Author

Tao Chen

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#artificial intelligence Preprint Oct 2026

DHCG: Dynamic Construction of Hierarchical Collaboration Graphs for LLM-Based Multi-Agent Reasoning

LLM-based multi-agent systems (MAS) have demonstrated strong capabilities in solving complex problems across diverse domains. Recently, the dynamic orchestration of agent systems has become an important research direction. However, existing methods suffer from limited composition, misaligned dependencies, and inflexibl...

Jie Ren, Jiakang Yuan, Chen-Yu Huang et al. · 0 citations

#artificial intelligence Preprint Oct 2026

DeReAct: Decomposed Reasoning and Acting for Reliable AI Agents

ReAct-based agents typically rely on a single LLM policy to propose actions, interact with the environment, and decide when a task is complete. This coupling makes action authorization and completion control difficult to enforce independently, allowing errors to propagate and unsupported completion claims to terminate...

Ajay Vohra, Tao Chen, Neeti Narayan et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Explicit Trajectory Diversity for RL-Based Post-Training of LLM Agents

LLM agents often admit multiple high-quality solutions to the same task, differing in reasoning structure, tool-use pattern, or interaction trajectory. Yet existing notions of diversity in LLM post-training are mostly implicit, arising from general stochasticity and regularization mechanisms rather than explicitly targ...

Huai-Yu Fu, Heng Cao, Hao Wang et al. · 0 citations

#natural language process... Review Sep 2026

Efficient GUI Agents: A Systems Survey of Observation, Memory, Action, and Runtime Optimization

This survey studies efficient GUI agents through an end-to-end systems lens that preserves the current technical axes of observation efficiency, context and memory efficiency, action efficiency, and planner-side/system efficiency.

Bizhe Bai, Jiakang Yuan, Hong-Zhou Wu et al. · 0 citations

Jul 2026

Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills

This work introduces Skill Self-Play (Skill-SP), a co-evolutionary framework comprising a proposer, a solver, and a dynamic skill controller that effectively bridges the gap between structured verification and open-ended exploration.

Siyuan Huang, Pengyu Cheng, Haotian Liu et al. · arXiv.org · 2 citations