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    AI agent memory reportedly isn't automatically portable between models

    A user's summary of the paper says fixed-format memory barely changed across two tested models, but accuracy with free-form notes fell 13.28 points when memory moved in one direction.

    RP
    3 Sources, 19d ago, first seen 19d ago

    TLDR

    A user summarizing a LinkedIn paper describes tests in which one model inherited another's memory. The summary says fixed-format memory barely changed across the two models, while free-form notes were less reliable. Rewriting compressed notes could not recover facts already lost.

    For retrieval, the post says mixing old and new embeddings—data representations used for search—in one index recovered only 4.96 of the 11.90-point gain from fully rebuilding it. The user recommends treating model upgrades as memory migrations: test compatibility, rebuild indexes fully, prefer structured memory where it fits, and retain protected raw history when policy allows.

    Combined views

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    3 Sources, first seen 19d ago

    Combined views

    6.5K

    3 Sources, first seen 19d ago

    65 likes
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    15 comments
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    27 reposts

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    15 comments
    35 saves
    27 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    3 Sources

    @rohanpaul_aiNew Linkedin paper shows Agent memory is not automatically portable: fixed-schema memory survived the model swap, free-form notes changed sharply, and mixed embeddings hurt retrieval, so treat upgrades as memory migrations. The paper tests what happens when 1 model inherits memory created by another. A new model may inherit the same memory store but remember differently, so agent upgrades should include memory compatibility tests rather than only model benchmarks. Memory saved in a fixed structure barely changed across the 2 tested models because each model used the same fields and format. Free-form notes were much less reliable. In 1 direction, accuracy dropped 13.28 points because the old model had already left out useful information. RAG failed differently. Mixing old and new embeddings in the same index recovered only 4.96 of the 11.90-point gain from fully rebuilding the index. Once compressed notes had lost a fact, rewriting them could not bring it back. So test memory whenever you change models, rebuild embedding indexes fully, prefer structured memory where it fits, and keep protected raw history when policy allows.

    3 Sources

    @rohanpaul_aiNew Linkedin paper shows Agent memory is not automatically portable: fixed-schema memory survived the model swap, free-form notes changed sharply, and mixed embeddings hurt retrieval, so treat upgrades as memory migrations. The paper tests what happens when 1 model inherits memory created by another. A new model may inherit the same memory store but remember differently, so agent upgrades should include memory compatibility tests rather than only model benchmarks. Memory saved in a fixed structure barely changed across the 2 tested models because each model used the same fields and format. Free-form notes were much less reliable. In 1 direction, accuracy dropped 13.28 points because the old model had already left out useful information. RAG failed differently. Mixing old and new embeddings in the same index recovered only 4.96 of the 11.90-point gain from fully rebuilding the index. Once compressed notes had lost a fact, rewriting them could not bring it back. So test memory whenever you change models, rebuild embedding indexes fully, prefer structured memory where it fits, and keep protected raw history when policy allows.