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 investigate this, we introduce RobotWorld, a challenging simulation testbed for robot use: turning instructions and observations into physic...
Zhiqin Yang, Chen-Xin Li, Xiao-Meng Hu et al.· 0 citations
This work provides a theoretically grounded condition under which a self-evolved harness can be reliably adopted and proposes a validation rule to make the evolved system satisfy the reliable adoption condition with theoretical guarantees.
Qi Cai, Yong-Gang Zhang, Jun Nie et al.· 2 citations· ⚡2
EvolveNet is introduced, a paradigm of collaborative harness evolution that moves experience extraction to the data and introduces scope-typed, evidence-guided program aggregation, which improves the shared harness in all five settings.
Jun Nie, Yong-Gang Zhang, Qi Cai et al.· 2 citations
This analysis provides a structured account of current approaches to scaling LRMs beyond human supervision and the open problems involved in developing self-sustaining learning systems toward superintelligence.
Zhiqin Yang, Jing-Wen Fu, Yu-Han Liu et al.· 4 citations
Zero2Skill is presented, a human-robot symbiotic agentic system in which corrections are retained and reused across rounds, and policies fine-tuned on Zero2Skill data match teleoperation-trained policy success at a fraction of collection human cost.
Boyuan Wang, Zhenyuan Zhang, Zhiqin Yang et al.· arXiv.org· 1 citation
DRNOISE, a 100-task benchmark for answer recovery under misleading evidence, is introduced, a 100-task benchmark for answer recovery under misleading evidence that requires active reconciliation of direct claims with record-level evidence.
Jun Nie, Zhiqin Yang, Zhenheng Tang et al.· arXiv.org· 2 citations
It is proved that the planner's suboptimality is bounded by twice this discrepancy between the predicted and the true plan-cost at the plan the planner commits to, whereas the data-averaged prediction error neither bounds nor tracks it.
Hanzhe You, Yonggang Zhang, Maohao Ran et al.· arXiv.org· 2 citations
Test-Time Harness Evolution is introduced, which treats the executable harness as the state of test-time adaptation for LLM agents as evolution over executable control programs and identifies execution-derived proxy reliability as a central challenge for robust unsupervised agent improvement.
Jun Nie, Yonggang Zhang, Jun Song et al.· arXiv.org· 3 citations
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