Xiang Xu

Yuliang Xu, Xiang Xu, Yao Wan et al.

Algorithmic problem solving serves as a rigorous testbed for evaluating structured reasoning in AI coding systems, as it directly reflects a model's ability to perform structured reasoning in complex scenarios.Existing approaches predominantly rely on model-centric strategies, such as architectural modifications and data scaling, which are costly and offer limited interpretability. Alternative methods leveraging external tools or prompting techniques (e.g., chain-of-thought) are often fragmented and lack a unified framework. In this paper, we propose MAS-Algorithm, a systematic multi-agent workflow for algorithmic problem solving inspired by the practices of competitive programmers and algorithm engineers. Our framework decomposes the end-to-end solving process into modular stages, enabling structured reasoning, tool integration, and flexible coordination among agents. The design emphasizes both rigor and extensibility, allowing it to generalize across diverse problem types.Experimental results on a self-constructed benchmark demonstrate consistent improvements across multiple Qwen series models, achieving an average gain of 6.48% in acceptance rate. In contrast, parameter-efficient fine-tuning on the same data yields only a marginal improvement of 0.89%. We further observe a 4.72% gain on LiveCodeBench-Pro, along with consistent improvements across additional accuracy and efficiency metrics.Beyond performance gains, we conduct comprehensive analyses to better understand the reasoning process within the workflow, including error patterns and cross-scenario behaviors. We further perform customized replacement and ablation studies to explore the upper bound of the framework, showing that individual agents can contribute improvements of up to 27.7%. These results highlight the strong potential of MAS-Algorithm for advancing AI-driven algorithmic reasoning.

Binxiong Li, Yuefei Wang, Binyu Zhao et al.

This study introduces the Multi-Scale Weight-Based Pairwise Coarsening and Contrastive Learning (MPCCL) model, a novel approach for attributed graph clustering that effectively bridges critical gaps in existing methods, including long-range dependency, feature collapse, and information loss. Traditional methods often struggle to capture high-order graph features due to their reliance on low-order attribute information, while contrastive learning techniques face limitations in feature diversity by overemphasizing local neighborhood structures. Similarly, conventional graph coarsening methods, though reducing graph scale, frequently lose fine-grained structural details. MPCCL addresses these challenges through an innovative multi-scale coarsening strategy, which progressively condenses the graph while prioritizing the merging of key edges based on global node similarity to preserve essential structural information. It further introduces a one-to-many contrastive learning paradigm, integrating node embeddings with augmented graph views and cluster centroids to enhance feature diversity, while mitigating feature masking issues caused by the accumulation of high-frequency node weights during multi-scale coarsening. By incorporating a graph reconstruction loss and KL divergence into its self-supervised learning framework, MPCCL ensures cross-scale consistency of node representations. Experimental evaluations reveal that MPCCL achieves a significant improvement in clustering performance, including a remarkable 15.24% increase in NMI on the ACM dataset and notable robust gains on smaller-scale datasets such as Citeseer, Cora and DBLP.

Lingling Zeng, Gen Zhang, Jialin Peng et al.

As AI cluster sizes continue to expand and the demand for large-language-model (LLM) training and inference workloads grows rapidly, traditional scheduling systems face significant challenges in balancing resource utilization, scheduling efficiency, and service quality. This paper presents and evaluates Kant: an efficient unified scheduling platform designed for large-scale AI container clusters, supporting the co-scheduling of both training and inference jobs. Based on the practical implementation of the Kant system, we systematically define a set of key evaluation metrics for AI clusters, including GPU Allocation Ratio (GAR), Scheduling Occupancy Rate (SOR), GPU Node Fragmentation Ratio (GFR), Job Waiting Time Distribution (JWTD), and Job Training Time Estimation Distribution (JTTED), providing a foundation for quantitative performance analysis. Experimental results demonstrate that Kant achieves exceptional performance in clusters ranging from hundreds to tens of thousands of GPUs. By leveraging scheduling strategies such as Backfill and Enhanced Binpack (E-Binpack), the system significantly improves resource utilization and scheduling efficiency, while effectively reducing resource fragmentation and communication overhead in distributed training. The system has been deployed in multiple AI data center clusters, where it stably supports large-scale intelligent computing workloads. This work provides a practical engineering approach for building high-performance, highly available, AI-native scheduling infrastructure.