1 citations · 1 across the 10 of their papers we have counts for
15 papers
HTN-Orchestrate: A Hierarchical Task Network Framework for Multi-Agent Orchestration
Yanchen Deng, Jianguo Huang, Yao Long Teng +6
Orchestrating multiple agents on a complex task requires reconciling local subtask execution with global task planning. Dynamic dispatch approaches provide execution flexibility bu…
AutoCompact: Learning When to Compact Context in Long-Horizon Coding Agents
Xuan Zhang, Longtao Zheng, Cunxiao Du +2
Coding agents solve repository-level software engineering tasks through long trajectories of code inspection, search, editing, and testing. As a task progresses, earlier exploratio…
Finding the Right Fit: Model-Harness Interactions across Agent Tasks
Yixuan Li, Yiyun Zhou, Yao Long Teng +6
Choosing an agent system means choosing both a language model and the harness through which it acts. We ask whether a strong model, harness, or pairing stays strong when the settin…
JET: Judge-Guided Evolution at Test Time for Agent Programs
Yao Long Teng, Jiayi Cai, Bo An
An agent's executable program governs how it uses tools, processes observations, and responds to failures. Evolving this program at test time can help adaptation, but deciding whic…
AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories
Zhiyi Lyu, Yewen Li, Longtao Zheng +7
LLM-based agents are increasingly deployed in real-world applications through tool-use APIs, yet training them for specific environments remains fundamentally difficult: real-world…
AppDeltaWorld: Transition-Grounded Delta Code World Model for Mobile GUI Agents
Weikai Xu, Yunren Feng, Haoxiang Lei +6
Mobile GUI agents can operate apps through pixel perception and touch actions, making them a promising interface for collecting and improving long-horizon mobile interaction polici…
How Mobile World Model Guides GUI Agents?
Weikai Xu, Kun Huang, Yunren Feng +10
Recent advances in vision-language models have enabled mobile GUI agents to perceive visual interfaces and execute user instructions, but reliable prediction of action consequences…
Argus: Evidence Assembly for Scalable Deep Research Agents
Zhen Zhang, Liangcai Su, Zhuo Chen +7
Deep research agents have achieved remarkable progress on complex information seeking tasks. Even long ReAct style rollouts explore only a single trajectory, while recent state of…
MIND-Skill: Quality-Guaranteed Skill Generation via Multi-Agent Induction and Deduction
Yixuan Li, Mingshu Cai, Ziyang Xiao +3
Large language model (LLM) powered AI agents have emerged as a promising paradigm for autonomous problem-solving, yet they continue to struggle with complex, multi-step real-world…
Hierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks
Shuo He, Lang Feng, Qi Wei +3
Group-based reinforcement learning (RL), such as GRPO, has advanced the capabilities of large language models on long-horizon agentic tasks. To enable more fine-grained policy upda…
Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systems
Lang Feng, Longtao Zheng, Shuo He +2
Multi-agent LLM systems enable advanced reasoning and tool use via role specialization, yet reliable reinforcement learning (RL) post-training for such systems remains difficult. I…
Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds
Weihao Tan, Xiangyang Li, Yunhao Fang +11
We introduce Lumine, the first open recipe for developing generalist agents capable of completing hours-long complex missions in real time within challenging 3D open-world environm…
SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning
Zhenghai Xue, Longtao Zheng, Qian Liu +4
Large Language Models (LLMs) can significantly improve their reasoning capabilities by interacting with external tools, a paradigm known as Tool-Integrated Reasoning (TIR). However…
Towards Efficient Online Tuning of VLM Agents via Counterfactual Soft Reinforcement Learning
Lang Feng, Weihao Tan, Zhiyi Lyu +5
Online fine-tuning vision-language model (VLM) agents with reinforcement learning (RL) has shown promise for equipping agents with multi-step, goal-oriented capabilities in dynamic…
Group-in-Group Policy Optimization for LLM Agent Training
Lang Feng, Zhenghai Xue, Tingcong Liu +1
Recent advances in group-based reinforcement learning (RL) have driven frontier large language models (LLMs) in single-turn tasks like mathematical reasoning. However, their scalab…