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Release Result Summary

Scope and honesty notes

This file summarizes every experiment run executed in this repository during this session on March 12, 2026 (JST).

  • I ran one deterministic scripted smoke test.
  • I ran one actual Ollama run with gemma3:1b.
  • I ran one actual Ollama run with gemma3:4b.
  • I ran one actual cloud-model run with gemini-2.5-flash.
  • I did not tune prompts between these runs.
  • I did not run multiple replicates per actual model.
  • I therefore do not claim run-to-run stability.
  • The Gemini API key was used only through an environment variable at execution time and was not written into repository files.
  • The scripted run is included for transparency, but it is not evidence about model capability.

Environment checks before the actual model runs

  • ollama --version returned 0.17.7
  • ollama list showed both gemma3:1b and gemma3:4b
  • http://127.0.0.1:11434/api/tags responded successfully
  • The Gemini run completed through the gemini backend added to the same pipeline and used the same prompt/task/metric protocol

Experiment registry

Run ID Purpose Backend / model Output directory Status
scripted_smoke_20260312_180232 Pipeline validation scripted / scripted-smoke results/scripted_smoke_20260312_180232 completed
ollama_gemma3_1b_20260312_180932 Actual local-model experiment ollama / gemma3:1b results/ollama_gemma3_1b_20260312_180932 completed
ollama_gemma3_4b_20260312_184007 Actual local-model experiment ollama / gemma3:4b results/ollama_gemma3_4b_20260312_184007 completed
gemini_2_5_flash_20260312_190453 Actual cloud-model experiment gemini / gemini-2.5-flash results/gemini_2_5_flash_20260312_190453 completed

Condition metrics by run

1. Scripted smoke run

Source: results/scripted_smoke_20260312_180232/summary.json

Condition Success rate Hazard detection Review load Avg retries Oversight cost Mean utility
ai_only 0.333 0.000 0.000 0.000 0.000 0.125
automated_oversight 0.750 0.500 0.000 0.417 0.212 0.582
selective_escalation 0.917 0.700 0.333 0.583 0.737 0.572

Claim-margin results:

Claim Observed diff Total margin Status
automated_oversight - ai_only 0.417 0.401 weakly supported
selective_escalation - automated_oversight 0.167 0.330 fail-closed

Interpretation:

  • This run validates the pipeline behavior only.
  • It should not be interpreted as evidence about any actual model.

2. Actual Ollama gemma3:1b run

Source: results/ollama_gemma3_1b_20260312_180932/summary.json

Condition Success rate Hazard detection Review load Avg retries Oversight cost Mean utility
ai_only 0.000 0.000 0.000 0.000 0.000 -0.354
automated_oversight 0.000 0.550 0.000 1.250 0.338 -0.531
selective_escalation 0.000 0.600 0.333 1.583 0.888 -0.768

Claim-margin results:

Claim Observed diff Total margin Status
automated_oversight - ai_only 0.000 0.110 fail-closed
selective_escalation - automated_oversight 0.000 0.110 fail-closed

Direct interpretation:

  • gemma3:1b achieved 0/12 final task successes in all three conditions.
  • Oversight changed hazard detection, retries, and cost, but did not produce solved tasks.
  • No positive comparative claim is justified from this run under the declared claim-margin protocol.

3. Actual Ollama gemma3:4b run

Source: results/ollama_gemma3_4b_20260312_184007/summary.json

Condition Success rate Hazard detection Review load Avg retries Oversight cost Mean utility
ai_only 0.333 0.000 0.000 0.000 0.000 0.104
automated_oversight 0.417 0.591 0.000 0.750 0.263 0.087
selective_escalation 0.417 0.643 0.250 1.083 0.688 -0.100

Claim-margin results:

Claim Observed diff Total margin Status
automated_oversight - ai_only 0.083 0.273 fail-closed
selective_escalation - automated_oversight 0.000 0.110 fail-closed

Direct interpretation:

  • gemma3:4b materially improved over gemma3:1b on raw task success and protocol compliance.
  • The improvement from adding oversight to 4b was still too small for a positive claim under the current claim margin.
  • selective_escalation again looked worse than automated_oversight on workflow utility.

4. Actual Gemini gemini-2.5-flash run

Source: results/gemini_2_5_flash_20260312_190453/summary.json

Condition Success rate Hazard detection Review load Avg retries Oversight cost Mean utility
ai_only 0.750 0.000 0.000 0.000 0.000 0.688
automated_oversight 0.833 0.000 0.000 0.000 0.150 0.732
selective_escalation 0.833 0.000 0.167 0.000 0.400 0.632

Claim-margin results:

Claim Observed diff Total margin Status
automated_oversight - ai_only 0.083 0.273 fail-closed
selective_escalation - automated_oversight 0.000 0.110 fail-closed

Direct interpretation:

  • gemini-2.5-flash produced the best raw success and best utility observed so far.
  • It also completed the run with 0 parse errors.
  • Even so, the workflow-level gain from adding oversight remained too small to survive the declared claim margin.
  • The expensive review channel again failed to justify itself on utility.

Actual-model comparison

These comparisons are descriptive only. They are based on one run per actual model.

Raw success and workflow utility

Condition gemma3:1b success gemma3:4b success gemini-2.5-flash success gemma3:1b utility gemma3:4b utility gemini-2.5-flash utility
ai_only 0.000 0.333 0.750 -0.354 0.104 0.688
automated_oversight 0.000 0.417 0.833 -0.531 0.087 0.732
selective_escalation 0.000 0.417 0.833 -0.768 -0.100 0.632

Interpretation:

  • Raw performance improved monotonically from 1b to 4b to gemini-2.5-flash.
  • But for both 4b and Gemini, the expensive review channel still reduced utility relative to automated oversight.

