STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?

• URL: http://arxiv.org/abs/2605.06527v1
• Date: Thu, 07 May 2026 16:31:15 GMT
• Title: STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?
• Abstract summary: Large Language Model (LLM) agents are increasingly expected to maintain coherent, long-term personalized memory.<n>Current benchmarks primarily measure static fact retrieval, overlooking the ability to revise stored beliefs when new evidence emerges.<n>We introduce STALE, a benchmark of 400 expert-validated conflict scenarios.<n>We present CUPMem, a prototype that strengthens write-time revision through structured state consolidation and propagation-aware search.
• Score: 5.361950931863979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 We propose a three-dimensional probing framework that tests State Resolution (detecting that a prior belief is outdated), Premise Resistance (rejecting queries that falsely presuppose a stale state), and Implicit Policy Adaptation (proactively applying updated states in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 Models often accept outdated assumptions embedded in a user's query, and they struggle to recognize when a change in one aspect of the user's state should invalidate related memories.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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