works on

From the 1 of 18 linked papers with an AI index.

activity
20242026
collaborators

18 papers

cs.AI2026

HiSkill: Empowering LLM Agents with Hierarchical Skill Graphs

Yu Hao, Jinxuan Cai, Qi Zhang +4

Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks. However, existing trajectory-…

cs.CV2026

Self-Aware Recursively Self-Improving Agents for Personal Singularity: A Goal-, Scope-, Tool-, and Benchmark-Driven Multi-Agent Architecture

Chengshuai Yang

The paper proposes a Self-Aware Recursively Self-Improving (SARSI) multi‑agent architecture that maintains a persistent self‑model to guide goal‑driven improvement and supports a p…

cs.CL2026

MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing

Yang Liu, Jinxuan Cai, Yishen Li +6

Large language model-based (LLM-based) multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. MAS workflows ca…

cs.SE2026

A Judge Agent Closes the Reliability Gap in AI-Generated Scientific Simulation

Chengshuai Yang

Large language models can generate scientific simulation code, but the generated code silently fails on most non-textbook problems. We show that classical mathematical validation -…

cs.CL2026

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…

cs.AI2025

GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration

Xin Li, Qizhi Chu, Yubin Chen +7

Graphs are widely used for modeling relational data in real-world scenarios, such as social networks and urban computing. Existing LLM-based graph analysis approaches either integr…