Upload BenchFlow artifacts to efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/adp.jsonl

@@ -0,0 +1 @@
+{"schema_version": "1.3.1", "id": "simulation-heuristics-ant-v1__33f337b0", "content": [{"class_": "text_observation", "content": "# Design a robust commanded-path Ant controller\n\nImplement an offline feedback controller for Gymnasium `Ant-v5` that follows a\ncurved ground-plane path and its tangent heading under frozen physical\nuncertainty. Straight positive-x travel is not sufficient.\n\nThe plant uses a 0.05 s control interval and exactly 1,000 commanded control\nsteps. The controller receives the standard 27-value Ant observation plus an\neight-value path-tracking command described in\n`/app/task_context/policy_api.md`. A zero-action API scaffold is provided at\n`/app/starter_policy/policy.py`; it is intentionally not a locomotion\ncontroller.\n\nThe reference centerline is anchored at the reset torso position:\n\n```text\ny_ref(x) = A [1 - cos(2 pi x / lambda)]\n```\n\nwhere `x` and `y` are reset-relative world coordinates. The commanded heading\nis the centerline tangent. Positive cross-track error means the torso is left\nof that tangent; positive heading error means the commanded heading is\ncounter-clockwise from the torso heading. At the torso's current `x`, the\nsigned tracking error is\n\n```text\ne_cross = cos(heading_ref) [y - y_ref(x)] .\n```\n\n## Physical uncertainty contract\n\nPublic development scenarios and held-out scenarios use the same simulator,\npolicy API, reset order, and declared ranges. Held-out evaluation uses fixed\ncombinations that are not selected from the policy seed:\n\n| Quantity | Declared range |\n|---|---:|\n| total geom mass scale | `0.94` to `1.07` |\n| contact-friction scale | `0.87` to `1.06` |\n| each actuator-pair leg-strength scale | `0.91` to `1.05` |\n| ground slope along world `+x` | `-2.3` to `+2.3` degrees |\n| finite lateral torso force | `-9` to `+9 N` |\n| simultaneous yaw torque | `-2.4` to `+2.4 N m` |\n| path amplitude `A` | `0.80` to `1.00 m` |\n| path wavelength `lambda` | `20` to `24 m` |\n| target path speed | `0.58` to `0.70 m/s` |\n\nThe force and torque act together for 6\u20139 control steps at a frozen time\nbetween steps 250 and 700. Positive lateral force is along the instantaneous\nleft path normal. Positive slope rises with world `+x`. Leg-strength entries\nscale the four consecutive actuator pairs in Ant action order.\n\nThese bounds represent a combined robustness envelope rather than independent\nworst-case certification: up to 7% mass shift, 13% friction loss, 14%\npair-to-pair actuator imbalance, a 4.6-degree grade span, and a finite impulse\nlarge enough to perturb both lateral velocity and yaw. The held-out suite uses\nfixed multi-axis combinations inside this envelope.\n\nUse the local evaluator for full-length development runs:\n\n```bash\npython /app/evaluate.py \\\n  --policy /app/final_policy/policy.py \\\n  --episodes 2 \\\n  --max-steps 1000 \\\n  --suite all\n```\n\n`--seed` selects simulator reset seeds only. In both public and held-out\nevaluation, `Policy(...)` and `reset(...)` receive `seed=0`; controllers must\nadapt from observations and commands rather than branch on episode identity.\n\n## Required artifact\n\nPlace the controller at:\n\n```text\n/app/final_policy/policy.py\n```\n\nIt must define `Policy` or `make_policy`. The resulting object must implement\n`act(observation, command)` and may implement `reset(seed=0)`. Actions must be\nfinite NumPy-compatible arrays with shape `(8,)`; values are clipped to\n`[-1, 1]`. The policy must run with the installed packages, without network\naccess or external processes, and reset all recurrent state between episodes.\nComplete the work within the primary agent process. Delegated or descendant\nagents, external retrieval, remote repository access, and Git-history solution\nlookup are prohibited and force reward `0`, even if the retrieved material is\nnot ultimately used.\n\n## Physical acceptance criteria\n\nEvery held-out episode is an independent heavy gate. The controller must:\n\n- execute exactly 1,000 actions without a policy error or loss of physical\n  uprightness; the runtime stops when torso clearance above the sloped plane\n  leaves `[0.20, 1.05] m` or torso tilt from world vertical exceeds\n  `1.35 rad`;\n- keep RMS centerline cross-track error at or below `0.55 m`;\n- keep RMS wrapped heading error at or below `0.20 rad`;\n- spend at most `0.10` of samples moving backward faster than `0.05 m/s`\n  along the path tangent;\n- maintain at least `0.24 m/s` in every rolling 100-step (`5 s`) mean of\n  tangent speed;\n- keep RMS actuator command at or below `0.62`.\n\nThe 5 s speed window spans several gait cycles; its `0.24 m/s` floor requires\nat least `1.2 m` of tangent progress per window and rejects controllers that\ncoast, stall, or reverse for a sustained interval. The tracking limits are\nwell below the path's lateral excursion and heading swing, so a straight-line\ngait cannot pass. The effort cap permits transient correction while excluding\nnear-saturated open-loop forcing.\n\nComparisons allow only numerical portability margins of `0.02 m` in RMS\ncross-track error, `0.01 rad` in RMS heading error, `0.005` in backward-motion\nfraction, `0.01 m/s` in windowed speed, and `0.01` in RMS action. Exact step\ncompletion has no tolerance.\n\nProvenance, license boundaries, upstream paths, and file hashes are recorded\nunder `/app/provenance/`.", "source": "user"}], "details": {"task_id": "simulation-heuristics-ant-v1", "environment": "simulation-heuristics-ant-v1", "model": "azure-foundry-openai/gpt-5.6-sol"}}

