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Mnemon agent uses dual-system approach for LLM long-term memory

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Mnemon, a novel memory agent designed for long-term context in large language models. Mnemon distinguishes between fast, judgment-based tasks (System 1) and slow, reasoning-based tasks (System 2), assigning the former to a decision model called Jev and the latter to an LLM. This approach allows Mnemon to maintain and effectively utilize extensive conversation histories, outperforming 14 other systems on the LoCoMo benchmark with GPT-4.1 mini and achieving top results on LongMemEval-S. AI

IMPACT This research could lead to more capable LLM assistants that can effectively recall and utilize information from very long conversations.

RANK_REASON The item is a research paper detailing a new method for LLM memory. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Mnemon agent uses dual-system approach for LLM long-term memory

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The item is a research paper detailing a new method for LLM memory. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Guangren Wang ·

    Mnemon: Raw Records, Fast Judgments, Slow Thoughts

    arXiv:2609.36059v1 Announce Type: cross Abstract: Long-term memory lets an LLM assistant use a history it can no longer reread, and most memory systems build it by rewriting conversations into facts, graphs or typed memories at write time. We argue that the work of memory divides…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Guangren Wang ·

    Mnemon: Raw Records, Fast Judgments, Slow Thoughts

    Long-term memory lets an LLM assistant use a history it can no longer reread, and most memory systems build it by rewriting conversations into facts, graphs or typed memories at write time. We argue that the work of memory divides, as thinking does, into two systems. Most of it i…

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