30 citations · 176 across the 66 of their papers we have counts for
12 papers · 1 filter
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…
Mary, the Cheeseburger-Eating Vegetarian: Do LLMs Recognize Incoherence in Narratives?
Karin de Langis, Püren Öncel, Ryan Peters +4
Leveraging a dataset of paired narratives, we investigate the extent to which large language models (LLMs) can reliably separate incoherent and coherent stories. A probing study fi…
Tracing How Annotators Think: Augmenting Preference Judgments with Reading Processes
Karin de Langis, William Walker, Khanh Chi Le +1
We propose an annotation approach that captures not only labels but also the reading process underlying annotators' decisions, e.g., what parts of the text they focus on, re-read o…
FlowForge: Guiding the Creation of Multi-agent Workflows with Design Space Visualization as a Thinking Scaffold
Pan Hao, Dongyeop Kang, Nicholas Hinds +1
Multi-agent workflows have become an effective strategy for tackling complicated tasks by decomposing them into multiple sub-tasks and assigning them to specialized agents. However…
How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs
Karin de Langis, Jong Inn Park, Andreas Schramm +5
Large language models (LLMs) exhibit increasingly sophisticated linguistic capabilities, yet the extent to which these behaviors reflect human-like cognition versus advanced patter…
Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models
Zae Myung Kim, Chanwoo Park, Vipul Raheja +2
Reward-based alignment methods for large language models (LLMs) face two key limitations: vulnerability to reward hacking, where models exploit flaws in the reward signal; and reli…
Stealing Creator's Workflow: A Creator-Inspired Agentic Framework with Iterative Feedback Loop for Improved Scientific Short-form Generation
Jong Inn Park, Maanas Taneja, Qianwen Wang +1
Generating engaging, accurate short-form videos from scientific papers is challenging due to content complexity and the gap between expert authors and readers. Existing end-to-end…
LawFlow: Collecting and Simulating Lawyers' Thought Processes on Business Formation Case Studies
Debarati Das, Khanh Chi Le, Ritik Sachin Parkar +8
Legal practitioners, particularly those early in their careers, face complex, high-stakes tasks that require adaptive, context-sensitive reasoning. While AI holds promise in suppor…
Align to Structure: Aligning Large Language Models with Structural Information
Zae Myung Kim, Anand Ramachandran, Farideh Tavazoee +3
Generating long, coherent text remains a challenge for large language models (LLMs), as they lack hierarchical planning and structured organization in discourse generation. We intr…
Strong Memory, Weak Control: An Empirical Study of Executive Functioning in LLMs
Karin de Langis, Jong Inn Park, Bin Hu +5
Working memory, or the ability to hold and manipulate information in the mind, is a critical component of human intelligence and executive functioning. It is correlated with perfor…
ScholaWrite: A Dataset of End-to-End Scholarly Writing Process
Khanh Chi Le, Linghe Wang, Minhwa Lee +3
Writing is a cognitively demanding activity that requires constant decision-making, heavy reliance on working memory, and frequent shifts between tasks of different goals. To build…