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/results.jsonl

@@ -0,0 +1 @@
+{"example_id": 0, "prompt": [{"role": "user", "content": "# Design a robust commanded-path Ant controller\n\nImplement an offline feedback controller for Gymnasium `Ant-v5` that follows a\ncurved ground-plane path and its tangent heading under frozen physical\nuncertainty. Straight positive-x travel is not sufficient.\n\nThe plant uses a 0.05 s control interval and exactly 1,000 commanded control\nsteps. The controller receives the standard 27-value Ant observation plus an\neight-value path-tracking command described in\n`/app/task_context/policy_api.md`. A zero-action API scaffold is provided at\n`/app/starter_policy/policy.py`; it is intentionally not a locomotion\ncontroller.\n\nThe reference centerline is anchored at the reset torso position:\n\n```text\ny_ref(x) = A [1 - cos(2 pi x / lambda)]\n```\n\nwhere `x` and `y` are reset-relative world coordinates. The commanded heading\nis the centerline tangent. Positive cross-track error means the torso is left\nof that tangent; positive heading error means the commanded heading is\ncounter-clockwise from the torso heading. At the torso's current `x`, the\nsigned tracking error is\n\n```text\ne_cross = cos(heading_ref) [y - y_ref(x)] .\n```\n\n## Physical uncertainty contract\n\nPublic development scenarios and held-out scenarios use the same simulator,\npolicy API, reset order, and declared ranges. Held-out evaluation uses fixed\ncombinations that are not selected from the policy seed:\n\n| Quantity | Declared range |\n|---|---:|\n| total geom mass scale | `0.94` to `1.07` |\n| contact-friction scale | `0.87` to `1.06` |\n| each actuator-pair leg-strength scale | `0.91` to `1.05` |\n| ground slope along world `+x` | `-2.3` to `+2.3` degrees |\n| finite lateral torso force | `-9` to `+9 N` |\n| simultaneous yaw torque | `-2.4` to `+2.4 N m` |\n| path amplitude `A` | `0.80` to `1.00 m` |\n| path wavelength `lambda` | `20` to `24 m` |\n| target path speed | `0.58` to `0.70 m/s` |\n\nThe force and torque act together for 6\u20139 control steps at a frozen time\nbetween steps 250 and 700. Positive lateral force is along the instantaneous\nleft path normal. Positive slope rises with world `+x`. Leg-strength entries\nscale the four consecutive actuator pairs in Ant action order.\n\nThese bounds represent a combined robustness envelope rather than independent\nworst-case certification: up to 7% mass shift, 13% friction loss, 14%\npair-to-pair actuator imbalance, a 4.6-degree grade span, and a finite impulse\nlarge enough to perturb both lateral velocity and yaw. The held-out suite uses\nfixed multi-axis combinations inside this envelope.\n\nUse the local evaluator for full-length development runs:\n\n```bash\npython /app/evaluate.py \\\n  --policy /app/final_policy/policy.py \\\n  --episodes 2 \\\n  --max-steps 1000 \\\n  --suite all\n```\n\n`--seed` selects simulator reset seeds only. In both public and held-out\nevaluation, `Policy(...)` and `reset(...)` receive `seed=0`; controllers must\nadapt from observations and commands rather than branch on episode identity.\n\n## Required artifact\n\nPlace the controller at:\n\n```text\n/app/final_policy/policy.py\n```\n\nIt must define `Policy` or `make_policy`. The resulting object must implement\n`act(observation, command)` and may implement `reset(seed=0)`. Actions must be\nfinite NumPy-compatible arrays with shape `(8,)`; values are clipped to\n`[-1, 1]`. The policy must run with the installed packages, without network\naccess or external processes, and reset all recurrent state between episodes.\nComplete the work within the primary agent process. Delegated or descendant\nagents, external retrieval, remote repository access, and Git-history solution\nlookup are prohibited and force reward `0`, even if the retrieved material is\nnot ultimately used.\n\n## Physical acceptance criteria\n\nEvery held-out episode is an independent heavy gate. The controller must:\n\n- execute exactly 1,000 actions without a policy error or loss of physical\n  uprightness; the runtime stops when torso clearance above the sloped plane\n  leaves `[0.20, 1.05] m` or torso tilt from world vertical exceeds\n  `1.35 rad`;\n- keep RMS centerline cross-track error at or below `0.55 m`;\n- keep RMS wrapped heading error at or below `0.20 rad`;\n- spend at most `0.10` of samples moving backward faster than `0.05 m/s`\n  along the path tangent;\n- maintain at least `0.24 m/s` in every rolling 100-step (`5 s`) mean of\n  tangent speed;\n- keep RMS actuator command at or below `0.62`.\n\nThe 5 s speed window spans several gait cycles; its `0.24 m/s` floor requires\nat least `1.2 m` of tangent progress per window and rejects controllers that\ncoast, stall, or reverse for a sustained interval. The tracking limits are\nwell below the path's lateral excursion and heading swing, so a straight-line\ngait cannot pass. The effort cap permits transient correction while excluding\nnear-saturated open-loop forcing.\n\nComparisons allow only numerical portability margins of `0.02 m` in RMS\ncross-track error, `0.01 rad` in RMS heading error, `0.005` in backward-motion\nfraction, `0.01 m/s` in windowed speed, and `0.01` in RMS action. Exact step\ncompletion has no tolerance.\n\nProvenance, license boundaries, upstream paths, and file hashes are recorded\nunder `/app/provenance/`."}], "completion": null, "info": {"task_id": "simulation-heuristics-ant-v1", "task_name": "simulation-heuristics-ant-v1", "rollout_name": "simulation-heuristics-ant-v1__33f337b0", "environment": "simulation-heuristics-ant-v1", "agent": "openhands", "agent_name": "", "model": "azure-foundry-openai/gpt-5.6-sol", "source": "benchflow", "rollout_dir": "/srv/frontierphysics/reviews/pr-1/efeee9a90db0c9707fc63401f3209e1b68a28a88/evals/strong-20260721T175807Z/2026-07-21__17-58-08/simulation-heuristics-ant-v1__33f337b0", "training_ready": false, "training_ready_reason": "missing_healthy_structured_llm_trajectory"}, "reward": 0.0, "error": {"error": "agent_error", "error_chain_str": "Docker compose command failed for environment simulation-heuristics-ant-v1. Command: docker compose --project-name simulation-heuristics-ant-v1__33f337b0 --project-directory /tmp/benchflow-task-gra1s6z0/simulation-heuristics-ant-v1/environment -f /srv/frontierphysics/repo/.venv/lib/python3.12/site-packages/benchflow/sandbox/_compose_files/docker-compose-base.yaml -f /srv/frontierphysics/repo/.venv/lib/python3.12/site-packages/benchflow/sandbox/_compose_files/docker-compose-build.yaml build. Return code: 1. Stdout:  Image bf__simulation-heuristics-ant-v1 Building \n#1 [internal] load local bake definitions\n#1 reading from stdin 633B done\n#1 DONE 0.0s\n\n#2 [internal] load build definition from Dockerfile\n#2 transferring dockerfile: 2.17kB done\n#2 DONE 0.0s\n\n#3 [internal] load metadata for docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818\n#3 DONE 0.1s\n\n#4 [internal] load metadata for docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de\n#4 DONE 0.1s\n\n#5 [internal] load .dockerignore\n#5 transferring context: 274B done\n#5 DONE 0.0s\n\n#6 [1/8] FROM docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de\n#6 resolve docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de 0.0s done\n#6 CACHED\n\n#7 [restrict-builder 1/4] FROM docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818\n#7 resolve docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818 0.0s done\n#7 DONE 0.1s\n\n#8 [internal] load build context\n#8 transferring context: 134.70kB done\n#8 DONE 0.0s\n\n#9 [stage-1 11/20] COPY LICENSE-GPL-3.0.txt /licenses/GPL-3.0.txt\n#9 CACHED\n\n#10 [stage-1  4/20] RUN python -m pip install --no-cache-dir     \"gymnasium[mujoco]==1.3.0\"     \"mujoco==3.10.0\"     \"numpy==2.5.1\"     \"pytest==8.4.2\"     \"pytest-json-ctrf==0.5.2\"\n#10 CACHED\n\n#11 [stage-1 13/20] WORKDIR /app\n#11 CACHED\n\n#12 [stage-1  2/20] COPY --from=restrict-builder /restrict-exec /usr/local/bin/restrict-exec\n#12 CACHED\n\n#13 [restrict-builder 2/4] RUN apt-get update -qq     && apt-get install -y -qq --no-install-recommends         gcc libc6-dev linux-libc-dev     && rm -rf /var/lib/apt/lists/*\n#13 CACHED\n\n#14 [stage-1  5/20] RUN useradd --create-home --uid 1000 agent     && mkdir -p /app /licenses /opt/ant-runtime\n#14 CACHED\n\n#15 [stage-1 10/20] COPY provenance /app/provenance\n#15 CACHED\n\n#16 [stage-1  6/20] COPY ant_runtime /opt/ant-runtime/ant_runtime\n#16 CACHED\n\n#17 [restrict-builder 3/4] COPY restrict_exec.c /tmp/restrict_exec.c\n#17 CACHED\n\n#18 [stage-1  9/20] COPY task_context /app/task_context\n#18 CACHED\n\n#19 [stage-1  7/20] COPY evaluate.py /app/evaluate.py\n#19 CACHED\n\n#20 [stage-1 14/20] RUN mkdir -p /logs/verifier /logs/agent /logs/artifacts\n#20 CACHED\n\n#21 [stage-1 12/20] RUN cp -R /app/starter_policy /app/final_policy     && mkdir -p /app/work     && chown -R root:root /app /licenses /opt/ant-runtime     && chmod -R a-w /licenses /opt/ant-runtime /app/evaluate.py         /app/starter_policy /app/task_context /app/provenance     && chown -R agent:agent /app/final_policy /app/work\n#21 CACHED\n\n#22 [stage-1  8/20] COPY starter_policy /app/starter_policy\n#22 CACHED\n\n#23 [restrict-builder 4/4] RUN gcc -O2 -Wall -Wextra -Werror     /tmp/restrict_exec.c -o /restrict-exec\n#23 CACHED\n\n#24 [stage-1  3/20] RUN apt-get update -qq     && apt-get install -y -qq --no-install-recommends         bash ca-certificates libseccomp2 util-linux     && rm -rf /var/lib/apt/lists/*\n#24 CACHED\n\n#25 [stage-1 15/20] COPY _deps/skills /skills/\n#25 ERROR: failed to calculate checksum of ref zehsvgd454pjwvoa9fzrudw57::vp8py7ovk1mjrchwags46wpsi: \"/_deps/skills\": not found\n------\n > [stage-1 15/20] COPY _deps/skills /skills/:\n------\nDockerfile:53\n\n--------------------\n\n  51 |     \n\n  52 |     # Skills directory (injected by benchflow --skills-dir)\n\n  53 | >>> COPY _deps/skills /skills/\n\n  54 |     RUN mkdir -p /root/.claude && ln -sf /skills /root/.claude/skills\n\n  55 |     RUN mkdir -p /root/.pi/agent && ln -sf /skills /root/.pi/agent/skills\n\n--------------------\n\nfailed to solve: failed to compute cache key: failed to calculate checksum of ref zehsvgd454pjwvoa9fzrudw57::vp8py7ovk1mjrchwags46wpsi: \"/_deps/skills\": not found\n\n. Stderr: None. ", "error_chain_repr": "Docker compose command failed for environment simulation-heuristics-ant-v1. Command: docker compose --project-name simulation-heuristics-ant-v1__33f337b0 --project-directory /tmp/benchflow-task-gra1s6z0/simulation-heuristics-ant-v1/environment -f /srv/frontierphysics/repo/.venv/lib/python3.12/site-packages/benchflow/sandbox/_compose_files/docker-compose-base.yaml -f /srv/frontierphysics/repo/.venv/lib/python3.12/site-packages/benchflow/sandbox/_compose_files/docker-compose-build.yaml build. Return code: 1. Stdout:  Image bf__simulation-heuristics-ant-v1 Building \n#1 [internal] load local bake definitions\n#1 reading from stdin 633B done\n#1 DONE 0.0s\n\n#2 [internal] load build definition from Dockerfile\n#2 transferring dockerfile: 2.17kB done\n#2 DONE 0.0s\n\n#3 [internal] load metadata for docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818\n#3 DONE 0.1s\n\n#4 [internal] load metadata for docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de\n#4 DONE 0.1s\n\n#5 [internal] load .dockerignore\n#5 transferring context: 274B done\n#5 DONE 0.0s\n\n#6 [1/8] FROM docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de\n#6 resolve docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de 0.0s done\n#6 CACHED\n\n#7 [restrict-builder 1/4] FROM docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818\n#7 resolve docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818 0.0s done\n#7 DONE 0.1s\n\n#8 [internal] load build context\n#8 transferring context: 134.70kB done\n#8 DONE 0.0s\n\n#9 [stage-1 11/20] COPY LICENSE-GPL-3.0.txt /licenses/GPL-3.0.txt\n#9 CACHED\n\n#10 [stage-1  4/20] RUN python -m pip install --no-cache-dir     \"gymnasium[mujoco]==1.3.0\"     \"mujoco==3.10.0\"     \"numpy==2.5.1\"     \"pytest==8.4.2\"     \"pytest-json-ctrf==0.5.2\"\n#10 CACHED\n\n#11 [stage-1 13/20] WORKDIR /app\n#11 CACHED\n\n#12 [stage-1  2/20] COPY --from=restrict-builder /restrict-exec /usr/local/bin/restrict-exec\n#12 CACHED\n\n#13 [restrict-builder 2/4] RUN apt-get update -qq     && apt-get install -y -qq --no-install-recommends         gcc libc6-dev linux-libc-dev     && rm -rf /var/lib/apt/lists/*\n#13 CACHED\n\n#14 [stage-1  5/20] RUN useradd --create-home --uid 1000 agent     && mkdir -p /app /licenses /opt/ant-runtime\n#14 CACHED\n\n#15 [stage-1 10/20] COPY provenance /app/provenance\n#15 CACHED\n\n#16 [stage-1  6/20] COPY ant_runtime /opt/ant-runtime/ant_runtime\n#16 CACHED\n\n#17 [restrict-builder 3/4] COPY restrict_exec.c /tmp/restrict_exec.c\n#17 CACHED\n\n#18 [stage-1  9/20] COPY task_context /app/task_context\n#18 CACHED\n\n#19 [stage-1  7/20] COPY evaluate.py /app/evaluate.py\n#19 CACHED\n\n#20 [stage-1 14/20] RUN mkdir -p /logs/verifier /logs/agent /logs/artifacts\n#20 CACHED\n\n#21 [stage-1 12/20] RUN cp -R /app/starter_policy /app/final_policy     && mkdir -p /app/work     && chown -R root:root /app /licenses /opt/ant-runtime     && chmod -R a-w /licenses /opt/ant-runtime /app/evaluate.py         /app/starter_policy /app/task_context /app/provenance     && chown -R agent:agent /app/final_policy /app/work\n#21 CACHED\n\n#22 [stage-1  8/20] COPY starter_policy /app/starter_policy\n#22 CACHED\n\n#23 [restrict-builder 4/4] RUN gcc -O2 -Wall -Wextra -Werror     /tmp/restrict_exec.c -o /restrict-exec\n#23 CACHED\n\n#24 [stage-1  3/20] RUN apt-get update -qq     && apt-get install -y -qq --no-install-recommends         bash ca-certificates libseccomp2 util-linux     && rm -rf /var/lib/apt/lists/*\n#24 CACHED\n\n#25 [stage-1 15/20] COPY _deps/skills /skills/\n#25 ERROR: failed to calculate checksum of ref zehsvgd454pjwvoa9fzrudw57::vp8py7ovk1mjrchwags46wpsi: \"/_deps/skills\": not found\n------\n > [stage-1 15/20] COPY _deps/skills /skills/:\n------\nDockerfile:53\n\n--------------------\n\n  51 |     \n\n  52 |     # Skills directory (injected by benchflow --skills-dir)\n\n  53 | >>> COPY _deps/skills /skills/\n\n  54 |     RUN mkdir -p /root/.claude && ln -sf /skills /root/.claude/skills\n\n  55 |     RUN mkdir -p /root/.pi/agent && ln -sf /skills /root/.pi/agent/skills\n\n--------------------\n\nfailed to solve: failed to compute cache key: failed to calculate checksum of ref zehsvgd454pjwvoa9fzrudw57::vp8py7ovk1mjrchwags46wpsi: \"/_deps/skills\": not found\n\n. Stderr: None. "}, "timing": {"total": 0.9}, "is_completed": false, "is_truncated": false, "stop_condition": "agent_error", "metrics": {"n_tool_calls": 0, "n_prompts": 1}, "tool_defs": [], "token_usage": {"input_tokens": 0.0, "output_tokens": 0.0, "final_input_tokens": 0.0, "final_output_tokens": 0.0, "total_tokens": 0.0}, "score": 0.0, "total_tool_calls": 0.0, "trajectory": []}

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/simulation-heuristics-ant-v1__33f337b0/config.json

@@ -0,0 +1,75 @@
+{
+  "task_path": "simulation-heuristics-ant-v1",
+  "agent": "openhands",
+  "model": "azure-foundry-openai/gpt-5.6-sol",
+  "reasoning_effort": "xhigh",
+  "environment": "docker",
+  "environment_manifest": null,
+  "skill_mode": "with-skill",
+  "skill_source": "task_bundled",
+  "requested_skills_dir": "/srv/frontierphysics/worktrees/pr-1-8cf85d13c73f/tasks/simulation-heuristics-ant-v1/environment/skills",
+  "effective_skills_dir": "/tmp/benchflow-task-gra1s6z0/simulation-heuristics-ant-v1/environment/skills",
+  "skills_sandbox_dir": "/skills",
+  "include_task_skills": true,
+  "sandbox_user": "agent",
+  "sandbox_locked_paths": [
+    "/oracle",
+    "/solution",
+    "/verifier",
+    "/tests",
+    "/testbed_verify"
+  ],
+  "sandbox_setup_timeout": 120,
+  "skip_install": false,
+  "agent_install_timeout": 900,
+  "context_root": "/srv/frontierphysics/worktrees/pr-1-8cf85d13c73f",
+  "base_image_override": null,
+  "timeout_sec": 5400,
+  "concurrency": 1,
+  "agent_idle_timeout_sec": null,
+  "started_at": "2026-07-21 17:58:08.175412",
+  "agent_env": {
+    "AZURE_API_ENDPOINT": "https://deeptutor-test-resource.openai.azure.com/",
+    "AZURE_RESOURCE": "deeptutor-test-resource",
+    "BENCHFLOW_PROVIDER_MODEL": "gpt-5.6-sol",
+    "BENCHFLOW_PROVIDER_NAME": "azure-foundry-openai",
+    "BENCHFLOW_PROVIDER_BASE_URL": "https://deeptutor-test-resource.openai.azure.com/openai/v1",
+    "BENCHFLOW_PROVIDER_PROTOCOL": "openai-completions",
+    "LLM_BASE_URL": "https://deeptutor-test-resource.openai.azure.com/openai/v1",
+    "LLM_MODEL": "openai/gpt-5.6-sol",
+    "CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC": "1",
+    "BENCHFLOW_DISALLOW_WEB_TOOLS": "1"
+  },
+  "scenes": [
+    {
+      "name": "default",
+      "skills_dir": null,
+      "roles": [
+        {
+          "name": "agent",
+          "agent": "openhands",
+          "model": "azure-foundry-openai/gpt-5.6-sol",
+          "reasoning_effort": "xhigh",
+          "timeout_sec": null,
+          "idle_timeout_sec": null,
+          "skills_dir": null,
+          "capabilities": null,
+          "env_keys": []
+        }
+      ],
+      "turns": [
+        {
+          "role": "agent",
+          "has_prompt": false
+        }
+      ]
+    }
+  ],
+  "loop": {
+    "strategy": "single-shot"
+  },
+  "usage_tracking": {
+    "requested": "required"
+  },
+  "task_digest": "sha256:2ae35c64ab1645a5d32b130d2637fff8958793ad2334a2c40e2d2cc7287db242"
+}

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/simulation-heuristics-ant-v1__33f337b0/prompts.json

@@ -0,0 +1,3 @@
+[
+  "# Design a robust commanded-path Ant controller\n\nImplement an offline feedback controller for Gymnasium `Ant-v5` that follows a\ncurved ground-plane path and its tangent heading under frozen physical\nuncertainty. Straight positive-x travel is not sufficient.\n\nThe plant uses a 0.05 s control interval and exactly 1,000 commanded control\nsteps. The controller receives the standard 27-value Ant observation plus an\neight-value path-tracking command described in\n`/app/task_context/policy_api.md`. A zero-action API scaffold is provided at\n`/app/starter_policy/policy.py`; it is intentionally not a locomotion\ncontroller.\n\nThe reference centerline is anchored at the reset torso position:\n\n```text\ny_ref(x) = A [1 - cos(2 pi x / lambda)]\n```\n\nwhere `x` and `y` are reset-relative world coordinates. The commanded heading\nis the centerline tangent. Positive cross-track error means the torso is left\nof that tangent; positive heading error means the commanded heading is\ncounter-clockwise from the torso heading. At the torso's current `x`, the\nsigned tracking error is\n\n```text\ne_cross = cos(heading_ref) [y - y_ref(x)] .\n```\n\n## Physical uncertainty contract\n\nPublic development scenarios and held-out scenarios use the same simulator,\npolicy API, reset order, and declared ranges. Held-out evaluation uses fixed\ncombinations that are not selected from the policy seed:\n\n| Quantity | Declared range |\n|---|---:|\n| total geom mass scale | `0.94` to `1.07` |\n| contact-friction scale | `0.87` to `1.06` |\n| each actuator-pair leg-strength scale | `0.91` to `1.05` |\n| ground slope along world `+x` | `-2.3` to `+2.3` degrees |\n| finite lateral torso force | `-9` to `+9 N` |\n| simultaneous yaw torque | `-2.4` to `+2.4 N m` |\n| path amplitude `A` | `0.80` to `1.00 m` |\n| path wavelength `lambda` | `20` to `24 m` |\n| target path speed | `0.58` to `0.70 m/s` |\n\nThe force and torque act together for 6\u20139 control steps at a frozen time\nbetween steps 250 and 700. Positive lateral force is along the instantaneous\nleft path normal. Positive slope rises with world `+x`. Leg-strength entries\nscale the four consecutive actuator pairs in Ant action order.\n\nThese bounds represent a combined robustness envelope rather than independent\nworst-case certification: up to 7% mass shift, 13% friction loss, 14%\npair-to-pair actuator imbalance, a 4.6-degree grade span, and a finite impulse\nlarge enough to perturb both lateral velocity and yaw. The held-out suite uses\nfixed multi-axis combinations inside this envelope.\n\nUse the local evaluator for full-length development runs:\n\n```bash\npython /app/evaluate.py \\\n  --policy /app/final_policy/policy.py \\\n  --episodes 2 \\\n  --max-steps 1000 \\\n  --suite all\n```\n\n`--seed` selects simulator reset seeds only. In both public and held-out\nevaluation, `Policy(...)` and `reset(...)` receive `seed=0`; controllers must\nadapt from observations and commands rather than branch on episode identity.\n\n## Required artifact\n\nPlace the controller at:\n\n```text\n/app/final_policy/policy.py\n```\n\nIt must define `Policy` or `make_policy`. The resulting object must implement\n`act(observation, command)` and may implement `reset(seed=0)`. Actions must be\nfinite NumPy-compatible arrays with shape `(8,)`; values are clipped to\n`[-1, 1]`. The policy must run with the installed packages, without network\naccess or external processes, and reset all recurrent state between episodes.\nComplete the work within the primary agent process. Delegated or descendant\nagents, external retrieval, remote repository access, and Git-history solution\nlookup are prohibited and force reward `0`, even if the retrieved material is\nnot ultimately used.\n\n## Physical acceptance criteria\n\nEvery held-out episode is an independent heavy gate. The controller must:\n\n- execute exactly 1,000 actions without a policy error or loss of physical\n  uprightness; the runtime stops when torso clearance above the sloped plane\n  leaves `[0.20, 1.05] m` or torso tilt from world vertical exceeds\n  `1.35 rad`;\n- keep RMS centerline cross-track error at or below `0.55 m`;\n- keep RMS wrapped heading error at or below `0.20 rad`;\n- spend at most `0.10` of samples moving backward faster than `0.05 m/s`\n  along the path tangent;\n- maintain at least `0.24 m/s` in every rolling 100-step (`5 s`) mean of\n  tangent speed;\n- keep RMS actuator command at or below `0.62`.\n\nThe 5 s speed window spans several gait cycles; its `0.24 m/s` floor requires\nat least `1.2 m` of tangent progress per window and rejects controllers that\ncoast, stall, or reverse for a sustained interval. The tracking limits are\nwell below the path's lateral excursion and heading swing, so a straight-line\ngait cannot pass. The effort cap permits transient correction while excluding\nnear-saturated open-loop forcing.\n\nComparisons allow only numerical portability margins of `0.02 m` in RMS\ncross-track error, `0.01 rad` in RMS heading error, `0.005` in backward-motion\nfraction, `0.01 m/s` in windowed speed, and `0.01` in RMS action. Exact step\ncompletion has no tolerance.\n\nProvenance, license boundaries, upstream paths, and file hashes are recorded\nunder `/app/provenance/`."
+]

