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ETAMem: Decoupling and Aligning Heterogeneous Memory for Long-Term Conversational Agents

Paper recorded by Signals 4 on 2026-10-27 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-27 on arXiv · recorded by Signals 4 on 2026-10-28

Category: cs.CL · 自然语言处理 · first seen 2026-10-28

Abstract

Long-term memory enables Large Language Models (LLMs) to maintain coherent and personalized interactions across extended conversations. However, existing memory systems face two key challenges: (1) Information Entanglement, where heterogeneous information is mixed within coarse-grained memory units; and (2) Organizational Mismatch, where memory structures based primarily on semantic similarity may

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#126 most recent of 660 cs.CL papers we have recorded · ↑ newer: Test-Time Communication in Trading Agent Swarms · ↓ older: Representing the Unseen: Rethinking Visual Imagination for Spatial Rea
Cite this page: ETAMem: Decoupling and Aligning Heterogeneous Memory for Long-Term Conversational Agents: the #126 most recent of 660 cs.CL papers we have recorded (as of 2026-10-27). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/etamem-decoupling-and-aligning-heterogeneous-memory-for-long-term-conversational.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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