activity
20242026
most citedAutomated Commit Message Generation with Large Language Models: An Empirical Study and Beyond

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Open-o3-Video-v2: Seek, Ground, and Reason with Verifiable Spatio-Temporal Evidence

Jiahao Meng, Zhongyuan Bao, Yue Tan +7

Reliable video reasoning requires models to produce correct answers and expose supporting visual evidence. Open-o3-Video enables verifiable reasoning by embedding timestamps and ob…

cs.CV2026

Towards One-to-Many Temporal Grounding

Qi Xu, Yue Tan, Shihao Chen +5

Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predominantly focuses on single-segment retrieval. Real-world scenarios, ho…

cs.CV2026

Watch, Remember, Reason: Human-View Video Understanding with MLLMs

Jiahao Meng, Yue Tan, Qi Xu +12

Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video…

cs.CL2026

EvoSpec: Evolving Speculative Decoding via Real-Time Vocabulary and Parameter Adaptation

Shuyu Zhang, Lingfeng Pan, Qicheng Wang +6

Speculative decoding accelerates Large Language Model inference through draft-then-verify generation, yet lightweight draft models face coupled efficiency and quality limitations:…

cs.CV2025

Open-o3-Video: Grounded Video Reasoning with Explicit Spatio-Temporal Evidence

Jiahao Meng, Xiangtai Li, Haochen Wang +8

Most video reasoning models only generate textual reasoning traces without indicating when and where key evidence appears. Recent models such as OpenAI-o3 have sparked wide interes…

cs.CV2025

CyberV: Cybernetics for Test-time Scaling in Video Understanding

Jiahao Meng, Shuyang Sun, Yue Tan +4

Current Multimodal Large Language Models (MLLMs) may struggle with understanding long or complex videos due to computational demands at test time, lack of robustness, and limited a…

cs.SE2024★ 1 cited

Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond

Pengyu Xue, Linhao Wu, Zhongxing Yu +5

Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs, which facilitate collaboration among developers and play a criti…