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Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents

Paper recorded by Signals 4 on 2026-10-01 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-01 on arXiv · recorded by Signals 4 on 2026-10-02

Category: cs.CL · 自然语言处理 · first seen 2026-10-02

Abstract

Large Language Model (LLM) agents now take part in organizational work, where many authors record decisions across documents over months. Because a revised decision arrives as a new document rather than an edit, answering a question requires knowing which version held at a given time. However, most memory systems compress the record at write time. By distilling each document into facts, notes or g

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#10 most recent of 311 cs.CL papers we have recorded · ↑ newer: Controllable Multi-label Video Safety Detection via Adaptive Tversky P · ↓ older: Mingbird: A Local-First Agent Harness Enabling Small Open Models to Co
Cite this page: Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents: the #10 most recent of 311 cs.CL papers we have recorded (as of 2026-10-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mem-non-destructive-memory-for-long-term-organizational-llm-agents.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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