5 citations · 5 across the 9 of their papers we have counts for
9 papers
Not Just Reason, Not Just Scan: Reinforcement Learning for Proactive Scientific Error Verification over Academic Paper
Rongjin Li, Yuanxin Liu, Hao Zhou +3
Multimodal large language models (MLLMs) are increasingly capable scientific assistants, yet they remain far from fully autonomous research. This transition requires models to acti…
Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning
Junpeng Ding, Zichen Tang, Haihong E +17
We introduce SPUR, a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from…
NeocorRAG: Less Irrelevant Information, More Explicit Evidence, and More Effective Recall via Evidence Chains
Shiyao Peng, Qianhe Zheng, Zhuodi Hao +7
Although precise recall is a core objective in Retrieval-Augmented Generation (RAG), a critical oversight persists in the field: improvements in retrieval performance do not consis…
RoadMapper: A Multi-Agent System for Roadmap Generation of Solving Complex Research Problems
Jiacheng Liu, Zichen Tang, Zhongjun Yang +8
People commonly leverage structured content to accelerate knowledge acquisition and research problem solving. Among these, roadmaps guide researchers through hierarchical subtasks…
Not Search, But Scan: Benchmarking MLLMs on Scan-Oriented Academic Paper Reasoning
Rongjin Li, Zichen Tang, Xianghe Wang +9
With the rapid progress of multimodal large language models (MLLMs), AI already performs well at literature retrieval and certain reasoning tasks, serving as a capable assistant to…
Generation-Augmented Generation: A Plug-and-Play Framework for Private Knowledge Injection in Large Language Models
Rongji Li, Jian Xu, Yi Chen +7
In domains such as materials science, biomedicine, and finance, high-stakes deployment of large language models (LLMs) requires injecting private, domain-specific knowledge that is…
FinMMDocR: Benchmarking Financial Multimodal Reasoning with Scenario Awareness, Document Understanding, and Multi-Step Computation
Zichen Tang, Haihong E, Rongjin Li +18
We introduce FinMMDocR, a novel bilingual multimodal benchmark for evaluating multimodal large language models (MLLMs) on real-world financial numerical reasoning. Compared to exis…
FinMMR: Make Financial Numerical Reasoning More Multimodal, Comprehensive, and Challenging
Zichen Tang, Haihong E, Jiacheng Liu +18
We present FinMMR, a novel bilingual multimodal benchmark tailored to evaluate the reasoning capabilities of multimodal large language models (MLLMs) in financial numerical reasoni…
FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging
Zichen Tang, Haihong E, Ziyan Ma +10
We introduce FinanceReasoning, a novel benchmark designed to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. Compare…