collaborators

10 papers

cs.CL2026

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

Wei Jia, Zhicong Lu, Yu Chen +6

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.AI2026

HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning

Juncheng Diao, Zhicong Lu, Peiguang Li +6

While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degrades in multi-turn long-hori…

cs.LG2026

Cache: Accelerating World Action Models with Cross Inference Chunk Cache

Weisen Zhao, Lam Nguyen, Zhicong Lu +1

World Action Models (WAMs) generalize better than standard Vision-Language-Action (VLA) policies to novel motions and environments, because a video-modeling objective lets them lea…

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…