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RSIGym: A Flexible Environment for Recursive Self-Improvement
Fanqing Meng, Lingxiao Du, Haocheng Lu +6
Recursive self-improvement requires carrying accepted changes into later improvement cycles, while studying agent-proposed changes also requires substantial research infrastructure…
RSIBench-Data: Benchmarking Data-Centric Research for Recursive Self-Improvement
Fanqing Meng, Lingxiao Du, Qiguang Chen +4
Recursive self-improvement requires turning evidence of model failures into better models. Data-centric post-training research entails diagnosing capability gaps, designing and val…
Scaling GUI Agents with Visual State Transitions
Xiangyan Liu, Kaixin Li, Haonan Wang +6
We introduce State Transition Pretraining (STP) as a new scaling axis for GUI agents. During the STP stage, we continually pretrain a unified multimodal model on visual state trans…
Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents
Zhuoming Chen, Xinrui Zhong, Qilong Feng +5
Sparse attention is becoming increasingly important for serving large language models (LLMs) as generation lengths continue to grow. However, deploying and evaluating new sparse at…
ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents
Fanqing Meng, Lingxiao Du, Zijian Wu +46
Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surrounding environment may change in…
Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows
Hardy Chen, Nancy Lau, Haoqin Tu +8
Frontier coding agents are increasingly used in workflows where users supervise progress primarily through repeated improvement of a public score, namely the reported score on a pu…
Gym-V: A Unified Vision Environment System for Agentic Vision Research
Fanqing Meng, Lingxiao Du, Jiawei Gu +9
As agentic systems increasingly rely on reinforcement learning from verifiable rewards, standardized ``gym'' infrastructure has become essential for rapid iteration, reproducibilit…
Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw
Zijun Wang, Haoqin Tu, Letian Zhang +11
OpenClaw, the most widely deployed personal AI agent in early 2026, operates with full local system access and integrates with sensitive services such as Gmail, Stripe, and the fil…
In-Context Reinforcement Learning for Tool Use in Large Language Models
Yaoqi Ye, Yiran Zhao, Keyu Duan +4
While large language models (LLMs) exhibit strong reasoning abilities, their performance on complex tasks is often constrained by the limitations of their internal knowledge. A com…
ImageEdit-R1: Boosting Multi-Agent Image Editing via Reinforcement Learning
Yiran Zhao, Yaoqi Ye, Xiang Liu +2
With the rapid advancement of commercial multi-modal models, image editing has garnered significant attention due to its widespread applicability in daily life. Despite impressive…
LongRLVR: Long-Context Reinforcement Learning Requires Verifiable Context Rewards
Guanzheng Chen, Michael Qizhe Shieh, Lidong Bing
Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Large Language Models (LLMs) by optimizing them against factual outco…