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    A proposal to pair recurrent memory with dense attention in language models for AI agents

    A post sharing @a1zhang’s blog describes an alternative to fitting an AI agent’s harness around a decoder-only Transformer: change the model’s input and output shape to fit the agent.

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    2 Sources, 11h ago, first seen 11h ago

    TLDR

    A post sharing @a1zhang’s blog says the proposal would use recurrent memory for older history while dense attention handles recent context. The blog suggests this could reduce the need for manual compaction in AI agents.

    Combined views

    4.7K

    2 Sources, first seen 11h ago

    119 likes

    Combined views

    4.7K

    2 Sources, first seen 11h ago

    119 likes
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    3 comments
    74 saves
    16 reposts

    Sentiment

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    Featured Source
    3 comments
    74 saves
    16 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    2 Sources

    @askalphaxivLanguage Model "Shape" Most AI agents force the harness around a decoder-only Transformer. But what if we change the model’s input/output shape to fit the agent? This blog by @a1zhang suggests using recurrent memory for old history while dense attention handles recent context, reducing the need for manual compaction. Read more about it: https://alexzhang13.github.io/blog/2026/shape/
    @a1zhangRT @askalphaxiv: Language Model "Shape" Most AI agents force the harness around a decoder-only Transformer. But what if we change the mod…

    2 Sources

    @askalphaxivLanguage Model "Shape" Most AI agents force the harness around a decoder-only Transformer. But what if we change the model’s input/output shape to fit the agent? This blog by @a1zhang suggests using recurrent memory for old history while dense attention handles recent context, reducing the need for manual compaction. Read more about it: https://alexzhang13.github.io/blog/2026/shape/
    @a1zhangRT @askalphaxiv: Language Model "Shape" Most AI agents force the harness around a decoder-only Transformer. But what if we change the mod…