14 papers
Safe Harness Self-Evolution: A Theoretical Analysis of Feasibility and Limits
Qianshu Cai, Yonggang Zhang, Jun Nie +6
Harness self-evolution is the process by which an agent modifies its prompts, tools, code, or orchestration in response to task feedback while keeping the underlying language model…
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,…
EvolveNet: Collaborative Harness Evolution for Agent Self-Improvement
Jun Nie, Yonggang Zhang, Qianshu Cai +3
The capabilities of an LLM agent depend not only on its model but on the harness: the executable program that constructs context, invokes tools, verifies results, and recovers from…
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…
DRNOISE: Benchmarking Deep Research Agents in Misleading Evidence Environments
Jun Nie, Zhiqin Yang, Zhenheng Tang +4
Deep research agents increasingly operate over the open web, where relevant records coexist with redundant summaries, outdated reports, and misleading documents. Existing evaluatio…
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…