32 citations · 42 across the 9 of their papers we have counts for
9 papers
Auditable agentic AI for evidence-grounded thyroid ultrasound diagnosis and reporting
Haifan Gong, Shiyu Chen, Bodong Wang +11
Thyroid ultrasound diagnosis requires coordinated lesion localization, measurement, risk stratification and reporting, yet most AI systems address these tasks in isolation and prov…
Adam's Law: Textual Frequency Law on Large Language Models
Hongyuan Adam Lu, Z. L., Victor Wei +5
While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel re…
Assay2Mol: large language model-based drug design using BioAssay context
Yifan Deng, Spencer S. Ericksen, Anthony Gitter
Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, molecule screening assays evaluate candidate molecules' functional res…
Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems
Haowei Wang, Rupeng Zhang, Junjie Wang +4
Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses. This approach…
FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging
Zichen Tang, Haihong E, Ziyan Ma +10
We introduce FinanceReasoning, a novel benchmark designed to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. Compare…
AdInject: Real-World Black-Box Attacks on Web Agents via Advertising Delivery
Haowei Wang, Junjie Wang, Xiaojun Jia +5
Vision-Language Model (VLM) based Web Agents represent a significant step towards automating complex tasks by simulating human-like interaction with websites. However, their deploy…
RouteNator: A Router-Based Multi-Modal Architecture for Generating Synthetic Training Data for Function Calling LLMs
Vibha Belavadi, Tushar Vatsa, Dewang Sultania +5
This paper addresses fine-tuning Large Language Models (LLMs) for function calling tasks when real user interaction data is unavailable. In digital content creation tools, where us…
From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection
Haowei Wang, Rupeng Zhang, Junjie Wang +4
Tool-calling has changed Large Language Model (LLM) applications by integrating external tools, significantly enhancing their functionality across diverse tasks. However, this inte…
A Hybrid Framework with Large Language Models for Rare Disease Phenotyping
Jinge Wu, Hang Dong, Zexi Li +7
Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valua…