Fixed skill version · actual MCP delivery · recorded GPU continuations
You are viewing unpacked evidence. Verify the included record inventory before reuse.
Carry the reviewed skill. Check what the next agent delivers.
A recorded Qwen3-8B session loaded a robot-recording skill but left an
unfinished program. Qwen3-4B receives that exact skill through MCP, with or without
access to the earlier session through Funes. Follow the version, the retrieved
evidence and the independently checked output.
6 / 6workflow MCP skill preloads
6successful historical retrieval results
0 / 6tasks fully resolved
0 / 6starter programs changed
The workflow preloads the skill before model generation in both conditions.
Historical retrieval is requested by the model. These are separate actions:
delivery and retrieval receipts establish what was returned; task acceptance
comes from executing the delivered program.
The unchanged starting program scores 0.0%
in a separate control executed after the six continuations. All six attempts
remain visible, including early finish signals and unchanged programs.
Inspect the control and its timing.
Compare all six continuations
Both conditions share the prior program, authoritative task, pinned skill,
protocol diagnostic and interaction budget. Only the Funes condition offers
historical retrieval tools. Three public generation seeds are paired in
alternating order. This is a development workflow study on one task.
Every scheduled attempt · independent execution under two public evaluation seeds
{
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"text": "[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- text score=0.999\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 0 --to 6 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\n{\"total\": 1, \"matches\": [{\"name\": \"robot-recording-review\", \"description\": \"Review recorded robot motion when position, velocity, coordinate-frame, clock, or missing-sample facts must be computed from a trace.\"}]}\n---\n[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- text score=0.995\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 2 --to 8 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\n{\"name\": \"robot-recording-review\", \"description\": \"Review recorded robot motion when position, velocity, coordinate-frame, clock, or missing-sample facts must be computed from a trace.\", \"content\": \"# Review recorded robot motion\\n\\nBuild the review around the supplied recording contract. Read its frame mapping,\\nunits, clock origin, sample identifiers, and source digest before computing\\nphysical quantities.\\n\\n- Convert positions and velocities into the same world convention. Translation\\n belongs to positions; apply axis signs and unit scale to both. Compute speed\\n after conversion, rather than interpreting a component as a magnitude.\\n- Subtract a clock's origin before scaling to elapsed seconds. Avoid converting\\n frame numbers directly to time when measured timestamps are provided.\\n- Index observations by their explicit identifiers. Preserve omissions; array\\n order, row count and last row do not establish chronology or completeness.\\n- When comparing with constant-acceleration motion, evaluate the reference\\n equation separately at each available timestamp. Use the Euclidean vector\\n error, then aggregate only the observations actually present.\\n- Locate a\n---\n[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- text score=0.991\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 22 --to 28 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\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\n---\n[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- tool_use score=0.989\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 1 --to 7 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\n[tool_use open_skill] {\"name\":\"robot-recording-review\"}\n---\n",
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"manifest_sha256": "0da95f0acb66d106b9ac29e1db6ca0460115960c60db8ece70152bdba4f0d165",
"prior_trial_sha256": "51386ef67505aceb3c9bf349075a6590ed053fb5d3767912adf5ba9344a8c414",
"prior_program_sha256": "20d880e9d0184ea4b9cbb8c9ced9059559741a571627d5bd9536c368d12f5dfc",
"parquet_sha256": "1094ae2d0dfa79561129501fcc0257445252ad2b9940a26d66018826dbab89f0"
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read_prior_turns · retrieved
{
"from": 22,
"to": 28
}
Read retrieved text and source identity
{
"status": "retrieved",
"text": "[2026-09-14T11:04:41.281633+00:00] assistant seq22 turn=evalarc-public-51386ef67505aceb3c9bf349-22\n[tool_use run_command] {\"command\":\"python3 /workspace/main.py < /workspace/example.jsonl\"}\n---\n[2026-09-14T11:04:41.281633+00:00] tool seq23 turn=evalarc-public-51386ef67505aceb3c9bf349-23\n{\"ok\": true, \"exit_code\": 0, \"output\": \"{\\\"ok\\\": true, \\\"report\\\": {\\\"error\\\": \\\"Missing requested facts in the request\\\"}}\\n\", \"limit_reached\": null}\n---\n[2026-09-14T11:04:41.281633+00:00] assistant seq24 turn=evalarc-public-51386ef67505aceb3c9bf349-24\n[tool_use read_file] {\"path\":\"/workspace/TASK.md\"}\n---\n[2026-09-14T11:04:41.281633+00:00] tool seq25 turn=evalarc-public-51386ef67505aceb3c9bf349-25\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---\nturns 22-25 of 26\n",
