252 citations · 554 across the 32 of their papers we have counts for
32 papers
Decoupled Physical Modeling and Execution for Physics Reasoning
Ye Zhang, Xuehang Guo, Rui Pan +4
Physics reasoning requires constructing a consistent model of the underlying physical system rather than relying solely on symbolic or formula-based manipulation. Although large la…
Consistency-Driven Co-Evolution for Self-Supervised Cross-Representation Learning
Xuehang Guo, Pengyuan Li, Tom Hope +3
As chart images, tabular data, and visualization code play increasingly important roles across diverse domains, cross-representation understanding across these modalities poses fun…
CURV: Enhancing Chart Understanding Through Curriculum Visual Grounded Reasoning
Xuehang Guo, Pingyue Zhang, Ruiyi Zhang +6
Chart question answering (CQA) requires multimodal large language models (MLLMs) to integrate visual comprehension with logical reasoning, yet current models struggle with accurate…
Enhancing Software Engineering Through Closed-Loop Memory Optimization
Xuehang Guo, Zora Zhiruo Wang, Qingyun Wang +2
Large language models (LLMs) have enabled powerful software engineering (SE) agents capable of navigating complex codebases and resolving real-world issues. However, these agents r…
When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning
Jiaqi Wei, Xuehang Guo, Pengfei Yu +5
In single-stream autoregressive interfaces, the same tokens both update the model state and constitute an irreversible public commitment. This coupling creates a silence tax: addit…
Augmenting Interface Usability Heuristics for Reliable Computer-Use Agents
Jiateng Liu, Rushi Wang, Bingxuan Li +6
Recent advances have enabled general computer-use agents that interpret screens and execute grounded actions from human instructions, yet they still struggle to generalize to unsee…
FinRule-Bench: A Benchmark for Joint Reasoning over Financial Tables and Principles
Arun Vignesh Malarkkan, Manan Roy Choudhury, Guangwei Zhang +4
Large language models (LLMs) are increasingly applied to financial analysis, yet their ability to audit structured financial statements under explicit accounting principles remains…
Anagent For Enhancing Scientific Table & Figure Analysis
Xuehang Guo, Zhiyong Lu, Tom Hope +1
In scientific research, analysis requires accurately interpreting complex multimodal knowledge, integrating evidence from different sources, and drawing inferences grounded in doma…
CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation
Yee Man Choi, Xuehang Guo, Yi R. Fung +1
Large Language Models (LLMs) have emerged as powerful assistants for scientific writing. However, concerns remain about the quality and reliability of the generated text, including…
Atomic Reasoning for Scientific Table Claim Verification
Yuji Zhang, Qingyun Wang, Cheng Qian +7
Scientific texts often convey authority due to their technical language and complex data. However, this complexity can sometimes lead to the spread of misinformation. Non-experts a…
MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing
Minghao Liu, Zhitao He, Zhiyuan Fan +2
Text-guided image editing has seen significant progress in natural image domains, but its application in medical imaging remains limited and lacks standardized evaluation framework…
Explain Less, Understand More: Data-Efficient Personalization of Reader-Dependent Jargons
Bohao Wu, Qingyun Wang, Yue Guo
Personalizing jargon detection and explanation is essential for making technical documents accessible to readers with diverse disciplinary backgrounds. However, tailoring models to…
MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness
Junsheng Huang, Zhitao He, Yucheng Huang +3
The hallucination of non-existent facts by LLMs is an important problem given its widespread adoption across various applications. Previous research addresses this problem by analy…
CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering
Yumeng Wang, Zhiyuan Fan, Qingyun Wang +2
Large Language Models (LLMs) are pretrained on extensive multilingual corpora to acquire both language-specific cultural knowledge and general knowledge. Ideally, while LLMs should…
Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production
Kexuan Xin, Qingyun Wang, Junyu Chen +3
In the rapidly evolving field of metabolic engineering, the quest for efficient and precise gene target identification for metabolite production enhancement presents significant ch…
Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play
Sha Li, Revanth Gangi Reddy, Khanh Duy Nguyen +7
Complex news events, such as natural disasters and socio-political conflicts, require swift responses from the government and society. Relying on historical events to project the f…
MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders
Cheng Li, May Fung, Qingyun Wang +4
Mental health disorders are one of the most serious diseases in the world. Most people with such a disease lack access to adequate care, which highlights the importance of training…
Self-Correction is More than Refinement: A Learning Framework for Visual and Language Reasoning Tasks
Jiayi He, Hehai Lin, Qingyun Wang +2
While Vision-Language Models (VLMs) have shown remarkable abilities in visual and language reasoning tasks, they invariably generate flawed responses. Self-correction that instruct…
Automating Intervention Discovery from Scientific Literature: A Progressive Ontology Prompting and Dual-LLM Framework
Yuting Hu, Dancheng Liu, Qingyun Wang +5
Identifying effective interventions from the scientific literature is challenging due to the high volume of publications, specialized terminology, and inconsistent reporting format…
MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents
Ruochen Li, Teerth Patel, Qingyun Wang +1
Autonomous machine learning research has gained significant attention recently. We present MLR-COPILOT, an autonomous Machine Learning Research framework powered by large language…
L+M-24: Building a Dataset for Language + Molecules @ ACL 2024
Carl Edwards, Qingyun Wang, Lawrence Zhao +1
Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molec…
Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences
Hongyi Liu, Qingyun Wang, Payam Karisani +1
Named entity recognition is a key component of Information Extraction (IE), particularly in scientific domains such as biomedicine and chemistry, where large language models (LLMs)…
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction
Qingyun Wang, Zixuan Zhang, Hongxiang Li +4
Fine-grained few-shot entity extraction in the chemical domain faces two unique challenges. First, compared with entity extraction tasks in the general domain, sentences from chemi…
SciMON: Scientific Inspiration Machines Optimized for Novelty
Qingyun Wang, Doug Downey, Heng Ji +1
We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. Work on literature-based hypothesis generation has trad…