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AGM: Achievement-Grounded Memory for Closed-Loop Agents with Frozen VLA Policies

Paper recorded by Signals 4 on 2026-08-30 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.AI · 人工智能 · first seen 2026-09-01

Abstract

Frozen vision-language-action (VLA) policies offer broad manipulation skills but execute open-loop action chunks without tracking task progress, so the agent cannot reliably decide whether to continue, retry, or terminate. External memory is a natural remedy, yet it can be harmful when attempted actions are treated as completed progress, turning local execution errors into persistent task-state er

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#278 most recent of 300 cs.AI papers we have recorded · ↑ newer: Evaluating LLMs on Conversational Text-to-SQL under Chain Ambiguity an · ↓ older: Integrating adaptive human behavior into epidemic models with large la
Cite this page: AGM: Achievement-Grounded Memory for Closed-Loop Agents with Frozen VLA Policies: the #278 most recent of 300 cs.AI papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/agm-achievement-grounded-memory-for-closed-loop-agents-with-frozen-vla-policies.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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