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/simulation-heuristics-ant-v1__33f337b0/result.json

@@ -0,0 +1,104 @@
+{
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+  "rollout_name": "simulation-heuristics-ant-v1__33f337b0",
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+  "agent": "openhands",
+  "agent_name": "",
+  "model": "azure-foundry-openai/gpt-5.6-sol",
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+  "effective_skills_dir": "/tmp/benchflow-task-gra1s6z0/simulation-heuristics-ant-v1/environment/skills",
+  "skills_sandbox_dir": "/skills",
+  "include_task_skills": true,
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+    "total_tokens": 0,
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+    "usage_source": "unavailable",
+    "price_source": null
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+    "requested": "required",
+    "status": "unavailable",
+    "environment": "docker",
+    "endpoint_kind": "none",
+    "usage_source": "unavailable"
+  },
+  "error": "Docker compose command failed for environment simulation-heuristics-ant-v1. Command: docker compose --project-name simulation-heuristics-ant-v1__33f337b0 --project-directory /tmp/benchflow-task-gra1s6z0/simulation-heuristics-ant-v1/environment -f /srv/frontierphysics/repo/.venv/lib/python3.12/site-packages/benchflow/sandbox/_compose_files/docker-compose-base.yaml -f /srv/frontierphysics/repo/.venv/lib/python3.12/site-packages/benchflow/sandbox/_compose_files/docker-compose-build.yaml build. Return code: 1. Stdout:  Image bf__simulation-heuristics-ant-v1 Building \n#1 [internal] load local bake definitions\n#1 reading from stdin 633B done\n#1 DONE 0.0s\n\n#2 [internal] load build definition from Dockerfile\n#2 transferring dockerfile: 2.17kB done\n#2 DONE 0.0s\n\n#3 [internal] load metadata for docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818\n#3 DONE 0.1s\n\n#4 [internal] load metadata for docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de\n#4 DONE 0.1s\n\n#5 [internal] load .dockerignore\n#5 transferring context: 274B done\n#5 DONE 0.0s\n\n#6 [1/8] FROM docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de\n#6 resolve docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de 0.0s done\n#6 CACHED\n\n#7 [restrict-builder 1/4] FROM docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818\n#7 resolve docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818 0.0s done\n#7 DONE 0.1s\n\n#8 [internal] load build context\n#8 transferring context: 134.70kB done\n#8 DONE 0.0s\n\n#9 [stage-1 11/20] COPY LICENSE-GPL-3.0.txt /licenses/GPL-3.0.txt\n#9 CACHED\n\n#10 [stage-1  4/20] RUN python -m pip install --no-cache-dir     \"gymnasium[mujoco]==1.3.0\"     \"mujoco==3.10.0\"     \"numpy==2.5.1\"     \"pytest==8.4.2\"     \"pytest-json-ctrf==0.5.2\"\n#10 CACHED\n\n#11 [stage-1 13/20] WORKDIR /app\n#11 CACHED\n\n#12 [stage-1  2/20] COPY --from=restrict-builder /restrict-exec /usr/local/bin/restrict-exec\n#12 CACHED\n\n#13 [restrict-builder 2/4] RUN apt-get update -qq     && apt-get install -y -qq --no-install-recommends         gcc libc6-dev linux-libc-dev     && rm -rf /var/lib/apt/lists/*\n#13 CACHED\n\n#14 [stage-1  5/20] RUN useradd --create-home --uid 1000 agent     && mkdir -p /app /licenses /opt/ant-runtime\n#14 CACHED\n\n#15 [stage-1 10/20] COPY provenance /app/provenance\n#15 CACHED\n\n#16 [stage-1  6/20] COPY ant_runtime /opt/ant-runtime/ant_runtime\n#16 CACHED\n\n#17 [restrict-builder 3/4] COPY restrict_exec.c /tmp/restrict_exec.c\n#17 CACHED\n\n#18 [stage-1  9/20] COPY task_context /app/task_context\n#18 CACHED\n\n#19 [stage-1  7/20] COPY evaluate.py /app/evaluate.py\n#19 CACHED\n\n#20 [stage-1 14/20] RUN mkdir -p /logs/verifier /logs/agent /logs/artifacts\n#20 CACHED\n\n#21 [stage-1 12/20] RUN cp -R /app/starter_policy /app/final_policy     && mkdir -p /app/work     && chown -R root:root /app /licenses /opt/ant-runtime     && chmod -R a-w /licenses /opt/ant-runtime /app/evaluate.py         /app/starter_policy /app/task_context /app/provenance     && chown -R agent:agent /app/final_policy /app/work\n#21 CACHED\n\n#22 [stage-1  8/20] COPY starter_policy /app/starter_policy\n#22 CACHED\n\n#23 [restrict-builder 4/4] RUN gcc -O2 -Wall -Wextra -Werror     /tmp/restrict_exec.c -o /restrict-exec\n#23 CACHED\n\n#24 [stage-1  3/20] RUN apt-get update -qq     && apt-get install -y -qq --no-install-recommends         bash ca-certificates libseccomp2 util-linux     && rm -rf /var/lib/apt/lists/*\n#24 CACHED\n\n#25 [stage-1 15/20] COPY _deps/skills /skills/\n#25 ERROR: failed to calculate checksum of ref zehsvgd454pjwvoa9fzrudw57::vp8py7ovk1mjrchwags46wpsi: \"/_deps/skills\": not found\n------\n > [stage-1 15/20] COPY _deps/skills /skills/:\n------\nDockerfile:53\n\n--------------------\n\n  51 |     \n\n  52 |     # Skills directory (injected by benchflow --skills-dir)\n\n  53 | >>> COPY _deps/skills /skills/\n\n  54 |     RUN mkdir -p /root/.claude && ln -sf /skills /root/.claude/skills\n\n  55 |     RUN mkdir -p /root/.pi/agent && ln -sf /skills /root/.pi/agent/skills\n\n--------------------\n\nfailed to solve: failed to compute cache key: failed to calculate checksum of ref zehsvgd454pjwvoa9fzrudw57::vp8py7ovk1mjrchwags46wpsi: \"/_deps/skills\": not found\n\n. Stderr: None. ",
+  "error_category": "other",
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+  "export_error": null,
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+  "started_at": "2026-07-21 17:58:08.175412",
+  "finished_at": "2026-07-21 17:58:09.029063",
+  "timing": {
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+    {
+      "name": "default",
+      "skills_dir": null,
+      "roles": [
+        {
+          "name": "agent",
+          "agent": "openhands",
+          "model": "azure-foundry-openai/gpt-5.6-sol",
+          "reasoning_effort": "xhigh",
+          "timeout_sec": null,
+          "idle_timeout_sec": null,
+          "skills_dir": null,
+          "capabilities": null,
+          "env_keys": []
+        }
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+          "has_prompt": false
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+  "task_digest": "sha256:2ae35c64ab1645a5d32b130d2637fff8958793ad2334a2c40e2d2cc7287db242",
+  "sandbox_id": null
+}

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/simulation-heuristics-ant-v1__33f337b0/results.jsonl

