4 papers
Requesting Expert Reasoning: Augmenting LLM Agents with Learned Collaborative Intervention
Zhiming Wang, Jinwei He, Feng Lu
Large Language Model (LLM) based agents excel at general reasoning but often fail in specialized domains where success hinges on long-tail knowledge absent from their training data…
Learning with Challenges: Adaptive Difficulty-Aware Data Generation for Mobile GUI Agent Training
Linjia Kang, Zhimin Wang, Yongkang Zhang +5
Large-scale, high-quality interaction trajectories are essential for advancing mobile Graphical User Interface (GUI) agents. While existing methods typically rely on labor-intensiv…
Collaborative Belief Reasoning with LLMs for Efficient Multi-Agent Collaboration
Zhimin Wang, Duo Wu, Shaokang He +6
Effective real-world multi-agent collaboration requires not only accurate planning but also the ability to reason about collaborators' intents--a crucial capability for avoiding mi…
Grounding Large Language Models as Generalizable Policies in Network Control
Duo Wu, Linjia Kang, Zhimin Wang +9
Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure. Yet network cont…