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Richeng Xuan

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Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?

RuVerBench is introduced, the first benchmark for assessing LaaJ reliability in rubric verification for agentic scenarios, and the impact of key LaaJ strategies, including prompt design, batching, and majority voting, on rubric verification is analyzed.

Yangda Peng, Yunjia Qi, Hao Peng et al. · 2 citations
Preprint Aug 2026

RepoProbe: Benchmarking Architecture-Aware Repository Comprehension with Checklists

This work introduces RepoProbe, a novel benchmark for evaluating repository-level code understanding through open-ended Q&A using GitHub Discussions, which focuses on open-ended architectural inquiries rather than defect reporting and proposes a Checklist-Based Verification Protocol that decomposes answers into atomic,...

Yue Yang, Alyssa Wu, Ji Luo et al. · 1 citation
#natural language process... Preprint Aug 2026

JPO: Juris Policy Optimization for Structured Legal Reasoning in Criminal Judgment Prediction

Juris Policy Optimization (JPO), a post-training framework for structured legal reasoning in Chinese criminal judgment prediction, is proposed and experiments show that JPO consistently improves both judgment prediction and reasoning quality over supervised fine-tuning and reinforcement learning baselines.

Zhao-Lu Kang, Yan-Tao Liu, Tai-Long Luo et al. · 1 citation
#natural language process... Preprint Aug 2026

Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning

SCEval (Structure-Corruption Evaluation) a diagnostic evaluation protocol that keeps the question, answer space, and modality channels fixed while applying controlled structural corruptions to text, vision, and audio individually and jointly is introduced.

Zhaolu Kang, Mei-Xin Wu, Yu Xue et al. · 1 citation
Preprint Aug 2026

TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories

TrajDebug is proposed, an error-lifecycle tracing framework that addresses long-trajectory error discovery with multi-granularity history compression and evidence-based error identification, and supports critical attribution by tracing each error's resolution status and terminal impact.

Yunjia Qi, Zehua Yin, Xin Shi et al. · 4 citations · ⚡1

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