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MemBodied: Recurrent Associative Memory for Vision-Language-Action Models

Paper recorded by Signals 4 on 2026-09-23 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-23 on arXiv · recorded by Signals 4 on 2026-09-24

Category: cs.AI · 人工智能 · first seen 2026-09-24

Abstract

Vision-Language-Action models provide a strong foundation for general-purpose robot control, yet a vast majority of policies do not preserve and leverage episode-level information beyond the current observation. This limitation is consequential in history-dependent manipulation tasks that depend on information available only in past observations. Retaining past observations in context can aid in r

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#15 most recent of 380 cs.AI papers we have recorded · ↑ newer: Shutdown Sabotage Propensities in Multi-Agent Systems · ↓ older: Controlling Collectives of AI Agents in Reasoning Space with Spatial T

Cite this page: MemBodied: Recurrent Associative Memory for Vision-Language-Action Models: the #15 most recent of 380 cs.AI papers we have recorded (as of 2026-09-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/membodied-recurrent-associative-memory-for-vision-language-action-models.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json

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