Author name cluster

Feng Qi

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

AAMAS Conference 2026 Conference Paper

RouterHGC: Optimized Router for LLM-based Multi-Agent Systems via Heterogeneous Graph Contrastive Learning

Yitao Xiao
Shaoyong Guo
Guoming Yang
Qingnan Wang
Yinlin Ren
Xuesong Qiu
Feng Qi

While Large Language Model (LLM)-based Multi-Agent Systems (MAS) offer scalable solutions for complex tasks, dynamically configuring the optimal combination of collaboration modes, agent roles, and LLMs remains challenging. Existing routing methods typically resort to computationally expensive post-response optimization or focus on single-agent settings. We introduce Router- HGC, the first framework to harness heterogeneous graph contrastive learning for MAS routing. We formalize routing as a node selection problem on a heterogeneous graph, capturing high-order dependencies among queries, collaboration patterns, and roles. To optimize this, we design a novel global-local contrastive loss that aligns query graphs with high-performing configurations while distancing them from suboptimal or costly ones. Experiments on five datasets demonstrate that RouterHGC significantly improves reasoning accuracy while reducing costs.

PDF

EAAI Journal 2024 Journal Article

Text-guided Graph Temporal Modeling for few-shot video classification

Fuqin Deng
Jiaming Zhong
Nannan Li
Lanhui Fu
Bingchun Jiang
Yi Ningbo
Feng Qi
He Xin

Large-scale pre-trained models and graph neural networks have recently demonstrated remarkable success in few-shot video classification tasks. However, they generally suffer from two key limitations: i) the temporal relations between adjacent frames tends to be ambiguous due to the lack of explicit temporal modeling. ii) the absence of multi-modal semantic knowledge in query videos results in inaccurate prototypes construction and an inability to achieve multi-modal temporal alignment metrics. To address these issues, we develop a Text-guided Graph Temporal Modeling (TgGTM) method that consists of two crucial components: a text-guided feature refinement module and a learnable Query text-token contrastive objective. Specifically, the former leverages the Temporal masking layer to guide the model in learning temporal relationships between adjacent frames. Additionally, it utilizes multi-modal information to refine video prototypes for comprehensive few-shot video classification. The latter addresses the feature discrepancy between multi-modal support features and single-modal query features by aligning a learnable Query text-token with corresponding base class text descriptions. Extensive experiments on four commonly used benchmarks demonstrate the effectiveness of our proposed method, which achieves mean accuracies of 54. 4%, 80. 3%, 91. 9%, and 96. 2% for 5-way 1-shot classification on SSV2-Small, HMDB51, Kinetics, and UCF101, respectively. These results are superior compared to existing state-of-the-art methods. A detailed ablation showcases the importance of learning temporal relationships between adjacent frames and obtaining Query text-token. The source code and models will be publicly available at https: //github. com/JiaMingZhong2621/TgGTM.

Details DOI

NeurIPS Conference 2021 Conference Paper

A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs

Runzhong Wang
Zhigang Hua
Gan Liu
Jiayi Zhang
Junchi Yan
Feng Qi
Shuang Yang
Jun Zhou

Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature. Traditionally such problems are approximately solved with heuristic algorithms which are usually fast but may sacrifice the solution quality. Currently, machine learning for combinatorial optimization (MLCO) has become a trending research topic, but most existing MLCO methods treat CO as a single-level optimization by directly learning the end-to-end solutions, which are hard to scale up and mostly limited by the capacity of ML models given the high complexity of CO. In this paper, we propose a hybrid approach to combine the best of the two worlds, in which a bi-level framework is developed with an upper-level learning method to optimize the graph (e. g. add, delete or modify edges in a graph), fused with a lower-level heuristic algorithm solving on the optimized graph. Such a bi-level approach simplifies the learning on the original hard CO and can effectively mitigate the demand for model capacity. The experiments and results on several popular CO problems like Directed Acyclic Graph scheduling, Graph Edit Distance and Hamiltonian Cycle Problem show its effectiveness over manually designed heuristics and single-level learning methods.

PDF Details

ICRA Conference 2001 Conference Paper

Trajectory Planning for a Four-Wheel-Steering Vehicle

Danwei Wang
Feng Qi

This paper develops a trajectory planning algorithm for a four-wheel-steering vehicle based on vehicle kinematics. The flexibility offered by the steering is utilized fully in the trajectory planning. A two-part trajectory planning algorithm consists of the steering planning and velocity planning. Limits of the vehicle mechanism and drive torque are taken into account. Simulation results are presented to illustrate the application of the proposed algorithm.

Details