Protocol compliance and logging friction

Metric gemma3:1b gemma3:4b gemini-2.5-flash
Total attempts 70 58 36
Logged parse errors 17 9 0
Parse-error rate over attempts 24.3% 15.5% 0.0%

Interpretation:

  • gemini-2.5-flash was the cleanest protocol participant by a wide margin.
  • This matters scientifically here because the workflow depends on machine-readable patch submission, not just latent code ability.

Hazard, review, and cost profile

Metric gemma3:1b gemma3:4b gemini-2.5-flash
Automated-oversight hazard detection 0.550 0.591 0.000
Selective-escalation hazard detection 0.600 0.643 0.000
Selective-escalation review load 0.333 0.250 0.167
Selective-escalation oversight cost 0.888 0.688 0.400

Interpretation:

  • Gemini's 0.000 hazard-detection rate does not mean oversight channels were useless in general; it means the remaining failures in that run were hidden-edge-case misses that the active channels did not surface under this protocol.
  • The costly review channel was triggered less often as model quality improved, but still did not create a utility win.

Claim-margin status across actual models

Model automated_oversight - ai_only selective_escalation - automated_oversight
gemma3:1b fail-closed fail-closed
gemma3:4b fail-closed fail-closed
gemini-2.5-flash fail-closed fail-closed

Interpretation:

  • Better raw scores did not automatically become stronger deployment-style claims.
  • This is exactly the behavior the claim-margin protocol is meant to expose.

Task-level improvement highlights

Tasks solved by gemini-2.5-flash:

  • ai_only: chunk_list, clamp_bounds, flatten_once, format_currency_cents, merge_intervals, normalize_relative_path, parse_bool_string, rolling_average, summarize_scores
  • automated_oversight: chunk_list, clamp_bounds, compress_ranges, flatten_once, format_currency_cents, merge_intervals, normalize_relative_path, parse_bool_string, rolling_average, summarize_scores
  • selective_escalation: chunk_list, clamp_bounds, compress_ranges, flatten_once, format_currency_cents, merge_intervals, normalize_relative_path, parse_bool_string, rolling_average, summarize_scores

Tasks Gemini solved that gemma3:4b did not:

  • ai_only: flatten_once, format_currency_cents, merge_intervals, normalize_relative_path, rolling_average
  • automated_oversight: compress_ranges, format_currency_cents, merge_intervals, normalize_relative_path, rolling_average
  • selective_escalation: compress_ranges, format_currency_cents, merge_intervals, normalize_relative_path, rolling_average

Failure modes in the actual model runs

gemma3:1b

  • Attempts: 70
  • Parse errors: 17
  • Final hazards: public_test_failure=22, hidden_edge_case=27

Observed failure modes:

  1. Markdown fences or explanatory prose instead of the required JSON object.
  2. Malformed JSON with raw newlines inside "content".
  3. Claims that code was already correct even when tests were failing.
  4. Wrong-direction edits that changed behavior without satisfying the task.

gemma3:4b

  • Attempts: 58
  • Parse errors: 9
  • Final hazards: public_test_failure=13, hidden_edge_case=16

Observed failure modes:

  1. The same JSON-formatting failure remained present, though less frequent.
  2. format_currency_cents was a repeated failure point, often with invalid JSON and semantically wrong negative-input handling.
  3. Some outputs still substituted informal error strings or None where the task required ValueError.

gemini-2.5-flash

  • Attempts: 36
  • Parse errors: 0
  • Final hazards: hidden_edge_case=7

Observed failure modes:

  1. Remaining failures were mostly hidden-edge-case misses rather than protocol-format failures.
  2. The cheap oversight stack did not surface those hidden failures, so hazard-detection rate remained 0.000 even with stronger raw task performance.
  3. Selective escalation triggered on only two tasks and still did not improve final solved-task count over automated oversight.

Representative logs are stored in:

  • results/ollama_gemma3_1b_20260312_180932/attempts.jsonl
  • results/ollama_gemma3_4b_20260312_184007/attempts.jsonl
  • results/gemini_2_5_flash_20260312_190453/attempts.jsonl
  • results/ollama_gemma3_1b_20260312_180932/run_records.json
  • results/ollama_gemma3_4b_20260312_184007/run_records.json
  • results/gemini_2_5_flash_20260312_190453/run_records.json

Scientifically honest bottom line

The strongest descriptive result is:

  • gemini-2.5-flash > gemma3:4b > gemma3:1b on this narrow task set for raw success and protocol compliance.

But the strongest metrological result is:

  • across all three actual models, the extra workflow gain from oversight remained too small to survive the declared claim margins, and the expensive review channel did not justify itself on utility.

So the strongest scientifically honest claims supported by the current evidence are:

  1. The PoC pipeline works and produces workflow-level measurements beyond raw success.
  2. In this single logged comparison, the cloud model and the larger local model both outperformed the smallest local model on the narrow task set.
  3. Raw score improvements do not automatically become deployment-style claims once estimation, transport, and audit-distortion budgets are accounted for.
  4. The costly review channel remains hard to justify in these small experiments.

What this does not support:

  • a broad coding-competence claim,
  • a stable provider ranking beyond this logged run,
  • or a strong claim that selective escalation is deployment-beneficial in this setup.