Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes
📰 ArXiv cs.AI
Medical LLMs can be decoded but not corrected by fixed residual-stream linear steering, highlighting a classification-correction gap in AI failure regimes
Action Steps
- Investigate the classification-correction gap in LLMs using Overthinking (OT) regime
- Apply linear decoding to LLM hidden states to identify failure signals
- Evaluate the effectiveness of fixed residual-stream linear steering in correcting model failures
- Analyze the results using metrics such as balanced accuracy and inter-annotator agreement
- Develop alternative correction strategies to address the limitations of linear steering
Who Needs to Know This
AI researchers and engineers working on medical LLMs can benefit from understanding the limitations of linear steering in correcting model failures, while data scientists and analysts can apply these insights to improve model evaluation and validation
Key Insight
💡 Linear decoding can identify failure signals in LLMs, but fixed residual-stream linear steering may not be sufficient to correct those failures
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🚨 Medical LLMs can be decoded but not corrected by fixed linear steering! 🤖💡
Key Takeaways
Medical LLMs can be decoded but not corrected by fixed residual-stream linear steering, highlighting a classification-correction gap in AI failure regimes
Full Article
Title: Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes
Abstract:
arXiv:2605.05715v1 Announce Type: new Abstract: Can linearly decodable failure signals in LLM hidden states be leveraged to correct those failures? We investigate this classification-correction gap via Overthinking (OT)--a stable behavioral regime (Jaccard >= 0.81, 94% inter-annotator agreement) in medical QA where models answer correctly under resampling yet fail in extended chain-of-thought. OT is linearly decodable at 71.6% balanced accuracy (p < 10^{-16}). Yet five families of fixed linear s
Abstract:
arXiv:2605.05715v1 Announce Type: new Abstract: Can linearly decodable failure signals in LLM hidden states be leveraged to correct those failures? We investigate this classification-correction gap via Overthinking (OT)--a stable behavioral regime (Jaccard >= 0.81, 94% inter-annotator agreement) in medical QA where models answer correctly under resampling yet fail in extended chain-of-thought. OT is linearly decodable at 71.6% balanced accuracy (p < 10^{-16}). Yet five families of fixed linear s
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