2 citations · 3 across the 12 of their papers we have counts for
13 papers
HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation
Mingyu Lu, Timothy Ossowski, Tristan Naumann +2
Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as text-to-SQL and text-to-Python…
HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents
Qianchu Liu, Sheng Zhang, Guanghui Qin +18
As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous evaluation is essential for measuring progress toward real-world healthcare applications. Howe…
CodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents
Timothy Ossowski, Xinchi Liu, Danyal Maqbool +6
Clinical reasoning agents based on large language models (LLMs) aim to automate tasks such as intensive care unit (ICU) monitoring and patient state tracking from electronic health…
RiskCueBench: Benchmarking Anticipatory Reasoning from Early Risk Cues in Video-Language Models
Sha Luo, Yogesh Prabhu, Timothy Ossowski +2
With the rapid growth of video centered social media, the ability to anticipate risky events from visual data is a promising direction for ensuring public safety and preventing rea…
Scaling medical imaging report generation with multimodal reinforcement learning
Qianchu Liu, Sheng Zhang, Guanghui Qin +10
Frontier models have demonstrated remarkable capabilities in understanding and reasoning with natural-language text, but they still exhibit major competency gaps in multimodal unde…
OctoMed: Data Recipes for State-of-the-Art Multimodal Medical Reasoning
Timothy Ossowski, Sheng Zhang, Qianchu Liu +5
High-quality and carefully curated data is a cornerstone of training medical large language models, as it directly impacts both generalization and robustness to unseen clinical tas…
X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains
Qianchu Liu, Sheng Zhang, Guanghui Qin +9
Recent proprietary models (e.g., o3) have begun to demonstrate strong multimodal reasoning capabilities. Yet, most existing open-source research concentrates on training text-only…
COMMA: A Communicative Multimodal Multi-Agent Benchmark
Timothy Ossowski, Danyal Maqbool, Jixuan Chen +3
The rapid advances of multimodal agents built on large foundation models have largely overlooked their potential for language-based communication between agents in collaborative ta…
OLIVE: Object Level In-Context Visual Embeddings
Timothy Ossowski, Junjie Hu
Recent generalist vision-language models (VLMs) have demonstrated impressive reasoning capabilities across diverse multimodal tasks. However, these models still struggle with fine-…
How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function Classes
Harmon Bhasin, Timothy Ossowski, Yiqiao Zhong +1
Large language models (LLM) have recently shown the extraordinary ability to perform unseen tasks based on few-shot examples provided as text, also known as in-context learning (IC…
Prompting Large Vision-Language Models for Compositional Reasoning
Timothy Ossowski, Ming Jiang, Junjie Hu
Vision-language models such as CLIP have shown impressive capabilities in encoding texts and images into aligned embeddings, enabling the retrieval of multimodal data in a shared e…
Multimodal Prompt Retrieval for Generative Visual Question Answering
Timothy Ossowski, Junjie Hu
Recent years have witnessed impressive results of pre-trained vision-language models on knowledge-intensive tasks such as visual question answering (VQA). Despite the recent advanc…
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment
Tuan Dinh, Jy-yong Sohn, Shashank Rajput +5
Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods. Recent findings have shown that the accuracy and robustness of unsupe…