + previous_response_id: str | None = None + input_tokens = 0 + output_tokens = 0 + cached_tokens = 0 + + for turn in range(1, MAX_TURNS + 1): + request: dict[str, Any] = { + "model": self._api_model, + "instructions": INSTRUCTIONS, + "input": next_input, + "tools": TOOLS, + "reasoning": {"effort": "high"}, + "max_output_tokens": 12_000, + "parallel_tool_calls": False, + } + if previous_response_id is not None: + request["previous_response_id"] = previous_response_id + response = await self._client.responses.create(**request) + usage = response.usage + input_tokens += self._usage_value(usage, "input_tokens") + output_tokens += self._usage_value(usage, "output_tokens") + cached_tokens += self._usage_value( + getattr(usage, "input_tokens_details", None), "cached_tokens" + ) + + calls = [item for item in response.output if item.type == "function_call"] + self._trace( + { + "turn": turn, + "response_id": response.id, + "output_text": response.output_text, + "function_calls": [ + {"name": call.name, "arguments": call.arguments} + for call in calls + ], + } + ) + if not calls: + if response.output_text.strip(): + self._submit(response.output_text) + context.metadata = {"turns": turn, "trace": "atlas-trace.jsonl"} + break + raise RuntimeError("model returned neither an answer nor a tool call") + + next_input = [] + submitted = False + for call in calls: + try: + arguments = json.loads(call.arguments) + except json.JSONDecodeError as error: + result: dict[str, Any] = {"error": f"invalid arguments: {error}"} + else: + if call.name == "run_shell": + result = await self._run_shell( + environment, arguments["command"] + ) + elif call.name == "submit_answer": + self._submit(arguments["answer"]) + result = {"submitted": True} + submitted = True + else: + result = {"error": f"unknown tool: {call.name}"} + self._trace({"turn": turn, "tool": call.name, "result": result}) + next_input.append( + { + "type": "function_call_output", + "call_id": call.call_id, + "output": json.dumps(result, ensure_ascii=False), + } + ) + if submitted: + break + previous_response_id = response.id + if submitted: + context.metadata = {"turns": turn, "trace": "atlas-trace.jsonl"} + break + else: + raise RuntimeError(f"agent exceeded {MAX_TURNS} turns") + + context.n_input_tokens = input_tokens + context.n_output_tokens = output_tokens + context.n_cache_tokens = cached_tokens diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/target/tests/test_agent.py b/harness-engineering-bench/swe-atlas-qna/baseline/target/tests/test_agent.py new file mode 100644 index 00000000..78f29b86 --- /dev/null +++ b/harness-engineering-bench/swe-atlas-qna/baseline/target/tests/test_agent.py @@ -0,0 +1,63 @@ +from types import SimpleNamespace + +import pytest + +from atlas_agent import AtlasAgent + + +class FakeEnvironment: + async def exec(self, command, **kwargs): + return SimpleNamespace(return_code=0, stdout="evidence", stderr="") + + +class FakeResponses: + async def create(self, **kwargs): + return SimpleNamespace( + id="response-1", + output=[ + SimpleNamespace( + type="function_call", + name="submit_answer", + arguments='{"answer":"See src/example.py:10."}', + call_id="call-1", + ) + ], + output_text="", + usage=SimpleNamespace( + input_tokens=100, + output_tokens=12, + input_tokens_details=SimpleNamespace(cached_tokens=25), + ), + ) + + +@pytest.mark.asyncio +async def test_agent_writes_wrapped_answer_and_context(tmp_path, monkeypatch): + monkeypatch.setenv("OPENAI_API_KEY", "test-key") + agent = AtlasAgent( + logs_dir=tmp_path / "logs", + model_name="openai/gpt-5.4-mini-2026-03-17", + ) + agent._client = SimpleNamespace(responses=FakeResponses()) + context = SimpleNamespace( + metadata=None, + n_input_tokens=None, + n_output_tokens=None, + n_cache_tokens=None, + ) + + await agent.run("Investigate the code", FakeEnvironment(), context) + + assert (tmp_path / "logs" / "answer.txt").read_text() == ( + "<>\nSee src/example.py:10.\n<>\n" + ) + assert context.n_input_tokens == 100 + assert context.n_output_tokens == 12 + assert context.n_cache_tokens == 25 + assert context.metadata == {"turns": 1, "trace": "atlas-trace.jsonl"} + + +def test_agent_requires_model(tmp_path, monkeypatch): + monkeypatch.setenv("OPENAI_API_KEY", "test-key") + with pytest.raises(ValueError, match="requires a Harbor model"): + AtlasAgent(logs_dir=tmp_path) diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/target/uv.lock b/harness-engineering-bench/swe-atlas-qna/baseline/target/uv.lock new file mode 100644 index 00000000..89c2ec7b --- /dev/null +++ b/harness-engineering-bench/swe-atlas-qna/baseline/target/uv.lock @@ -0,0 +1,2127 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" + +[[package]] +name = "aiohappyeyeballs" +version = 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talks to the canonical +tau3 simulated user and domain environment through the task's `tau3-runtime` +MCP server. It spans airline, retail, telecom, and banking-knowledge domains. + +The pinned 375-task dataset is split 75/150/150 for development, validation, +and test. The deterministic split is stratified by domain. + +Regenerate or verify the committed split from an exported dataset: + +```bash +python harness-engineering-bench/scripts/partition_dataset.py tau3 \ + --tasks-dir /path/to/exported/dataset \ + --output-dir harness-engineering-bench/candidates/tau3/partitions \ + --fetch-registry + +python harness-engineering-bench/scripts/partition_dataset.py tau3 \ + --tasks-dir /path/to/exported/dataset \ + --output-dir harness-engineering-bench/candidates/tau3/partitions \ + --check +``` diff --git a/harness-engineering-bench/tau3/baseline/.gitignore b/harness-engineering-bench/tau3/baseline/.gitignore new file mode 100644 index 00000000..35e4297b --- /dev/null +++ b/harness-engineering-bench/tau3/baseline/.gitignore @@ -0,0 +1,2 @@ +compiled/ +.vero/ diff --git a/harness-engineering-bench/tau3/baseline/README.md b/harness-engineering-bench/tau3/baseline/README.md new file mode 100644 index 00000000..eb904111 --- /dev/null +++ b/harness-engineering-bench/tau3/baseline/README.md @@ -0,0 +1,25 @@ +# tau3 MCP customer-service agent + +This leaf benchmark optimizes a Harbor-native agent that connects to the MCP +server declared by each tau3 task, obtains the first simulated-user message, +and carries the conversation through domain and communication tool calls. The +editable program controls its prompt, tool selection, context management, and +conversation policy; the task-owned MCP runtime and canonical evaluator remain +outside the candidate repository. + +The trusted build pins the target model to `gpt-5.4-mini-2026-03-17` and pins +the Harbor dataset, split, budgets, access policy, and final test partition. +The simulated user and natural-language assertion grader use the canonical +model defaults encoded in the pinned tau3 tasks. + +Compile from the repository root: + +```bash +cd vero +VERO_SKIP_SECRET_CHECK=1 uv run vero harbor build \ + --config ../harness-engineering-bench/candidates/tau3/baseline/build.yaml \ + --output ../harness-engineering-bench/candidates/tau3/baseline/compiled +``` + +For a real run, provide the OpenAI and Modal credentials declared in +`build.yaml`. diff --git a/harness-engineering-bench/tau3/baseline/build.yaml b/harness-engineering-bench/tau3/baseline/build.yaml new file mode 100644 index 00000000..67cbcef0 --- /dev/null +++ b/harness-engineering-bench/tau3/baseline/build.yaml @@ -0,0 +1,90 @@ +name: vero/optimize-tau3-baseline +description: >- + Improve a customer-service agent on canonical tau3-bench tasks while + preserving the task-owned MCP protocol and evaluation. +agent_repo: target +task_source: sierra-research/tau3-bench@sha256:a57304f682894ac061090769af771a3617664f3ff6e5417d4eadf8e30433e4d9 +task_manifest: ../partitions/manifest.json +agent_import_path: tau3_agent.agent:Tau3Agent +harbor_requirement: harbor[modal]==0.18.0 + +partition_files: + development: ../partitions/development.json + validation: ../partitions/validation.json + test: ../partitions/test.json + +agent_access: + - partition: development + disclosure: full + expose_case_resources: true + total_runs: 12 + total_cases: 120 + - partition: validation + disclosure: aggregate + expose_case_resources: false + min_aggregate_cases: 5 + total_runs: 8 + total_cases: 300 + +selection_partition: validation +targets: + - partition: test + reward_key: reward + failure_value: 0.0 + max_attempts: 1 + +evaluation_set_name: tau3 +objective: + selector: + metric: score + direction: maximize +reward_mode: auto_best +baseline_floor: true +score_baseline: true +rescore_top_k: 3 +rescore_attempts: 1 + +model: openai/gpt-5.4-mini-2026-03-17 +environment_name: modal +harbor_python_version: "3.12" +n_attempts: 1 +max_retries: 1 +infrastructure_max_attempts: 3 +infrastructure_retry_delay_seconds: 5 +aggregate_attempts: best +feedback_transcripts: true +feedback_max_bytes: 16000 +expose_attempt_detail: false +timeout_seconds: 14400 +max_concurrency: 8 +error_rate_threshold: 0.1 +verifier_timeout_seconds: 14400 +secrets: + - MODAL_TOKEN_ID + - MODAL_TOKEN_SECRET +inference_gateway: + upstream_api_key_env: OPENAI_API_KEY + upstream_base_url_env: OPENAI_BASE_URL + producer: + allowed_models: ["${optimizer_model:-gpt-5.4}"] + max_concurrency: 8 + evaluation: + allowed_models: [gpt-5.4-mini-2026-03-17] + max_requests: 15000 + max_tokens: 100000000 + max_concurrency: 64 +instruct_multifidelity: true +instruct_exhaust_budget: true + +# tau3's environment runs its own LLM services — the user-simulator (inside the +# task container) and the NL-assertions grader (verifier) — which cannot reach the +# compose-internal inference gateway. Give them the real upstream directly while +# the candidate agent keeps the metered/allow-listed gateway (VERO_AGENT_INFERENCE_*). +task_services_use_upstream: true +# Harness isolation is off for tau3: its task-owned services need the real +# upstream credential, which uid isolation cannot hide from the harness env. +# Re-enable once task-service credentials are delivered off the harness env. +harness_user: null +task_environment: + TAU2_USER_MODEL: openai/gpt-5.4-mini-2026-03-17 + TAU2_NL_ASSERTIONS_MODEL: openai/gpt-5.4-mini-2026-03-17 diff --git a/harness-engineering-bench/tau3/baseline/target/pyproject.toml b/harness-engineering-bench/tau3/baseline/target/pyproject.toml new file mode 100644 index 00000000..b101024e --- /dev/null +++ b/harness-engineering-bench/tau3/baseline/target/pyproject.toml @@ -0,0 +1,25 @@ +[project] +name = "vero-tau3-agent" +version = "0.1.0" +description = "Editable Harbor-native tau3 baseline for VeRO" +requires-python = ">=3.12" +dependencies = [ + "harbor==0.18.0", + "openai>=2.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/tau3_agent"] + +[dependency-groups] +dev = [ + "pytest>=9.0.2", + "pytest-asyncio>=1.3.0", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] diff --git a/harness-engineering-bench/tau3/baseline/target/src/tau3_agent/__init__.py b/harness-engineering-bench/tau3/baseline/target/src/tau3_agent/__init__.py new file mode 100644 index 00000000..e170cbf9 --- /dev/null +++ b/harness-engineering-bench/tau3/baseline/target/src/tau3_agent/__init__.py @@ -0,0 +1,5 @@ +"""Harbor-native tau3 target agent.""" + +from tau3_agent.agent import Tau3Agent + +__all__ = ["Tau3Agent"] diff --git a/harness-engineering-bench/tau3/baseline/target/src/tau3_agent/agent.py b/harness-engineering-bench/tau3/baseline/target/src/tau3_agent/agent.py new file mode 100644 index 00000000..4a98cd5a --- /dev/null +++ b/harness-engineering-bench/tau3/baseline/target/src/tau3_agent/agent.py @@ -0,0 +1,382 @@ +"""Harbor agent for tau3: a host-side MCP conversation loop. + +The tau3 task exposes its runtime as a streamable-http MCP server that lives on +the task's internal compose network (`tau3-runtime:8000`), reachable only from +inside the `main` container. Harbor's ``BaseAgent.run`` runs host-side, so this +agent drives the whole conversation from the host: it calls the target model +directly (Harbor already places the inference credentials in this process's +environment), and it reaches the MCP server by shelling ``curl`` into ``main`` +via ``environment.exec``. The MCP session is header-keyed (``Mcp-Session-Id``), +so it survives across independent ``curl`` invocations — no persistent +in-container process, no uploaded runner, no credential forwarding. + +This whole file is the optimizable surface: the system instructions and the +tool-use loop are what an optimizer edits to improve the agent. +""" + +from __future__ import annotations + +import base64 +import json +import os +from typing import Any, override + +from harbor.agents.base import BaseAgent +from harbor.environments.base import BaseEnvironment +from harbor.models.agent.context import AgentContext +from openai import AsyncOpenAI + +MAX_TURNS = 80 +MAX_TOOL_OUTPUT_CHARS = 30_000 +PROTOCOL_VERSION = "2025-06-18" + +INSTRUCTIONS = """You are a careful customer-service agent in a simulated environment. + +The task instruction contains the binding domain policy. Follow it exactly. Continue the +conversation by making one MCP tool call at a time. Use send_message_to_user for every +message to the customer, use domain tools only after satisfying policy prerequisites, +and never invent tool results, customer data, or policy. Before any consequential action, +double-check identity, state, allowed parameters, and required confirmation. Use +end_conversation only when the request is resolved or the policy requires ending it. +""" + + +def _truncate(value: str) -> str: + if len(value) <= MAX_TOOL_OUTPUT_CHARS: + return value + half = MAX_TOOL_OUTPUT_CHARS // 2 + omitted = len(value) - (2 * half) + return f"{value[:half]}\n...[{omitted} characters omitted]...