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Gym-Anything-RT: Scaling Real-Time Environments for Vision-Language Agents

Paper recorded by Signals 4 on 2026-10-27 in cs.AI. 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.AI · 人工智能 · first seen 2026-10-28

Abstract

AI agents have made substantial progress in domains where they have a non-trivial amount of time to deliberate between actions, such as mathematics, software engineering, and performing tasks on desktop applications. It is less clear how such models fare in settings that demand real-time interaction, such as playing a video game or operating a physical device. In these settings, the environment st

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#117 most recent of 940 cs.AI papers we have recorded · ↑ newer: TeamGym: A Gym for Team Decision-Making by AI Agents with Distributed · ↓ older: When uncertainty quantification fails: structural vulnerabilities and
Cite this page: Gym-Anything-RT: Scaling Real-Time Environments for Vision-Language Agents: the #117 most recent of 940 cs.AI papers we have recorded (as of 2026-10-27). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/gym-anything-rt-scaling-real-time-environments-for-vision-language-agents.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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