Read retrieved text and source identity
{
"status": "retrieved",
"text": "[2026-09-14T11:15:45.309261+00:00] tool seq5 turn=evalarc-public-2f665fd5734bb89435224913-5\n{\"ok\": true, \"content\": \"# Robot recording evidence review\\n\\nImplement a JSON-lines service that reviews recorded positions and velocities.\\nEach stdin line is one request. Write exactly one JSON response per request to\\nstdout; diagnostics belong on stderr. Exit cleanly when stdin reaches EOF.\\n\\nThe input contains an `op` of `review`, a `query_frame`, and a `recording`:\\n\\n- `source.sha256` identifies the original recorded file. Preserve it in the report.\\n- `metadata.expected_frames` lists every frame that should be present.\\n- `metadata.world_from_sensor` describes the coordinate convention.\\n For world component `j`, take sensor component `axes[j]`, multiply by\\n `signs[j]` and `meters_per_unit`, then add `origin_m[j]` for positions.\\n Velocities use the same axis/sign/unit mapping without translation.\\n- `metadata.clock` defines elapsed seconds as\\n `(tick - origin_tick) * seconds_per_tick`.\\n- `metadata.analytic` gives initial position, initial velocity and constant\\n acceleration in world metres and seconds.\\n- `observations` contains objects with `frame`, `tick`, `position`, and `velocity`.\\n Input order is arbitrary. Some expected frames may be absent. The queried frame\\n is present. Do not interpolate absent observations.\\n\\nReturn exactly `{\\\"ok\\\": true, \\\"report\\\": {...}}`. The report contains these fields:\\n\\n| Field | Required value |\\n| --- | --- |\\n| `source_sha256` | The provided original source digest |\\n| `frame` | The requested integer frame |\\n| `time_seconds` | Its elapsed time, after clock conversion |\\n| `position_m` | Its three world-coordinate position components |\\n| `speed_m_s` | Euclidean magnitude of its world velocity |\\n| `max_position_error_m` | Maximum Euclidean position error across **available** observations against `p0 + v0*t + 0.5*g*t*t` |\\n| `peak_frame` | Available frame with greatest world z; choose the lowest frame number on an exact tie |\\n| `peak_height_m` | That frame's world z position |\\n| `missing_frames` | Sorted list of expected but absent integer frames |\\n\\nUse JSON numbers for numerical values, never Boolean substitutes, NaN or Infinity.\\nChecks allow absolute numerical error up to 1e-6 or relative error up to 1e-7.\\nDo not infer metres, seconds, axis order, or completeness from filenames.\\n\\nThese inputs are derived representations of six real CUDA recordings published\\nby Robot Reel. Unit, coordinate, clock and omission variants are generated for this\\npublic development task. They are not new physical experiments or hidden test data.\\nReporting a source digest provides attribution; it does not authenticate the\\noriginal recording's producer.\\n\"}\n---\n[2026-09-14T11:15:45.309261+00:00] assistant seq6 turn=evalarc-public-2f665fd5734bb89435224913-6\n[tool_use read_file] {\"path\":\"/workspace/main.py\"}\n---\n[2026-09-14T11:15:45.309261+00:00] tool seq7 turn=evalarc-public-2f665fd5734bb89435224913-7\n{\"ok\": true, \"content\": \"\\\"\\\"\\\"Implement the recording-review contract described in TASK.md.