2 citations · 2 across the 8 of their papers we have counts for
10 papers
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…
Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning
Yunghwei Lai, Kaiming Liu, Ziyue Wang +2
The professionalism of a human doctor in outpatient service depends on two core abilities: the ability to make accurate medical decisions and the medical consultation skill to cond…
Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning
Xuanyu Lei, Chenliang Li, Yuning Wu +7
Recent advances in Large Language Models(LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited: Supervised Fine-Tuning (SFT) rem…
Agent-Environment Alignment via Automated Interface Generation
Kaiming Liu, Xuanyu Lei, Ziyue Wang +2
Large language model (LLM) agents have shown impressive reasoning capabilities in interactive decision-making tasks. These agents interact with environment through intermediate int…
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation
Weitao Li, Kaiming Liu, Xiangyu Zhang +3
Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for knowledge injection during large language model (LLM) inference in recent years. However, due to t…
AIGS: Generating Science from AI-Powered Automated Falsification
Zijun Liu, Kaiming Liu, Yiqi Zhu +5
Rapid development of artificial intelligence has drastically accelerated the development of scientific discovery. Trained with large-scale observation data, deep neural networks ex…
Towards Unified Alignment Between Agents, Humans, and Environment
Zonghan Yang, An Liu, Zijun Liu +11
The rapid progress of foundation models has led to the prosperity of autonomous agents, which leverage the universal capabilities of foundation models to conduct reasoning, decisio…