3 citations · 4 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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 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…