---
title: "STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?"
source: HuggingFace Daily Papers · 2026-05-17
url: https://arxiv.org/abs/2605.06527
date: 2026-05-18
published_at: 2026-05-17T12:00:00+00:00
tag: 论文研究
item_id: 333fc34a612fca9e
---
# Computer Science > Computation and Language

[Submitted on 7 May 2026]

# Title:STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?

[View PDF](https://arxiv.org/pdf/2605.06527)

[HTML (experimental)](https://arxiv.org/html/2605.06527v1)

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 We propose a three-dimensional probing framework that tests State Resolution (detecting that a prior belief is outdated), Premise Resistance (rejecting queries that falsely presuppose a stale state), and Implicit Policy Adaptation (proactively applying updated states in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 Models often accept outdated assumptions embedded in a user's query, and they struggle to recognize when a change in one aspect of the user's state should invalidate related memories.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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