Don't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting Lessons

A paper on arXiv (2610.31846) introduces Self-Direction, a mechanism that lets LLM agents learn from their own mistakes by adding adapters equal to roughly 0.02% of the base model's parameter count for each lesson learned. The method improves overall success rates by 8.9 to 17.0 percentage points and nearly doubles the reliability of an agent's corrections, according to the paper's reported results.

Agents & Inference arXiv https://arxiv.org/abs/2610.31846 Don't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting Lessons Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated. Summary A Agents can now learn from mistakes with a new mechanism called Self-Direction, which adds adapters equivalent to ~0.02% of the base model's parameter count for each lesson learned, improving overall success rates by 8.9 to 17.0 percentage points and nearly doubling the reliability of an agent's corrections, enabling more robust and reliable LLM-based systems in production. Summary B Mistral Large quota or rate limit — check usage and plan. Original headline: Don't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting Lessons 0 picks