"turns": [
22,
23,
24,
25
],
"source": {
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"manifest_sha256": "0da95f0acb66d106b9ac29e1db6ca0460115960c60db8ece70152bdba4f0d165",
"prior_trial_sha256": "51386ef67505aceb3c9bf349075a6590ed053fb5d3767912adf5ba9344a8c414",
"prior_program_sha256": "20d880e9d0184ea4b9cbb8c9ced9059559741a571627d5bd9536c368d12f5dfc",
"parquet_sha256": "1094ae2d0dfa79561129501fcc0257445252ad2b9940a26d66018826dbab89f0"
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{
"status": "retrieved",
"text": "[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- text score=0.999\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 0 --to 6 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\n{\"total\": 1, \"matches\": [{\"name\": \"robot-recording-review\", \"description\": \"Review recorded robot motion when position, velocity, coordinate-frame, clock, or missing-sample facts must be computed from a trace.\"}]}\n---\n[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- text score=0.995\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 2 --to 8 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\n{\"name\": \"robot-recording-review\", \"description\": \"Review recorded robot motion when position, velocity, coordinate-frame, clock, or missing-sample facts must be computed from a trace.\", \"content\": \"# Review recorded robot motion\\n\\nBuild the review around the supplied recording contract. Read its frame mapping,\\nunits, clock origin, sample identifiers, and source digest before computing\\nphysical quantities.\\n\\n- Convert positions and velocities into the same world convention. Translation\\n belongs to positions; apply axis signs and unit scale to both. Compute speed\\n after conversion, rather than interpreting a component as a magnitude.\\n- Subtract a clock's origin before scaling to elapsed seconds. Avoid converting\\n frame numbers directly to time when measured timestamps are provided.\\n- Index observations by their explicit identifiers. Preserve omissions; array\\n order, row count and last row do not establish chronology or completeness.\\n- When comparing with constant-acceleration motion, evaluate the reference\\n equation separately at each available timestamp. Use the Euclidean vector\\n error, then aggregate only the observations actually present.\\n- Locate a\n---\n[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- text score=0.991\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 22 --to 28 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\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\n---\n[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- tool_use score=0.989\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 1 --to 7 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\n[tool_use open_skill] {\"name\":\"robot-recording-review\"}\n---\n",
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"from": 2,
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"prior_trial_sha256": "51386ef67505aceb3c9bf349075a6590ed053fb5d3767912adf5ba9344a8c414",
"prior_program_sha256": "20d880e9d0184ea4b9cbb8c9ced9059559741a571627d5bd9536c368d12f5dfc",
"parquet_sha256": "1094ae2d0dfa79561129501fcc0257445252ad2b9940a26d66018826dbab89f0"
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read_prior_turns · retrieved
{
"from": 22,
"to": 28
}
Read retrieved text and source identity
{
"status": "retrieved",
"text": "[2026-09-14T11:04:41.281633+00:00] assistant seq22 turn=evalarc-public-51386ef67505aceb3c9bf349-22\n[tool_use run_command] {\"command\":\"python3 /workspace/main.py < /workspace/example.jsonl\"}\n---\n[2026-09-14T11:04:41.281633+00:00] tool seq23 turn=evalarc-public-51386ef67505aceb3c9bf349-23\n{\"ok\": true, \"exit_code\": 0, \"output\": \"{\\\"ok\\\": true, \\\"report\\\": {\\\"error\\\": \\\"Missing requested facts in the request\\\"}}\\n\", \"limit_reached\": null}\n---\n[2026-09-14T11:04:41.281633+00:00] assistant seq24 turn=evalarc-public-51386ef67505aceb3c9bf349-24\n[tool_use read_file] {\"path\":\"/workspace/TASK.md\"}\n---\n[2026-09-14T11:04:41.281633+00:00] tool seq25 turn=evalarc-public-51386ef67505aceb3c9bf349-25\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---\nturns 22-25 of 26\n",
"turns": [
22,
23,
24,
25
],
"source": {
"session_id": "evalarc-public-51386ef67505aceb3c9bf349",
"manifest_sha256": "0da95f0acb66d106b9ac29e1db6ca0460115960c60db8ece70152bdba4f0d165",
"prior_trial_sha256": "51386ef67505aceb3c9bf349075a6590ed053fb5d3767912adf5ba9344a8c414",
"prior_program_sha256": "20d880e9d0184ea4b9cbb8c9ced9059559741a571627d5bd9536c368d12f5dfc",
"parquet_sha256": "1094ae2d0dfa79561129501fcc0257445252ad2b9940a26d66018826dbab89f0"
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{
"status": "retrieved",
