2 citations · 3 across the 9 of their papers we have counts for
9 papers
From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG
Guanhua Chen, Chuyue Huang, Yutong Yao +4
Multimodal Retrieval-Augmented Generation (RAG) systems retrieve evidence at coarse granularities (entire images or scenes), creating a mismatch with fine-grained user queries and…
Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA
Guanhua Chen, Yutong Yao, Shenghe Sun +5
Recent advances in vision-language models (VLMs) have achieved impressive results on standard image-text tasks, yet their potential for visual procedure question answering (VP-QA)…
EmoS: A High-Fidelity Multimodal Benchmark for Fine-grained Streaming Emotional Understanding
Pengze Guo, Jingxi Liang, Zhiwen Xie +2
In the context of today's high-pressure, aging society, the demand for large-scale emotional models capable of providing empathetic support is more critical than ever. However, exi…
Agri-R1: Agricultural Reasoning for Disease Diagnosis via Automated-Synthesis and Reinforcement Learning
Wentao Zhang, Mingkun Xu, Qi Zhang +6
Agricultural disease diagnosis challenges VLMs, as conventional fine-tuning requires extensive labels, lacks interpretability, and generalizes poorly. While reasoning improves mode…
CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward
Shudong Liu, Hongwei Liu, Junnan Liu +8
Answer verification is crucial not only for evaluating large language models (LLMs) by matching their unstructured outputs against standard answers, but also serves as the reward m…
Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models
Rui Wang, Hongru Wang, Boyang Xue +9
Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to perform complex reasoning tasks, transitioning from fast and intuitive thinking (Sy…
CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries
Shudong Liu, Yiqiao Jin, Cheng Li +6
Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding, often misinterpreting symbols, gestures, and artifacts due to biases in p…
Learning from "Silly" Questions Improves Large Language Models, But Only Slightly
Tingyuan Zhu, Shudong Liu, Yidong Wang +4
Constructing high-quality Supervised Fine-Tuning (SFT) datasets is critical for the training of large language models (LLMs). Recent studies have shown that using data from a speci…
Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist
Zihao Zhou, Shudong Liu, Maizhen Ning +6
Exceptional mathematical reasoning ability is one of the key features that demonstrate the power of large language models (LLMs). How to comprehensively define and evaluate the mat…