6 papers
EvoPrune: Early-Stage Visual Token Pruning for Efficient MLLMs
Yuhao Chen, Bin Shan, Xin Ye +1
Multimodal Large Language Models (MLLMs) have shown strong performance in vision-language tasks, but their inference efficiency is severely limited by the exponential growth of vis…
MEML-GRPO: Heterogeneous Multi-Expert Mutual Learning for RLVR Advancement
Weitao Jia, Jinghui Lu, Haiyang Yu +17
Recent advances demonstrate that reinforcement learning with verifiable rewards (RLVR) significantly enhances the reasoning capabilities of large language models (LLMs). However, s…
Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning
Jinghui Lu, Haiyang Yu, Siliang Xu +9
Recent advancements in reasoning have significantly enhanced the capabilities of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) across diverse tasks. How…
OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning
Ling Fu, Zhebin Kuang, Jiajun Song +21
Scoring the Optical Character Recognition (OCR) capabilities of Large Multimodal Models (LMMs) has witnessed growing interest. Existing benchmarks have highlighted the impressive p…
MCTBench: Multimodal Cognition towards Text-Rich Visual Scenes Benchmark
Bin Shan, Xiang Fei, Wei Shi +6
The comprehension of text-rich visual scenes has become a focal point for evaluating Multi-modal Large Language Models (MLLMs) due to their widespread applications. Current benchma…
ParGo: Bridging Vision-Language with Partial and Global Views
An-Lan Wang, Bin Shan, Wei Shi +7
This work presents ParGo, a novel Partial-Global projector designed to connect the vision and language modalities for Multimodal Large Language Models (MLLMs). Unlike previous work…