AGM: Achievement-Grounded Memory for Closed-Loop Agents with Frozen VLA Policies
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…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
