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Large language models (LLMs) offer a scalable approach to social simulation, but their credibility depends on how agents are constructed. Existing methods can partially reproduce population-level patterns, yet often fail to capture human-like diversity. Our analysis shows that static-profile agents exhibit stronger dem...
Ranking is a fundamental component of modern information access systems. Reinforcement learning (RL) provides a flexible framework for directly optimizing coarse-grained feedback and system-level objectives defined over the complete ranking list. However, existing RL-based ranking methods typically treat each sampled p...
This work introduces LexRubric, a rubric-based benchmark for evaluating open-ended Chinese legal tasks and evaluates 18 recent general and legal-domain LLMs on LexRubric, showing that different models exhibit distinct capability profiles, and that open-ended legal tasks remain challenging for current LLMs.
GenRubric is introduced, a self-evolving framework that improves rubric generation from unlabeled queries without requiring additional human annotations during self-evolution, and experiments show that self-evolution improves the agreement between evaluations induced by generated rubrics and those induced by expert-wri...
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