@@ -0,0 +1 @@
+{"example_id": 0, "prompt": [{"role": "user", "content": "# Design a robust commanded-path Ant controller\n\nImplement an offline feedback controller for Gymnasium `Ant-v5` that follows a\ncurved ground-plane path and its tangent heading under frozen physical\nuncertainty. Straight positive-x travel is not sufficient.\n\nThe plant uses a 0.05 s control interval and exactly 1,000 commanded control\nsteps. The controller receives the standard 27-value Ant observation plus an\neight-value path-tracking command described in\n`/app/task_context/policy_api.md`. A zero-action API scaffold is provided at\n`/app/starter_policy/policy.py`; it is intentionally not a locomotion\ncontroller.\n\nThe reference centerline is anchored at the reset torso position:\n\n```text\ny_ref(x) = A [1 - cos(2 pi x / lambda)]\n```\n\nwhere `x` and `y` are reset-relative world coordinates. The commanded heading\nis the centerline tangent. Positive cross-track error means the torso is left\nof that tangent; positive heading error means the commanded heading is\ncounter-clockwise from the torso heading. At the torso's current `x`, the\nsigned tracking error is\n\n```text\ne_cross = cos(heading_ref) [y - y_ref(x)] .\n```\n\n## Physical uncertainty contract\n\nPublic development scenarios and held-out scenarios use the same simulator,\npolicy API, reset order, and declared ranges. Held-out evaluation uses fixed\ncombinations that are not selected from the policy seed:\n\n| Quantity | Declared range |\n|---|---:|\n| total geom mass scale | `0.94` to `1.07` |\n| contact-friction scale | `0.87` to `1.06` |\n| each actuator-pair leg-strength scale | `0.91` to `1.05` |\n| ground slope along world `+x` | `-2.3` to `+2.3` degrees |\n| finite lateral torso force | `-9` to `+9 N` |\n| simultaneous yaw torque | `-2.4` to `+2.4 N m` |\n| path amplitude `A` | `0.80` to `1.00 m` |\n| path wavelength `lambda` | `20` to `24 m` |\n| target path speed | `0.58` to `0.70 m/s` |\n\nThe force and torque act together for 6\u20139 control steps at a frozen time\nbetween steps 250 and 700. Positive lateral force is along the instantaneous\nleft path normal. Positive slope rises with world `+x`. Leg-strength entries\nscale the four consecutive actuator pairs in Ant action order.\n\nThese bounds represent a combined robustness envelope rather than independent\nworst-case certification: up to 7% mass shift, 13% friction loss, 14%\npair-to-pair actuator imbalance, a 4.6-degree grade span, and a finite impulse\nlarge enough to perturb both lateral velocity and yaw. The held-out suite uses\nfixed multi-axis combinations inside this envelope.\n\nUse the local evaluator for full-length development runs:\n\n```bash\npython /app/evaluate.py \\\n  --policy /app/final_policy/policy.py \\\n  --episodes 2 \\\n  --max-steps 1000 \\\n  --suite all\n```\n\n`--seed` selects simulator reset seeds only. In both public and held-out\nevaluation, `Policy(...)` and `reset(...)` receive `seed=0`; controllers must\nadapt from observations and commands rather than branch on episode identity.\n\n## Required artifact\n\nPlace the controller at:\n\n```text\n/app/final_policy/policy.py\n```\n\nIt must define `Policy` or `make_policy`. The resulting object must implement\n`act(observation, command)` and may implement `reset(seed=0)`. Actions must be\nfinite NumPy-compatible arrays with shape `(8,)`; values are clipped to\n`[-1, 1]`. The policy must run with the installed packages, without network\naccess or external processes, and reset all recurrent state between episodes.\nComplete the work within the primary agent process. Delegated or descendant\nagents, external retrieval, remote repository access, and Git-history solution\nlookup are prohibited and force reward `0`, even if the retrieved material is\nnot ultimately used.\n\n## Physical acceptance criteria\n\nEvery held-out episode is an independent heavy gate. The controller must:\n\n- execute exactly 1,000 actions without a policy error or loss of physical\n  uprightness; the runtime stops when torso clearance above the sloped plane\n  leaves `[0.20, 1.05] m` or torso tilt from world vertical exceeds\n  `1.35 rad`;\n- keep RMS centerline cross-track error at or below `0.55 m`;\n- keep RMS wrapped heading error at or below `0.20 rad`;\n- spend at most `0.10` of samples moving backward faster than `0.05 m/s`\n  along the path tangent;\n- maintain at least `0.24 m/s` in every rolling 100-step (`5 s`) mean of\n  tangent speed;\n- keep RMS actuator command at or below `0.62`.\n\nThe 5 s speed window spans several gait cycles; its `0.24 m/s` floor requires\nat least `1.2 m` of tangent progress per window and rejects controllers that\ncoast, stall, or reverse for a sustained interval. The tracking limits are\nwell below the path's lateral excursion and heading swing, so a straight-line\ngait cannot pass. The effort cap permits transient correction while excluding\nnear-saturated open-loop forcing.\n\nComparisons allow only numerical portability margins of `0.02 m` in RMS\ncross-track error, `0.01 rad` in RMS heading error, `0.005` in backward-motion\nfraction, `0.01 m/s` in windowed speed, and `0.01` in RMS action. Exact step\ncompletion has no tolerance.\n\nProvenance, license boundaries, upstream paths, and file hashes are recorded\nunder `/app/provenance/`."}], "completion": null, "info": {"task_id": "simulation-heuristics-ant-v1", "task_name": "simulation-heuristics-ant-v1", "rollout_name": "simulation-heuristics-ant-v1__33f337b0", "environment": "simulation-heuristics-ant-v1", "agent": "openhands", "agent_name": "", "model": "azure-foundry-openai/gpt-5.6-sol", "source": "benchflow", "rollout_dir": "/srv/frontierphysics/reviews/pr-1/efeee9a90db0c9707fc63401f3209e1b68a28a88/evals/strong-20260721T175807Z/2026-07-21__17-58-08/simulation-heuristics-ant-v1__33f337b0", "training_ready": false, "training_ready_reason": "missing_healthy_structured_llm_trajectory"}, "reward": 0.0, "error": {"error": "agent_error", "error_chain_str": "Docker compose command failed for environment simulation-heuristics-ant-v1. Command: docker compose --project-name simulation-heuristics-ant-v1__33f337b0 --project-directory /tmp/benchflow-task-gra1s6z0/simulation-heuristics-ant-v1/environment -f /srv/frontierphysics/repo/.venv/lib/python3.12/site-packages/benchflow/sandbox/_compose_files/docker-compose-base.yaml -f /srv/frontierphysics/repo/.venv/lib/python3.12/site-packages/benchflow/sandbox/_compose_files/docker-compose-build.yaml build. Return code: 1. Stdout:  Image bf__simulation-heuristics-ant-v1 Building \n#1 [internal] load local bake definitions\n#1 reading from stdin 633B done\n#1 DONE 0.0s\n\n#2 [internal] load build definition from Dockerfile\n#2 transferring dockerfile: 2.17kB done\n#2 DONE 0.0s\n\n#3 [internal] load metadata for docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818\n#3 DONE 0.1s\n\n#4 [internal] load metadata for docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de\n#4 DONE 0.1s\n\n#5 [internal] load .dockerignore\n#5 transferring context: 274B done\n#5 DONE 0.0s\n\n#6 [1/8] FROM docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de\n#6 resolve docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de 0.0s done\n#6 CACHED\n\n#7 [restrict-builder 1/4] FROM docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818\n#7 resolve docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818 0.0s done\n#7 DONE 0.1s\n\n#8 [internal] load build context\n#8 transferring context: 134.70kB done\n#8 DONE 0.0s\n\n#9 [stage-1 11/20] COPY LICENSE-GPL-3.0.txt /licenses/GPL-3.0.txt\n#9 CACHED\n\n#10 [stage-1  4/20] RUN python -m pip install --no-cache-dir     \"gymnasium[mujoco]==1.3.0\"     \"mujoco==3.10.0\"     \"numpy==2.5.1\"     \"pytest==8.4.2\"     \"pytest-json-ctrf==0.5.2\"\n#10 CACHED\n\n#11 [stage-1 13/20] WORKDIR /app\n#11 CACHED\n\n#12 [stage-1  2/20] COPY --from=restrict-builder /restrict-exec /usr/local/bin/restrict-exec\n#12 CACHED\n\n#13 [restrict-builder 2/4] RUN apt-get update -qq     && apt-get install -y -qq --no-install-recommends         gcc libc6-dev linux-libc-dev     && rm -rf /var/lib/apt/lists/*\n#13 CACHED\n\n#14 [stage-1  5/20] RUN useradd --create-home --uid 1000 agent     && mkdir -p /app /licenses /opt/ant-runtime\n#14 CACHED\n\n#15 [stage-1 10/20] COPY provenance /app/provenance\n#15 CACHED\n\n#16 [stage-1  6/20] COPY ant_runtime /opt/ant-runtime/ant_runtime\n#16 CACHED\n\n#17 [restrict-builder 3/4] COPY restrict_exec.c /tmp/restrict_exec.c\n#17 CACHED\n\n#18 [stage-1  9/20] COPY task_context /app/task_context\n#18 CACHED\n\n#19 [stage-1  7/20] COPY evaluate.py /app/evaluate.py\n#19 CACHED\n\n#20 [stage-1 14/20] RUN mkdir -p /logs/verifier /logs/agent /logs/artifacts\n#20 CACHED\n\n#21 [stage-1 12/20] RUN cp -R /app/starter_policy /app/final_policy     && mkdir -p /app/work     && chown -R root:root /app /licenses /opt/ant-runtime     && chmod -R a-w /licenses /opt/ant-runtime /app/evaluate.py         /app/starter_policy /app/task_context /app/provenance     && chown -R agent:agent /app/final_policy /app/work\n#21 CACHED\n\n#22 [stage-1  8/20] COPY starter_policy /app/starter_policy\n#22 CACHED\n\n#23 [restrict-builder 4/4] RUN gcc -O2 -Wall -Wextra -Werror     /tmp/restrict_exec.c -o /restrict-exec\n#23 CACHED\n\n#24 [stage-1  3/20] RUN apt-get update -qq     && apt-get install -y -qq --no-install-recommends         bash ca-certificates libseccomp2 util-linux     && rm -rf /var/lib/apt/lists/*\n#24 CACHED\n\n#25 [stage-1 15/20] COPY _deps/skills /skills/\n#25 ERROR: failed to calculate checksum of ref zehsvgd454pjwvoa9fzrudw57::vp8py7ovk1mjrchwags46wpsi: \"/_deps/skills\": not found\n------\n > [stage-1 15/20] COPY _deps/skills /skills/:\n------\nDockerfile:53\n\n--------------------\n\n  51 |     \n\n  52 |     # Skills directory (injected by benchflow --skills-dir)\n\n  53 | >>> COPY _deps/skills /skills/\n\n  54 |     RUN mkdir -p /root/.claude && ln -sf /skills /root/.claude/skills\n\n  55 |     RUN mkdir -p /root/.pi/agent && ln -sf /skills /root/.pi/agent/skills\n\n--------------------\n\nfailed to solve: failed to compute cache key: failed to calculate checksum of ref zehsvgd454pjwvoa9fzrudw57::vp8py7ovk1mjrchwags46wpsi: \"/_deps/skills\": not found\n\n. Stderr: None. ", "error_chain_repr": "Docker compose command failed for environment simulation-heuristics-ant-v1. Command: docker compose --project-name simulation-heuristics-ant-v1__33f337b0 --project-directory /tmp/benchflow-task-gra1s6z0/simulation-heuristics-ant-v1/environment -f /srv/frontierphysics/repo/.venv/lib/python3.12/site-packages/benchflow/sandbox/_compose_files/docker-compose-base.yaml -f /srv/frontierphysics/repo/.venv/lib/python3.12/site-packages/benchflow/sandbox/_compose_files/docker-compose-build.yaml build. Return code: 1. Stdout:  Image bf__simulation-heuristics-ant-v1 Building \n#1 [internal] load local bake definitions\n#1 reading from stdin 633B done\n#1 DONE 0.0s\n\n#2 [internal] load build definition from Dockerfile\n#2 transferring dockerfile: 2.17kB done\n#2 DONE 0.0s\n\n#3 [internal] load metadata for docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818\n#3 DONE 0.1s\n\n#4 [internal] load metadata for docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de\n#4 DONE 0.1s\n\n#5 [internal] load .dockerignore\n#5 transferring context: 274B done\n#5 DONE 0.0s\n\n#6 [1/8] FROM docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de\n#6 resolve docker.io/library/python:3.12-slim@sha256:57cd7c3a7a273101a6485ba99423ee568157882804b1124b4dd04266317710de 0.0s done\n#6 CACHED\n\n#7 [restrict-builder 1/4] FROM docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818\n#7 resolve docker.io/library/debian:bookworm-slim@sha256:7b140f374b289a7c2befc338f42ebe6441b7ea838a042bbd5acbfca6ec875818 0.0s done\n#7 DONE 0.1s\n\n#8 [internal] load build context\n#8 transferring context: 134.70kB done\n#8 DONE 0.0s\n\n#9 [stage-1 11/20] COPY LICENSE-GPL-3.0.txt /licenses/GPL-3.0.txt\n#9 CACHED\n\n#10 [stage-1  4/20] RUN python -m pip install --no-cache-dir     \"gymnasium[mujoco]==1.3.0\"     \"mujoco==3.10.0\"     \"numpy==2.5.1\"     \"pytest==8.4.2\"     \"pytest-json-ctrf==0.5.2\"\n#10 CACHED\n\n#11 [stage-1 13/20] WORKDIR /app\n#11 CACHED\n\n#12 [stage-1  2/20] COPY --from=restrict-builder /restrict-exec /usr/local/bin/restrict-exec\n#12 CACHED\n\n#13 [restrict-builder 2/4] RUN apt-get update -qq     && apt-get install -y -qq --no-install-recommends         gcc libc6-dev linux-libc-dev     && rm -rf /var/lib/apt/lists/*\n#13 CACHED\n\n#14 [stage-1  5/20] RUN useradd --create-home --uid 1000 agent     && mkdir -p /app /licenses /opt/ant-runtime\n#14 CACHED\n\n#15 [stage-1 10/20] COPY provenance /app/provenance\n#15 CACHED\n\n#16 [stage-1  6/20] COPY ant_runtime /opt/ant-runtime/ant_runtime\n#16 CACHED\n\n#17 [restrict-builder 3/4] COPY restrict_exec.c /tmp/restrict_exec.c\n#17 CACHED\n\n#18 [stage-1  9/20] COPY task_context /app/task_context\n#18 CACHED\n\n#19 [stage-1  7/20] COPY evaluate.py /app/evaluate.py\n#19 CACHED\n\n#20 [stage-1 14/20] RUN mkdir -p /logs/verifier /logs/agent /logs/artifacts\n#20 CACHED\n\n#21 [stage-1 12/20] RUN cp -R /app/starter_policy /app/final_policy     && mkdir -p /app/work     && chown -R root:root /app /licenses /opt/ant-runtime     && chmod -R a-w /licenses /opt/ant-runtime /app/evaluate.py         /app/starter_policy /app/task_context /app/provenance     && chown -R agent:agent /app/final_policy /app/work\n#21 CACHED\n\n#22 [stage-1  8/20] COPY starter_policy /app/starter_policy\n#22 CACHED\n\n#23 [restrict-builder 4/4] RUN gcc -O2 -Wall -Wextra -Werror     /tmp/restrict_exec.c -o /restrict-exec\n#23 CACHED\n\n#24 [stage-1  3/20] RUN apt-get update -qq     && apt-get install -y -qq --no-install-recommends         bash ca-certificates libseccomp2 util-linux     && rm -rf /var/lib/apt/lists/*\n#24 CACHED\n\n#25 [stage-1 15/20] COPY _deps/skills /skills/\n#25 ERROR: failed to calculate checksum of ref zehsvgd454pjwvoa9fzrudw57::vp8py7ovk1mjrchwags46wpsi: \"/_deps/skills\": not found\n------\n > [stage-1 15/20] COPY _deps/skills /skills/:\n------\nDockerfile:53\n\n--------------------\n\n  51 |     \n\n  52 |     # Skills directory (injected by benchflow --skills-dir)\n\n  53 | >>> COPY _deps/skills /skills/\n\n  54 |     RUN mkdir -p /root/.claude && ln -sf /skills /root/.claude/skills\n\n  55 |     RUN mkdir -p /root/.pi/agent && ln -sf /skills /root/.pi/agent/skills\n\n--------------------\n\nfailed to solve: failed to compute cache key: failed to calculate checksum of ref zehsvgd454pjwvoa9fzrudw57::vp8py7ovk1mjrchwags46wpsi: \"/_deps/skills\": not found\n\n. Stderr: None. "}, "timing": {"total": 0.9}, "is_completed": false, "is_truncated": false, "stop_condition": "agent_error", "metrics": {"n_tool_calls": 0, "n_prompts": 1}, "tool_defs": [], "token_usage": {"input_tokens": 0.0, "output_tokens": 0.0, "final_input_tokens": 0.0, "final_output_tokens": 0.0, "total_tokens": 0.0}, "score": 0.0, "total_tool_calls": 0.0, "trajectory": []}

