Multi-Head Recurrent Memory Agents

Recurrent memory agents extend LLMs to arbitrarily long contexts by iteratively consolidating input into a fixed-size memory window. Despite their scalability, these agents exhibit a well-documented reliability problem: end-to-end performance degrades systematically as context length grows. We diagnose this failure by decomposing performance into two factors--memory capture and memory retention--and quantitatively confirm that retention is the dominant bottleneck. Retention collapses because exi

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ethics.ai (1 July 2026), “Multi-Head Recurrent Memory Agents,” evidence record 339, https://ethics.ai/record/339 (originally published by arXiv).

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