\n{value[-half:]}" + + +def _usage_value(value: Any, name: str) -> int: + result = getattr(value, name, 0) if value is not None else 0 + return int(result or 0) + + +def _looks_stopped(value: str) -> bool: + return "###STOP###" in value + + +class Tau3Agent(BaseAgent): + """Drive the tau3 MCP conversation host-side, reaching MCP via ``curl``.""" + + @staticmethod + @override + def name() -> str: + return "tau3-responses-baseline" + + @override + def version(self) -> str: + return "0.2.0" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + if self.model_name is None: + raise ValueError("tau3 agent requires a Harbor model") + self._api_model = self.model_name.removeprefix("openai/") + self._request_id = 0 + + def _server_url(self) -> str: + urls = [ + str(server.url) + for server in self.mcp_servers + if getattr(server, "transport", None) == "streamable-http" + and getattr(server, "url", None) + ] + if len(urls) != 1: + raise RuntimeError( + "tau3 agent requires exactly one streamable-http MCP server" + ) + return urls[0] + + @override + async def setup(self, environment: BaseEnvironment) -> None: + # The loop reaches MCP by shelling `curl` into `main`; ensure it (and the + # base64 decoder we pipe payloads through) are present. base64 is coreutils; + # curl is installed only if the base image lacks it. + command = ( + "mkdir -p /logs/agent; " + "if ! command -v curl >/dev/null 2>&1; then " + " (apt-get update && apt-get install -y --no-install-recommends curl) " + " || apk add --no-cache curl || true; " + "fi; " + "command -v curl >/dev/null 2>&1 && command -v base64 >/dev/null 2>&1" + ) + result = await environment.exec(command, timeout_sec=300) + if result.return_code != 0: + raise RuntimeError( + "tau3 agent requires curl and base64 in the task environment" + ) + + def _next_id(self) -> int: + self._request_id += 1 + return self._request_id + + async def _mcp_raw( + self, + environment: BaseEnvironment, + payload: dict[str, Any], + *, + session_id: str | None, + ) -> str: + """POST one JSON-RPC message to the MCP server via `curl` inside `main`. + + The payload is base64-encoded so arbitrary tool arguments survive the shell + untouched. Returns curl's raw stdout (response headers followed by the SSE + body), which the callers parse for the session id and the `data:` line. + """ + encoded = base64.b64encode( + json.dumps(payload, ensure_ascii=False).encode("utf-8") + ).decode("ascii") + session_header = ( + f"-H 'mcp-session-id: {session_id}' " if session_id is not None else "" + ) + command = ( + f"printf '%s' '{encoded}' | base64 -d | " + f"curl -sS -D - -X POST '{self._mcp_url}' " + f"-H 'Content-Type: application/json' " + f"-H 'Accept: application/json, text/event-stream' " + f"{session_header}--data-binary @-" + ) + result = await environment.exec(command, timeout_sec=120) + if result.return_code != 0: + raise RuntimeError( + f"MCP request failed (rc={result.return_code}): " + f"{result.stderr or result.stdout}" + ) + return result.stdout + + @staticmethod + def _session_id_from(stdout: str) -> str | None: + for line in stdout.splitlines(): + if line.lower().startswith("mcp-session-id:"): + return line.split(":", 1)[1].strip() + return None + + @staticmethod + def _jsonrpc_from(stdout: str) -> dict[str, Any] | None: + parsed: list[dict[str, Any]] = [] + for line in stdout.splitlines(): + stripped = line.strip() + if stripped.startswith("data:"): + data = stripped[len("data:"):].strip() + if not data: + continue + try: + message = json.loads(data) + except json.JSONDecodeError: + continue + if isinstance(message, dict): + parsed.append(message) + for message in parsed: + if "result" in message or "error" in message: + return message + return parsed[-1] if parsed else None + + async def _call_tool( + self, + environment: BaseEnvironment, + session_id: str, + name: str, + arguments: dict[str, Any], + ) -> str: + stdout = await self._mcp_raw( + environment, + { + "jsonrpc": "2.0", + "id": self._next_id(), + "method": "tools/call", + "params": {"name": name, "arguments": arguments}, + }, + session_id=session_id, + ) + response = self._jsonrpc_from(stdout) + if response is None: + raise RuntimeError(f"no JSON-RPC response for tool {name!r}") + if "error" in response: + return _truncate(json.dumps(response["error"], ensure_ascii=False)) + return _truncate( + json.dumps(response["result"], ensure_ascii=False, default=str) + ) + + async def _open_session(self, environment: BaseEnvironment) -> tuple[str, list[Any]]: + init = await self._mcp_raw( + environment, + { + "jsonrpc": "2.0", + "id": self._next_id(), + "method": "initialize", + "params": { + "protocolVersion": PROTOCOL_VERSION, + "capabilities": {}, + "clientInfo": {"name": "vero-tau3", "version": self.version()}, + }, + }, + session_id=None, + ) + session_id = self._session_id_from(init) + if not session_id: + raise RuntimeError("MCP initialize did not return a session id") + await self._mcp_raw( + environment, + {"jsonrpc": "2.0", "method": "notifications/initialized"}, + session_id=session_id, + ) + listed = self._jsonrpc_from( + await self._mcp_raw( + environment, + {"jsonrpc": "2.0", "id": self._next_id(), "method": "tools/list"}, + session_id=session_id, + ) + ) + if listed is None or "result" not in listed: + raise RuntimeError("MCP tools/list returned no result") + tools = listed["result"].get("tools", []) + available = {tool["name"] for tool in tools} + required = {"start_conversation", "send_message_to_user", "end_conversation"} + missing = sorted(required - available) + if missing: + raise RuntimeError(f"tau3 MCP server is missing tools: {missing}") + return session_id, tools + + @staticmethod + def _openai_tools(tools: list[Any]) -> list[dict[str, Any]]: + return [ + { + "type": "function", + "name": tool["name"], + "description": tool.get("description") or f"Call {tool['name']}.", + "parameters": tool["inputSchema"], + } + for tool in tools + if tool["name"] != "start_conversation" + ] + + def _trace(self, event: dict[str, Any]) -> None: + self.logs_dir.mkdir(parents=True, exist_ok=True) + path = self.logs_dir / "tau3-trace.jsonl" + with path.open("a", encoding="utf-8") as file: + file.write(json.dumps(event, ensure_ascii=False, default=str) + "\n") + + @override + async def run( + self, + instruction: str, + environment: BaseEnvironment, + context: AgentContext, + ) -> None: + self._mcp_url = self._server_url() + # When the eval reroutes OPENAI_* to the task's own LLM services + # (user-sim/grader), the candidate agent's metered gateway is supplied on + # dedicated vars; otherwise fall back to OPENAI_* from the environment. + gateway_key = os.environ.get("VERO_AGENT_INFERENCE_API_KEY") + gateway_url = os.environ.get("VERO_AGENT_INFERENCE_BASE_URL") + if gateway_key and gateway_url: + client = AsyncOpenAI(api_key=gateway_key, base_url=gateway_url) + else: + client = AsyncOpenAI() # OPENAI_API_KEY / OPENAI_BASE_URL from the env + input_tokens = output_tokens = cached_tokens = turns = 0 + + session_id, tools = await self._open_session(environment) + openai_tools = self._openai_tools(tools) + + first_text = await self._call_tool( + environment, session_id, "start_conversation", {} + ) + self._trace({"turn": 0, "tool": "start_conversation", "result": first_text}) + next_input: Any = ( + f"{instruction}\n\nThe conversation has been started exactly once. " + f"The current user message is in this MCP result:\n{first_text}" + ) + previous_response_id: str | None = None + + for turn in range(1, MAX_TURNS + 1): + turns = turn + request: dict[str, Any] = { + "model": self._api_model, + "instructions": INSTRUCTIONS, + "input": next_input, + "tools": openai_tools, + "reasoning": {"effort": "medium"}, + "max_output_tokens": 8_000, + "parallel_tool_calls": False, + } + if previous_response_id is not None: + request["previous_response_id"] = previous_response_id + response = await client.responses.create(**request) + usage = response.usage + input_tokens += _usage_value(usage, "input_tokens") + output_tokens += _usage_value(usage, "output_tokens") + cached_tokens += _usage_value( + getattr(usage, "input_tokens_details", None), "cached_tokens" + ) + + calls = [item for item in response.output if item.type == "function_call"] + self._trace( + { + "turn": turn, + "response_id": response.id, + "output_text": response.output_text, + "function_calls": [ + {"name": call.name, "arguments": call.arguments} + for call in calls + ], + } + ) + + if calls: + call = calls[0] + try: + arguments = json.loads(call.arguments) + result_text = await self._call_tool( + environment, session_id, call.name, arguments + ) + except Exception as error: # noqa: BLE001 - feed failures back to model + result_text = json.dumps( + {"error": f"{type(error).__name__}: {error}"} + ) + self._trace({"turn": turn, "tool": call.name, "result": result_text}) + next_input = [ + { + "type": "function_call_output", + "call_id": call.call_id, + "output": result_text, + } + ] + previous_response_id = response.id + if call.name == "end_conversation" or _looks_stopped(result_text): + break + continue + + message = response.output_text.strip() + if not message: + raise RuntimeError( + "model returned neither a customer message nor a tool call" + ) + fallback_tool = ( + "end_conversation" if message == "###STOP###" else "send_message_to_user" + ) + result_text = await self._call_tool( + environment, session_id, fallback_tool, {"message": message} + ) + self._trace({"turn": turn, "tool": fallback_tool, "result": result_text}) + if fallback_tool == "end_conversation" or _looks_stopped(result_text): + break + next_input = ( + "Your previous text was delivered to the user. " + f"Their next response is in this MCP result:\n{result_text}" + ) + previous_response_id = None + else: + await self._call_tool( + environment, + session_id, + "end_conversation", + {"message": "I'm sorry, but I'm unable to complete this request."}, + ) + + context.n_input_tokens = input_tokens + context.n_output_tokens = output_tokens + context.n_cache_tokens = cached_tokens + context.metadata = {"turns": turns, "trace": "tau3-trace.jsonl"} diff --git a/harness-engineering-bench/tau3/baseline/target/tests/test_agent.py b/harness-engineering-bench/tau3/baseline/target/tests/test_agent.py new file mode 100644 index 00000000..eaeb4c10 --- /dev/null +++ b/harness-engineering-bench/tau3/baseline/target/tests/test_agent.py @@ -0,0 +1,236 @@ +import base64 +import json +import re +from types import SimpleNamespace + +import pytest + +from tau3_agent import Tau3Agent +import tau3_agent.agent as agent_module + + +def _agent(tmp_path, monkeypatch, **kwargs): + monkeypatch.setenv("OPENAI_API_KEY", "test-key") + monkeypatch.setenv("OPENAI_BASE_URL", "http://gateway.invalid/v1") + return Tau3Agent( + logs_dir=tmp_path / "logs", + model_name="openai/gpt-5.4-mini-2026-03-17", + **kwargs, + ) + + +def _server(url="http://tau3-runtime:8000/mcp"): + return SimpleNamespace(name="tau3-runtime", transport="streamable-http", url=url) + + +def _sse(message: dict, *, headers: str = "") -> str: + head = "HTTP/1.1 200 OK\r\ncontent-type: text/event-stream\r\n" + headers + "\r\n" + return f"{head}\nevent: message\ndata: {json.dumps(message)}\n\n" + + +def test_agent_resolves_one_streamable_http_server(tmp_path, monkeypatch): + agent = _agent(tmp_path, monkeypatch, mcp_servers=[_server()]) + assert agent._server_url() == "http://tau3-runtime:8000/mcp" + + +def test_agent_requires_model(tmp_path, monkeypatch): + monkeypatch.setenv("OPENAI_API_KEY", "test-key") + with pytest.raises(ValueError, match="requires a Harbor model"): + Tau3Agent(logs_dir=tmp_path) + + +def test_session_id_parsed_from_headers(): + stdout = "HTTP/1.1 200 OK\r\nMcp-Session-Id: abc123\r\n\r\ndata: {}" + assert Tau3Agent._session_id_from(stdout) == "abc123" + assert Tau3Agent._session_id_from("HTTP/1.1 200 OK\r\n\r\n") is None + + +def test_jsonrpc_prefers_result_over_other_events(): + stdout = ( + "data: {\"jsonrpc\":\"2.0\",\"method\":\"notifications/message\"}\n" + "data: {\"jsonrpc\":\"2.0\",\"id\":3,\"result\":{\"ok\":true}}\n" + ) + assert Tau3Agent._jsonrpc_from(stdout) == { + "jsonrpc": "2.0", + "id": 3, + "result": {"ok": True}, + } + + +def test_openai_tools_excludes_start_conversation(): + tools = [ + {"name": "start_conversation", "description": "x", "inputSchema": {}}, + {"name": "send_message_to_user", "description": "y", "inputSchema": {}}, + ] + names = {t["name"] for t in Tau3Agent._openai_tools(tools)} + assert names == {"send_message_to_user"} + + +class _RecordingEnv: + """Fake environment that decodes each base64 MCP payload and returns canned SSE.""" + + def __init__(self): + self.commands = [] + + async def exec(self, command, **kwargs): + self.commands.append(command) + if "curl" not in command: # setup() probe + return SimpleNamespace(return_code=0, stdout="", stderr="") + encoded = re.search(r"printf '%s' '([^']+)'", command).group(1) + payload = json.loads(base64.b64decode(encoded)) + method = payload.get("method") + if method == "initialize": + return SimpleNamespace( + return_code=0, + stdout=_sse( + {"jsonrpc": "2.0", "id": payload["id"], "result": {}}, + headers="mcp-session-id: sess-1\r\n", + ), + stderr="", + ) + if method == "notifications/initialized": + return SimpleNamespace(return_code=0, stdout="HTTP/1.1 202\r\n\r\n", stderr="") + if method == "tools/list": + tools = [ + {"name": n, "description": n, "inputSchema": {"type": "object"}} + for n in ( + "start_conversation", + "send_message_to_user", + "end_conversation", + ) + ] + return SimpleNamespace( + return_code=0, + stdout=_sse( + {"jsonrpc": "2.0", "id": payload["id"], "result": {"tools": tools}} + ), + stderr="", + ) + # tools/call + name = payload["params"]["name"] + result = {"content": [{"type": "text", "text": f"ran:{name}"}]} + return SimpleNamespace( + return_code=0, + stdout=_sse({"jsonrpc": "2.0", "id": payload["id"], "result": result}), + stderr="", + ) + + +def _response(*, calls=None, text="", rid="r1"): + output = calls or [] + return SimpleNamespace( + id=rid, + output=output, + output_text=text, + usage=SimpleNamespace( + input_tokens=10, + output_tokens=5, + input_tokens_details=SimpleNamespace(cached_tokens=3), + ), + ) + + +def _call(name, arguments): + return SimpleNamespace( + type="function_call", + name=name, + arguments=json.dumps(arguments), + call_id=f"c-{name}", + ) + + +@pytest.mark.asyncio +async def test_setup_requires_curl_and_base64(tmp_path, monkeypatch): + agent = _agent(tmp_path, monkeypatch, mcp_servers=[_server()]) + + class _OkEnv: + async def exec(self, command, **kwargs): + return SimpleNamespace(return_code=0, stdout="", stderr="") + + await agent.setup(_OkEnv()) # no exception + + class _FailEnv: + async def exec(self, command, **kwargs): + return SimpleNamespace(return_code=1, stdout="", stderr="no curl") + + with pytest.raises(RuntimeError, match="curl and base64"): + await agent.setup(_FailEnv()) + + +@pytest.mark.asyncio +async def test_run_drives_loop_and_records_usage(tmp_path, monkeypatch): + agent = _agent(tmp_path, monkeypatch, mcp_servers=[_server()]) + env = _RecordingEnv() + + # turn 1: plain text -> send_message_to_user; turn 2: end_conversation tool call. + script = [ + _response(text="Hello, how can I help?", rid="r1"), + _response(calls=[_call("end_conversation", {"message": "bye"})], rid="r2"), + ] + + class _FakeResponses: + def __init__(self, script): + self._script, self._i = script, 0 + + async def create(self, **kwargs): + item = self._script[self._i] + self._i += 1 + return item + + class _FakeClient: + def __init__(self): + self.responses = _FakeResponses(script) + + monkeypatch.setattr(agent_module, "AsyncOpenAI", lambda *a, **k: _FakeClient()) + + context = SimpleNamespace( + n_input_tokens=0, n_output_tokens=0, n_cache_tokens=0, metadata=None + ) + await agent.run("POLICY", env, context) + + # base64 payloads + threaded session id on tool calls + assert any("mcp-session-id: sess-1" in c for c in env.commands if "curl" in c) + assert any("base64 -d" in c for c in env.commands if "curl" in c) + # two model turns => usage accumulated twice + assert context.n_input_tokens == 20 + assert context.n_output_tokens == 10 + assert context.n_cache_tokens == 6 + assert context.metadata["turns"] == 2 + + +@pytest.mark.asyncio +async def test_agent_prefers_dedicated_gateway_creds(tmp_path, monkeypatch): + # When the eval reroutes OPENAI_* to the task's own LLM services, the agent's + # metered gateway arrives on VERO_AGENT_INFERENCE_* — the agent must use those. + agent = _agent(tmp_path, monkeypatch, mcp_servers=[_server()]) + monkeypatch.setenv("VERO_AGENT_INFERENCE_API_KEY", "gw-key") + monkeypatch.setenv("VERO_AGENT_INFERENCE_BASE_URL", "http://gw/scopes/evaluation/e/v1") + + captured = {} + script = [_response(calls=[_call("end_conversation", {"message": "bye"})], rid="r1")] + + class _FakeResponses: + def __init__(self): + self._i = 0 + + async def create(self, **kwargs): + item = script[self._i] + self._i += 1 + return item + + class _FakeClient: + def __init__(self): + self.responses = _FakeResponses() + + def _fake(*a, **k): + captured.update(k) + return _FakeClient() + + monkeypatch.setattr(agent_module, "AsyncOpenAI", _fake) + context = SimpleNamespace( + n_input_tokens=0, n_output_tokens=0, n_cache_tokens=0, metadata=None + ) + await agent.run("POLICY", _RecordingEnv(), context) + + assert captured.get("api_key") == "gw-key" + assert captured.get("base_url") == "http://gw/scopes/evaluation/e/v1" diff --git a/harness-engineering-bench/tau3/baseline/target/uv.lock 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"sierra-research/tau3-bench__tau3-telecom-service-issue-airplane-mode-on-overdue-bill-suspension-persona-none", + "sierra-research/tau3-bench__tau3-telecom-service-issue-break-apn-settings-contract-end-suspension-lock-sim-card-pin-unseat-sim-card-persona-hard" +] From 2254f234bdff867c47f6fe328b9a65b90fa3950f Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 11:53:04 -0700 Subject: [PATCH 02/16] gaia: baseline_floor off (feedback #1) The floor gates a ship on a validation comparison while gaia's reward is on the test partition (different distributions), so keep it opt-in / off. Now that the harbor default is false this is explicit-for-clarity. --- harness-engineering-bench/gaia/baseline/build.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/harness-engineering-bench/gaia/baseline/build.yaml b/harness-engineering-bench/gaia/baseline/build.yaml index 654ed2a6..dcf7319d 100644 --- a/harness-engineering-bench/gaia/baseline/build.yaml +++ b/harness-engineering-bench/gaia/baseline/build.yaml @@ -38,7 +38,7 @@ objective: metric: score direction: maximize reward_mode: submit # agent picks; falls back to auto_best, then current version -baseline_floor: true +baseline_floor: false # gates on validation while reward is on test; opt-in only score_baseline: true rescore_top_k: 3 rescore_attempts: 1 From 7f754b713fa439da952f982f537351b4954a10c3 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 13:00:24 -0700 Subject: [PATCH 03/16] gaia: re-enable harness isolation (harness_user: harness) Now that the sidecar image pre-warms the harness uv cache, the inner eval runs as the unprivileged harness user again, so the candidate harness cannot read the held-out records, budgets, or admin token. --- harness-engineering-bench/gaia/baseline/build.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/harness-engineering-bench/gaia/baseline/build.yaml b/harness-engineering-bench/gaia/baseline/build.yaml index dcf7319d..11a6d37d 100644 --- a/harness-engineering-bench/gaia/baseline/build.yaml +++ b/harness-engineering-bench/gaia/baseline/build.yaml @@ -66,8 +66,8 @@ secrets: - WANDB_API_KEY - WANDB_BASE_URL # self-hosted W&B (scaleai.wandb.io) -# off: breaks the eval's uv resolution of gaia_agent; needs an isolation fix. -harness_user: null +# candidate harness runs as an unprivileged uid, unable to read held-out state. +harness_user: harness wandb: project: vero-gaia From abbb093d237bbfa05996e9b7c6cc1b6ba1e3e00e Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 16:03:03 -0700 Subject: [PATCH 04/16] Bump harbor to 0.20.0 across benchmark configs and regenerate locks Move each benchmark to harbor 0.20.0 in lockstep so the eval resolves: build.yaml harbor_requirement (the evaluator's --with harbor[modal]) and target/pyproject.toml (the candidate's own harbor dep) must match, else `uv run --project --with harbor[modal]==0.20.0` is unsatisfiable. uv.lock regenerated with `uv lock` (not hand-edited). Covers gaia, officeqa, swe-atlas-qna, tau3. Co-Authored-By: Claude Opus 4.8 (1M context) --- harness-engineering-bench/gaia/baseline/build.yaml | 2 +- .../gaia/baseline/target/pyproject.toml | 2 +- harness-engineering-bench/gaia/baseline/target/uv.lock | 8 ++++---- harness-engineering-bench/officeqa/baseline/build.yaml | 2 +- .../officeqa/baseline/target/pyproject.toml | 2 +- .../officeqa/baseline/target/uv.lock | 8 ++++---- .../swe-atlas-qna/baseline/build.yaml | 2 +- .../swe-atlas-qna/baseline/target/pyproject.toml | 2 +- .../swe-atlas-qna/baseline/target/uv.lock | 8 ++++---- harness-engineering-bench/tau3/baseline/build.yaml | 2 +- .../tau3/baseline/target/pyproject.toml | 2 +- harness-engineering-bench/tau3/baseline/target/uv.lock | 8 ++++---- 12 files changed, 24 insertions(+), 24 deletions(-) diff --git a/harness-engineering-bench/gaia/baseline/build.yaml b/harness-engineering-bench/gaia/baseline/build.yaml index 11a6d37d..6e7bdc20 100644 --- a/harness-engineering-bench/gaia/baseline/build.yaml +++ b/harness-engineering-bench/gaia/baseline/build.yaml @@ -6,7 +6,7 @@ agent_repo: target task_source: gaia/gaia@sha256:bbc356f476e0b70ba77da11a9be7d6345918d1e4a2daade0d6dfb82ee6f7b761 task_manifest: ../partitions/manifest.json agent_import_path: gaia_agent.agent:GaiaAgent -harbor_requirement: harbor[modal]==0.18.0 +harbor_requirement: harbor[modal]==0.20.0 partition_files: development: ../partitions/development.json diff --git a/harness-engineering-bench/gaia/baseline/target/pyproject.toml b/harness-engineering-bench/gaia/baseline/target/pyproject.toml index 23136ade..016d6341 100644 --- a/harness-engineering-bench/gaia/baseline/target/pyproject.toml +++ b/harness-engineering-bench/gaia/baseline/target/pyproject.toml @@ -4,7 +4,7 @@ version = "0.1.0" description = "Editable Harbor-native GAIA baseline for VeRO" requires-python = ">=3.12" dependencies = [ - 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"harbor==0.18.0", + "harbor==0.20.0", "openai>=2.0", ] diff --git a/harness-engineering-bench/tau3/baseline/target/uv.lock b/harness-engineering-bench/tau3/baseline/target/uv.lock index b392ab67..9e97b014 100644 --- a/harness-engineering-bench/tau3/baseline/target/uv.lock +++ b/harness-engineering-bench/tau3/baseline/target/uv.lock @@ -611,7 +611,7 @@ wheels = [ [[package]] name = "harbor" -version = "0.18.0" +version = "0.20.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "dirhash" }, @@ -636,9 +636,9 @@ dependencies = [ { name = "typer" }, { name = "uvicorn" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/38/24/7222433d3b9f665db1759456c281a9c136b921300be5d7af269f183ee59e/harbor-0.18.0.tar.gz", hash = "sha256:9b918b99ec38b4e16db7e0b797dcebf92bfc8be9d9ba22a2d2ed6ebb8feb38df", size = 1535447, upload-time = "2026-07-07T20:29:46.878Z" } +sdist = { url = "https://files.pythonhosted.org/packages/1e/da/5a26998c6e7d9455321ab39bc8e9122993892ece13c3ad4f2b0de986aaf6/harbor-0.20.0.tar.gz", hash = "sha256:e2e5e88f772690fd121553ca34fd5d6dd6b4aaa51c8fae635abc84b112303112", size = 1590870, upload-time = "2026-07-18T21:25:22.578Z" } wheels = [ - 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Compiled trees are build outputs (not committed) and pick this up on rebuild. Co-Authored-By: Claude Fable 5 --- harness-engineering-bench/ale-bench/environment/Dockerfile | 2 +- harness-engineering-bench/ale-bench/instruction.md | 6 +++--- harness-engineering-bench/ale-bench/solution/solve.sh | 4 ++-- harness-engineering-bench/gaia/README.md | 4 ++-- harness-engineering-bench/gaia/baseline/README.md | 6 +++--- harness-engineering-bench/swe-atlas-qna/baseline/.gitignore | 1 + harness-engineering-bench/tau3/baseline/.gitignore | 1 + 7 files changed, 13 insertions(+), 11 deletions(-) diff --git a/harness-engineering-bench/ale-bench/environment/Dockerfile b/harness-engineering-bench/ale-bench/environment/Dockerfile index 9f2e4e6f..99fbbac7 100644 --- a/harness-engineering-bench/ale-bench/environment/Dockerfile +++ b/harness-engineering-bench/ale-bench/environment/Dockerfile @@ -1,6 +1,6 @@ # Main service: the optimizer workbench a coding agent works in. # Holds the editable target (solution.cpp) plus the VeRO CLI so the agent can -# call `vero harbor eval` / `vero harbor status` against the trusted sidecar. +# call `evals run` / `evals status` against the trusted sidecar. FROM ghcr.io/astral-sh/uv:python3.12-bookworm RUN apt-get update \ diff --git a/harness-engineering-bench/ale-bench/instruction.md b/harness-engineering-bench/ale-bench/instruction.md index f26c762c..8e50fc15 100644 --- a/harness-engineering-bench/ale-bench/instruction.md +++ b/harness-engineering-bench/ale-bench/instruction.md @@ -15,11 +15,11 @@ the statement specifies. 3. Score the current commit on the validation set (public seeds): ```bash - vero harbor eval --backend cmd --evaluation-set ale-bench \ + evals run --backend cmd --evaluation-set ale-bench \ --partition validation --start 0 --stop 1 ``` - Then `vero harbor eval-status JOB_ID` / `vero harbor eval-result JOB_ID`. -4. Use `vero harbor status` to see evaluation access. VeRO pools your measurements, + Then `evals status JOB_ID` / `evals result JOB_ID`. +4. Use `evals status` to see evaluation access. VeRO pools your measurements, re-scores the best on the held-out private seeds, and ships the best candidate (the unmodified baseline is a floor, so a regression is never shipped). diff --git a/harness-engineering-bench/ale-bench/solution/solve.sh b/harness-engineering-bench/ale-bench/solution/solve.sh index 7bf3ea71..fdecfa05 100755 --- a/harness-engineering-bench/ale-bench/solution/solve.sh +++ b/harness-engineering-bench/ale-bench/solution/solve.sh @@ -2,5 +2,5 @@ set -eu cd /work/agent git config user.name optimizer; git config user.email o@v.test -vero harbor eval --backend cmd --evaluation-set ale-bench --partition validation --start 0 --stop 1 -vero harbor status +evals run --backend cmd --evaluation-set ale-bench --partition validation --start 0 --stop 1 +evals status diff --git a/harness-engineering-bench/gaia/README.md b/harness-engineering-bench/gaia/README.md index a2d89c8b..b79f53ad 100644 --- a/harness-engineering-bench/gaia/README.md +++ b/harness-engineering-bench/gaia/README.md @@ -15,10 +15,10 @@ All 165 cases come from the immutable dataset reference recorded in available to the optimization agent with full result disclosure. Validation is aggregate-only and is used to select candidates. Test is held out until Harbor grades the completed outer task. The complete development task packages and -attachments are mounted read-only under `.vero/cases/`; successful and failed +attachments are mounted read-only under `.evals/tasks/`; successful and failed development evaluations place their complete Harbor trial records—including exact failures and target-agent logs—under -`.vero/evaluations/`. Neither validation nor test resources are mounted. +`.evals/results/`. Neither validation nor test resources are mounted. The pinned GAIA tasks declare a 600-second Harbor agent timeout. The build config sets `case_timeout_seconds: 180` and diff --git a/harness-engineering-bench/gaia/baseline/README.md b/harness-engineering-bench/gaia/baseline/README.md index d8846ce0..9f69a362 100644 --- a/harness-engineering-bench/gaia/baseline/README.md +++ b/harness-engineering-bench/gaia/baseline/README.md @@ -34,7 +34,7 @@ program. A separate inference-gateway container holds it. The outer coding agent uses a producer-scoped token for `gpt-5.4` or `gpt-5.5`; GAIA candidates use an evaluation-scoped token restricted to `gpt-5.4-mini-2026-03-17`. Their request and token budgets are recorded separately and are visible through -`vero harbor status`. +`evals status`. Run the outer optimization through VeRO so it can give Harbor's coding-agent adapter the scoped credential while forwarding the upstream credential only to @@ -70,10 +70,10 @@ run it locally against Modal eval backends. The coding agent edits only `target/`; inner evaluations run the candidate against the pinned GAIA tasks on Modal. Complete development tasks and -attachments are mounted read-only under `.vero/cases/`. After each development +attachments are mounted read-only under `.evals/tasks/`. After each development evaluation, complete Harbor trial records for every case—including exact failures and target-agent logs—are available under -`.vero/evaluations/`. Validation remains aggregate-only, and test remains +`.evals/results/`. Validation remains aggregate-only, and test remains verifier-only. Before Modal teardown, the shared verifier exports the complete VeRO session diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/.gitignore b/harness-engineering-bench/swe-atlas-qna/baseline/.gitignore index 35e4297b..ed297509 100644 --- a/harness-engineering-bench/swe-atlas-qna/baseline/.gitignore +++ b/harness-engineering-bench/swe-atlas-qna/baseline/.gitignore @@ -1,2 +1,3 @@ compiled/ .vero/ +.evals/ diff --git a/harness-engineering-bench/tau3/baseline/.gitignore b/harness-engineering-bench/tau3/baseline/.gitignore index 35e4297b..ed297509 100644 --- a/harness-engineering-bench/tau3/baseline/.gitignore +++ b/harness-engineering-bench/tau3/baseline/.gitignore @@ -1,2 +1,3 @@ compiled/ .vero/ +.evals/ From 1831a1908641372d422b383a7cdf03e87da3913b Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 20:49:20 -0700 Subject: [PATCH 06/16] Benchmarks: run inner eval sandboxes in a dedicated Modal app All four vero-compiled benchmarks pass --ek app_name=harness-engineering-bench so their Modal sandboxes are grouped under one app instead of __harbor__. Docker inner environments ignore the kwarg. --- harness-engineering-bench/gaia/baseline/build.yaml | 2 ++ harness-engineering-bench/officeqa/baseline/build.yaml | 2 ++ harness-engineering-bench/swe-atlas-qna/baseline/build.yaml | 2 ++ harness-engineering-bench/tau3/baseline/build.yaml | 2 ++ 4 files changed, 8 insertions(+) diff --git a/harness-engineering-bench/gaia/baseline/build.yaml b/harness-engineering-bench/gaia/baseline/build.yaml index 6e7bdc20..3680500f 100644 --- a/harness-engineering-bench/gaia/baseline/build.yaml +++ b/harness-engineering-bench/gaia/baseline/build.yaml @@ -45,6 +45,8 @@ rescore_attempts: 1 model: openai/gpt-5.4-mini-2026-03-17 environment_name: ${inner_env:-modal} +# inner eval sandboxes share a dedicated Modal app instead of the __harbor__ default +extra_harbor_args: ["--ek", "app_name=harness-engineering-bench"] harbor_python_version: "3.12" n_attempts: 1 max_retries: 1 diff --git a/harness-engineering-bench/officeqa/baseline/build.yaml b/harness-engineering-bench/officeqa/baseline/build.yaml index 13e1e2eb..aac79878 100644 --- a/harness-engineering-bench/officeqa/baseline/build.yaml +++ b/harness-engineering-bench/officeqa/baseline/build.yaml @@ -53,6 +53,8 @@ rescore_attempts: 1 model: openai/gpt-5.4-mini-2026-03-17 environment_name: modal +# inner eval sandboxes share a dedicated Modal app instead of the __harbor__ default +extra_harbor_args: ["--ek", "app_name=harness-engineering-bench"] harbor_python_version: "3.12" n_attempts: 1 max_retries: 1 diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml index 010dd354..d93c8cb9 100644 --- a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml +++ b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml @@ -47,6 +47,8 @@ rescore_attempts: 1 model: openai/gpt-5.4-mini-2026-03-17 environment_name: modal +# inner eval sandboxes share a dedicated Modal app instead of the __harbor__ default +extra_harbor_args: ["--ek", "app_name=harness-engineering-bench"] harbor_python_version: "3.12" n_attempts: 1 max_retries: 1 diff --git a/harness-engineering-bench/tau3/baseline/build.yaml b/harness-engineering-bench/tau3/baseline/build.yaml index 22d49674..d5224c46 100644 --- a/harness-engineering-bench/tau3/baseline/build.yaml +++ b/harness-engineering-bench/tau3/baseline/build.yaml @@ -46,6 +46,8 @@ rescore_attempts: 1 model: openai/gpt-5.4-mini-2026-03-17 environment_name: modal +# inner eval sandboxes share a dedicated Modal app instead of the __harbor__ default +extra_harbor_args: ["--ek", "app_name=harness-engineering-bench"] harbor_python_version: "3.12" n_attempts: 1 max_retries: 1 From 2be2ed583ddfd4729644318191f241382a88e6b6 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 22:28:18 -0700 Subject: [PATCH 07/16] swe-atlas-qna: submit-mode selection, matching gaia --- harness-engineering-bench/swe-atlas-qna/baseline/build.