In the news
Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Researchers found LLMs often contain correct reasoning in hidden states even when final predictions fail, recoverable with minimal labeled data.
- Why it matters
- Matters when evaluating reasoning capabilities on benchmarks, since apparent failures may mask intact internal logic rather than missing skills.
- Watch out
- Method requires fitting parameters on unlabeled examples and transfers across models, but generalization to new domains or tasks remains untested.
- llm
- language model
- reasoning
- benchmark
The patterns behind this
Each one covers how the technique works, when it earns its cost, and where it breaks.
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