\\\"\\\"\\\"\\n\\nimport json\\nimport sys\\n\\n\\ndef review(request):\\n raise NotImplementedError(\\\"Convert recorded observations and return the requested facts\\\")\\n\\n\\nfor line in sys.stdin:\\n print(json.dumps({\\\"ok\\\": True, \\\"report\\\": review(json.loads(line))}), flush=True)\\n\"}\n---\n[2026-09-14T11:15:45.309261+00:00] assistant seq8 turn=evalarc-public-2f665fd5734bb89435224913-8\n[tool_use write_file] {\"path\":\"/workspace/main.py\",\"content\":\"\\\"\\\"\\\"Implement the recording-review contract described in TASK.md.\\\"\\\"\\\"\\n\\nimport json\\nimport sys\\n\\n\\ndef review(request):\\n # Extract the query frame\\n query_frame = request[\\\"query_frame\\\"]\\n recording = request[\\\"recording\\\"]\\n source_sha256 = recording[\\\"source\\\"][\\\"sha256\\\"]\\n metadata = recording[\\\"metadata\\\"]\\n observations = recording[\\\"observations\\\"]\\n\\n # Process metadata\\n expected_frames = metadata[\\\"expected_frames\\\"]\\n world_from_sensor = metadata[\\\"world_from_sensor\\\"]\\n axes = world_from_sensor[\\\"axes\\\"]\\n signs = world_from_sensor[\\\"signs\\\"]\\n origin_m = world_from_sensor[\\\"origin_m\\\"]\\n meters_per_unit = world_from_sensor[\\\"meters_per_unit\\\"]\\n clock = metadata[\\\"clock\\\"]\\n origin_tick = clock[\\\"origin_tick\\\"]\\n seconds_per_tick = clock[\\\"seconds_per_tick\\\"]\\n analytic = metadata[\\\"analytic\\\"]\\n position0_m = analytic[\\\"position0_m\\\"]\\n velocity0_m_s = analytic[\\\"velocity0_m_s\\\"]\\n gravity_m_s2 = analytic[\\\"gravity_m_s2\\\"]\\n\\n # Process observations\\n observations_dict = {obs[\\\"frame\\\"]: obs for obs in observations}\\n available_frames = sorted(observations_dict.keys())\\n\\n # Calculate time_seconds for the query frame\\n query_tick = observations_dict[query_frame][\\\"tick\\\"]\\n time_seconds = (query_tick - origin_tick) * seconds_per_tick\\n\\n # Calculate position_m for the query frame\\n query_position = observations_dict[query_frame][\\\"position\\\"]\\n position_m = [\\n query_position[axes[0]] * signs[0] * meters_per_unit + origin_m[0],\\n query_position[axes[1]] * signs[1] * meters_per_unit + origin_m[1],\\n query_position[axes[2]] * signs[2] * meters_per_unit + origin_m[2]\\n ]\\n\\n # Calculate speed_m_s for the query frame\\n query_velocity = observations_dict[query_frame][\\\"velocity\\\"]\\n speed_m_s = (query_velocity[axes[0]] ** 2 + query_velocity[axes[1]] ** 2 + query_velocity[axes[2]] ** 2) ** 0.5\\n\\n # Calculate max_position_error_m\\n max_position_error_m = 0.0\\n for frame in available_frames:\\n obs = observations_dict[frame]\\n tick = obs[\\\"tick\\\"]\\n position = obs[\\\"position\\\"]\\n # Convert position to world coordinates\\n world_position = [\\n position[axes[0]] * signs[0] * meters_per_unit + origin_m[0],\\n position[axes[1]] * signs[1] * meters_per_unit + origin_m[1],\\n position[axes[2]] * signs[2] * meters_per_unit + origin_m[2]\\n ]\\n # Calculate expected position using analytic model\\n t = (tick - origin_tick) * seconds_per_tick\\n expected_position = [\\n position0_m[0] + velocity0_m_s[0] * t + 0.5 * gravity_m_s2[0] * t ** 2,\\n position0_m[1] + velocity0_m_s[1] * t + 0.5 * gravity_m_s2[1] * t ** 2,\\n position0_m[2] + velocity0_m_s[2] * t + 0.5 * gravity_m_s2[2] * t ** 2\\n ]\\n # Calculate position error\\n error = (world_position[0] - expected_position[0]) ** 2 + (world_position[1] - expected_position[1]) ** 2 + (world_position[2] - expected_position[2]) ** 2\\n error = error ** 0.5\\n if error > max_position_error_m:\\n max_position_error_m = error\\n\\n # Find peak_frame and peak_height_m\\n peak_frame = None\\n peak_height_m = -float('inf')\\n for frame in available_frames:\\n obs = observations_dict[frame]\\n position = obs[\\\"position\\\"]\\n # Convert position to world coordinates\\n world_position = [\\n position[axes[0]] * signs[0] * meters_per_unit + origin_m[0],\\n position[axes[1]] * signs[1] * meters_per_unit + origin_m[1],\\n position[axes[2]] * signs[2] * meters_per_unit + origin_m[2]\\n ]\\n if world_position[2] > peak_height_m:\\n peak_height_m = world_position[2]\\n peak_frame = frame\\n elif world_position[2] == peak_height_m:\\n if frame < peak_frame:\\n peak_frame = frame\\n\\n # Find missing_frames\\n missing_frames = []\\n for frame in expected_frames:\\n if frame not in observations_dict:\\n missing_frames.append(frame)\\n missing_frames.sort()\\n\\n # Create report\\n report = {\\n \\\"source_sha256\\\": source_sha256,\\n \\\"frame\\\": query_frame,\\n \\\"time_seconds\\\": time_seconds,\\n \\\"position_m\\\": position_m,\\n \\\"speed_m_s\\\": speed_m_s,\\n \\\"max_position_error_m\\\": max_position_error_m,\\n \\\"peak_frame\\\": peak_frame,\\n \\\"peak_height_m\\\": peak_height_m,\\n \\\"missing_frames\\\": missing_frames\\n }\\n return report\\n\\n\\nfor line in sys.stdin:\\n print(json.dumps({\\\"ok\\\": True, \\\"report\\\": review(json.loads(line))}), flush=True)\\n\"}\n---\n[2026-09-14T11:15:45.309261+00:00] tool seq9 turn=evalarc-public-2f665fd5734bb89435224913-9\n{\"ok\": true, \"written_bytes\": 4547}\n---\n[2026-09-14T11:15:45.309261+00:00] assistant seq10 turn=evalarc-public-2f665fd5734bb89435224913-10\n[tool_use run_command] {\"command\":\"python3 /workspace/main.py < /workspace/example.jsonl\"}\n---\n[2026-09-14T11:15:45.309261+00:00] tool seq11 turn=evalarc-public-2f665fd5734bb89435224913-11\n{\"ok\": true, \"exit_code\": 0, \"output\": \"{\\\"ok\\\": true, \\\"report\\\": {\\\"source_sha256\\\": \\\"ed8caf612cae5c056d5a9f32b2074f1f7c6cdd1e50214b26ad70ebeaecb56b2e\\\", \\\"frame\\\": 31, \\\"time_seconds\\\": 1.033333333333372, \\\"position_m\\\": [2.066678047180176, 0.0, 6.085323333740234], \\\"speed_m_s\\\": 2004.6877195638112, \\\"max_position_error_m\\\": 0.02046407548115492, \\\"peak_frame\\\": 31, \\\"peak_height_m\\\": 6.085323333740234, \\\"missing_frames\\\": []}}\\n\", \"limit_reached\": null}\n---\nturns 5-11 of 14\n",
"turns": [
5,
6,
7,
8,
9,
10,
11
],
"source": {
"session_id": "evalarc-public-2f665fd5734bb89435224913",
"manifest_sha256": "40ae2debeef57610aaacf01b4f086eba2b926cceece9bfcc81c6d051b121d925",
"prior_trial_sha256": "2f665fd5734bb89435224913d43b4a69d91d53495c33d5ba9c375d2c59710004",
"prior_program_sha256": "0f8d610ce4894ef43620e6f356a39c79775a09a4613b61e6e9818f34371bbf2a",
"parquet_sha256": "4f38babfcd1c0fb84eac003667d0ac465f2024068c8616cc38a3da44e4f9ca4e"
}
}