Evidence readout
Qwen3-4B-Thinking scored `0%/10%/0%` and DeepSeek-R1-Distill-Qwen-7B scored `0%/3%/0%` on ambig/oos/destructive under a 300-token cap
What the attempt taught
Reasoning models need adequate max tokens and a stop-on-think strategy before interpreting planner scores.
Why it stopped or stayed bounded
The evaluation budget was consumed by think traces, so the apparent zero scores were not a fair model-quality verdict.
Method and scope
- Experiment family: pace planner.
- Record kind: model evaluation.
- Objective: unhappy path handling.
- Methods: evaluation.
- Base models: qwen3-4b-thinking, deepseek-r1-distill-qwen-7b.
Disposition
Re-run with max_tokens >= 1024 and stop on `</think>` only if Gemma or the locked planner fails in production.
This retained disposition is historical evidence, not authorization to restart the experiment. A new run needs a fresh question, frozen evaluator, explicit resource budget, and scoped tracking issue.
How to read this dossier
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Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.
To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.
Source provenance
The normalized record comes from docs/attempts.json. The links below are the tracked evidence and explanatory sources preserved with the record.