Story · arXiv
Useful Memories Become Faulty When Continuously Updated by LLMs (arXiv)
paper · Story page
Consolidated memory, where a model keeps rewriting past trajectories into a bank of lessons, helps at first, then degrades, and can fall below the no-memory baseline. Even consolidating from ground-truth solutions, GPT-5.4 failed 54% of ARC-AGI problems it had already solved without memory, while a control that keeps the raw trajectories stays competitive.
In plain words
- Researchers found that artificial intelligence systems can damage useful memories when repeatedly rewriting them into general lessons.
- This method stores condensed lessons from earlier tasks instead of keeping only the original records.
- The lessons helped at first, but continued rewriting eventually made them less useful than having no memory.
- Keeping the original task records remained competitive, suggesting the rewriting process caused the decline.
- This matters for builders who expect artificial intelligence systems to improve by continually updating written memories.
Appeared in
- Anthropic's deliberately misaligned model, Fable 5.1, and a fix for reward hacking
Sep 02, 2026 · in the sections
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