STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?

Hugging Face Daily Papers Papers
llm-agents memory-updating benchmark implicit-conflict state-aware commonsense-reasoning evaluation

Summary

This paper identifies a critical failure mode in LLM agents where they fail to update personalized memories when new evidence conflicts with prior beliefs. It introduces the STALE benchmark and a three-dimensional probing framework, revealing that even the best models achieve only 55.2% accuracy, and proposes CUPMem as a prototype for robust memory revision.

Large Language Model (LLM) agents are increasingly expected to maintain coherent, long-term personalized memory, yet current benchmarks primarily measure static fact retrieval, overlooking the ability to revise stored beliefs when new evidence emerges. We identify a critical and underexplored failure mode, Implicit Conflict: a later observation invalidates an earlier memory without explicit negation, requiring contextual inference and commonsense reasoning to detect. To rigorously evaluate this capability, we introduce STALE, a benchmark of 400 expert-validated conflict scenarios (1,200 evaluation queries across three probing dimensions) spanning over 100 everyday topics with contexts up to 150K tokens. We propose a three-dimensional probing framework that tests State Resolution (detecting that a prior belief is outdated), Premise Resistance (rejecting queries that falsely presuppose a stale state), and Implicit Policy Adaptation (proactively applying updated states in downstream behavior). A systematic evaluation of frontier LLMs and specialized memory frameworks reveals a pervasive gap between retrieving updated evidence and acting on it, with even the best evaluated model achieving only 55.2% overall accuracy. Models often accept outdated assumptions embedded in a user's query, and they struggle to recognize when a change in one aspect of the user's state should invalidate related memories. To establish an initial baseline for state-aware memory, we further present CUPMem, a prototype that strengthens write-time revision through structured state consolidation and propagation-aware search, suggesting that explicit state adjudication is a promising direction for robust agentic memory.
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Paper page - STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?

Source: https://huggingface.co/papers/2605.06527

Abstract

Large language models struggle to update personalized memories when new evidence emerges, requiring contextual inference and commonsense reasoning to detect implicit conflicts, as demonstrated by a comprehensive benchmark and evaluation of state-aware memory systems.

Large Language Model (LLM) agents are increasingly expected to maintain coherent, long-termpersonalized memory, yet current benchmarks primarily measure static fact retrieval, overlooking the ability to revise stored beliefs when new evidence emerges. We identify a critical and underexplored failure mode,Implicit Conflict: a later observation invalidates an earlier memory without explicit negation, requiring contextual inference and commonsense reasoning to detect. To rigorously evaluate this capability, we introduce STALE, a benchmark of 400 expert-validated conflict scenarios (1,200 evaluation queries across three probing dimensions) spanning over 100 everyday topics with contexts up to 150K tokens. We propose a three-dimensional probing framework that testsState Resolution(detecting that a prior belief is outdated),Premise Resistance(rejecting queries that falsely presuppose a stale state), andImplicit Policy Adaptation(proactively applying updated states in downstream behavior). A systematic evaluation of frontier LLMs and specialized memory frameworks reveals a pervasive gap between retrieving updated evidence and acting on it, with even the best evaluated model achieving only 55.2% overall accuracy. Models often accept outdated assumptions embedded in a user’s query, and they struggle to recognize when a change in one aspect of the user’s state should invalidate related memories. To establish an initial baseline for state-aware memory, we further presentCUPMem, a prototype that strengthens write-time revision throughstructured state consolidationandpropagation-aware search, suggesting that explicit state adjudication is a promising direction for robust agentic memory.

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