collaborators

10 papers

cs.CL2026

Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy

Gengxian Zhou, Jian Xu, Zichen Tang +3

Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterogeneous knowledge bases (larg…

cs.AI2026

Can LLMs Follow Medical Expert Logic? A Benchmark for Hierarchical Logical Consistency in Risk-of-Bias Assessment

Jiayu Huang, Zichen Tang, Qianhui Ling +2

Evidence-based medicine demands strict logical consistency, yet current evaluations of large language models (LLMs) prioritize superficial label matching over genuine reasoning. We…

cs.CV2026

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…

cs.CV2026

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images

Bo Zhang, Tzu-Yen Ma, Zichen Tang +18

We introduce AEGIS, A holistic benchmark for Evaluating forensic analysis of AI-Generated academic ImageS. Compared to existing benchmarks, AEGIS features three key advances: (1) D…

cs.CL2026

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…

cs.CV2026

Ride the Wave: Precision-Allocated Sparse Attention for Smooth Video Generation

Wentai Zhang, Ronghui Xi, Shiyao Peng +4

Video Diffusion Transformers have revolutionized high-fidelity video generation but suffer from the massive computational burden of self-attention. While sparse attention provides…

cs.AI2026

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…

cs.CV2026

THEMIS: Towards Holistic Evaluation of MLLMs for Scientific Paper Fraud Forensics

Tzu-Yen Ma, Bo Zhang, Zichen Tang +20

We present THEMIS, a novel multi-task benchmark designed to comprehensively evaluate multimodal large language models (MLLMs) on visual fraud reasoning within real-world academic s…

cs.CV2025

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…

cs.CV2025

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…