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More Memory, Worse Agents: Error Reproduction and Anti-Persistence in LLM Agents
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research shows LLM agents using external memory can reproduce errors and degrade performance over time, challenging persistent memory designs.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 21 October 2026
- Collected by OneBench
- 22 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2610.27406v1 Announce Type: cross Abstract: LLM agents can improve across tasks without updating model parameters by storing experience from their own executions in external memory…
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The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.
OneBench interpretation
Institutional assessment
So what
Unvetted long-term memory in autonomous agents can compound execution errors and compromise operational reliability.
Do what
Review agentic architecture memory patterns with the team responsible for model risk and orchestration controls.