Full-scale eval crashed in the pure-LLM QA-judge path: pure_llm emits
free-form JSON whose 'segments' list can contain entries like
["CPU Usage", 30] (channel name instead of an int). _seg_from_entry called
int() unconditionally and raised ValueError, killing the whole report run
(model predictor had already finished; result lost).
Fix: wrap _seg_from_entry in try/except (ValueError, TypeError) → return
None for any unparseable entry, so good segments alongside junk are still
extracted. Verified: {["CPU Usage",30],[10,20]} → [(10,20)]. + unit test.
Also archive logs/train_full_run.log (stage1 31250 EMA1.26, stage2 9375 EMA0.75).
张宗平
17ed7947d3