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml index d93c8cb9..b25ec3af 100644 --- a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml +++ b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml @@ -39,8 +39,8 @@ objective: selector: metric: score direction: maximize -reward_mode: auto_best -baseline_floor: true +reward_mode: submit # agent picks; falls back to auto_best, then current version +baseline_floor: true # only guards the no-submission fallback path score_baseline: true rescore_top_k: 3 rescore_attempts: 1 From 440360a6fc47193c5193e724be9086f95da3f8d3 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 22:38:58 -0700 Subject: [PATCH 08/16] Benchmarks: enable W&B reporting on officeqa, swe-atlas-qna, and tau3 --- harness-engineering-bench/officeqa/baseline/build.yaml | 8 ++++++++ .../swe-atlas-qna/baseline/build.yaml | 8 ++++++++ harness-engineering-bench/tau3/baseline/build.yaml | 8 ++++++++ 3 files changed, 24 insertions(+) diff --git a/harness-engineering-bench/officeqa/baseline/build.yaml b/harness-engineering-bench/officeqa/baseline/build.yaml index aac79878..d66973e4 100644 --- a/harness-engineering-bench/officeqa/baseline/build.yaml +++ b/harness-engineering-bench/officeqa/baseline/build.yaml @@ -73,6 +73,14 @@ verifier_timeout_seconds: 3600 secrets: - MODAL_TOKEN_ID - MODAL_TOKEN_SECRET + - WANDB_API_KEY + - WANDB_BASE_URL # self-hosted W&B (scaleai.wandb.io) + +wandb: + project: vero-officeqa + group: officeqa + log_traces: true + inference_gateway: upstream_api_key_env: OPENAI_API_KEY upstream_base_url_env: OPENAI_BASE_URL diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml index b25ec3af..5a3a2db2 100644 --- a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml +++ b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml @@ -65,6 +65,14 @@ verifier_timeout_seconds: 14400 secrets: - MODAL_TOKEN_ID - MODAL_TOKEN_SECRET + - WANDB_API_KEY + - WANDB_BASE_URL # self-hosted W&B (scaleai.wandb.io) + +wandb: + project: vero-swe-atlas-qna + group: swe-atlas-qna + log_traces: true + inference_gateway: upstream_api_key_env: OPENAI_API_KEY upstream_base_url_env: OPENAI_BASE_URL diff --git a/harness-engineering-bench/tau3/baseline/build.yaml b/harness-engineering-bench/tau3/baseline/build.yaml index d5224c46..30b04d5f 100644 --- a/harness-engineering-bench/tau3/baseline/build.yaml +++ b/harness-engineering-bench/tau3/baseline/build.yaml @@ -64,6 +64,14 @@ verifier_timeout_seconds: 14400 secrets: - MODAL_TOKEN_ID - MODAL_TOKEN_SECRET + - WANDB_API_KEY + - WANDB_BASE_URL # self-hosted W&B (scaleai.wandb.io) + +wandb: + project: vero-tau3 + group: tau3 + log_traces: true + inference_gateway: upstream_api_key_env: OPENAI_API_KEY upstream_base_url_env: OPENAI_BASE_URL From 363c608054fcc8a401525ac2fc3af2afc017523f Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 23:03:46 -0700 Subject: [PATCH 09/16] swe-atlas-qna: route the in-container rubric judge to the upstream The task's verifier runs inside the Modal task container and templates EVAL_BASE_URL from OPENAI_API_BASE, unreachable-from-Modal gateway URLs aside. Enable task_services_use_upstream so the judge gets the real upstream on OPENAI_*, and point the baseline agent at the metered gateway via VERO_AGENT_INFERENCE_* (falling back to OPENAI_* locally). --- .../swe-atlas-qna/baseline/build.yaml | 9 +++++++++ .../baseline/target/src/atlas_agent/agent.py | 10 +++++++++- 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml index 5a3a2db2..2df84f83 100644 --- a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml +++ b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml @@ -86,3 +86,12 @@ inference_gateway: max_concurrency: 64 instruct_multifidelity: true instruct_exhaust_budget: true +# The rubric judge runs inside the Modal task container and cannot reach the +# compose-internal gateway; it gets the real upstream on OPENAI_* while the +# candidate agent keeps the metered gateway on VERO_AGENT_INFERENCE_*. +task_services_use_upstream: true +# ACCEPTED EXPOSURE (non-adversarial optimizer): with isolation off, candidate +# harness code shares the sidecar uid, so it could read the upstream key and +# the held-out session state. Runs using this config get a post-run leakage +# audit of every candidate version. Proper fix: per-role egress isolation. +harness_user: null diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py b/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py index 62d5e12c..0544c105 100644 --- a/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py +++ b/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py @@ -3,6 +3,7 @@ from __future__ import annotations import json +import os from typing import Any, override from harbor.agents.base import BaseAgent @@ -77,7 +78,14 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: if self.model_name is None: raise ValueError("SWE-Atlas agent requires a Harbor model") self._api_model = self.model_name.removeprefix("openai/") - self._client = AsyncOpenAI() + # The metered per-evaluation gateway arrives on dedicated variables + # (OPENAI_* carries the upstream for the task's rubric judge instead). + self._client = AsyncOpenAI( + api_key=os.environ.get("VERO_AGENT_INFERENCE_API_KEY") + or os.environ.get("OPENAI_API_KEY"), + base_url=os.environ.get("VERO_AGENT_INFERENCE_BASE_URL") + or os.environ.get("OPENAI_BASE_URL"), + ) @override async def setup(self, environment: BaseEnvironment) -> None: From a2fcd197eddb7f13d5857ce872e727c4b635f48f Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 23:41:00 -0700 Subject: [PATCH 10/16] officeqa: merge duplicate extra_harbor_args keys The Modal app-name commit added a second extra_harbor_args key; YAML last-key-wins silently dropped --no-force-build, which would have force-built the guard Dockerfile instead of pulling the prebuilt corpus image. --- harness-engineering-bench/officeqa/baseline/build.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/harness-engineering-bench/officeqa/baseline/build.yaml b/harness-engineering-bench/officeqa/baseline/build.yaml index d66973e4..25cfd8af 100644 --- a/harness-engineering-bench/officeqa/baseline/build.yaml +++ b/harness-engineering-bench/officeqa/baseline/build.yaml @@ -13,7 +13,6 @@ task_source: ../tasks task_manifest: ../partitions/manifest.json agent_import_path: officeqa_agent.agent:OfficeQaAgent harbor_requirement: harbor[modal]==0.20.0 -extra_harbor_args: ["--no-force-build"] partition_files: development: ../partitions/development.json @@ -53,8 +52,9 @@ rescore_attempts: 1 model: openai/gpt-5.4-mini-2026-03-17 environment_name: modal -# inner eval sandboxes share a dedicated Modal app instead of the __harbor__ default -extra_harbor_args: ["--ek", "app_name=harness-engineering-bench"] +# --no-force-build pulls the prebuilt officeqa-corpus image (see task_source +# note above); the --ek groups inner sandboxes under one dedicated Modal app. +extra_harbor_args: ["--no-force-build", "--ek", "app_name=harness-engineering-bench"] harbor_python_version: "3.12" n_attempts: 1 max_retries: 1 From badc42cf19c56c894bb65ac2baeef08950647365 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Thu, 23 Jul 2026 23:42:18 -0700 Subject: [PATCH 11/16] officeqa: submit-mode selection, matching gaia --- harness-engineering-bench/officeqa/baseline/build.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/harness-engineering-bench/officeqa/baseline/build.yaml b/harness-engineering-bench/officeqa/baseline/build.yaml index 25cfd8af..2d213596 100644 --- a/harness-engineering-bench/officeqa/baseline/build.yaml +++ b/harness-engineering-bench/officeqa/baseline/build.yaml @@ -44,8 +44,8 @@ objective: selector: metric: score direction: maximize -reward_mode: auto_best -baseline_floor: true +reward_mode: submit # agent picks; falls back to auto_best, then current version +baseline_floor: true # only guards the no-submission fallback path score_baseline: true rescore_top_k: 3 rescore_attempts: 1 From 0072a5ca7e662e7f7390847aa7e61cf181a47953 Mon Sep 17 00:00:00 2001 From: Varun Ursekar Date: Fri, 24 Jul 2026 08:32:43 -0700 Subject: [PATCH 12/16] Benchmarks: bound orphaned infrastructure from killed runs Killing the outer harbor process leaves the compose topology and any in-flight Modal sandboxes running (each layer's cleanup lives in the layer above). Cap sandbox idle lifetime at 1h via --ek, and add scripts/cleanup_orphans.sh to tear down task__* containers and terminate sandboxes in the dedicated Modal app. --- .../gaia/baseline/build.yaml | 2 +- .../officeqa/baseline/build.yaml | 2 +- .../scripts/cleanup_orphans.sh | 50 +++++++++++++++++++ .../swe-atlas-qna/baseline/build.yaml | 2 +- .../tau3/baseline/build.yaml | 2 +- 5 files changed, 54 insertions(+), 4 deletions(-) create mode 100755 harness-engineering-bench/scripts/cleanup_orphans.sh diff --git a/harness-engineering-bench/gaia/baseline/build.yaml b/harness-engineering-bench/gaia/baseline/build.yaml index 3680500f..7aae5416 100644 --- a/harness-engineering-bench/gaia/baseline/build.yaml +++ b/harness-engineering-bench/gaia/baseline/build.yaml @@ -46,7 +46,7 @@ rescore_attempts: 1 model: openai/gpt-5.4-mini-2026-03-17 environment_name: ${inner_env:-modal} # inner eval sandboxes share a dedicated Modal app instead of the __harbor__ default -extra_harbor_args: ["--ek", "app_name=harness-engineering-bench"] +extra_harbor_args: ["--ek", "app_name=harness-engineering-bench", "--ek", "sandbox_idle_timeout_secs=3600"] harbor_python_version: "3.12" n_attempts: 1 max_retries: 1 diff --git a/harness-engineering-bench/officeqa/baseline/build.yaml b/harness-engineering-bench/officeqa/baseline/build.yaml index 2d213596..5e05863a 100644 --- a/harness-engineering-bench/officeqa/baseline/build.yaml +++ b/harness-engineering-bench/officeqa/baseline/build.yaml @@ -54,7 +54,7 @@ model: openai/gpt-5.4-mini-2026-03-17 environment_name: modal # --no-force-build pulls the prebuilt officeqa-corpus image (see task_source # note above); the --ek groups inner sandboxes under one dedicated Modal app. -extra_harbor_args: ["--no-force-build", "--ek", "app_name=harness-engineering-bench"] +extra_harbor_args: ["--no-force-build", "--ek", "app_name=harness-engineering-bench", "--ek", "sandbox_idle_timeout_secs=3600"] harbor_python_version: "3.12" n_attempts: 1 max_retries: 1 diff --git a/harness-engineering-bench/scripts/cleanup_orphans.sh b/harness-engineering-bench/scripts/cleanup_orphans.sh new file mode 100755 index 00000000..f29e89d2 --- /dev/null +++ b/harness-engineering-bench/scripts/cleanup_orphans.sh @@ -0,0 +1,50 @@ +#!/usr/bin/env bash +# Tear down orphaned benchmark infrastructure after a killed `vero harbor run`. +# +# Killing the outer harbor process orphans everything below it: the docker +# compose topology (daemon-owned, survives its creator) and any in-flight +# Modal sandboxes (server-side, default 24h timeout). This removes both. +# Scope is strictly ours: `task__*` compose containers locally, and sandboxes +# in the dedicated `harness-engineering-bench` Modal app. +# +# Usage: bash scripts/cleanup_orphans.sh [--dry-run] +# Requires MODAL_TOKEN_ID/MODAL_TOKEN_SECRET in the environment (or a dotenv +# sourced beforehand); skips Modal cleanup if they are absent. +set -euo pipefail + +dry_run=false +[ "${1:-}" = "--dry-run" ] && dry_run=true + +containers=$(docker ps --format '{{.Names}}' | grep -i '^task__' || true) +if [ -n "$containers" ]; then + echo "orphaned containers:"; echo "$containers" + if ! $dry_run; then + echo "$containers" | xargs docker rm -f + fi +else + echo "no orphaned task__* containers" +fi + +if [ -z "${MODAL_TOKEN_ID:-}" ]; then + echo "MODAL_TOKEN_ID not set; skipping Modal sandbox cleanup" + exit 0 +fi + +uv run --quiet --python 3.12 --with modal python - "$dry_run" <<'PY' +import sys +import modal + +dry_run = sys.argv[1] == "true" +try: + app = modal.App.lookup("harness-engineering-bench", create_if_missing=False) +except Exception as error: + print(f"no harness-engineering-bench app: {error}") + raise SystemExit(0) +count = 0 +for sandbox in modal.Sandbox.list(app_id=app.app_id): + print(("would terminate" if dry_run else "terminating"), sandbox.object_id) + if not dry_run: + sandbox.terminate() + count += 1 +print(f"{count} running sandbox(es) in harness-engineering-bench") +PY diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml index 2df84f83..cb00788f 100644 --- a/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml +++ b/harness-engineering-bench/swe-atlas-qna/baseline/build.yaml @@ -48,7 +48,7 @@ rescore_attempts: 1 model: openai/gpt-5.4-mini-2026-03-17 environment_name: modal # inner eval sandboxes share a dedicated Modal app instead of the __harbor__ default -extra_harbor_args: ["--ek", "app_name=harness-engineering-bench"] +extra_harbor_args: ["--ek", "app_name=harness-engineering-bench", "--ek", "sandbox_idle_timeout_secs=3600"] harbor_python_version: "3.12" n_attempts: 1 max_retries: 1 diff --git a/harness-engineering-bench/tau3/baseline/build.yaml b/harness-engineering-bench/tau3/baseline/build.yaml index 30b04d5f..5d8a0892 100644 --- a/harness-engineering-bench/tau3/baseline/build.yaml +++ b/harness-engineering-bench/tau3/baseline/build.yaml @@ -47,7 +47,7 @@ rescore_attempts: 1 model: openai/gpt-5.4-mini-2026-03-17 environment_name: modal # inner eval sandboxes share a dedicated Modal app instead of the __harbor__ default -extra_harbor_args: ["--ek", "app_name=harness-engineering-bench"] +extra_harbor_args: ["--ek", "app_name=harness-engineering-bench", "--ek", "sandbox_idle_timeout_secs=3600"] harbor_python_version: "3.12" n_attempts: 1 max_retries: 1 From ccc7d07a8745f5ad1a152fa69979d2625264f924 Mon Sep 17 00:00:00 2001 From: Shehab Yasser Date: Fri, 24 Jul 2026 19:32:44 +0300 Subject: [PATCH 13/16] feat: scaffold SWE-bench-Pro baseline for the vero optimizer WIP scaffold: a working agent + build/config skeleton for a SWE-bench-Pro optimization target, mirroring the gaia baseline layout. Two things are stubbed and must be completed before it runs: (1) the task_source digest in baseline/build.yaml is a clearly-marked placeholder that must be pinned to the real SWE-bench-Pro Harbor task package, and (2) the partitions/ files are empty placeholders that must be regenerated from the real task list via scripts/partition_swe_bench_pro.py. The agent wraps responses.create in a retry-with-backoff helper from the start (the unguarded call is what scored the gaia baseline 0.0). See harness-engineering-bench/swe-bench-pro/README.md. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../swe-bench-pro/README.md | 135 +++++ .../swe-bench-pro/baseline/.gitignore | 4 + .../swe-bench-pro/baseline/build.yaml | 101 ++++ .../baseline/secrets.env.example | 23 + .../baseline/target/pyproject.toml | 30 ++ .../target/src/swebench_pro_agent/__init__.py | 5 + .../target/src/swebench_pro_agent/agent.py | 472 ++++++++++++++++++ .../baseline/target/tests/test_agent.py | 135 +++++ .../swe-bench-pro/partitions/development.json | 1 + .../swe-bench-pro/partitions/manifest.json | 23 + .../swe-bench-pro/partitions/test.json | 1 + .../swe-bench-pro/partitions/validation.json | 1 + .../scripts/partition_swe_bench_pro.py | 280 +++++++++++ 13 files changed, 1211 insertions(+) create mode 100644 harness-engineering-bench/swe-bench-pro/README.md create mode 100644 harness-engineering-bench/swe-bench-pro/baseline/.gitignore create mode 100644 harness-engineering-bench/swe-bench-pro/baseline/build.yaml create mode 100644 harness-engineering-bench/swe-bench-pro/baseline/secrets.env.example create mode 100644 harness-engineering-bench/swe-bench-pro/baseline/target/pyproject.toml create mode 100644 harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/__init__.py create mode 100644 harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/agent.py create mode 100644 harness-engineering-bench/swe-bench-pro/baseline/target/tests/test_agent.py create mode 100644 harness-engineering-bench/swe-bench-pro/partitions/development.json create mode 100644 harness-engineering-bench/swe-bench-pro/partitions/manifest.json create mode 100644 harness-engineering-bench/swe-bench-pro/partitions/test.json create mode 100644 harness-engineering-bench/swe-bench-pro/partitions/validation.json create mode 100644 harness-engineering-bench/swe-bench-pro/scripts/partition_swe_bench_pro.py diff --git a/harness-engineering-bench/swe-bench-pro/README.md b/harness-engineering-bench/swe-bench-pro/README.md new file mode 100644 index 00000000..979fc11e --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/README.md @@ -0,0 +1,135 @@ +# SWE-bench-Pro (scaffold) + +This benchmark optimizes a code-editing agent on SWE-bench-Pro tasks. Each task +checks a real project out at `/app/repo` and describes a bug fix or feature. The +editable agent (`baseline/target/src/swebench_pro_agent/agent.py`) edits the +repository in place; the **task-source verifier** applies the resulting repo state +and runs the task's hidden test suite for the reward. The agent does not +self-grade and writes no answer file: reward comes entirely from the suite. + +## Status: this is a SCAFFOLD, not a runnable benchmark + +Everything except the actual task data is in place: a robust Harbor-native agent, +a `build.yaml`, partition scaffolding, a split script, and this README. It will +**not run yet** because the real SWE-bench-Pro task dataset must come from a Harbor +task source that is not committed here. + +### DONE (working skeleton) + +- `baseline/target/src/swebench_pro_agent/agent.py`: a tool-using code-editing + agent on the OpenAI Responses API (`run_shell`, `read_file`, `write_file`, + `apply_patch`, `run_tests`, `submit`), `MAX_TURNS = 50`. The + `self._client.responses.create(...)` call is wrapped in a retry-with-backoff + helper from the start (the GAIA baseline scored 0.0 because an unguarded call + crashed on a transient error; that retry was the optimizer's winning fix). +- `baseline/build.yaml`: the full VeRO/Harbor optimization config (agent import + path, inference gateway, partitions wiring, timeouts, secrets, W&B). +- `baseline/target/pyproject.toml`, smoke test, `secrets.env.example`, `.gitignore`. +- `scripts/partition_swe_bench_pro.py`: a deterministic, sha256-keyed stratified + split with a `--check` mode, modeled on `gaia/scripts/partition_gaia.py`. + +### STUBBED (must be completed before it runs) + +Two things must be finished: + +1. **Pin the real `task_source`.** `baseline/build.yaml` carries a clearly-marked + placeholder: + + ``` + task_source: scale-ai/swe-bench-pro@sha256:REPLACE_WITH_REAL_DIGEST + ``` + + SWE-bench-Pro is a Scale benchmark. Find the real pinned task package in the + Harbor task registry (ask the DEX-harness / Harbor team if it is not obvious). + The baseline in this directory provides only the **agent + config**; the task + package provides the **repos, tests, gold patches, and grading**. Replace the + placeholder digest here and the `DATASET_DIGEST` constant in + `scripts/partition_swe_bench_pro.py`. + +2. **Regenerate the partitions from the real task list.** The committed + `partitions/{development,validation,test}.json` are empty placeholders and + `partitions/manifest.json` is a stub (`"_stub": true`). Once the task source is + pinned, set `TOTAL_TASKS`/`TARGET_COUNTS` and confirm `_read_tasks` against the + real `task.toml` layout in `scripts/partition_swe_bench_pro.py`, then generate + and verify the split: + + ```bash + uv run --python 3.12 \ + harness-engineering-bench/swe-bench-pro/scripts/partition_swe_bench_pro.py \ + --tasks-dir /path/to/exported/swe-bench-pro \ + --fetch-registry + + uv run --python 3.12 \ + harness-engineering-bench/swe-bench-pro/scripts/partition_swe_bench_pro.py \ + --tasks-dir /path/to/exported/swe-bench-pro \ + --check + ``` + + Also update the placeholder `total_cases` under `agent_access` in + `baseline/build.yaml` to real disclosure counts, and run `uv lock` in + `baseline/target/` so the compiled task pins an exact resolution. + +## Split design + +The committed split is intended to be deterministic and stratified by source +repository (so each represented codebase appears across all three partitions): + +- development: 20% (full result disclosure to the optimizer) +- validation: 40% (aggregate-only; used to select candidates) +- test: 40% (held out until Harbor grades the completed outer task) + +Exact counts are TODO until the task source is pinned. + +## Build the outer Harbor task + +From the repository root: + +```bash +cd vero +VERO_SKIP_SECRET_CHECK=1 uv run vero harbor build \ + --config ../harness-engineering-bench/swe-bench-pro/baseline/build.yaml \ + --output ../harness-engineering-bench/swe-bench-pro/baseline/compiled +``` + +Omit `VERO_SKIP_SECRET_CHECK=1` for a real build so VeRO verifies that the OpenAI +and Modal credentials declared in `build.yaml` are present. Set `OPENAI_BASE_URL` +to your OpenAI-compatible endpoint. The `compiled/` directory is generated and +intentionally ignored. (This build will fail to resolve tasks until the +placeholder `task_source` above is replaced with the real digest.) + +## Run the optimization + +`vero harbor run` compiles the `build.yaml` to a temporary task, wires credentials +(relocating the upstream inference key to the gateway and handing the optimizer a +scoped producer token), and invokes Harbor. Copy the secrets template first: + +```bash +cp harness-engineering-bench/swe-bench-pro/baseline/secrets.env.example \ + harness-engineering-bench/swe-bench-pro/baseline/secrets.env # then edit it + +cd vero +uv run vero harbor run \ + --config ../harness-engineering-bench/swe-bench-pro/baseline/build.yaml \ + --env-file ../harness-engineering-bench/swe-bench-pro/baseline/secrets.env \ + --environment modal \ + --agent codex \ + --model gpt-5.3-codex \ + --yes \ + -o ../runs/swe-bench-pro-full/jobs +``` + +`--env-file` values override the ambient shell and are passed only through the +subprocess environment, never on the command line. Anything after the known +options (here `--yes -o ...`) is forwarded verbatim to `harbor run`. + +The producer scope's allow-list in `build.yaml` is `gpt-5.3-codex`, so pass +`--model gpt-5.3-codex` (or an aligned model) to keep the optimizer's model and +the gateway allow-list in lockstep and avoid a 403 mismatch. The evaluation scope +is pinned to `gpt-4o` (matching `model: openai/gpt-4o`). + +**Modal endpoint requirement:** `--environment modal` runs eval sandboxes on +Modal, which must be able to reach the model endpoint in `OPENAI_BASE_URL`. Use a +Modal-reachable endpoint (for example the Azure +`https://.openai.azure.com/openai/v1` endpoint), **not** a VPN-internal +host that only resolves on the Scale network. Use `--environment docker` to run +the optimizer locally against Modal eval backends instead. diff --git a/harness-engineering-bench/swe-bench-pro/baseline/.gitignore b/harness-engineering-bench/swe-bench-pro/baseline/.gitignore new file mode 100644 index 00000000..5441cda5 --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/baseline/.gitignore @@ -0,0 +1,4 @@ +compiled/ +target/.venv/ +target/.pytest_cache/ +secrets.env diff --git a/harness-engineering-bench/swe-bench-pro/baseline/build.yaml b/harness-engineering-bench/swe-bench-pro/baseline/build.yaml new file mode 100644 index 00000000..e625ea29 --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/baseline/build.yaml @@ -0,0 +1,101 @@ +name: vero/optimize-swe-bench-pro-baseline +description: >- + Improve a code-editing SWE-bench-Pro agent while preserving the Harbor agent + interface. The target must edit a checked-out repository so the task's hidden + test suite passes; the task-source verifier runs the suite for the reward. +agent_repo: target +# TODO: pin the real SWE-bench-Pro Harbor task source (see README). This is a +# clearly-marked placeholder, NOT a real digest, and will fail to resolve until +# replaced with the actual `scale-ai/swe-bench-pro@sha256:` from the +# Harbor task registry. +task_source: scale-ai/swe-bench-pro@sha256:REPLACE_WITH_REAL_DIGEST # TODO: pin the real SWE-bench-Pro Harbor task source (see README) +task_manifest: ../partitions/manifest.json +agent_import_path: swebench_pro_agent.agent:SweBenchProAgent +harbor_requirement: harbor[modal]==0.20.0 + +partition_files: + development: ../partitions/development.json + validation: ../partitions/validation.json + test: ../partitions/test.json + +# TODO: set real disclosure counts once the partitions are regenerated from the +# pinned task list. total_cases below are placeholders scaled to the stub sizes. +agent_access: + - partition: development + disclosure: full + expose_case_resources: true + total_cases: 0 + - partition: validation + disclosure: aggregate + expose_case_resources: false + min_aggregate_cases: 5 + total_cases: 0 + +selection_partition: validation +targets: + - partition: test + reward_key: reward + failure_value: 0.0 + max_attempts: 1 + +evaluation_set_name: swe-bench-pro +objective: + selector: + metric: score + direction: maximize +reward_mode: submit # agent picks; falls back to auto_best, then current version +baseline_floor: false # gates on validation while reward is on test; opt-in only +score_baseline: true +rescore_top_k: 3 +rescore_attempts: 1 + +model: openai/gpt-4o +environment_name: ${inner_env:-modal} +# inner eval sandboxes share a dedicated Modal app instead of the __harbor__ default +extra_harbor_args: ["--ek", "app_name=harness-engineering-bench", "--ek", "sandbox_idle_timeout_secs=3600"] +harbor_python_version: "3.12" +n_attempts: 1 +max_retries: 1 +infrastructure_max_attempts: 3 +infrastructure_retry_delay_seconds: 5 +aggregate_attempts: best +feedback_transcripts: true +feedback_max_bytes: 16000 +expose_attempt_detail: false +# Timeouts are bumped well above the GAIA baseline: SWE-bench-Pro tasks build a +# real repository and run its (often slow) test suite, so both the per-case and +# the verifier budgets need substantially more headroom than a short-answer task. +timeout_seconds: 28800 +case_timeout_seconds: 1800 +task_agent_timeout_seconds: 3600 +max_concurrency: 8 +error_rate_threshold: 0.1 +verifier_timeout_seconds: 28800 +secrets: + - MODAL_TOKEN_ID + - MODAL_TOKEN_SECRET + - WANDB_API_KEY + - WANDB_BASE_URL # self-hosted W&B (scaleai.wandb.io) + +# candidate harness runs as an unprivileged uid, unable to read held-out state. +harness_user: harness + +wandb: + project: vero-swe-bench-pro + group: swe-bench-pro + log_traces: true + +inference_gateway: + upstream_api_key_env: OPENAI_API_KEY + upstream_base_url_env: OPENAI_BASE_URL + producer: + # Both prefixed and bare forms: the gateway model match is prefix-sensitive. + allowed_models: ["gpt-5.3-codex", "openai/gpt-5.3-codex"] + max_concurrency: 8 + evaluation: + allowed_models: ["gpt-4o", "openai/gpt-4o"] + max_requests: 15000 + max_tokens: 100000000 + max_concurrency: 64 +instruct_multifidelity: true +instruct_exhaust_budget: true diff --git a/harness-engineering-bench/swe-bench-pro/baseline/secrets.env.example b/harness-engineering-bench/swe-bench-pro/baseline/secrets.env.example new file mode 100644 index 00000000..eb7444c1 --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/baseline/secrets.env.example @@ -0,0 +1,23 @@ +# Run secrets for `vero harbor run --env-file`. +# Copy to `secrets.env` (gitignored) and fill in. File values override the +# ambient shell and never appear on the command line. + +# Upstream inference credentials. VeRO reads these as the real provider key/URL +# (per the compiled gateway's *_source fields), relocates them to the +# VERO_INFERENCE_UPSTREAM_* target vars, and hands the optimizer a scoped +# producer token instead — so the raw key never reaches the coding agent. +# +# For `--environment modal`, OPENAI_BASE_URL must be reachable from Modal's +# network (for example the Azure `https://.openai.azure.com/openai/v1` +# endpoint), NOT a VPN-internal host. See the README. +OPENAI_API_KEY=sk-your-upstream-inference-key +OPENAI_BASE_URL=https://your-openai-compatible-endpoint/v1 + +# Modal credentials for the eval sidecar backends (declared required by the +# compiled task.toml). Copy from your ~/.modal.toml profile. +MODAL_TOKEN_ID=ak-... +MODAL_TOKEN_SECRET=as-... + +# Self-hosted Weights & Biases (scaleai.wandb.io) for trace/metric logging. +WANDB_API_KEY=your-wandb-key +WANDB_BASE_URL=https://scaleai.wandb.io diff --git a/harness-engineering-bench/swe-bench-pro/baseline/target/pyproject.toml b/harness-engineering-bench/swe-bench-pro/baseline/target/pyproject.toml new file mode 100644 index 00000000..2ec07101 --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/baseline/target/pyproject.toml @@ -0,0 +1,30 @@ +[project] +name = "swebench-pro-agent" +version = "0.1.0" +description = "Editable Harbor-native SWE-bench-Pro baseline for VeRO" +# Cap below 3.14: an uncapped ">=3.11" lets uv resolve to 3.14, whose litellm/pyo3 +# build fails. Keep this bounded until the toolchain supports 3.14. +requires-python = ">=3.11,<3.14" +dependencies = [ + "harbor==0.20.0", + "openai==2.46.