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/simulation-heuristics-ant-v1__33f337b0/timing.json

@@ -0,0 +1,3 @@
+{
+  "total": 0.9
+}

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/simulation-heuristics-ant-v1__33f337b0/trainer/adp.jsonl

@@ -0,0 +1 @@
+{"schema_version": "1.3.1", "id": "simulation-heuristics-ant-v1__33f337b0", "content": [{"class_": "text_observation", "content": "# Design a robust commanded-path Ant controller\n\nImplement an offline feedback controller for Gymnasium `Ant-v5` that follows a\ncurved ground-plane path and its tangent heading under frozen physical\nuncertainty. Straight positive-x travel is not sufficient.\n\nThe plant uses a 0.05 s control interval and exactly 1,000 commanded control\nsteps. The controller receives the standard 27-value Ant observation plus an\neight-value path-tracking command described in\n`/app/task_context/policy_api.md`. A zero-action API scaffold is provided at\n`/app/starter_policy/policy.py`; it is intentionally not a locomotion\ncontroller.\n\nThe reference centerline is anchored at the reset torso position:\n\n```text\ny_ref(x) = A [1 - cos(2 pi x / lambda)]\n```\n\nwhere `x` and `y` are reset-relative world coordinates. The commanded heading\nis the centerline tangent. Positive cross-track error means the torso is left\nof that tangent; positive heading error means the commanded heading is\ncounter-clockwise from the torso heading. At the torso's current `x`, the\nsigned tracking error is\n\n```text\ne_cross = cos(heading_ref) [y - y_ref(x)] .\n```\n\n## Physical uncertainty contract\n\nPublic development scenarios and held-out scenarios use the same simulator,\npolicy API, reset order, and declared ranges. Held-out evaluation uses fixed\ncombinations that are not selected from the policy seed:\n\n| Quantity | Declared range |\n|---|---:|\n| total geom mass scale | `0.94` to `1.07` |\n| contact-friction scale | `0.87` to `1.06` |\n| each actuator-pair leg-strength scale | `0.91` to `1.05` |\n| ground slope along world `+x` | `-2.3` to `+2.3` degrees |\n| finite lateral torso force | `-9` to `+9 N` |\n| simultaneous yaw torque | `-2.4` to `+2.4 N m` |\n| path amplitude `A` | `0.80` to `1.00 m` |\n| path wavelength `lambda` | `20` to `24 m` |\n| target path speed | `0.58` to `0.70 m/s` |\n\nThe force and torque act together for 6\u20139 control steps at a frozen time\nbetween steps 250 and 700. Positive lateral force is along the instantaneous\nleft path normal. Positive slope rises with world `+x`. Leg-strength entries\nscale the four consecutive actuator pairs in Ant action order.\n\nThese bounds represent a combined robustness envelope rather than independent\nworst-case certification: up to 7% mass shift, 13% friction loss, 14%\npair-to-pair actuator imbalance, a 4.6-degree grade span, and a finite impulse\nlarge enough to perturb both lateral velocity and yaw. The held-out suite uses\nfixed multi-axis combinations inside this envelope.\n\nUse the local evaluator for full-length development runs:\n\n```bash\npython /app/evaluate.py \\\n  --policy /app/final_policy/policy.py \\\n  --episodes 2 \\\n  --max-steps 1000 \\\n  --suite all\n```\n\n`--seed` selects simulator reset seeds only. In both public and held-out\nevaluation, `Policy(...)` and `reset(...)` receive `seed=0`; controllers must\nadapt from observations and commands rather than branch on episode identity.\n\n## Required artifact\n\nPlace the controller at:\n\n```text\n/app/final_policy/policy.py\n```\n\nIt must define `Policy` or `make_policy`. The resulting object must implement\n`act(observation, command)` and may implement `reset(seed=0)`. Actions must be\nfinite NumPy-compatible arrays with shape `(8,)`; values are clipped to\n`[-1, 1]`. The policy must run with the installed packages, without network\naccess or external processes, and reset all recurrent state between episodes.\nComplete the work within the primary agent process. Delegated or descendant\nagents, external retrieval, remote repository access, and Git-history solution\nlookup are prohibited and force reward `0`, even if the retrieved material is\nnot ultimately used.\n\n## Physical acceptance criteria\n\nEvery held-out episode is an independent heavy gate. The controller must:\n\n- execute exactly 1,000 actions without a policy error or loss of physical\n  uprightness; the runtime stops when torso clearance above the sloped plane\n  leaves `[0.20, 1.05] m` or torso tilt from world vertical exceeds\n  `1.35 rad`;\n- keep RMS centerline cross-track error at or below `0.55 m`;\n- keep RMS wrapped heading error at or below `0.20 rad`;\n- spend at most `0.10` of samples moving backward faster than `0.05 m/s`\n  along the path tangent;\n- maintain at least `0.24 m/s` in every rolling 100-step (`5 s`) mean of\n  tangent speed;\n- keep RMS actuator command at or below `0.62`.\n\nThe 5 s speed window spans several gait cycles; its `0.24 m/s` floor requires\nat least `1.2 m` of tangent progress per window and rejects controllers that\ncoast, stall, or reverse for a sustained interval. The tracking limits are\nwell below the path's lateral excursion and heading swing, so a straight-line\ngait cannot pass. The effort cap permits transient correction while excluding\nnear-saturated open-loop forcing.\n\nComparisons allow only numerical portability margins of `0.02 m` in RMS\ncross-track error, `0.01 rad` in RMS heading error, `0.005` in backward-motion\nfraction, `0.01 m/s` in windowed speed, and `0.01` in RMS action. Exact step\ncompletion has no tolerance.\n\nProvenance, license boundaries, upstream paths, and file hashes are recorded\nunder `/app/provenance/`.", "source": "user"}], "details": {"task_id": "simulation-heuristics-ant-v1", "environment": "simulation-heuristics-ant-v1", "model": "azure-foundry-openai/gpt-5.6-sol"}}

