We study cross-model frozen-memory transfer, showing that target-side readers can extract and align representations across model architectures and tokenizers.</p>\n","updatedAt":"2026-08-19T07:37:10.580Z","author":{"_id":"617a92e16f37340367d5d791","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/617a92e16f37340367d5d791/omgyzmaF90KBLa3YgFxhS.png","fullname":"Shaoxiong","name":"jisx","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":12,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.867068886756897},"editors":["jisx"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/617a92e16f37340367d5d791/omgyzmaF90KBLa3YgFxhS.png"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2608.17050","authors":[{"_id":"6a855cdc536bdd3bdd48f923","name":"Mingyuan Li","hidden":false},{"_id":"6a855cdc536bdd3bdd48f924","name":"Guangsheng Yu","hidden":false},{"_id":"6a855cdc536bdd3bdd48f925","name":"Xu Wang","hidden":false},{"_id":"6a855cdc536bdd3bdd48f926","name":"Shaoxiong Ji","hidden":false}],"publishedAt":"2026-08-17T00:00:00.000Z","submittedOnDailyAt":"2026-08-19T00:00:00.000Z","title":"Cross-Model Memory Transfer via Target-Side Reader Adaptation","submittedOnDailyBy":{"_id":"617a92e16f37340367d5d791","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/617a92e16f37340367d5d791/omgyzmaF90KBLa3YgFxhS.png","isPro":false,"fullname":"Shaoxiong","user":"jisx","type":"user","name":"jisx"},"summary":"Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrieval latency, context overhead, and only shallow integration with the backbone. Parametric adaptation is efficient at inference time, but entangles knowledge with model weights and can be hard to update, audit, or transfer. Engram-style hashed memory occupies a middle regime: it stores learned information in an external, addressable table, yet consumes that table through a small learned reader. This raises a basic question: when such a memory is moved across backbones, what matters more, the frozen memory itself or the target-side reader? We study this question through cross-model frozen-memory extraction, in which a memory trained on a source model is frozen and attached to a different target model, with only a lightweight reader trained. Ablations show that learned memory content and correct addressing both matter, but the transferred table becomes useful only through a reader aligned to the target model. In downstream question answering tasks, a dual-layer, four-branch reader nearly closes the gap between same-model and cross-model reuse, achieving an average score of 38.8 under our controlled evaluation protocol. Moreover, when the provider reader is directly compatible with the target interface, the frozen artifact can provide substantial utility without target-side training, while optional reader adaptation yields further improvement. These results suggest that Engram can serve as a reusable external knowledge artifact, provided that the target has access to a compatible reader interface; target-side adaptation can further improve alignment when direct reader reuse is insufficient.","upvotes":2,"discussionId":"6a855cdc536bdd3bdd48f927","projectPage":"https://www.olaresearch.org/XMemTransfer/","githubRepo":"https://github.com/OLAResearch/XMemTransfer","githubRepoAddedBy":"user","ai_summary":"Cross-model reuse of frozen external memory tables depends primarily on aligning a lightweight target-side reader rather than the memory content alone, enabling reusable knowledge artifacts with optional adaptation.","ai_keywords":["Engram","hashed memory","cross-model frozen-memory extraction","reader alignment","dual-layer four-branch reader","external addressable table"],"ai_summary_model":"thinkingmachines/Inkling-Small","githubStars":0,"organization":{"_id":"6a1ffda6310029dbc8ac9adc","name":"OLAResearchX","fullname":"Omni Language AI Research","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/617a92e16f37340367d5d791/dMU_WZq7clwnvUTMragYe.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"617a92e16f37340367d5d791","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/617a92e16f37340367d5d791/omgyzmaF90KBLa3YgFxhS.png","isPro":false,"fullname":"Shaoxiong","user":"jisx","type":"user"},{"_id":"67f4d3bfa5ea8948d62539c0","avatarUrl":"/avatars/c0e600debf2339257a92595aca62c31d.svg","isPro":false,"fullname":"yuan","user":"leehenry","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"6a1ffda6310029dbc8ac9adc","name":"OLAResearchX","fullname":"Omni Language AI Research","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/617a92e16f37340367d5d791/dMU_WZq7clwnvUTMragYe.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2608/2608.17050.md","query":{}}">
Cross-Model Memory Transfer via Target-Side Reader Adaptation
Abstract
Cross-model reuse of frozen external memory tables depends primarily on aligning a lightweight target-side reader rather than the memory content alone, enabling reusable knowledge artifacts with optional adaptation.
Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrieval latency, context overhead, and only shallow integration with the backbone. Parametric adaptation is efficient at inference time, but entangles knowledge with model weights and can be hard to update, audit, or transfer. Engram-style hashed memory occupies a middle regime: it stores learned information in an external, addressable table, yet consumes that table through a small learned reader. This raises a basic question: when such a memory is moved across backbones, what matters more, the frozen memory itself or the target-side reader? We study this question through cross-model frozen-memory extraction, in which a memory trained on a source model is frozen and attached to a different target model, with only a lightweight reader trained. Ablations show that learned memory content and correct addressing both matter, but the transferred table becomes useful only through a reader aligned to the target model. In downstream question answering tasks, a dual-layer, four-branch reader nearly closes the gap between same-model and cross-model reuse, achieving an average score of 38.8 under our controlled evaluation protocol. Moreover, when the provider reader is directly compatible with the target interface, the frozen artifact can provide substantial utility without target-side training, while optional reader adaptation yields further improvement. These results suggest that Engram can serve as a reusable external knowledge artifact, provided that the target has access to a compatible reader interface; target-side adaptation can further improve alignment when direct reader reuse is insufficient.
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We study cross-model frozen-memory transfer, showing that target-side readers can extract and align representations across model architectures and tokenizers.
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