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Kunlun Zhu

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Review Aug 2026

Apodex Discovery: Reality Benchmarks and Environments for Evaluating and Building Discoverative Artificial Intelligence

Apollo did not reach the Moon merely because its engineers could solve difficult equations. It succeeded by turning a distant ambition into a mission architecture of explicit objectives, simulation, verification, and repeated correction. AI now faces a similar transition: frontier models can solve difficult tasks once...

Brian Wang, Bin Feng, Xiaoman Pan et al. · 1 citation
#artificial intelligence Preprint Sep 2026

Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training

An unsuccessful LLM agent rollout contains more information than its final reward: the observations available to the agent, the actions it chose, and the environment's responses. Reusing this experience for learning requires identifying a decision to revise and testing a concrete alternative. We introduce the Agent Err...

Kun-Lun Zhu, Xu-Yan Ye, Yi-Bo Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Learning Meta-Skills for Agent Harness Design in Test-Time AI4AI

Across Harness-Bench and NewtonBench, full-bank meta-skills improve macro-average performance by 8.95 percentage points over no-skill construction, and 12.02 points over direct delivery of the same bank to the Target.

Cheng Qian, Kun-Lun Zhu, Bei-Bin Li et al. · 0 citations
#artificial intelligence Review Sep 2026

Relic: From Multi-Agent Collaboration to Persistent Organizational Capability

The introduction of Relic, which turns recurring collaboration failures into organization-owned, executable protocols, and how collaboration experience can become persistent organizational state that remains useful beyond the members who created it are shown.

Hong-Yi Du, Tian-Yi Zhang, Wei-Jia Zhang et al. · 0 citations
Preprint Jul 2026

CUADebug: Diagnosing and Repairing Computer-Use Agent Failures

Results show that CUA root-cause diagnosis can provide actionable repair signals rather than merely post-hoc explanations and show that CUA root-cause diagnosis can provide actionable repair signals.

Wei-Jia Zhang, Kunlun Zhu, Ze-Yi Liu et al. · 1 citation
Preprint Aug 2026

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers

This work presents a unified formulation of LLM routing as a sequential decision process characterized by five components: context encoders, model encoders, scoring functions, decision rules, and learning signals, covering single-turn, multi-turn, and personalized routing.

Tao Feng, Fangxu Yu, Haozhen Zhang et al. · 1 citation

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