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cs.CL2026

CAVE: Competence-Aware Visual Boundary Evidence Alignment for Video Temporal Grounding

Wei Jia, Zhicong Lu, Yu Chen +5

Large vision-language models (LVLMs) have achieved substantial performance gains in Video Temporal Grounding (VTG) through reinforcement learning (RL). However, existing methods pr…

cs.CL2026

Orthogonal Representation Editing: Decoupling Semantic Entanglement in Batch Knowledge Editing of LLMs

Wenhao Yu, Zhicong Lu, Bo Lv +4

Knowledge editing aims to efficiently update factual information in Large Language Models (LLMs) without full retraining. However, existing methods still suffer from performance de…

cs.CL2026

CORA: Analyzing and bridging thinking-answer gap in Multimodal RLVR via Consistency-Oriented Reasoning Alignment

Jiayue Cao, Zhicong Lu, Xuehan Sun +6

Reinforcement learning with verifiable rewards (RLVR) has successfully elicited the reasoning capabilities of large language models, motivating its extension to multimodal scenario…

cs.CL2026

Faithful-MR1: Faithful Multimodal Reasoning via Anchoring and Reinforcing Visual Attention

Changyuan Tian, Zhicong Lu, Huaxing Liu +7

Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for advancing complex reasoning in large language models, and recent work extends RLVR to…

cs.CL2025

Rectify Evaluation Preference: Improving LLMs' Critique on Math Reasoning via Perplexity-aware Reinforcement Learning

Changyuan Tian, Zhicong Lu, Shuang Qian +8

To improve Multi-step Mathematical Reasoning (MsMR) of Large Language Models (LLMs), it is crucial to obtain scalable supervision from the corpus by automatically critiquing mistak…

cs.CL2025

HyCoRA: Hyper-Contrastive Role-Adaptive Learning for Role-Playing

Shihao Yang, Zhicong Lu, Yong Yang +3

Multi-character role-playing aims to equip models with the capability to simulate diverse roles. Existing methods either use one shared parameterized module across all roles or ass…