6 papers · 1 filter
Text-Centric Post-Training for Omni-Modal Reasoning
Ziyang Cheng, Yuhao Wang, Hongcheng Liu +5
Improving joint audio-visual reasoning in Omni Large Language Models typically incurs substantial data construction and training costs. Our diagnostics reveal multi-hop reasoning d…
DevicesWorld: Benchmarking Cross-Device Agents in Heterogeneous Environments
Huatao Li, Xinwei Geng, Yuheng Wang +9
LLM-based agents have rapidly improved at operating individual digital environments such as mobile applications, desktop systems, and smart homes. However, real-world user goals of…
OctoNest: Adaptive Cross-Device Execution through Stateful Control
Shu Yao, Yuhua Luo, Qian Long +7
Computer use agents are expanding from single-device operation toward cross-device systems that coordinate tasks across heterogeneous environments. Execution conditions are often o…
Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science
Jingru Fan, Dewen Liu, Yufan Dang +15
Recent advancements in Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS), demonstrating significant effectiveness across a wide range…
Multi-Agent Collaboration via Evolving Orchestration
Yufan Dang, Chen Qian, Xueheng Luo +11
Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-s…
AgentRM: Enhancing Agent Generalization with Reward Modeling
Yu Xia, Jingru Fan, Weize Chen +7
Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. Hence, some recent work focus on fine-tuning t…