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/swebench_pro_agent"] + +[dependency-groups] +dev = [ + "pytest>=9.0.2", + "pytest-asyncio>=1.3.0", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] + +# NOTE: no uv.lock is committed yet. Run `uv lock` in this directory when +# finalizing the baseline so the compiled task pins an exact resolution. diff --git a/harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/__init__.py b/harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/__init__.py new file mode 100644 index 00000000..a4e65b5b --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/__init__.py @@ -0,0 +1,5 @@ +"""Harbor-native SWE-bench-Pro target agent.""" + +from swebench_pro_agent.agent import SweBenchProAgent + +__all__ = ["SweBenchProAgent"] diff --git a/harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/agent.py b/harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/agent.py new file mode 100644 index 00000000..961c5b85 --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/agent.py @@ -0,0 +1,472 @@ +"""A compact, tool-using SWE-bench-Pro baseline built on the OpenAI Responses API. + +This module is the editable optimization target. A VeRO coding agent may rewrite +the prompts, control flow, tool definitions, patching strategy, or dependencies, +but it must keep the Harbor agent interface (``harbor.agents.base.BaseAgent``) and +must not touch the dataset, verifier, split, model, or test target. + +The task-source verifier is what grades a solution: it applies whatever the agent +left in the repository and runs the task's own test suite for the reward. The agent +does NOT self-grade and does NOT write an answer file; it edits code in place and +calls ``submit`` to signal that it believes the task is complete. +""" + +from __future__ import annotations + +import asyncio +import json +import os +import random +from typing import Any, override + +from harbor.agents.base import BaseAgent +from harbor.environments.base import BaseEnvironment +from harbor.models.agent.context import AgentContext +from openai import AsyncOpenAI + +# SWE-bench-Pro tasks build a real repository and run its test suite, so the agent +# needs more turns than a short-answer benchmark like GAIA. +MAX_TURNS = 50 +MAX_TOOL_OUTPUT_CHARS = 20_000 +MAX_FILE_READ_CHARS = 60_000 + +# Repository checkout location inside the task environment. SWE-bench-Pro task +# packages check the target project out here; adjust in one place if the pinned +# task source uses a different mount. +REPO_DIR = "/app/repo" + +# Retry policy for the Responses API. The GAIA baseline scored 0.0 in the first +# VeRO run because a single transient error on ``responses.create`` crashed the +# whole rollout; the optimizer's winning fix was exactly this retry-with-backoff. +# It ships here from the start so the baseline is robust before optimization. +MAX_API_RETRIES = 6 +API_RETRY_BASE_DELAY = 2.0 +API_RETRY_MAX_DELAY = 60.0 + +INSTRUCTIONS = f"""You are a careful software engineer solving a SWE-bench-Pro task. + +A real project is checked out at {REPO_DIR}. The task describes a bug to fix or a +feature to implement. Your job is to edit the repository so that the task's hidden +test suite passes. Work like an engineer: read the failing area, reproduce the +problem, make the smallest correct change, and re-run the relevant tests. + +Tools available to you: +- run_shell: run non-interactive shell commands with the repo as the working dir. +- read_file: read a file (optionally a line range) from the repo. +- write_file: overwrite a file with new contents. +- apply_patch: apply a unified diff to the repo (prefer this for surgical edits). +- run_tests: run the project's test suite (or a targeted subset you name). +- submit: signal that you believe the task is complete. + +Rules: +- Change only what the task requires. Do not rewrite unrelated code or reformat. +- Never edit, delete, or weaken the task's own tests, the verifier, or any grading + files, and never hard-code expected outputs. The grader runs the hidden suite; a + solution that only edits tests will be rejected. +- Verify your change with run_tests before calling submit. There is no answer file + and no self-grading: the task-source verifier runs the suite for the reward. + +When you are confident the change is correct and the tests you can see pass, call +submit. Do not merely describe what you would do.""" + +TOOLS: list[dict[str, Any]] = [ + { + "type": "function", + "name": "run_shell", + "description": ( + "Run a non-interactive shell command with the repository at " + f"{REPO_DIR} as the working directory." + ), + "parameters": { + "type": "object", + "properties": { + "command": {"type": "string", "description": "Shell command to run"} + }, + "required": ["command"], + "additionalProperties": False, + }, + "strict": True, + }, + { + "type": "function", + "name": "read_file", + "description": ( + "Read a repository-relative file. Optionally restrict to a 1-indexed, " + "inclusive line range to focus on a region." + ), + "parameters": { + "type": "object", + "properties": { + "path": { + "type": "string", + "description": f"Repo-relative path under {REPO_DIR}", + }, + "start_line": { + "type": ["integer", "null"], + "description": "1-indexed first line to read, or null for start", + }, + "end_line": { + "type": ["integer", "null"], + "description": "1-indexed last line to read, or null for end", + }, + }, + "required": ["path", "start_line", "end_line"], + "additionalProperties": False, + }, + "strict": True, + }, + { + "type": "function", + "name": "write_file", + "description": ( + "Overwrite a repository-relative file with the given contents, creating " + "parent directories as needed." + ), + "parameters": { + "type": "object", + "properties": { + "path": { + "type": "string", + "description": f"Repo-relative path under {REPO_DIR}", + }, + "content": {"type": "string", "description": "Full new file contents"}, + }, + "required": ["path", "content"], + "additionalProperties": False, + }, + "strict": True, + }, + { + "type": "function", + "name": "apply_patch", + "description": ( + "Apply a unified diff to the repository with `git apply`. The diff must " + "use repo-relative paths (a/ and b/ prefixes are accepted)." + ), + "parameters": { + "type": "object", + "properties": { + "patch": {"type": "string", "description": "Unified diff text"} + }, + "required": ["patch"], + "additionalProperties": False, + }, + "strict": True, + }, + { + "type": "function", + "name": "run_tests", + "description": ( + "Run the project's tests. Provide an optional command override (for " + "example a single pytest node id) to target a subset; otherwise a " + "sensible default is used." + ), + "parameters": { + "type": "object", + "properties": { + "command": { + "type": ["string", "null"], + "description": "Test command to run, or null for the default", + } + }, + "required": ["command"], + "additionalProperties": False, + }, + "strict": True, + }, + { + "type": "function", + "name": "submit", + "description": ( + "Signal that the edits are complete and the task should be graded. " + "Takes no arguments; the task-source verifier runs the hidden suite." + ), + "parameters": { + "type": "object", + "properties": {}, + "required": [], + "additionalProperties": False, + }, + "strict": True, + }, +] + + +class SweBenchProAgent(BaseAgent): + """Code-editing agent whose source is the editable optimization target.""" + + @staticmethod + @override + def name() -> str: + return "swe-bench-pro-responses-baseline" + + @override + def version(self) -> str: + return "0.1.0" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + if self.model_name is None: + raise ValueError("SWE-bench-Pro agent requires a Harbor model") + self._api_model = self.model_name.removeprefix("openai/") + # The metered per-evaluation gateway arrives on dedicated variables when + # VeRO relocates the upstream key; fall back to OPENAI_* for a plain run. + self._client = AsyncOpenAI( + api_key=os.environ.get("VERO_AGENT_INFERENCE_API_KEY") + or os.environ.get("OPENAI_API_KEY"), + base_url=os.environ.get("VERO_AGENT_INFERENCE_BASE_URL") + or os.environ.get("OPENAI_BASE_URL"), + ) + + @override + async def setup(self, environment: BaseEnvironment) -> None: + result = await environment.exec(f"test -d {REPO_DIR}", timeout_sec=30) + if result.return_code != 0: + raise RuntimeError( + result.stderr or f"task repository is missing at {REPO_DIR}" + ) + + async def _responses_create(self, **request: Any) -> Any: + """Call the Responses API with retry-and-backoff on transient errors. + + This is the load-bearing robustness fix: an unguarded ``create`` call is + what made the first GAIA baseline score 0.0. Any exception is retried with + exponential backoff and jitter up to ``MAX_API_RETRIES``; the final failure + is re-raised so a genuine, persistent error still surfaces. + """ + last_error: Exception | None = None + for attempt in range(1, MAX_API_RETRIES + 1): + try: + return await self._client.responses.create(**request) + except Exception as error: # noqa: BLE001 - transient classes vary by SDK + last_error = error + if attempt == MAX_API_RETRIES: + break + delay = min( + API_RETRY_BASE_DELAY * (2 ** (attempt - 1)), + API_RETRY_MAX_DELAY, + ) + delay += random.uniform(0, delay / 2) + self._trace( + { + "event": "responses_retry", + "attempt": attempt, + "delay": round(delay, 2), + "error": repr(error), + } + ) + await asyncio.sleep(delay) + assert last_error is not None + raise last_error + + @staticmethod + def _truncate(value: str) -> str: + if len(value) <= MAX_TOOL_OUTPUT_CHARS: + return value + half = MAX_TOOL_OUTPUT_CHARS // 2 + omitted = len(value) - (2 * half) + return f"{value[:half]}\n...[{omitted} characters omitted]...\n{value[-half:]}" + + def _trace(self, event: dict[str, Any]) -> None: + self.logs_dir.mkdir(parents=True, exist_ok=True) + trace_path = self.logs_dir / "swe-bench-pro-trace.jsonl" + with trace_path.open("a", encoding="utf-8") as file: + file.write(json.dumps(event, ensure_ascii=False, default=str) + "\n") + + async def _run_shell( + self, environment: BaseEnvironment, command: str, timeout_sec: int = 300 + ) -> dict[str, Any]: + result = await environment.exec(command, cwd=REPO_DIR, timeout_sec=timeout_sec) + return { + "return_code": result.return_code, + "stdout": self._truncate(result.stdout or ""), + "stderr": self._truncate(result.stderr or ""), + } + + async def _read_file( + self, + environment: BaseEnvironment, + path: str, + start_line: int | None, + end_line: int | None, + ) -> dict[str, Any]: + # `cat -A`-free read via shell so we do not need a download round-trip. + result = await environment.exec( + f"cat -- {json.dumps(path)}", cwd=REPO_DIR, timeout_sec=60 + ) + if result.return_code != 0: + return {"error": self._truncate(result.stderr or "could not read file")} + lines = (result.stdout or "").splitlines() + total = len(lines) + lo = max(1, start_line or 1) + hi = min(total, end_line or total) + selected = lines[lo - 1 : hi] if total else [] + body = "\n".join(selected) + if len(body) > MAX_FILE_READ_CHARS: + body = body[:MAX_FILE_READ_CHARS] + "\n...[truncated]..." + return {"path": path, "start_line": lo, "end_line": hi, "content": body} + + async def _write_file( + self, environment: BaseEnvironment, path: str, content: str + ) -> dict[str, Any]: + local_path = self.logs_dir / "staged" / path + local_path.parent.mkdir(parents=True, exist_ok=True) + local_path.write_text(content, encoding="utf-8") + remote_path = f"{REPO_DIR}/{path}" + mkdir = await environment.exec( + f"mkdir -p -- {json.dumps(os.path.dirname(remote_path) or REPO_DIR)}", + timeout_sec=30, + ) + if mkdir.return_code != 0: + return {"error": self._truncate(mkdir.stderr or "could not create dirs")} + await environment.upload_file(local_path, remote_path) + return {"written": path, "bytes": len(content.encode("utf-8"))} + + async def _apply_patch( + self, environment: BaseEnvironment, patch: str + ) -> dict[str, Any]: + local_path = self.logs_dir / "patches" / f"patch-{self._patch_index}.diff" + local_path.parent.mkdir(parents=True, exist_ok=True) + local_path.write_text(patch if patch.endswith("\n") else patch + "\n", "utf-8") + remote_path = "/tmp/swebp-agent.patch" + await environment.upload_file(local_path, remote_path) + self._patch_index += 1 + result = await environment.exec( + f"git apply --whitespace=nowarn {remote_path}", + cwd=REPO_DIR, + timeout_sec=120, + ) + if result.return_code != 0: + # Retry with a looser fuzz factor before giving up. + result = await environment.exec( + f"git apply --whitespace=nowarn -C1 {remote_path}", + cwd=REPO_DIR, + timeout_sec=120, + ) + return { + "applied": result.return_code == 0, + "return_code": result.return_code, + "stderr": self._truncate(result.stderr or ""), + } + + async def _run_tests( + self, environment: BaseEnvironment, command: str | None + ) -> dict[str, Any]: + # Default is intentionally generic; the pinned task source may ship its own + # entrypoint. The verifier runs the authoritative suite regardless. + test_command = command or "python -m pytest -q" + return await self._run_shell(environment, test_command, timeout_sec=1200) + + @staticmethod + def _usage_value(value: Any, name: str) -> int: + result = getattr(value, name, 0) if value is not None else 0 + return int(result or 0) + + @override + async def run( + self, + instruction: str, + environment: BaseEnvironment, + context: AgentContext, + ) -> None: + self._patch_index = 0 + next_input: Any = instruction + previous_response_id: str | None = None + input_tokens = 0 + output_tokens = 0 + cached_tokens = 0 + + for turn in range(1, MAX_TURNS + 1): + request: dict[str, Any] = { + "model": self._api_model, + "instructions": INSTRUCTIONS, + "input": next_input, + "tools": TOOLS, + "reasoning": {"effort": "high"}, + "max_output_tokens": 12_000, + "parallel_tool_calls": False, + } + if previous_response_id is not None: + request["previous_response_id"] = previous_response_id + response = await self._responses_create(**request) + usage = response.usage + input_tokens += self._usage_value(usage, "input_tokens") + output_tokens += self._usage_value(usage, "output_tokens") + cached_tokens += self._usage_value( + getattr(usage, "input_tokens_details", None), "cached_tokens" + ) + + calls = [item for item in response.output if item.type == "function_call"] + self._trace( + { + "turn": turn, + "response_id": response.id, + "output_text": response.output_text, + "function_calls": [ + {"name": call.name, "arguments": call.arguments} + for call in calls + ], + } + ) + if not calls: + # No tool call and no submit: the model believes it is done. The + # verifier grades the repository state as-is; nothing to