3 citations · 4 across the 11 of their papers we have counts for
19 papers
Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows
Shirley Anugrah Hayati, Patricia Anugrah Setiani, Ruizi Wang +4
When humans collaborate with AI agents on long-horizon tasks, what happens to the agents' contributions? For solving long-horizon tasks, humans place greater importance on ongoing…
Into the Unknown: Benchmarking AI agents on open scientific research problems
Seungone Kim, Jiayi Geng, Young-Jun Lee +25
Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper along with a research direction…
SanSi: A Looped Typed Decision Model for System 1.5 Thinking
Shuyu Gan, Young-Jun Lee, Dongyeop Kang
Typed decision models answer a declared question without generating text: a decision head returns a probability for each of the declared options in a single forward pass. A single…
EvoDuet: Bilevel Co-Evolution of Web Searching and Task Solving for Scientific Discovery
Young-Jun Lee, Jinheon Baek, Soyeong Jeong +5
Evolutionary search with large language models (LLMs) can stall when progress requires external knowledge the model lacks. Supplying relevant documents helps, but simply adding web…
Foundations of Proactive Agents: Principles, Technical Layers, and Proactivity-Gym
Jio Oh, Seunghyun Do, Young-Jun Lee +2
Proactive LLM agents can turn idle compute into useful support before users ask. Yet even correct work can misread user context, impose review costs, or undermine trust. This work…
Orchestrating GenAI for Interdisciplinary Research
Shirley Anugrah Hayati, Moyan Zhou, Patricia Anugrah Setiani +3
As researchers tackle interdisciplinary problems, they face the need to deepen expertise in primary areas while rapidly acquiring knowledge in secondary domains. Generative AI (Gen…
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…
Meta: Recursive Self-Improvement through Emergent Depth
Zae Myung Kim, Young-Jun Lee, Seungyeon Jwa +1
Self-improving LLM agents refine answers, not the process that produces those answers. Systems that add a meta-level hold that level fixed, and those that edit themselves must leav…
Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks
Young-Jun Lee, Seungone Kim, Minki Kang +5
Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture? Large Language Models (LLMs) integrated into evolutionary search h…
Effects of Varying LLM Access on Essay Writing Behavior
Julia Christenson, Karin de Langis, Shirley Anugrah Hayati +1
Investigating the degree to which large language models (LLMs) affect teaching and learning in universities can help identify strategies for integrating LLMs in a way that supports…
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…
Do LLMs Recognize Your Latent Preferences? A Benchmark for Latent Information Discovery in Personalized Interaction
Ioannis Tsaknakis, Bingqing Song, Shuyu Gan +5
Large Language Models (LLMs) excel at producing broadly relevant text, but this generality becomes a limitation when user-specific preferences are required, such as recommending re…
When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs
Soyeong Jeong, Taehee Jung, Sung Ju Hwang +2
Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning b…
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…
Learning Explainable Dense Reward Shapes via Bayesian Optimization
Ryan Koo, Ian Yang, Vipul Raheja +3
Current reinforcement learning from human feedback (RLHF) pipelines for large language model (LLM) alignment typically assign scalar rewards to sequences, using the final token as…
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
Quan Wei, Chung-Yiu Yau, Hoi-To Wai +4
Supervised fine-tuning is a standard method for adapting pre-trained large language models (LLMs) to downstream tasks. Quantization has been recently studied as a post-training tec…
LearnerVoice: A Dataset of Non-Native English Learners' Spontaneous Speech
Haechan Kim, Junho Myung, Seoyoung Kim +3
Prevalent ungrammatical expressions and disfluencies in spontaneous speech from second language (L2) learners pose unique challenges to Automatic Speech Recognition (ASR) systems.…
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment
Chenliang Li, Siliang Zeng, Zeyi Liao +4
Aligning human preference and value is an important requirement for building contemporary foundation models and embodied AI. However, popular approaches such as reinforcement learn…
Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in Automatic Related Work Composition
Anna Martin-Boyle, Aahan Tyagi, Marti A. Hearst +1
Numerous AI-assisted scholarly applications have been developed to aid different stages of the research process. We present an analysis of AI-assisted scholarly writing generated w…