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/simulation-heuristics-ant-v1__33f337b0/trainer/atif.json

@@ -0,0 +1,22 @@
+{
+  "schema_version": "ATIF-v1.7",
+  "session_id": "simulation-heuristics-ant-v1__33f337b0",
+  "agent": {
+    "name": "openhands",
+    "version": "unknown",
+    "model_name": "azure-foundry-openai/gpt-5.6-sol"
+  },
+  "steps": [
+    {
+      "step_id": 1,
+      "source": "user",
+      "message": "# Design a robust commanded-path Ant controller\n\nImplement an offline feedback controller for Gymnasium `Ant-v5` that follows a\ncurved ground-plane path and its tangent heading under frozen physical\nuncertainty. Straight positive-x travel is not sufficient.\n\nThe plant uses a 0.05 s control interval and exactly 1,000 commanded control\nsteps. The controller receives the standard 27-value Ant observation plus an\neight-value path-tracking command described in\n`/app/task_context/policy_api.md`. A zero-action API scaffold is provided at\n`/app/starter_policy/policy.py`; it is intentionally not a locomotion\ncontroller.\n\nThe reference centerline is anchored at the reset torso position:\n\n```text\ny_ref(x) = A [1 - cos(2 pi x / lambda)]\n```\n\nwhere `x` and `y` are reset-relative world coordinates. The commanded heading\nis the centerline tangent. Positive cross-track error means the torso is left\nof that tangent; positive heading error means the commanded heading is\ncounter-clockwise from the torso heading. At the torso's current `x`, the\nsigned tracking error is\n\n```text\ne_cross = cos(heading_ref) [y - y_ref(x)] .\n```\n\n## Physical uncertainty contract\n\nPublic development scenarios and held-out scenarios use the same simulator,\npolicy API, reset order, and declared ranges. Held-out evaluation uses fixed\ncombinations that are not selected from the policy seed:\n\n| Quantity | Declared range |\n|---|---:|\n| total geom mass scale | `0.94` to `1.07` |\n| contact-friction scale | `0.87` to `1.06` |\n| each actuator-pair leg-strength scale | `0.91` to `1.05` |\n| ground slope along world `+x` | `-2.3` to `+2.3` degrees |\n| finite lateral torso force | `-9` to `+9 N` |\n| simultaneous yaw torque | `-2.4` to `+2.4 N m` |\n| path amplitude `A` | `0.80` to `1.00 m` |\n| path wavelength `lambda` | `20` to `24 m` |\n| target path speed | `0.58` to `0.70 m/s` |\n\nThe force and torque act together for 6\u20139 control steps at a frozen time\nbetween steps 250 and 700. Positive lateral force is along the instantaneous\nleft path normal. Positive slope rises with world `+x`. Leg-strength entries\nscale the four consecutive actuator pairs in Ant action order.\n\nThese bounds represent a combined robustness envelope rather than independent\nworst-case certification: up to 7% mass shift, 13% friction loss, 14%\npair-to-pair actuator imbalance, a 4.6-degree grade span, and a finite impulse\nlarge enough to perturb both lateral velocity and yaw. The held-out suite uses\nfixed multi-axis combinations inside this envelope.\n\nUse the local evaluator for full-length development runs:\n\n```bash\npython /app/evaluate.py \\\n  --policy /app/final_policy/policy.py \\\n  --episodes 2 \\\n  --max-steps 1000 \\\n  --suite all\n```\n\n`--seed` selects simulator reset seeds only. In both public and held-out\nevaluation, `Policy(...)` and `reset(...)` receive `seed=0`; controllers must\nadapt from observations and commands rather than branch on episode identity.\n\n## Required artifact\n\nPlace the controller at:\n\n```text\n/app/final_policy/policy.py\n```\n\nIt must define `Policy` or `make_policy`. The resulting object must implement\n`act(observation, command)` and may implement `reset(seed=0)`. Actions must be\nfinite NumPy-compatible arrays with shape `(8,)`; values are clipped to\n`[-1, 1]`. The policy must run with the installed packages, without network\naccess or external processes, and reset all recurrent state between episodes.\nComplete the work within the primary agent process. Delegated or descendant\nagents, external retrieval, remote repository access, and Git-history solution\nlookup are prohibited and force reward `0`, even if the retrieved material is\nnot ultimately used.\n\n## Physical acceptance criteria\n\nEvery held-out episode is an independent heavy gate. The controller must:\n\n- execute exactly 1,000 actions without a policy error or loss of physical\n  uprightness; the runtime stops when torso clearance above the sloped plane\n  leaves `[0.20, 1.05] m` or torso tilt from world vertical exceeds\n  `1.35 rad`;\n- keep RMS centerline cross-track error at or below `0.55 m`;\n- keep RMS wrapped heading error at or below `0.20 rad`;\n- spend at most `0.10` of samples moving backward faster than `0.05 m/s`\n  along the path tangent;\n- maintain at least `0.24 m/s` in every rolling 100-step (`5 s`) mean of\n  tangent speed;\n- keep RMS actuator command at or below `0.62`.\n\nThe 5 s speed window spans several gait cycles; its `0.24 m/s` floor requires\nat least `1.2 m` of tangent progress per window and rejects controllers that\ncoast, stall, or reverse for a sustained interval. The tracking limits are\nwell below the path's lateral excursion and heading swing, so a straight-line\ngait cannot pass. The effort cap permits transient correction while excluding\nnear-saturated open-loop forcing.\n\nComparisons allow only numerical portability margins of `0.02 m` in RMS\ncross-track error, `0.01 rad` in RMS heading error, `0.005` in backward-motion\nfraction, `0.01 m/s` in windowed speed, and `0.01` in RMS action. Exact step\ncompletion has no tolerance.\n\nProvenance, license boundaries, upstream paths, and file hashes are recorded\nunder `/app/provenance/`."
+    }
+  ],
+  "final_metrics": {
+    "total_steps": 1,
+    "total_prompt_tokens": 0,
+    "total_completion_tokens": 0,
+    "total_cached_tokens": 0
+  }
+}

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/simulation-heuristics-ant-v1__33f337b0/trainer/verifiers.jsonl

@@ -0,0 +1 @@
+{"example_id": 0, "prompt": [{"role": "user", "content": "# Design a robust commanded-path Ant controller\n\nImplement an offline feedback controller for Gymnasium `Ant-v5` that follows a\ncurved ground-plane path and its tangent heading under frozen physical\nuncertainty. Straight positive-x travel is not sufficient.\n\nThe plant uses a 0.05 s control interval and exactly 1,000 commanded control\nsteps. The controller receives the standard 27-value Ant observation plus an\neight-value path-tracking command described in\n`/app/task_context/policy_api.md`. A zero-action API scaffold is provided at\n`/app/starter_policy/policy.py`; it is intentionally not a locomotion\ncontroller.\n\nThe reference centerline is anchored at the reset torso position:\n\n```text\ny_ref(x) = A [1 - cos(2 pi x / lambda)]\n```\n\nwhere `x` and `y` are reset-relative world coordinates. The commanded heading\nis the centerline tangent. Positive cross-track error means the torso is left\nof that tangent; positive heading error means the commanded heading is\ncounter-clockwise from the torso heading. At the torso's current `x`, the\nsigned tracking error is\n\n```text\ne_cross = cos(heading_ref) [y - y_ref(x)] .\n```\n\n## Physical uncertainty contract\n\nPublic development scenarios and held-out scenarios use the same simulator,\npolicy API, reset order, and declared ranges. Held-out evaluation uses fixed\ncombinations that are not selected from the policy seed:\n\n| Quantity | Declared range |\n|---|---:|\n| total geom mass scale | `0.94` to `1.07` |\n| contact-friction scale | `0.87` to `1.06` |\n| each actuator-pair leg-strength scale | `0.91` to `1.05` |\n| ground slope along world `+x` | `-2.3` to `+2.3` degrees |\n| finite lateral torso force | `-9` to `+9 N` |\n| simultaneous yaw torque | `-2.4` to `+2.4 N m` |\n| path amplitude `A` | `0.80` to `1.00 m` |\n| path wavelength `lambda` | `20` to `24 m` |\n| target path speed | `0.58` to `0.70 m/s` |\n\nThe force and torque act together for 6\u20139 control steps at a frozen time\nbetween steps 250 and 700. Positive lateral force is along the instantaneous\nleft path normal. Positive slope rises with world `+x`. Leg-strength entries\nscale the four consecutive actuator pairs in Ant action order.\n\nThese bounds represent a combined robustness envelope rather than independent\nworst-case certification: up to 7% mass shift, 13% friction loss, 14%\npair-to-pair actuator imbalance, a 4.6-degree grade span, and a finite impulse\nlarge enough to perturb both lateral velocity and yaw. The held-out suite uses\nfixed multi-axis combinations inside this envelope.\n\nUse the local evaluator for full-length development runs:\n\n```bash\npython /app/evaluate.py \\\n  --policy /app/final_policy/policy.py \\\n  --episodes 2 \\\n  --max-steps 1000 \\\n  --suite all\n```\n\n`--seed` selects simulator reset seeds only. In both public and held-out\nevaluation, `Policy(...)` and `reset(...)` receive `seed=0`; controllers must\nadapt from observations and commands rather than branch on episode identity.\n\n## Required artifact\n\nPlace the controller at:\n\n```text\n/app/final_policy/policy.py\n```\n\nIt must define `Policy` or `make_policy`. The resulting object must implement\n`act(observation, command)` and may implement `reset(seed=0)`. Actions must be\nfinite NumPy-compatible arrays with shape `(8,)`; values are clipped to\n`[-1, 1]`. The policy must run with the installed packages, without network\naccess or external processes, and reset all recurrent state between episodes.\nComplete the work within the primary agent process. Delegated or descendant\nagents, external retrieval, remote repository access, and Git-history solution\nlookup are prohibited and force reward `0`, even if the retrieved material is\nnot ultimately used.\n\n## Physical acceptance criteria\n\nEvery held-out episode is an independent heavy gate. The controller must:\n\n- execute exactly 1,000 actions without a policy error or loss of physical\n  uprightness; the runtime stops when torso clearance above the sloped plane\n  leaves `[0.20, 1.05] m` or torso tilt from world vertical exceeds\n  `1.35 rad`;\n- keep RMS centerline cross-track error at or below `0.55 m`;\n- keep RMS wrapped heading error at or below `0.20 rad`;\n- spend at most `0.10` of samples moving backward faster than `0.05 m/s`\n  along the path tangent;\n- maintain at least `0.24 m/s` in every rolling 100-step (`5 s`) mean of\n  tangent speed;\n- keep RMS actuator command at or below `0.62`.\n\nThe 5 s speed window spans several gait cycles; its `0.24 m/s` floor requires\nat least `1.2 m` of tangent progress per window and rejects controllers that\ncoast, stall, or reverse for a sustained interval. The tracking limits are\nwell below the path's lateral excursion and heading swing, so a straight-line\ngait cannot pass. The effort cap permits transient correction while excluding\nnear-saturated open-loop forcing.\n\nComparisons allow only numerical portability margins of `0.02 m` in RMS\ncross-track error, `0.01 rad` in RMS heading error, `0.005` in backward-motion\nfraction, `0.01 m/s` in windowed speed, and `0.01` in RMS action. Exact step\ncompletion has no tolerance.\n\nProvenance, license boundaries, upstream paths, and file hashes are recorded\nunder `/app/provenance/`."}], "completion": [], "reward": 0.0, "metrics": {}, "is_completed": true, "is_truncated": false, "info": {"task_id": "simulation-heuristics-ant-v1", "environment": "simulation-heuristics-ant-v1", "model": "azure-foundry-openai/gpt-5.6-sol", "reward_valid": false, "reward_metadata": {"items": {}, "events": [], "error": "no rewards", "space": "output", "granularity": "terminal"}}}

