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20242026
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cs.CL2026

Beyond Token-Level Policy Gradients for Complex Reasoning with Large Language Models

Mufan Xu, Kehai Chen, Xuefeng Bai +4

Existing policy-gradient methods for auto-regressive language models typically select subsequent tokens one at a time as actions in the policy. While effective for many generation…

cs.CL2026

Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition

Jinlong Ma, Yu Zhang, Xuefeng Bai +5

Grounded Multimodal Named Entity Recognition (GMNER) aims to extract text-based entities, assign them semantic categories, and ground them to corresponding visual regions. In this…

cs.CL2026

Character-R1: Enhancing Role-Aware Reasoning in Role-Playing Agents via RLVR

Yihong Tang, Kehai Chen, Xuefeng Bai +4

Current role-playing agents (RPAs) are typically constructed by imitating surface-level behaviors, but this approach lacks internal cognitive consistency, often causing out-of-char…

cs.CL2025

Evaluating and Improving Cultural Awareness of Reward Models for LLM Alignment

Hongbin Zhang, Kehai Chen, Xuefeng Bai +2

Reward models (RMs) are crucial for aligning large language models (LLMs) with diverse cultures. Consequently, evaluating their cultural awareness is essential for further advancin…

cs.CL2025

HITSZ's End-To-End Speech Translation Systems Combining Sequence-to-Sequence Auto Speech Recognition Model and Indic Large Language Model for IWSLT 2025 in Indic Track

Xuchen Wei, Yangxin Wu, Yaoyin Zhang +4

This paper presents HITSZ's submission for the IWSLT 2025 Indic track, focusing on speech-to-text translation (ST) for English-to-Indic and Indic-to-English language pairs. To enha…

cs.CL2025

Evaluating and Steering Modality Preferences in Multimodal Large Language Model

Yu Zhang, Jinlong Ma, Yongshuai Hou +5

Multi-modal large language models (MLLMs) have achieved remarkable success on complex multi-modal tasks. However, it remains insufficiently explored whether they exhibit $\textbf{m…