2 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
Instructions Are Evidence, Not Objectives: Measuring Outcome Alignment of Agents
Kaiming Liu, Yuxuan Liu, Jinrui Ju +4
Large language model agents can follow complex human instructions to solve real-world tasks. In practice, however, initial instructions are often incomplete. In our analysis of 100…
Illusion of Alignment: Detecting Hidden Disagreement in Collaborative Dialogue
Kaiming Liu, Fuwen Luo, Ziyue Wang +6
Collaborative dialogue can end with apparent agreement while participants still differ on goals, assumptions, or execution plans, creating an \textbf{illusion of alignment (IoA)}.…
State2State: Environment-Derived Mid-Training for LLM Agents
Xuanyu Lei, Yiqi Zhu, Chenliang Li +6
Training LLM agents commonly relies on supervised fine-tuning from expert trajectories or online reinforcement learning over human-specified tasks with handcrafted verifiers. Thoug…
BASIS: Evaluating Belief-Aware Solutions in Interactive Settings beyond Instructions
Minyuan Ruan, Ziyue Wang, Kaiming Liu +3
Large language model (LLM) agents are increasingly capable of carrying out complex, long-horizon tasks, yet effective assistance requires more than faithfully executing user instru…