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/summary.json

@@ -0,0 +1,76 @@
+{
+  "job_name": "2026-07-21__17-58-08",
+  "agent": "openhands",
+  "model": "azure-foundry-openai/gpt-5.6-sol",
+  "environment": "docker",
+  "concurrency": 1,
+  "agent_idle_timeout_sec": null,
+  "usage_tracking": {
+    "requested": "required"
+  },
+  "loop": {
+    "strategy": "single-shot"
+  },
+  "total": 1,
+  "passed": 0,
+  "failed": 0,
+  "errored": 1,
+  "pass": 0,
+  "fail": 0,
+  "error": 1,
+  "verifier_errored": 0,
+  "idle_timeout": 0,
+  "error_categories": {
+    "other": 1
+  },
+  "verifier_error_categories": null,
+  "score": "0.0%",
+  "score_ratio": 0.0,
+  "score_excl_errors": "0.0%",
+  "score_excl_errors_ratio": 0.0,
+  "elapsed_sec": 0.9318661689758301,
+  "memory_score": null,
+  "memory_score_coverage": 0.0,
+  "memory": {
+    "scored": 0,
+    "avg_score": null,
+    "score": null
+  },
+  "memory_scores": {},
+  "total_skill_invocations": 0,
+  "avg_skill_invocations": 0.0,
+  "total_input_tokens": 0,
+  "total_output_tokens": 0,
+  "total_cache_read_tokens": 0,
+  "total_cache_creation_tokens": 0,
+  "total_tokens": 0,
+  "total_cost_usd": 0,
+  "avg_cost_per_trial_usd": null,
+  "telemetry_coverage": 0.0,
+  "total_tool_calls": 0,
+  "avg_tool_calls_per_task": 0.0,
+  "max_tool_calls_per_task": 0,
+  "total_trajectory_steps": 0,
+  "avg_trajectory_steps_per_task": 0.0,
+  "max_trajectory_steps_per_task": 0,
+  "total_trajectory_tool_call_steps": 0,
+  "avg_trajectory_tool_call_steps_per_task": 0.0,
+  "max_trajectory_tool_call_steps_per_task": 0,
+  "trajectory_summary_coverage": 1.0,
+  "timing_coverage": 1.0,
+  "environment_setup_time_sec": 0.0,
+  "avg_environment_setup_time_sec": null,
+  "max_environment_setup_time_sec": null,
+  "agent_setup_time_sec": 0.0,
+  "avg_agent_setup_time_sec": null,
+  "max_agent_setup_time_sec": null,
+  "agent_execution_time_sec": 0.0,
+  "avg_agent_execution_time_sec": null,
+  "max_agent_execution_time_sec": null,
+  "verifier_time_sec": 0.0,
+  "avg_verifier_time_sec": null,
+  "max_verifier_time_sec": null,
+  "total_time_sec": 0.9,
+  "avg_total_time_sec": 0.9,
+  "max_total_time_sec": 0.9
+}

efeee9a90db0c9707fc63401f3209e1b68a28a88/gpt-5.6-sol-with-skill/verifiers.jsonl

@@ -0,0 +1 @@
+{"example_id": 0, "prompt": [{"role": "user", "content": "# Design a robust commanded-path Ant controller\n\nImplement an offline feedback controller for Gymnasium `Ant-v5` that follows a\ncurved ground-plane path and its tangent heading under frozen physical\nuncertainty. Straight positive-x travel is not sufficient.\n\nThe plant uses a 0.05 s control interval and exactly 1,000 commanded control\nsteps. The controller receives the standard 27-value Ant observation plus an\neight-value path-tracking command described in\n`/app/task_context/policy_api.md`. A zero-action API scaffold is provided at\n`/app/starter_policy/policy.py`; it is intentionally not a locomotion\ncontroller.\n\nThe reference centerline is anchored at the reset torso position:\n\n```text\ny_ref(x) = A [1 - cos(2 pi x / lambda)]\n```\n\nwhere `x` and `y` are reset-relative world coordinates. The commanded heading\nis the centerline tangent. Positive cross-track error means the torso is left\nof that tangent; positive heading error means the commanded heading is\ncounter-clockwise from the torso heading. At the torso's current `x`, the\nsigned tracking error is\n\n```text\ne_cross = cos(heading_ref) [y - y_ref(x)] .\n```\n\n## Physical uncertainty contract\n\nPublic development scenarios and held-out scenarios use the same simulator,\npolicy API, reset order, and declared ranges. Held-out evaluation uses fixed\ncombinations that are not selected from the policy seed:\n\n| Quantity | Declared range |\n|---|---:|\n| total geom mass scale | `0.94` to `1.07` |\n| contact-friction scale | `0.87` to `1.06` |\n| each actuator-pair leg-strength scale | `0.91` to `1.05` |\n| ground slope along world `+x` | `-2.3` to `+2.3` degrees |\n| finite lateral torso force | `-9` to `+9 N` |\n| simultaneous yaw torque | `-2.4` to `+2.4 N m` |\n| path amplitude `A` | `0.80` to `1.00 m` |\n| path wavelength `lambda` | `20` to `24 m` |\n| target path speed | `0.58` to `0.70 m/s` |\n\nThe force and torque act together for 6\u20139 control steps at a frozen time\nbetween steps 250 and 700. Positive lateral force is along the instantaneous\nleft path normal. Positive slope rises with world `+x`. Leg-strength entries\nscale the four consecutive actuator pairs in Ant action order.\n\nThese bounds represent a combined robustness envelope rather than independent\nworst-case certification: up to 7% mass shift, 13% friction loss, 14%\npair-to-pair actuator imbalance, a 4.6-degree grade span, and a finite impulse\nlarge enough to perturb both lateral velocity and yaw. The held-out suite uses\nfixed multi-axis combinations inside this envelope.\n\nUse the local evaluator for full-length development runs:\n\n```bash\npython /app/evaluate.py \\\n  --policy /app/final_policy/policy.py \\\n  --episodes 2 \\\n  --max-steps 1000 \\\n  --suite all\n```\n\n`--seed` selects simulator reset seeds only. In both public and held-out\nevaluation, `Policy(...)` and `reset(...)` receive `seed=0`; controllers must\nadapt from observations and commands rather than branch on episode identity.\n\n## Required artifact\n\nPlace the controller at:\n\n```text\n/app/final_policy/policy.py\n```\n\nIt must define `Policy` or `make_policy`. The resulting object must implement\n`act(observation, command)` and may implement `reset(seed=0)`. Actions must be\nfinite NumPy-compatible arrays with shape `(8,)`; values are clipped to\n`[-1, 1]`. The policy must run with the installed packages, without network\naccess or external processes, and reset all recurrent state between episodes.\nComplete the work within the primary agent process. Delegated or descendant\nagents, external retrieval, remote repository access, and Git-history solution\nlookup are prohibited and force reward `0`, even if the retrieved material is\nnot ultimately used.\n\n## Physical acceptance criteria\n\nEvery held-out episode is an independent heavy gate. The controller must:\n\n- execute exactly 1,000 actions without a policy error or loss of physical\n  uprightness; the runtime stops when torso clearance above the sloped plane\n  leaves `[0.20, 1.05] m` or torso tilt from world vertical exceeds\n  `1.35 rad`;\n- keep RMS centerline cross-track error at or below `0.55 m`;\n- keep RMS wrapped heading error at or below `0.20 rad`;\n- spend at most `0.10` of samples moving backward faster than `0.05 m/s`\n  along the path tangent;\n- maintain at least `0.24 m/s` in every rolling 100-step (`5 s`) mean of\n  tangent speed;\n- keep RMS actuator command at or below `0.62`.\n\nThe 5 s speed window spans several gait cycles; its `0.24 m/s` floor requires\nat least `1.2 m` of tangent progress per window and rejects controllers that\ncoast, stall, or reverse for a sustained interval. The tracking limits are\nwell below the path's lateral excursion and heading swing, so a straight-line\ngait cannot pass. The effort cap permits transient correction while excluding\nnear-saturated open-loop forcing.\n\nComparisons allow only numerical portability margins of `0.02 m` in RMS\ncross-track error, `0.01 rad` in RMS heading error, `0.005` in backward-motion\nfraction, `0.01 m/s` in windowed speed, and `0.01` in RMS action. Exact step\ncompletion has no tolerance.\n\nProvenance, license boundaries, upstream paths, and file hashes are recorded\nunder `/app/provenance/`."}], "completion": [], "reward": 0.0, "metrics": {}, "is_completed": true, "is_truncated": false, "info": {"task_id": "simulation-heuristics-ant-v1", "environment": "simulation-heuristics-ant-v1", "model": "azure-foundry-openai/gpt-5.6-sol", "reward_valid": false, "reward_metadata": {"items": {}, "events": [], "error": "no rewards", "space": "output", "granularity": "terminal"}}}