6 papers
SERUM: State Extraction and Refinement for User Modeling
Andy J. Phu, Karin de Langis, James Mooney +2
Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow. However, building these models from raw, unstructured scre…
Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output
James Mooney, Zae Myung Kim, Young-Jun Lee +1
Scientific discovery is an extended process of ideation -- surveying prior work, forming hypotheses, and refining reasoning -- yet existing LLM research agents treat this phase as…
Reasoning Beyond Literal: Cross-style Multimodal Reasoning for Figurative Language Understanding
Seyyed Saeid Cheshmi, Hahnemann Ortiz, James Mooney +1
Vision-language models (VLMs) have demonstrated strong reasoning abilities in literal multimodal tasks such as visual mathematics and science question answering. However, figurativ…
Scaling Unverifiable Rewards: A Case Study on Visual Insights
Shuyu Gan, James Mooney, Pan Hao +4
Large Language Model (LLM) agents can increasingly automate complex reasoning through Test-Time Scaling (TTS), iterative refinement guided by reward signals. However, many real-wor…
A2P-Vis: an Analyzer-to-Presenter Agentic Pipeline for Visual Insights Generation and Reporting
Shuyu Gan, Renxiang Wang, James Mooney +1
Automating end-to-end data science pipeline with AI agents still stalls on two gaps: generating insightful, diverse visual evidence and assembling it into a coherent, professional…
Are LLM Agents Behaviorally Coherent? A Latent Profiling Approach for Social Simulation
James Mooney, Josef Woldense, Zheng Robert Jia +4
LLM agents are increasingly proposed as substitutes for human participants in social research, usually on the strength of survey responses that match those of the humans they repre…