Study: AI Agents Lose Half Their Accuracy When Asked to Rewrite Their Own Memory

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A 2026 paper by Zhang et al. from UIUC found that repeatedly asking large language models to consolidate their own memory caused task accuracy to drop from 100% to 52.6% on the ARC-AGI benchmark. The research, tested across multiple environments including ALFWorld, WebShop, and ScienceWorld, identified three failure mechanisms: selection bias, rewriting drift, and a compounding feedback loop of corrupted memory. Episodic-only memory — retaining raw records without abstraction — matched or outperformed consolidation-based approaches in the study. The findings suggest that having an AI "clean up" its memory introduces distortion, as each rewrite reflects the model's current context rather than preserving original facts. Practitioners are now exploring append-only memory architectures that preserve raw data and avoid automated summarization entirely.

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