Author

J. Wu

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Jul 2026

AssumptionMiner: Extracting, Tracing, and Revising Implicit Assumptions in LLM Code Generation

AssumptionMiner is presented, a framework that makes implicit assumptions a first-class artifact of LLM-based code generation, and demonstrates that making assumptions explicit improves the transparency and controllability of LLM-based code generation.

J. Wu · arXiv.org · 0 citations

Preprint Aug 2026

AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning

This work proposes AgentOPSD, a critic-free, recursive method for turn-level credit assignment in agentic reinforcement learning that aggregates token-level teacher-student log-probability gaps into turn-level evidence and recursively updates a Bayesian belief state in log-odds space.

Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao et al. · 1 citation