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RobotWorld: Benchmarking Multimodal Agents for Robot Use Across Diverse Tasks and Embodiments
Zhiqin Yang, Chenxin Li, Xiaomeng Hu +30
General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carry into the physical world. To…
A Theory of Reliable Self-Evolution for Agent Harnesses
Qianshu Cai, Yonggang Zhang, Jun Nie +6
In harness self-evolution, agents modify their own prompts, code, tools, and orchestration while keeping the underlying language model fixed. Recent work has shown that agents can…
Scaling Large Reasoning Models beyond Human Supervision: A Path toward Superintelligence
Zhiqin Yang, Jingwen Fu, Yuhan Liu +16
Recent advances in large reasoning models (LRMs) have shown that reinforcement learning with verifiable rewards (RLVR) can substantially improve reasoning in mathematics and code,…
Failure-Informed Image Self-Augmentation for Multimodal Large Language Model Self-Improvement
Chunyang Jiang, Pingping Zhang, Yuzhi Zhao +9
Multimodal large language models (MLLMs) have achieved remarkable performance across vision-language tasks, but their progress depends heavily on large-scale, high-quality multimod…
Zero2Skill: Bootstrapping Robot Skills through Autonomous Data Collection, Training, and Deployment
Boyuan Wang, Zhenyuan Zhang, Zhiqin Yang +16
Autonomous data collection governs the volume and quality of real-world trajectories for manipulation policy learning. Existing pipelines reduce human effort via self-resetting, VL…
A Control Theory of Predictability in Latent World Models
Hanzhe You, Yonggang Zhang, Maohao Ran +6
Latent world models are trained to predict future states in a learned representation and are then deployed inside a planner that selects actions by simulating them forward. Current…
MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems
Qianshu Cai, Yonggang Zhang, Xianzhang Jia +5
Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human-driven update ships a…
Conditional Equivalence of DPO and RLHF: Implicit Assumption, Failure Modes, and Provable Alignment
Zhiqin Yang, Yonggang Zhang, Wei Xue +3
Direct Preference Optimization (DPO) has emerged as a popular alternative to Reinforcement Learning from Human Feedback (RLHF), offering theoretical equivalence with simpler implem…
Attention Hijacking: Response Manipulation Across Queries in Vision-Language Models
Zhiqiang Wang, Dongrui Liu, Yan Li +4
Existing adversarial attacks on vision-language models (VLMs) can steer model outputs toward attacker-specified target responses, but their effectiveness often degrades when the sa…
DC-W2S: Dual-Consensus Weak-to-Strong Training for Reliable Process Reward Modeling in Biological Reasoning
Chi-Min Chan, Ehsan Hajiramezanali, Xiner Li +6
In scientific reasoning tasks, the veracity of the reasoning process is as critical as the final outcome. While Process Reward Models (PRMs) offer a solution to the coarse-grained…