Signals 4 · free daily AI digest

AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents

Paper recorded by Signals 4 on 2026-10-14 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.CL · 自然语言处理 · first seen 2026-10-15

Abstract

Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy, but embodied RSI methods either refine the harness around a frozen executor and so cannot add a missing motion, or train the executor on persistent failures without asking whether a failure lies in how the executor is called or in what it can do. We

Read on arXiv →

#245 most recent of 660 cs.CL papers we have recorded · ↑ newer: Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models · ↓ older: ChronoCulture: Evaluating Cultural Awareness Across Time in Large Lang
Cite this page: AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents: the #245 most recent of 660 cs.CL papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/adarsi-on-demand-recursive-self-improvement-for-embodied-agents.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CL papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions