41 citations · 60 across the 25 of their papers we have counts for
10 papers · 1 filter
DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning
Nithin Sivakumaran, Justin Chih-Yao Chen, David Wan +4
Specialized visual tools can augment large language models or vision language models with expert knowledge (e.g., grounding, spatial reasoning, medical knowledge, etc.), but knowin…
PRInTS: Reward Modeling for Long-Horizon Information Seeking
Jaewoo Lee, Archiki Prasad, Justin Chih-Yao Chen +3
Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories. However, such multi-step infor…
Think Right: Learning to Mitigate Under-Over Thinking via Adaptive, Attentive Compression
Joykirat Singh, Justin Chih-Yao Chen, Archiki Prasad +3
Recent thinking models are capable of solving complex reasoning tasks by scaling test-time compute, but this scaling should be allocated in line with task difficulty. On one hand,…
Nudging the Boundaries of LLM Reasoning
Justin Chih-Yao Chen, Becky Xiangyu Peng, Prafulla Kumar Choubey +4
Current online reinforcement learning (RL) algorithms like GRPO share a key limitation in LLM reasoning: they cannot learn from problems that are "unsolvable" to the model. In othe…
MedGemma Technical Report
Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri +78
Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks,…
MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration
David Wan, Justin Chih-Yao Chen, Elias Stengel-Eskin +1
Multi-agent collaboration among models has shown promise in reasoning tasks but is underexplored in long-form generation tasks like summarization and question-answering. We extend…
Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills
Justin Chih-Yao Chen, Sukwon Yun, Elias Stengel-Eskin +2
Combining existing pre-trained LLMs is a promising approach for diverse reasoning tasks. However, task-level expert selection is often too coarse-grained, since different instances…
Private Text Generation by Seeding Large Language Model Prompts
Supriya Nagesh, Justin Y. Chen, Nina Mishra +1
We explore how private synthetic text can be generated by suitably prompting a large language model (LLM). This addresses a challenge for organizations like hospitals, which hold s…
Scalable Private Partition Selection via Adaptive Weighting
Justin Y. Chen, Vincent Cohen-Addad, Alessandro Epasto +1
In the differentially private partition selection problem (a.k.a. private set union, private key discovery), users hold subsets of items from an unbounded universe. The goal is to…
Learning to Generate Unit Tests for Automated Debugging
Archiki Prasad, Elias Stengel-Eskin, Justin Chih-Yao Chen +2
Unit tests (UTs) play an instrumental role in assessing code correctness as well as providing feedback to large language models (LLMs), motivating automated test generation. Howeve…