4 citations · 8 across the 7 of their papers we have counts for
8 papers
InfoEdit: Probing Global Layout Reasoning in Infographic Editing
Cheng Yang, Chufan Shi, Huijuan Wang +6
Multimodal foundation models edit natural photographs at production quality, yet the same models struggle with structured visual content such as infographics. Unlike photographs, i…
Are VLMs Seeing or Just Saying? Uncovering the Illusion of Visual Re-examination
Chufan Shi, Cheng Yang, Yaokang Wu +4
Vision-Language Models (VLMs) often produce self-reflective statements like "let me check the figure again" during reasoning. Do such statements trigger genuine visual re-examinati…
Knowledge Synthesis Graph: An LLM-Based Approach for Modeling Student Collaborative Discourse
Bo Shui, Xinran Zhu
Asynchronous, text-based discourse-such as students' posts in discussion forums-is widely used to support collaborative learning. However, the distributed and evolving nature of su…
UReason: Benchmarking Reasoning-to-Generation Alignment in Unified Multimodal Models
Cheng Yang, Chufan Shi, Bo Shui +7
Unified multimodal models (UMMs) aim to integrate multimodal understanding and generation within a unified architecture, yet it remains unclear to what extent textual and visual mo…
LLM2: Let Large Language Models Harness System 2 Reasoning
Cheng Yang, Chufan Shi, Siheng Li +3
Large language models (LLMs) have exhibited impressive capabilities across a myriad of tasks, yet they occasionally yield undesirable outputs. We posit that these limitations are r…
"Ghost of the past": identifying and resolving privacy leakage from LLM's memory through proactive user interaction
Shuning Zhang, Lyumanshan Ye, Xin Yi +5
Memories, encompassing past inputs in context window and retrieval-augmented generation (RAG), frequently surface during human-LLM interactions, yet users are often unaware of thei…
ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation
Cheng Yang, Chufan Shi, Yaxin Liu +11
We introduce a new benchmark, ChartMimic, aimed at assessing the visually-grounded code generation capabilities of large multimodal models (LMMs). ChartMimic utilizes information-i…
ContextVis: Envision Contextual Learning and Interaction with Generative Models
Bo Shui, Chufan Shi, Yujiu Yang +1
ContextVis introduces a workflow by integrating generative models to create contextual learning materials. It aims to boost knowledge acquisition through the creation of resources…