write. + context.metadata = {"turns": turn, "trace": "swe-bench-pro-trace.jsonl"} + break + + next_input = [] + submitted = False + for call in calls: + try: + arguments = json.loads(call.arguments or "{}") + except json.JSONDecodeError as error: + result: dict[str, Any] = {"error": f"invalid arguments: {error}"} + else: + if call.name == "run_shell": + result = await self._run_shell( + environment, arguments["command"] + ) + elif call.name == "read_file": + result = await self._read_file( + environment, + arguments["path"], + arguments.get("start_line"), + arguments.get("end_line"), + ) + elif call.name == "write_file": + result = await self._write_file( + environment, arguments["path"], arguments["content"] + ) + elif call.name == "apply_patch": + result = await self._apply_patch( + environment, arguments["patch"] + ) + elif call.name == "run_tests": + result = await self._run_tests( + environment, arguments.get("command") + ) + elif call.name == "submit": + result = {"submitted": True} + submitted = True + else: + result = {"error": f"unknown tool: {call.name}"} + self._trace({"turn": turn, "tool": call.name, "result": result}) + next_input.append( + { + "type": "function_call_output", + "call_id": call.call_id, + "output": json.dumps(result, ensure_ascii=False), + } + ) + if submitted: + break + if submitted: + context.metadata = {"turns": turn, "trace": "swe-bench-pro-trace.jsonl"} + break + previous_response_id = response.id + else: + raise RuntimeError(f"SWE-bench-Pro agent exceeded {MAX_TURNS} turns") + + context.n_input_tokens = input_tokens + context.n_output_tokens = output_tokens + context.n_cache_tokens = cached_tokens diff --git a/harness-engineering-bench/swe-bench-pro/baseline/target/tests/test_agent.py b/harness-engineering-bench/swe-bench-pro/baseline/target/tests/test_agent.py new file mode 100644 index 00000000..30701a20 --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/baseline/target/tests/test_agent.py @@ -0,0 +1,135 @@ +from types import SimpleNamespace + +import pytest + +from swebench_pro_agent import SweBenchProAgent + + +class FakeEnvironment: + def __init__(self): + self.commands: list[str] = [] + + async def exec(self, command, **kwargs): + self.commands.append(command) + return SimpleNamespace(return_code=0, stdout="", stderr="") + + async def upload_file(self, source_path, target_path): # pragma: no cover + pass + + +class FakeResponses: + """Returns a single submit call, then would loop forever if asked again.""" + + def __init__(self): + self.calls = 0 + + async def create(self, **kwargs): + self.calls += 1 + return SimpleNamespace( + id=f"response-{self.calls}", + output=[ + SimpleNamespace( + type="function_call", + name="submit", + arguments="{}", + call_id="call-1", + ) + ], + output_text="", + usage=SimpleNamespace( + input_tokens=200, + output_tokens=12, + input_tokens_details=SimpleNamespace(cached_tokens=30), + ), + ) + + +class FlakyResponses: + """Fails once, then returns a submit call: exercises the retry helper.""" + + def __init__(self): + self.calls = 0 + + async def create(self, **kwargs): + self.calls += 1 + if self.calls == 1: + raise RuntimeError("transient upstream error") + return SimpleNamespace( + id="response-ok", + output=[ + SimpleNamespace( + type="function_call", + name="submit", + arguments="{}", + call_id="call-1", + ) + ], + output_text="", + usage=SimpleNamespace( + input_tokens=10, + output_tokens=1, + input_tokens_details=SimpleNamespace(cached_tokens=0), + ), + ) + + +@pytest.mark.asyncio +async def test_agent_submits_and_populates_context(tmp_path, monkeypatch): + monkeypatch.setenv("OPENAI_API_KEY", "test-key") + agent = SweBenchProAgent( + logs_dir=tmp_path / "logs", + model_name="openai/gpt-4o", + ) + agent._client = SimpleNamespace(responses=FakeResponses()) + environment = FakeEnvironment() + context = SimpleNamespace( + metadata=None, + n_input_tokens=None, + n_output_tokens=None, + n_cache_tokens=None, + ) + + await agent.run("Fix the failing test in the repository.", environment, context) + + assert context.n_input_tokens == 200 + assert context.n_output_tokens == 12 + assert context.n_cache_tokens == 30 + assert context.metadata == { + "turns": 1, + "trace": "swe-bench-pro-trace.jsonl", + } + + +@pytest.mark.asyncio +async def test_responses_create_retries_transient_errors(tmp_path, monkeypatch): + monkeypatch.setenv("OPENAI_API_KEY", "test-key") + # Avoid real backoff sleeps in the test. + monkeypatch.setattr("swebench_pro_agent.agent.API_RETRY_BASE_DELAY", 0.0) + monkeypatch.setattr("swebench_pro_agent.agent.API_RETRY_MAX_DELAY", 0.0) + agent = SweBenchProAgent( + logs_dir=tmp_path / "logs", + model_name="openai/gpt-4o", + ) + flaky = FlakyResponses() + agent._client = SimpleNamespace(responses=flaky) + environment = FakeEnvironment() + context = SimpleNamespace( + metadata=None, + n_input_tokens=None, + n_output_tokens=None, + n_cache_tokens=None, + ) + + await agent.run("Fix the failing test in the repository.", environment, context) + + assert flaky.calls == 2 # one failure, then one success + assert context.metadata == { + "turns": 1, + "trace": "swe-bench-pro-trace.jsonl", + } + + +def test_agent_requires_model(tmp_path, monkeypatch): + monkeypatch.setenv("OPENAI_API_KEY", "test-key") + with pytest.raises(ValueError, match="requires a Harbor model"): + SweBenchProAgent(logs_dir=tmp_path) diff --git a/harness-engineering-bench/swe-bench-pro/partitions/development.json b/harness-engineering-bench/swe-bench-pro/partitions/development.json new file mode 100644 index 00000000..fe51488c --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/partitions/development.json @@ -0,0 +1 @@ +[] diff --git a/harness-engineering-bench/swe-bench-pro/partitions/manifest.json b/harness-engineering-bench/swe-bench-pro/partitions/manifest.json new file mode 100644 index 00000000..5aa42fd8 --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/partitions/manifest.json @@ -0,0 +1,23 @@ +{ + "_stub": true, + "_todo": "STUB manifest. Regenerate with scripts/partition_swe_bench_pro.py once the real task_source is pinned (see ../../README.md). The empty partition files and the placeholder task_source in baseline/build.yaml must both be replaced before this benchmark can run.", + "schema_version": 1, + "task_source": "scale-ai/swe-bench-pro@sha256:REPLACE_WITH_REAL_DIGEST", + "dataset_name": "scale-ai/swe-bench-pro", + "dataset_version": "REPLACE_WITH_REAL_DIGEST", + "seed": "vero-swe-bench-pro-v1", + "ratios": { + "development": 0.2, + "validation": 0.4, + "test": 0.4 + }, + "stratified_by": ["repository"], + "partition_counts": { + "development": 0, + "validation": 0, + "test": 0 + }, + "partition_digest": "sha256:REPLACE_AFTER_REGENERATION", + "stratum_counts": {}, + "tasks": [] +} diff --git a/harness-engineering-bench/swe-bench-pro/partitions/test.json b/harness-engineering-bench/swe-bench-pro/partitions/test.json new file mode 100644 index 00000000..fe51488c --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/partitions/test.json @@ -0,0 +1 @@ +[] diff --git a/harness-engineering-bench/swe-bench-pro/partitions/validation.json b/harness-engineering-bench/swe-bench-pro/partitions/validation.json new file mode 100644 index 00000000..fe51488c --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/partitions/validation.json @@ -0,0 +1 @@ +[] diff --git a/harness-engineering-bench/swe-bench-pro/scripts/partition_swe_bench_pro.py b/harness-engineering-bench/swe-bench-pro/scripts/partition_swe_bench_pro.py new file mode 100644 index 00000000..724ecc58 --- /dev/null +++ b/harness-engineering-bench/swe-bench-pro/scripts/partition_swe_bench_pro.py @@ -0,0 +1,280 @@ +#!/usr/bin/env python3 +"""Create and verify the committed SWE-bench-Pro development/validation/test split. + +STUB STATUS +----------- +This script is a faithful adaptation of ``gaia/scripts/partition_gaia.py`` for +SWE-bench-Pro, but it cannot produce real partitions until the SWE-bench-Pro +Harbor task source is pinned. Two constants and one reader must be confirmed +against the real task package before running without ``--check``: + + 1. ``DATASET_DIGEST`` — the real content digest (see baseline/build.yaml). + 2. ``TOTAL_TASKS`` / ``TARGET_COUNTS`` — the real task count and split sizes. + 3. ``_read_tasks`` — how each task exposes its canonical name and the field + used to stratify (assumed here to be the source repository). Adjust the + TOML keys once the pinned task.toml layout is known. + +The deterministic, sha256-keyed stratified split and the ``--check`` mode mirror +the GAIA script exactly, so once the constants are correct the committed split is +reproducible and verifiable. +""" + +from __future__ import annotations + +import argparse +import asyncio +import hashlib +import json +import tomllib +from collections import Counter, defaultdict +from fractions import Fraction +from pathlib import Path +from typing import Any + +DATASET_NAME = "scale-ai/swe-bench-pro" +# TODO: replace with the real content digest once the task source is pinned. +DATASET_DIGEST = "REPLACE_WITH_REAL_DIGEST" +TASK_SOURCE = f"{DATASET_NAME}@sha256:{DATASET_DIGEST}" +SEED = "vero-swe-bench-pro-v1" +PARTITIONS = ("development", "validation", "test") +RATIOS = { + "development": Fraction(1, 5), + "validation": Fraction(2, 5), + "test": Fraction(2, 5), +} + +# TODO: set to the real task count and split sizes (dev/val/test) once the task +# source is pinned. These placeholders keep the arithmetic self-consistent +# (20/40/40 of TOTAL_TASKS) but are NOT the real dataset size. +TOTAL_TASKS = 0 +TARGET_COUNTS = {"development": 0, "validation": 0, "test": 0} + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument( + "--tasks-dir", + type=Path, + required=True, + help="Directory containing the exported SWE-bench-Pro task directories", + ) + parser.add_argument( + "--output-dir", + type=Path, + default=Path(__file__).parents[1] / "partitions", + ) + parser.add_argument("--fetch-registry", action="store_true") + parser.add_argument("--check", action="store_true") + return parser.parse_args() + + +def _task_root(path: Path) -> Path: + path = path.expanduser().resolve() + if len(list(path.glob("*/task.toml"))) == TOTAL_TASKS: + return path + nested = path / "swe-bench-pro" + if len(list(nested.glob("*/task.toml"))) == TOTAL_TASKS: + return nested + raise ValueError( + f"{path} does not contain exactly {TOTAL_TASKS} exported " + "SWE-bench-Pro tasks (update TOTAL_TASKS once the source is pinned)" + ) + + +def _read_tasks(path: Path) -> list[dict[str, str]]: + """Read the exported tasks and the field used to stratify. + + STUB: the exact task.toml layout for SWE-bench-Pro is not yet known. This + reader assumes each task exposes a canonical ``[task].name`` and a source + repository under ``[metadata].repository`` (or a ``repo:`` tag). Adjust + the key lookups below to match the real package before generating partitions. + """ + tasks: list[dict[str, str]] = [] + for task_dir in sorted(_task_root(path).iterdir()): + if not task_dir.is_dir(): + continue + config = tomllib.loads((task_dir / "task.toml").read_text(encoding="utf-8")) + canonical_name = config.get("task", {}).get("name") + if not canonical_name: + raise ValueError(f"{task_dir.name} has no [task].name") + metadata = config.get("metadata", {}) + repository = metadata.get("repository") + if not repository: + tags = metadata.get("tags", []) + repo_tags = [t for t in tags if t.startswith("repo:")] + repository = repo_tags[0].removeprefix("repo:") if repo_tags else "unknown" + tasks.append({"name": canonical_name, "repository": repository}) + if len(tasks) != TOTAL_TASKS: + raise ValueError(f"expected {TOTAL_TASKS} tasks, found {len(tasks)}") + return tasks + + +async def _fetch_registry_refs() -> tuple[str, dict[str, str]]: + try: + from harbor.registry.client.package import PackageDatasetClient + except ImportError as error: + raise RuntimeError( + "--fetch-registry requires the exactly pinned Harbor package" + ) from error + + metadata = await PackageDatasetClient().get_dataset_metadata(TASK_SOURCE) + refs = {task.get_name(): str(task.ref) for task in metadata.task_ids} + return str(metadata.version), refs + + +def _existing_refs(output_dir: Path) -> tuple[str, dict[str, str]]: + manifest_path = output_dir / "manifest.json" + if not manifest_path.is_file(): + raise ValueError("no existing manifest; rerun with --fetch-registry") + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + refs = {item["name"]: item["ref"] for item in manifest["tasks"]} + return manifest["dataset_version"], refs + + +def _stable_key(task_name: str) -> str: + return hashlib.sha256(f"{SEED}:{task_name}".encode()).hexdigest() + + +def _allocate(tasks: list[dict[str, str]]) -> dict[str, list[str]]: + strata: dict[str, list[dict[str, str]]] = defaultdict(list) + for task in tasks: + strata[task["repository"]].append(task) + + allocation: dict[str, dict[str, int]] = {} + remaining_by_stratum: dict[str, int] = {} + totals: Counter[str] = Counter() + for stratum, members in strata.items(): + row = { + partition: int(len(members) * RATIOS[partition]) for partition in PARTITIONS + } + allocation[stratum] = row + totals.update(row) + remaining_by_stratum[stratum] = len(members) - sum(row.values()) + + deficits = { + partition: TARGET_COUNTS[partition] - totals[partition] + for partition in PARTITIONS + } + while sum(remaining_by_stratum.values()): + choices: list[tuple[Fraction, str, str]] = [] + for stratum, remaining in remaining_by_stratum.items(): + if remaining == 0: + continue + size = len(strata[stratum]) + for partition in PARTITIONS: + if deficits[partition] == 0: + continue + ideal = size * RATIOS[partition] + shortfall = ideal - allocation[stratum][partition] + choices.append((shortfall, stratum, partition)) + if not choices: + raise RuntimeError("could not satisfy exact partition sizes") + _, stratum, partition = max( + choices, + key=lambda item: (item[0], -PARTITIONS.index(item[2]), item[1]), + ) + allocation[stratum][partition] += 1 + remaining_by_stratum[stratum] -= 1 + deficits[partition] -= 1 + + result: dict[str, list[str]] = {partition: [] for partition in PARTITIONS} + for stratum in sorted(strata): + members = sorted(strata[stratum], key=lambda task: _stable_key(task["name"])) + cursor = 0 + for partition in PARTITIONS: + count = allocation[stratum][partition] + result[partition].extend( + task["name"] for task in members[cursor : cursor + count] + ) + cursor += count + for partition in PARTITIONS: + result[partition].sort() + if len(result[partition]) != TARGET_COUNTS[partition]: + raise RuntimeError(f"wrong {partition} size: {len(result[partition])}") + return result + + +def _render( + tasks: list[dict[str, str]], + partitions: dict[str, list[str]], + dataset_version: str, + refs: dict[str, str], +) -> dict[str, str]: + names = {task["name"] for task in tasks} + if names != set(refs): + raise ValueError( + "downloaded task names do not match the pinned registry dataset" + ) + membership = { + name: partition + for partition, partition_tasks in partitions.items() + for name in partition_tasks + } + partition_digest = hashlib.sha256( + json.dumps(partitions, sort_keys=True, separators=(",", ":")).encode() + ).hexdigest() + stratum_counts = Counter(task["repository"] for task in tasks) + manifest: dict[str, Any] = { + "schema_version": 1, + "task_source": TASK_SOURCE, + "dataset_name": DATASET_NAME, + "dataset_version": dataset_version, + "seed": SEED, + "ratios": {partition: float(RATIOS[partition]) for partition in PARTITIONS}, + "stratified_by": ["repository"], + "partition_counts": TARGET_COUNTS, + "partition_digest": f"sha256:{partition_digest}", + "stratum_counts": dict(sorted(stratum_counts.items())), + "tasks": [ + { + **task, + "ref": refs[task["name"]], + "partition": membership[task["name"]], + } + for task in sorted(tasks, key=lambda item: item["name"]) + ], + } + rendered = { + f"{partition}.json": json.dumps(partitions[partition], indent=2) + "\n" + for partition in PARTITIONS + } + rendered["manifest.json"] = json.dumps(manifest, indent=2) + "\n" + return rendered + + +def main() -> None: + if DATASET_DIGEST == "REPLACE_WITH_REAL_DIGEST" or TOTAL_TASKS == 0: + raise SystemExit( + "STUB: pin the real DATASET_DIGEST and TOTAL_TASKS/TARGET_COUNTS " + "(and confirm _read_tasks against the real task.toml) before running. " + "See the module docstring and ../../README.md." + ) + args = _parse_args() + tasks = _read_tasks(args.tasks_dir) + args.output_dir = args.output_dir.expanduser().resolve() + if args.fetch_registry: + dataset_version, refs = asyncio.run(_fetch_registry_refs()) + else: + dataset_version, refs = _existing_refs(args.output_dir) + rendered = _render(tasks, _allocate(tasks), dataset_version, refs) + + if args.check: + changed = [ + filename + for filename, content in rendered.items() + if not (args.output_dir / filename).is_file() + or (args.output_dir / filename).read_text(encoding="utf-8") != content + ] + if changed: + raise SystemExit("partition files are stale: " + ", ".join(changed)) + print("SWE-bench-Pro partitions match the pinned dataset and split algorithm") + return + + args.output_dir.mkdir(parents=True, exist_ok=True) + for filename, content in rendered.items(): + (args.output_dir / filename).write_text(content, encoding="utf-8") + print(f"wrote {len(rendered)} files to {args.output_dir}") + + +if __name__ == "__main__": + main() From 407e56f206a8c459aec46eff41d9ee9b8b49d1ea Mon Sep 17 00:00:00 2001 From: Shehab Yasser Date: Sat, 25 Jul 2026 06:56:23 +0300 Subject: [PATCH 14/16] fix: classify Modal sandbox / tests-dir loss as transient_infra The harbor backend's error taxonomy records any unrecognized case failure as TASK_FAILURE: an informative, scoreable sample at the failure value with status SUCCESS. That is right for a candidate that crashes on its own, but Modal sandbox lifecycle failures and a missing held-out tests fixture are infrastructure the candidate did not cause. Seen on a swe-atlas-qna run (2026-07-24): all 396 cases returned status=success, error_category=task_failure, score=0.0. Terminal exceptions were AddTestsDirError x144, Modal sandbox NotFound x125, RuntimeError x67, BadRequestError x60. Classified as task failures, the aggregate reported error_rate 0.0 and objective feasible at 0.0, so a fully broken environment looked like a real, shippable zero and the optimizer ran with no gradient (baseline and every candidate 0.0). Add the harness/Modal environment-loss signals (sandbox lifecycle, AddTestsDirError / "failed to add tests directory", container fetch failures, StreamTerminatedError) to the TRANSIENT_INFRA patterns. Those cases are now excluded from the aggregate and counted toward invalidity, so a collapsed environment surfaces as an invalid run instead of a silent zero. A candidate's own bug (ValueError, KeyError, no answer) is still a task failure, unchanged. Covered by two new tests; existing behavior asserted unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) --- vero/src/vero/evaluation/error_taxonomy.py | 16 ++++++++++ vero/tests/test_v05_error_taxonomy.py | 37 ++++++++++++++++++++++ 2 files changed, 53 insertions(+) diff --git a/vero/src/vero/evaluation/error_taxonomy.py b/vero/src/vero/evaluation/error_taxonomy.py index beacf7e8..8637ee2d 100644 --- a/vero/src/vero/evaluation/error_taxonomy.py +++ b/vero/src/vero/evaluation/error_taxonomy.py @@ -158,6 +158,22 @@ def policy(category: ErrorCategory) -> CategoryPolicy: ), ErrorCategory.TRANSIENT_INFRA, ), + ( + # Harness / Modal environment loss: the task sandbox died, was never + # created, or its container or held-out tests fixture could not be + # provisioned. This is infrastructure the candidate did not cause, so + # it is excluded from the aggregate and counted toward invalidity, + # never scored as an informative task failure. Without this, a run + # whose sandboxes all collapsed reports a fully "successful" 0.0 + # instead of an invalid aggregate. + re.compile( + r"sandbox|streamterminated|" + r"failed.?to.?add.?tests.?directory|addtestsdirerror|" + r"loading.?container|fetchspec", + re.IGNORECASE, + ), + ErrorCategory.TRANSIENT_INFRA, + ), ] diff --git a/vero/tests/test_v05_error_taxonomy.py b/vero/tests/test_v05_error_taxonomy.py index 5b15cc46..fad81e1c 100644 --- a/vero/tests/test_v05_error_taxonomy.py +++ b/vero/tests/test_v05_error_taxonomy.py @@ -58,3 +58,40 @@ def test_policy_encodes_the_intended_treatment(): task = policy(ErrorCategory.TASK_FAILURE) assert task.is_informative_sample assert not task.counts_toward_invalidity and not task.terminating + + +def test_classify_signal_recognizes_harness_environment_loss(): + # Modal sandbox lifecycle failures and a missing held-out tests fixture are + # infrastructure the candidate did not cause, not an informative zero. + assert classify_signal("AddTestsDirError") is ErrorCategory.TRANSIENT_INFRA + assert ( + classify_signal("Failed to add tests directory to environment.") + is ErrorCategory.TRANSIENT_INFRA + ) + assert ( + classify_signal("The Sandbox is unavailable. It may have already shut down.") + is ErrorCategory.TRANSIENT_INFRA + ) + assert ( + classify_signal("Modal Sandbox with container ID ta-01 not found.") + is ErrorCategory.TRANSIENT_INFRA + ) + assert ( + classify_signal("FetchSpec failed: loading container: file does not exist") + is ErrorCategory.TRANSIENT_INFRA + ) + + +def test_classify_case_treats_environment_loss_as_infra_not_task_failure(): + # Regression for the swe-atlas-qna flat-zero: the majority of cases died on + # Modal sandbox / tests-dir provisioning yet were masked as scoreable task + # failures at 0.0. They must be excluded infrastructure, not informative. + assert classify_case(["AddTestsDirError"]) is ErrorCategory.TRANSIENT_INFRA + assert ( + classify_case( + ["NotFoundError", "Modal Sandbox with container ID ta-01 not found."] + ) + is ErrorCategory.TRANSIENT_INFRA + ) + # A candidate's own bug is still an informative task failure, unchanged. + assert classify_case(["ValueError"]) is ErrorCategory.TASK_FAILURE From fc5b80d3056e43358f51d17d91fe2ef84854b795 Mon Sep 17 00:00:00 2001 From: Shehab Yasser Date: Sat, 25 Jul 2026 06:56:25 +0300 Subject: [PATCH 15/16] fix: only send reasoning.effort to models that support it Each harness-engineering-bench target agent builds its Responses API request with a hard-coded "reasoning": {"effort": ...} on every call, regardless of the configured model. Reasoning models (gpt-5.x, o-series, *-codex) accept it, but gpt-4o and other non-reasoning models reject the whole request with HTTP 400: "Unsupported parameter: 'reasoning.effort' is not supported with this model". On a swe-atlas-qna run pointed at a gpt-4o agent model this produced a wall of 400s (60 cases died with BadRequestError). The gaia and tau3 configs point their agent model at gpt-4o in the same setup, so they share the latent bug. Gate reasoning.effort on the model family: send it only when the resolved model is a reasoning model (gpt-5.x / o1 / o3 / o4 / *-codex), otherwise omit it. Each agent keeps its own effort level (high for atlas and swe-bench-pro, medium for gaia, officeqa and tau3). No behavior change for reasoning models; gpt-4o stops 400ing. Applied uniformly to all five bench agents. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../gaia/baseline/target/src/gaia_agent/agent.py | 6 +++++- .../officeqa/baseline/target/src/officeqa_agent/agent.py | 6 +++++- .../swe-atlas-qna/baseline/target/src/atlas_agent/agent.py | 6 +++++- .../baseline/target/src/swebench_pro_agent/agent.py | 6 +++++- .../tau3/baseline/target/src/tau3_agent/agent.py | 6 +++++- 5 files changed, 25 insertions(+), 5 deletions(-) diff --git a/harness-engineering-bench/gaia/baseline/target/src/gaia_agent/agent.py b/harness-engineering-bench/gaia/baseline/target/src/gaia_agent/agent.py index e7611b31..e0002455 100644 --- a/harness-engineering-bench/gaia/baseline/target/src/gaia_agent/agent.py +++ b/harness-engineering-bench/gaia/baseline/target/src/gaia_agent/agent.py @@ -185,10 +185,14 @@ async def run( "instructions": INSTRUCTIONS, "input": next_input, "tools": TOOLS, - "reasoning": {"effort": "medium"}, "max_output_tokens": 8000, "parallel_tool_calls": False, } + # gpt-4o and other non-reasoning models reject `reasoning.effort` + # with HTTP 400; only send it to reasoning-capable models. + _model = self._api_model.lower() + if _model.startswith(("gpt-5", "o1", "o3", "o4")) or "codex" in _model: + request["reasoning"] = {"effort": "medium"} if previous_response_id is not None: request["previous_response_id"] = previous_response_id response = await self._client.responses.create(**request) diff --git a/harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/agent.py b/harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/agent.py index 46bcde33..4db94c5a 100644 --- a/harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/agent.py +++ b/harness-engineering-bench/officeqa/baseline/target/src/officeqa_agent/agent.py @@ -189,10 +189,14 @@ async def run( "instructions": INSTRUCTIONS, "input": next_input, "tools": TOOLS, - "reasoning": {"effort": "medium"}, "max_output_tokens": 8000, "parallel_tool_calls": False, } + # gpt-4o and other non-reasoning models reject `reasoning.effort` + # with HTTP 400; only send it to reasoning-capable models. + _model = self._api_model.lower() + if _model.startswith(("gpt-5", "o1", "o3", "o4")) or "codex" in _model: + request["reasoning"] = {"effort": "medium"} if previous_response_id is not None: request["previous_response_id"] = previous_response_id response = await self._client.responses.create(**request) diff --git a/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py b/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py index 0544c105..c83f9042 100644 --- a/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py +++ b/harness-engineering-bench/swe-atlas-qna/baseline/target/src/atlas_agent/agent.py @@ -150,10 +150,14 @@ async def run( "instructions": INSTRUCTIONS, "input": next_input, "tools": TOOLS, - "reasoning": {"effort": "high"}, "max_output_tokens": 12_000, "parallel_tool_calls": False, } + # gpt-4o and other non-reasoning models reject `reasoning.effort` + # with HTTP 400; only send it to reasoning-capable models. + _model = self._api_model.lower() + if _model.startswith(("gpt-5", "o1", "o3", "o4")) or "codex" in _model: + request["reasoning"] = {"effort": "high"} if previous_response_id is not None: request["previous_response_id"] = previous_response_id response = await self._client.responses.create(**request) diff --git a/harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/agent.py b/harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/agent.py index 961c5b85..12af7ade 100644 --- a/harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/agent.py +++ b/harness-engineering-bench/swe-bench-pro/baseline/target/src/swebench_pro_agent/agent.py @@ -382,10 +382,14 @@ async def run( "instructions": INSTRUCTIONS, "input": next_input, "tools": TOOLS, - "reasoning": {"effort": "high"}, "max_output_tokens": 12_000, "parallel_tool_calls": False, } + # gpt-4o and other non-reasoning models reject `reasoning.effort` + # with HTTP 400; only send it to reasoning-capable models. + _model = self._api_model.lower() + if _model.startswith(("gpt-5", "o1", "o3", "o4")) or "codex" in _model: + request["reasoning"] = {"effort": "high"} if previous_response_id is not None: request["previous_response_id"] = previous_response_id response = await self._responses_create(**request) diff --git a/harness-engineering-bench/tau3/baseline/target/src/tau3_agent/agent.py b/harness-engineering-bench/tau3/baseline/target/src/tau3_agent/agent.py index 4a98cd5a..f1700806 100644 --- a/harness-engineering-bench/tau3/baseline/target/src/tau3_agent/agent.py +++ b/harness-engineering-bench/tau3/baseline/target/src/tau3_agent/agent.py @@ -298,10 +298,14 @@ async def run( "instructions": INSTRUCTIONS, "input": next_input, "tools": openai_tools, - "reasoning": {"effort": "medium"}, "max_output_tokens": 8_000, "parallel_tool_calls": False, } + # gpt-4o and other non-reasoning models reject `reasoning.effort` + # with HTTP 400; only send it to reasoning-capable models. + _model = self._api_model.lower() + if _model.startswith(("gpt-5", "o1", "o3", "o4")) or "codex" in _model: + request["reasoning"] = {"effort": "medium"} if previous_response_id is not None: request["previous_response_id"] = previous_response_id response = await client.responses.create(**request) From 0092c1f6d6d853f292408eeb3f258206860fd7da Mon Sep 17 00:00:00 2001 From: Shehab Yasser Date: Sat, 25 Jul 2026 06:56:28 +0300 Subject: [PATCH 16/16] fix: raise tau3 optimizer trial max_retries to survive transient Modal drops A tau3 optimizer run (2026-07-24) died about 25 minutes in when the Modal command-router gRPC stream dropped mid-optimization (grpclib StreamTerminatedError: Connection lost). With max_retries: 1 the whole trial errored and nothing was scored, even though the drop was transient infrastructure and the run had already committed candidates. Raise max_retries from 1 to 3 so a transient stream drop retries the trial instead of failing the run. This matches infrastructure_max_attempts: 3 already set for per-case infra. A deeper fix (running the eval under the Modal VM runtime to remove the single host-networking gRPC stream SPOF) is noted for follow-up but not applied here, since it needs validation against harbor 0.20.0. Co-Authored-By: Claude Opus 4.8 (1M context) --- harness-engineering-bench/tau3/baseline/build.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/harness-engineering-bench/tau3/baseline/build.yaml b/harness-engineering-bench/tau3/baseline/build.yaml index 5d8a0892..a628c108 100644 --- a/harness-engineering-bench/tau3/baseline/build.yaml +++ b/harness-engineering-bench/tau3/baseline/build.yaml @@ -50,7 +50,7 @@ environment_name: modal extra_harbor_args: ["--ek", "app_name=harness-engineering-bench", "--ek", "sandbox_idle_timeout_secs=3600"] harbor_python_version: "3.12" n_attempts: 1 -max_retries: 1 +max_retries: 3 infrastructure_max_attempts: 3 infrastructure_retry_delay_seconds: 5 aggregate_attempts: best