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Generative reward models (GRMs) are important for LLM optimization. Unlike scalar reward models, GRMs generate natural-language critiques alongside preference judgments, providing finer-grained evaluation signals. Their effectiveness depends heavily on critique reliability. However, existing GRM training typically uses...
This work proposes LifeMem, a longitudinal memory framework that combines structured life-event retrieval with agent-specific parametric memory for experience integration, and shows that LifeMem improves alignment with human data in terms of response distributions, overall and within-group diversity, and patterns of wi...
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...
SlimPer unifies sparse, dense, and sequence features within a single backbone and provides inherent interpretability through its attention mechanism, which yields measurable improvements in user engagement while streamlining the overall system and enabling effective modeling of 10k+ fine-grained user history events.
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...
This work introduces LongJudgeBench, a comprehensive benchmark for evaluating LLM judges on long-form outputs across diverse real-world scenarios and judging protocols, and systematically evaluates a broad range of LLM judges, covering multiple base models and judging settings.
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