Graph Retrieval Augmented Generation (GraphRAG) effectively enhances external knowledge integration capabilities by explicitly modeling knowledge relationships, thereby improving the factual accuracy and generation quality of Large Language Models (LLMs) in specialized domains.However, existing methods suffer from two inherent limitations: 1) Inefficient Information Aggregation: They rely on a single agent and fixed iterative patterns, making it difficult to adaptively capture multi-level textual, structural, and degree information within graph data.2) Rigid Reasoning Mechanism: They employ preset reasoning schemes, which cannot dynamically adjust reasoning depth nor achieve precise semantic correction.To overcome these limitations, we propose Graph Counselor, an GraphRAG method based on multi-agent collaboration.This method uses the Adaptive Graph Information Extraction Module (AGIEM), where Planning, Thought, and Execution Agents work together to precisely model complex graph structures and dynamically adjust information extraction strategies, addressing the challenges of multi-level dependency modeling and adaptive reasoning depth.Additionally, the Self-Reflection with Multiple Perspectives (SR) module improves the accuracy and semantic consistency of reasoning results through self-reflection and backward reasoning mechanisms.Experiments demonstrate that Graph Counselor outperforms existing methods in multiple graph reasoning tasks, exhibiting higher reasoning accuracy and generalization ability.Our code is available at Graph-Counselor.

This paper studies the problem of trajectory tracking control for robotic manipulators subject to model uncertainties, external disturbance and actuator saturation. At first, a nonsingular fixed-time sliding mode variable is developed based on a novel fixed-time stable system, which solves the singularity problem brought by the terminal sliding mode control and obtains a fast convergence rate. After that, a novel fixed-time sliding mode observer is designed to estimate and compensate the model uncertainties and external disturbance. The fixed-time stability of the system under the proposed composite control is proved by Lyapunov stability theory. Finally, the effectiveness of the method is verified by comparative simulation.