Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents
Factual evidence
What the source reports
Researchers identify safety risks in agentic AI memory caching, showing failure counters fail to detect stale reasoning after fact changes.
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:2610.17544v2 Announce Type: replace Abstract: Thought templates, skill libraries, agent memories and caches turn many reasoning steps into one. We call this reasoning contraction. A…
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OneBench interpretation
Institutional assessment
So what
Cached reasoning memory in agentic workflows creates silent drift and wrong outputs when underlying facts change without invalidating stored routines.
Do what
Review agentic architecture memory-invalidation controls with the team responsible for model risk management.