16 citations · 26 across the 12 of their papers we have counts for
12 papers
MMCL-Bench: Multimodal Context Learning from Visual Rules, Procedures, and Evidence
Yifan Chen, Fei Yin, Qingyan Bai +2
We introduce MMCL-Bench, a benchmark for multimodal context learning: learning task-local rules, procedures, and empirical patterns from visual or mixed-modality teaching context a…
Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO
Yiran Xu, Yiming Ren, Zicheng Lin +8
We identify a new dimension for enhancing rollout diversity in Group Relative Policy Optimization (GRPO) for LLMs. While GRPO relies on diverse rollouts, prevailing strategies prim…
Seg-ReSearch: Segmentation with Interleaved Reasoning and External Search
Tianming Liang, Qirui Du, Jian-Fang Hu +3
Segmentation based on language has been a popular topic in computer vision. While recent advances in multimodal large language models (MLLMs) have endowed segmentation systems with…
Qwen3-VL Technical Report
Shuai Bai, Yuxuan Cai, Ruizhe Chen +61
We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively…
MCFormer: A Multi-Cost-Volume Network and Comprehensive Benchmark for Particle Image Velocimetry
Zicheng Lin, Xiaoqiang Li, Yichao Wang +1
Particle Image Velocimetry (PIV) is fundamental to fluid dynamics, yet deep learning applications face significant hurdles. A critical gap exists: the lack of comprehensive evaluat…
AnyCap Project: A Unified Framework, Dataset, and Benchmark for Controllable Omni-modal Captioning
Yiming Ren, Zhiqiang Lin, Yu Li +8
Controllable captioning is essential for precise multimodal alignment and instruction following, yet existing models often lack fine-grained control and reliable evaluation protoco…
Unlocking Multimodal Mathematical Reasoning via Process Reward Model
Ruilin Luo, Zhuofan Zheng, Yifan Wang +9
Process Reward Models (PRMs) have shown promise in enhancing the mathematical reasoning capabilities of Large Language Models (LLMs) through Test-Time Scaling (TTS). However, their…
Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability
Zicheng Lin, Tian Liang, Jiahao Xu +7
Mathematical reasoning tasks pose significant challenges for large language models (LLMs) because they require precise logical deduction and sequence analysis. In this work, we int…
PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL
Ruilin Luo, Liyuan Wang, Binghuai Lin +2
Large Language Models (LLMs) have emerged as powerful tools for Text-to-SQL tasks, exhibiting remarkable reasoning capabilities. Different from tasks such as math word problems and…
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant
Zhengchao Huang, Bin Xia, Zicheng Lin +3
The rapid advancement of deepfake technologies has sparked widespread public concern, particularly as face forgery poses a serious threat to public information security. However, t…
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion
Ruilin Luo, Tianle Gu, Haoling Li +4
Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps by leveraging established temporal structural know…
CriticBench: Benchmarking LLMs for Critique-Correct Reasoning
Zicheng Lin, Zhibin Gou, Tian Liang +3
The ability of Large Language Models (LLMs) to critique and refine their reasoning is crucial for their application in evaluation, feedback provision, and self-improvement. This pa…