We would greatly appreciate it if you could upvote.</p>\n","updatedAt":"2026-05-15T04:17:00.352Z","author":{"_id":"62281c11236b7b2eefa7f198","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62281c11236b7b2eefa7f198/O-LoLaDkIoWcP19mzkNgS.jpeg","fullname":"Zhaowei Wang","name":"ZhaoweiWang","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":8,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9889910221099854},"editors":["ZhaoweiWang"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/62281c11236b7b2eefa7f198/O-LoLaDkIoWcP19mzkNgS.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.06527","authors":[{"_id":"6a05d7c4b1a8cbabc9f09505","name":"Hanxiang Chao","hidden":false},{"_id":"6a05d7c4b1a8cbabc9f09506","name":"Yihan Bai","hidden":false},{"_id":"6a05d7c4b1a8cbabc9f09507","name":"Rui Sheng","hidden":false},{"_id":"6a05d7c4b1a8cbabc9f09508","name":"Tianle Li","hidden":false},{"_id":"6a05d7c4b1a8cbabc9f09509","name":"Yushi Sun","hidden":false}],"publishedAt":"2026-05-07T00:00:00.000Z","submittedOnDailyAt":"2026-05-15T00:00:00.000Z","title":"STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?","submittedOnDailyBy":{"_id":"62281c11236b7b2eefa7f198","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62281c11236b7b2eefa7f198/O-LoLaDkIoWcP19mzkNgS.jpeg","isPro":true,"fullname":"Zhaowei Wang","user":"ZhaoweiWang","type":"user","name":"ZhaoweiWang"},"summary":"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 We propose a three-dimensional probing framework that tests State Resolution (detecting that a prior belief is outdated), Premise Resistance (rejecting queries that falsely presuppose a stale state), and Implicit Policy Adaptation (proactively applying updated states in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.","upvotes":37,"discussionId":"6a05d7c4b1a8cbabc9f0950a","ai_summary":"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.","ai_keywords":["large language model agents","personalized memory","implicit conflict","state resolution","premise resistance","implicit policy adaptation","STALE benchmark","CUPMem","structured state consolidation","propagation-aware search"],"organization":{"_id":"647693e442e5f529745b9ba6","name":"hkust-nlp","fullname":"HKUST NLP Group","avatar":"https://www.gravatar.com/avatar/9f5aca83deec76c97aeb9d60139a79e8?d=retro&size=100"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"656c5b5cfa91c816094cecaf","avatarUrl":"/avatars/25e58c53bc2a14f05307023d45129246.svg","isPro":false,"fullname":"Yushi SUN","user":"Yushi98","type":"user"},{"_id":"64bce857796f20daad639795","avatarUrl":"/avatars/36d46ab089bcac562f98fbd0448895db.svg","isPro":false,"fullname":"Dylan","user":"Dylannnnnnnn","type":"user"},{"_id":"65f2b36733725f138b252311","avatarUrl":"/avatars/69897dd235c24a961c3ebf4e961a9dc0.svg","isPro":false,"fullname":"Patricia","user":"Patriciayufish","type":"user"},{"_id":"68c14e8a883c1c5eb5bcf04f","avatarUrl":"/avatars/b617a7c5f5dadaa4d2887b89aabff956.svg","isPro":false,"fullname":"Michael","user":"iMichael00","type":"user"},{"_id":"6a0690f0b8a232dd5b4821f8","avatarUrl":"/avatars/1ec6f6f831a6f59aeffb5c9f6e15b7eb.svg","isPro":false,"fullname":"Yonggang Li","user":"yonggang13","type":"user"},{"_id":"65ed927fd767680a0c9a5a5c","avatarUrl":"/avatars/66e89d64847a58dec50ea5a9b9566fba.svg","isPro":false,"fullname":"XT","user":"AlgernonFe","type":"user"},{"_id":"62ef93ea5ea8f0d87a7776c3","avatarUrl":"/avatars/081df0c8cfdece1039800411a0aa18df.svg","isPro":false,"fullname":"Xu Ziyang","user":"FelliYang","type":"user"},{"_id":"677e8b330ef2084985c0a4f5","avatarUrl":"/avatars/d34bbc7cdd9a006c73c85e85ad53429a.svg","isPro":false,"fullname":"Yanjie Zhang","user":"doudouwer","type":"user"},{"_id":"62281c11236b7b2eefa7f198","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62281c11236b7b2eefa7f198/O-LoLaDkIoWcP19mzkNgS.jpeg","isPro":true,"fullname":"Zhaowei Wang","user":"ZhaoweiWang","type":"user"},{"_id":"662374fcdd416a585a26faff","avatarUrl":"/avatars/62ad033fea6e8db0df650e08f500ea77.svg","isPro":false,"fullname":"Haobo Li","user":"Haobo55654","type":"user"},{"_id":"692a831efda18e0c7649a5bc","avatarUrl":"/avatars/c65c7fc5a26c614bb6376517a905945d.svg","isPro":false,"fullname":"Hanxiang Chao","user":"CHXc","type":"user"},{"_id":"6855d31b10d4ecbc0d04e4bf","avatarUrl":"/avatars/72d9646f3c0bed092e6b09ca0cf276b9.svg","isPro":false,"fullname":"Hugh","user":"aHugh19","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"647693e442e5f529745b9ba6","name":"hkust-nlp","fullname":"HKUST NLP Group","avatar":"https://www.gravatar.com/avatar/9f5aca83deec76c97aeb9d60139a79e8?d=retro&size=100"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.06527.md"}">
STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?
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.
AI-generated summary
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.
Community
We would greatly appreciate it if you could upvote.
Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.
Tap or paste here to upload images
Cite arxiv.org/abs/2605.06527 in a model README.md to link it from this page.
Cite arxiv.org/abs/2605.06527 in a dataset README.md to link it from this page.
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.