Recent approaches in personalized reward modeling have primarily focused on leveraging user interaction history to align model judgments with individual preferences.However, existing approaches largely treat user context as a static or implicit conditioning signal, failing to capture the dynamic and multi-faceted nature of human judgment.In this paper, we propose P-CHECK, a novel personalized reward modeling framework, designed to train a plugand-play checklist generator that synthesizes dynamic evaluation criteria for guiding the reward prediction.To better align these checklists with personalized nuances, we introduce Preference-Contrastive Criterion Weighting, a training strategy that assigns saliency scores to criteria based on their discriminative power for personalized judgment.We conduct extensive experiments and demonstrate that P-CHECK not only improves reward accuracy but also enhances downstream personalized generation, and remains robust in OOD scenarios.[CODE]

Recent advancements in table-based reasoning have expanded beyond factoid-level QA to address insight-level tasks, where systems should synthesize implicit knowledge in the table to provide explainable analyses. Although effective, existing studies remain confined to scenarios where a single gold table is given alongside the user query, failing to address cases where users seek comprehensive insights from multiple unknown tables. To bridge these gaps, we propose MT-RAIG Bench, design to evaluate systems on Retrieval-Augmented Insight Generation over Mulitple-Tables. Additionally, to tackle the suboptimality of existing automatic evaluation methods in the table domain, we further introduce a fine-grained evaluation framework MT-RAIG Eval, which achieves better alignment with human quality judgments on the generated insights. We conduct extensive experiments and reveal that even frontier LLMs still struggle with complex multi-table reasoning, establishing our MT-RAIG Bench as a challenging testbed for future research.

Recent approaches in domain-specific named entity recognition (NER), such as biomedical NER, have shown remarkable advances.However, they still lack of faithfulness, producing erroneous predictions.We assume that knowledge of entities can be useful in verifying the correctness of the predictions.Despite the usefulness of knowledge, resolving such errors with knowledge is nontrivial, since the knowledge itself does not directly indicate the groundtruth label.To this end, we propose VER-IFINER, a post-hoc verification framework that identifies errors from existing NER methods using knowledge and revises them into more faithful predictions.Our framework leverages the reasoning abilities of large language models to adequately ground on knowledge and the contextual information in the verification process.We validate effectiveness of VERIFINER through extensive experiments on biomedical datasets.The results suggest that VERIFINER can successfully verify errors from existing models as a model-agnostic approach.Further analyses on out-of-domain and low-resource settings show the usefulness of VERIFINER on real-world applications.1