LLM Self-Correction Paradox: Weaker Models Outperform in Error Recovery
Published:Jan 6, 2026 05:00
•1 min read
•ArXiv AI
Analysis
This research highlights a critical flaw in the assumption that stronger LLMs are inherently better at self-correction, revealing a counterintuitive relationship between accuracy and correction rate. The Error Depth Hypothesis offers a plausible explanation, suggesting that advanced models generate more complex errors that are harder to rectify internally. This has significant implications for designing effective self-refinement strategies and understanding the limitations of current LLM architectures.
Key Takeaways
Reference
“We propose the Error Depth Hypothesis: stronger models make fewer but deeper errors that resist self-correction.”