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New "Memory as Code" framework boosts LLM long-term memory

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced "Memory as Code" (MaC), a novel framework designed to enhance the long-term memory capabilities of Large Language Model (LLM) agents. MaC unifies memory construction and retrieval using a shared code abstraction, progressing from atomic functions to modular compositions. This approach allows for declarative retrieval, translating queries into structured programs that operate over the memory structure. Experiments on benchmark datasets demonstrated significant improvements in LLM-judge accuracy and reduced token consumption compared to existing methods. AI

IMPACT This framework could enable LLM agents to handle more complex, long-horizon tasks by improving their ability to recall and compose information.

RANK_REASON The cluster contains a research paper detailing a new framework for LLM memory systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New "Memory as Code" framework boosts LLM long-term memory

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The cluster contains a research paper detailing a new framework for LLM memory systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Aoting Zeng, Yuxin Du, Jianwei Wang, Yizhang He, Shan Jiang, Xuemin Lin, Kai Wang ·

    Memory as Code: Declarative Retrieval over Codified Long-Term Memory

    arXiv:2610.27700v1 Announce Type: new Abstract: In long-term memory systems, memories are constructed from interaction histories and retrieved in response to queries, enhancing the reasoning ability of LLM agents. In complex scenarios, facts are scattered across sessions and thei…

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