Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456 Paper page - CloneMem: Benchmarking Long-Term Memory for AI Clones
https://arxivexplained.com/papers/clonemem-benchmarking-long-term-memory-for-ai-clones\n","updatedAt":"2026-01-16T18:01:53.209Z","author":{"_id":"65d9fc2a0e6ad24551d87a1e","avatarUrl":"/avatars/3aedb9522cc3cd08349d654f523fd792.svg","fullname":"Grant Singleton","name":"grantsing","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":4,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.6920873522758484},"editors":["grantsing"],"editorAvatarUrls":["/avatars/3aedb9522cc3cd08349d654f523fd792.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2601.07023","authors":[{"_id":"6967e29e30096756c8afaac9","name":"Sen Hu","hidden":false},{"_id":"6967e29e30096756c8afaaca","user":{"_id":"69600c1c76b6794073c3da2d","avatarUrl":"/avatars/754c92d08a1b428980a71b233cbd65c1.svg","isPro":false,"fullname":"Zhiyu Zhang","user":"ZhiyuZhangA","type":"user"},"name":"Zhiyu Zhang","status":"claimed_verified","statusLastChangedAt":"2026-01-15T15:03:48.009Z","hidden":false},{"_id":"6967e29e30096756c8afaacb","name":"Yuxiang Wei","hidden":false},{"_id":"6967e29e30096756c8afaacc","name":"Xueran Han","hidden":false},{"_id":"6967e29e30096756c8afaacd","name":"Zhenheng Tang","hidden":false},{"_id":"6967e29e30096756c8afaace","user":{"_id":"6603d56ab4344a2b07cd6d21","avatarUrl":"/avatars/1569bb60166532317c85e80da722ba1c.svg","isPro":false,"fullname":"Huacan Wang","user":"Huacan-Wang","type":"user"},"name":"Huacan Wang","status":"claimed_verified","statusLastChangedAt":"2026-01-15T15:03:44.738Z","hidden":false},{"_id":"6967e29e30096756c8afaacf","name":"Ronghao Chen","hidden":false}],"publishedAt":"2026-01-11T18:33:12.000Z","title":"CloneMem: Benchmarking Long-Term Memory for AI Clones","summary":"AI Clones aim to simulate an individual's thoughts and behaviors to enable long-term, personalized interaction, placing stringent demands on memory systems to model experiences, emotions, and opinions over time. Existing memory benchmarks primarily rely on user-agent conversational histories, which are temporally fragmented and insufficient for capturing continuous life trajectories. We introduce CloneMem, a benchmark for evaluating longterm memory in AI Clone scenarios grounded in non-conversational digital traces, including diaries, social media posts, and emails, spanning one to three years. CloneMem adopts a hierarchical data construction framework to ensure longitudinal coherence and defines tasks that assess an agent's ability to track evolving personal states. Experiments show that current memory mechanisms struggle in this setting, highlighting open challenges for life-grounded personalized AI. Code and dataset are available at https://github.com/AvatarMemory/CloneMemBench","upvotes":3,"discussionId":"6967e29e30096756c8afaad0","githubRepo":"https://github.com/AvatarMemory/CloneMemBench","githubRepoAddedBy":"user","ai_summary":"CloneMem benchmark evaluates long-term memory in AI clones using multi-year digital traces from diaries, social media, and emails to assess personal state tracking capabilities.","ai_keywords":["AI clones","long-term memory","digital traces","hierarchical data construction","personal state tracking"],"githubStars":22,"organization":{"_id":"68b33ab6a9ed99140481cf44","name":"QuantaAlpha","fullname":"QuantaAlpha","avatar":"https://cdn-uploads.huggingface.co/production/uploads/63f7767fbd28622c9b9915e9/DRN8PvmnpKmn2MSLQ7qhF.jpeg"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"69600c1c76b6794073c3da2d","avatarUrl":"/avatars/754c92d08a1b428980a71b233cbd65c1.svg","isPro":false,"fullname":"Zhiyu Zhang","user":"ZhiyuZhangA","type":"user"},{"_id":"678d88d2ff9242f6ac0c6b3e","avatarUrl":"/avatars/43fbfec1ea4b20600a70117286bebe79.svg","isPro":false,"fullname":"YUXIANG WEI","user":"stanjsx","type":"user"},{"_id":"658071c1da07e791d8a6e9dc","avatarUrl":"/avatars/66348bae88b5945cc54012c52cff6aa2.svg","isPro":false,"fullname":"Xiangchi Yuan","user":"Xiangchi","type":"user"}],"acceptLanguages":["*"],"organization":{"_id":"68b33ab6a9ed99140481cf44","name":"QuantaAlpha","fullname":"QuantaAlpha","avatar":"https://cdn-uploads.huggingface.co/production/uploads/63f7767fbd28622c9b9915e9/DRN8PvmnpKmn2MSLQ7qhF.jpeg"}}">
CloneMem benchmark evaluates long-term memory in AI clones using multi-year digital traces from diaries, social media, and emails to assess personal state tracking capabilities.
AI-generated summary
AI Clones aim to simulate an individual's thoughts and behaviors to enable long-term, personalized interaction, placing stringent demands on memory systems to model experiences, emotions, and opinions over time. Existing memory benchmarks primarily rely on user-agent conversational histories, which are temporally fragmented and insufficient for capturing continuous life trajectories. We introduce CloneMem, a benchmark for evaluating longterm memory in AI Clone scenarios grounded in non-conversational digital traces, including diaries, social media posts, and emails, spanning one to three years. CloneMem adopts a hierarchical data construction framework to ensure longitudinal coherence and defines tasks that assess an agent's ability to track evolving personal states. Experiments show that current memory mechanisms struggle in this setting, highlighting open challenges for life-grounded personalized AI. Code and dataset are available at https://github.com/AvatarMemory/CloneMemBench