LLM edits convnet weights with common-sense reasoning — and it actually lifts accuracy
dansitu · x · 2026-11-03
dansitu continues testing an LLM that analyzes and edits weights of a small convnet audio classifier (bird call recognition). The model's reasoning reads as common sense — averaging the final layer's 25 per-position readout weights across time, lowering a parakeet bias by 1.0 to fix frequent false positives (equivalent to multiplying its odds by 0.37), probing a noise fallback and stronger sparrow detectors then rejecting them on the full dev set — yet it already delivers a decent lift on this small classifier. The author notes this doesn't imply "understanding the weights" beyond what a human data scientist could do; small models are just easy to analyze, and it is undeniably effective. Next he plans to ask it to generate weights from scratch without training, given a feature set.
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