5 papers
Making medical vision-language models think causally across modalities with retrieval-augmented cross-modal reasoning
Weiqin Yang, Haowen Xue, Qingyi Peng +3
Medical vision-language models (VLMs) achieve strong performance in diagnostic reporting and image-text alignment, yet their underlying reasoning mechanisms remain fundamentally co…
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…
BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
Yusuf Roohani, Andrew Lee, Qian Huang +6
Agents based on large language models have shown great potential in accelerating scientific discovery by leveraging their rich background knowledge and reasoning capabilities. In t…
AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning
Shirley Wu, Shiyu Zhao, Qian Huang +7
Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and reduce hallucinations. However, developi…
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
Shirley Wu, Shiyu Zhao, Michihiro Yasunaga +7
Answering real-world complex queries, such as complex product search, often requires accurate retrieval from semi-structured knowledge bases that involve blend of unstructured (e.g…