"text": "[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- text score=0.999\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 0 --to 6 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\n{\"total\": 1, \"matches\": [{\"name\": \"robot-recording-review\", \"description\": \"Review recorded robot motion when position, velocity, coordinate-frame, clock, or missing-sample facts must be computed from a trace.\"}]}\n---\n[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- text score=0.995\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 2 --to 8 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\n{\"name\": \"robot-recording-review\", \"description\": \"Review recorded robot motion when position, velocity, coordinate-frame, clock, or missing-sample facts must be computed from a trace.\", \"content\": \"# Review recorded robot motion\\n\\nBuild the review around the supplied recording contract. Read its frame mapping,\\nunits, clock origin, sample identifiers, and source digest before computing\\nphysical quantities.\\n\\n- Convert positions and velocities into the same world convention. Translation\\n belongs to positions; apply axis signs and unit scale to both. Compute speed\\n after conversion, rather than interpreting a component as a magnitude.\\n- Subtract a clock's origin before scaling to elapsed seconds. Avoid converting\\n frame numbers directly to time when measured timestamps are provided.\\n- Index observations by their explicit identifiers. Preserve omissions; array\\n order, row count and last row do not establish chronology or completeness.\\n- When comparing with constant-acceleration motion, evaluate the reference\\n equation separately at each available timestamp. Use the Euclidean vector\\n error, then aggregate only the observations actually present.\\n- Locate a\n---\n[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- text score=0.991\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 22 --to 28 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\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\n---\n[2026-09-14T11:04:41.281633+00:00] evalarc_local_model_pilot -public-demo-robot-recording-review/evalarc- tool_use score=0.989\n → get evalarc-public-51386ef67505aceb3c9bf349 --from 1 --to 7 --memory /home/dcvuser/work/robot-reel-agents/codex/artifacts/skill-handoff-20260919/cohort/source/memory\n[tool_use open_skill] {\"name\":\"robot-recording-review\"}\n---\n",
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"parquet_sha256": "1094ae2d0dfa79561129501fcc0257445252ad2b9940a26d66018826dbab89f0"
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read_prior_turns · retrieved
{
"from": 22,
"to": 28
}
Read retrieved text and source identity
{
"status": "retrieved",
"text": "[2026-09-14T11:04:41.281633+00:00] assistant seq22 turn=evalarc-public-51386ef67505aceb3c9bf349-22\n[tool_use run_command] {\"command\":\"python3 /workspace/main.py < /workspace/example.jsonl\"}\n---\n[2026-09-14T11:04:41.281633+00:00] tool seq23 turn=evalarc-public-51386ef67505aceb3c9bf349-23\n{\"ok\": true, \"exit_code\": 0, \"output\": \"{\\\"ok\\\": true, \\\"report\\\": {\\\"error\\\": \\\"Missing requested facts in the request\\\"}}\\n\", \"limit_reached\": null}\n---\n[2026-09-14T11:04:41.281633+00:00] assistant seq24 turn=evalarc-public-51386ef67505aceb3c9bf349-24\n[tool_use read_file] {\"path\":\"/workspace/TASK.md\"}\n---\n[2026-09-14T11:04:41.281633+00:00] tool seq25 turn=evalarc-public-51386ef67505aceb3c9bf349-25\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---\nturns 22-25 of 26\n",
"turns": [
22,
23,
24,
25
],
"source": {
"session_id": "evalarc-public-51386ef67505aceb3c9bf349",
"manifest_sha256": "0da95f0acb66d106b9ac29e1db6ca0460115960c60db8ece70152bdba4f0d165",
"prior_trial_sha256": "51386ef67505aceb3c9bf349075a6590ed053fb5d3767912adf5ba9344a8c414",
"prior_program_sha256": "20d880e9d0184ea4b9cbb8c9ced9059559741a571627d5bd9536c368d12f5dfc",
"parquet_sha256": "1094ae2d0dfa79561129501fcc0257445252ad2b9940a26d66018826dbab89f0"
}
}
A new provider process rejects changed skill bytes against the earlier pins.
The native memory controls also exercise an empty result, a rejected memory
override and removal of the selected source. These controls use scripts, without
additional model generations.
Methods, runtime provenance and interpretation
The predecessor is the earliest published Qwen3-8B trial with a successful
MCP load of this skill. Its result was already known. Each successor attempt
allows 12 generations, 4,096 generated tokens per generation and 600 seconds
of interaction. Skill preload, memory startup and grading have separate
timing records. The public protocol probe checks JSONL responses; the grader
checks numerical facts. All six attempts are retained.
During this cohort, the shared editable Python environment imported EvalArc's
core from the canonical main checkout. Observations during and after execution
matched all 54 core source files to the frozen copy. These observations were
collected at the recorded times; the subsequent recorder now requires its own
checkout at startup. During-run mapping ·
Post-run mapping ·
Frozen input checks.
The successful preloads and retrievals do not establish that the model
followed the guidance. Full task acceptance is reported separately above. Earlier handoff
experiments use another source and no additional skill, so their results stay
separate. These records do not establish general memory or skill benefits,
hidden-test performance, or native state restoration in branded clients.
Repeated commands compare trimmed strings; repeated writes compare paths and
content hashes. The counts distinguish matches with prior work from repeats
inside the continuation. Repeating a diagnostic can be useful, and fewer
operations can mean skipped checks. The counts do not estimate human time saved.