1 citations · 1 across the 4 of their papers we have counts for
6 papers
ChemExplain: A Multimodal Benchmark for Explanatory Chemical Reasoning
Aotian Chen, Frazier N. Baker, Reza Averly +5
Current chemistry benchmarks primarily evaluate outcome prediction or reaction pathway generation, but rarely test whether a model can explain why an observed chemical effect occur…
Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams
Weihao Bo, Shan Zhang, Yanpeng Sun +7
Multimodal Large Language Models (MLLMs) have been growing the capability for scientific writing and collaboration. For example, OpenAI Prism is a free workspace for scientific wri…
Hierarchical Process Reward Models are Symbolic Vision Learners
Shan Zhang, Aotian Chen, Kai Zou +3
Symbolic computer vision represents diagrams through explicit logical rules and structured representations, enabling interpretable understanding in machine vision. This requires fu…
Math Blind: Failures in Diagram Understanding Undermine Reasoning in MLLMs
Yanpeng Sun, Shan Zhang, Wei Tang +5
Diagrams represent a form of visual language that encodes abstract concepts and relationships through structured symbols and their spatial arrangements. Unlike natural images, they…
Open Eyes, Then Reason: Fine-grained Visual Mathematical Understanding in MLLMs
Shan Zhang, Aotian Chen, Yanpeng Sun +6
Current multimodal large language models (MLLMs) often underperform on mathematical problem-solving tasks that require fine-grained visual understanding. The limitation is largely…
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
Yao Ni, Shan Zhang, Piotr Koniusz
Parameter-Efficient Fine-Tuning (PEFT) effectively adapts pre-trained transformers to downstream tasks. However, the optimization of tasks performance often comes at the cost of ge…