Author

Jing Yu

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Preprint Aug 2026

The Chase Is the Curriculum, the Capture Anchors the Credit: Pursuit-Evasion Self-Play for Zero-Data LLM Reasoning

In LURE, an LLM evader positions tasks along each environment's difficulty axis to stay one step ahead of a planner-executor pursuer that hunts it down through verifiable interaction, and the unified model attains stronger aggregate OOD zero-shot accuracy than all trained baselines across nine held-out benchmarks from...

Jing Yu, Shengchao Chen, Yiyun Tan · 0 citations

Conference Jul 2026

Zero-Shot Embodied Decision Making with Structured Prior Knowledge and Adaptive Calibration in ALFWorld

Large-language-model (LLM) agents perform well in embodied benchmarks but are costly, stochastic, and difficult to audit. We propose an LLM-free zero-shot decision agent for ALFWorld that combines structured commonsense priors with adaptive calibration. The agent uses object-location priors, task templates, synonym map...

Shengjie Ma, Jin-Han Li, Guo-An Zhang et al. · 2026 3rd World Conference on... · 0 citations