arXiv subjects

Ziyu Cheng

Publications and source records attributed to Ziyu Cheng.

9 recordsLinked to original sources

arXiv2026

Weighted Randomized Kaczmarz for Sparsely Corrupted Linear Systems

In this paper, we introduce a generalized weighted randomized Kaczmarz framework to approximate solutions to overdetermined linear systems whose measurements are perturbed by sparse, additive corruptions. The proposed framework samples equations with probabilities proportional to a user-defined weighting function of their corresponding residual magnitudes. Our work focuses on weighting functions that will reduce the influence of potentially corrupted equations, and provides analyses for a general class of non-increasing weighting functions. We derive one-step expected squared error bounds that separate progress on uncorrupted equations from the contribution of corrupted equations, and provide sufficient conditions for geometric convergence under one specific family of weighting functions -- threshold weighting. We also investigate adaptive exponential weights scaled by the median or median absolute deviation of residual magnitudes, requiring no prior knowledge of the corruption fraction or magnitudes. Lastly, we consider a family of inverse residual weighting functions. Numerical experiments demonstrate the benefit of such a generalized framework in its flexibility in developing approaches for settings in which information about the corruptions is known or unknown.

math.NA↗

arXiv2026

How Far Can Sharpness and Complexity Jointly Explain Generalization?

Large-scale empirical studies have revealed that many widely used generalization measures correlate only weakly with generalization, suggesting that relying on a single factor is insufficient to explain it. In this work, we examine sharpness and complexity jointly, placing the reach of the two-factor view at the center: how far can they jointly explain generalization, what determines when they succeed, and where do they fall short? We investigate these questions quantitatively, using sixteen alternative metric pairs as probes and Pareto analysis and linear regression as evaluation tools. Our results show that suitably defined $(S,C)$ pairs provide strong explanatory power across a range of settings. The sharpness perturbation scale substantially affects this success, and the complexity representation influences its robustness. The remaining failures leave open whether more refined measurements would suffice or additional factors are needed.

cs.LG↗

arXiv2026

From Social Reasoning to Embodied Interaction: An Agentic Framework for Social Robots

Natural face-to-face human--robot interaction requires a robot to understand an evolving social situation, decide when to engage, and express its intent through coordinated physical behavior. Yet existing approaches rarely close this loop: foundation-model agents provide increasingly capable multimodal reasoning and memory but remain largely disembodied, while expressive virtual agents do not face the physical constraints of real robots, and physical social robots typically address social reasoning and embodied expression only partially. We present ARISE, a unified framework that bridges Agentic Reasoning and Interactive Social Embodiment on the Sophia humanoid robot. ARISE integrates multimodal context understanding, long-term memory, and reactive and proactive interaction to determine when and what to communicate, and translates social intent into robot-native gestures coordinated with speech and mechanical facial expressions through streaming execution. Extensive evaluations on Sophia demonstrate strong perceived interaction quality, expressive and well-coordinated embodied behavior, and substantial latency reductions through streaming execution. These results highlight the importance of jointly reasoning about what to communicate, when to engage, and how to physically express social intent for natural interaction with humanoid robots. Project Page: https://robosocial.github.io/

cs.RO↗

arXiv2026

Interference-Aware K-Step Reachable Communication in Multi-Agent Reinforcement Learning

Effective communication is pivotal for addressing complex collaborative tasks in multi-agent reinforcement learning (MARL). Yet, limited communication bandwidth and dynamic, intricate environmental topologies present significant challenges in identifying high-value communication partners. Agents must consequently select collaborators under uncertainty, lacking a priori knowledge of which partners can deliver task-critical information. To this end, we propose Interference-Aware K-Step Reachable Communication (IA-KRC), a novel framework that enhances cooperation via two core components: (1) a K-Step reachability protocol that confines message passing to physically accessible neighbors, and (2) an interference-prediction module that optimizes partner choice by minimizing interference while maximizing utility. Compared to existing methods, IA-KRC enables substantially more persistent and efficient cooperation despite environmental interference. Comprehensive evaluations confirm that IA-KRC achieves superior performance compared to state-of-the-art baselines, while demonstrating enhanced robustness and scalability in complex topological and highly dynamic multi-agent scenarios.

cs.AI↗

arXiv2025

Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learning

As cloud computing and microservice architectures become increasingly prevalent, API rate limiting has emerged as a critical mechanism for ensuring system stability and service quality. Traditional rate limiting algorithms, such as token bucket and sliding window, while widely adopted, struggle to adapt to dynamic traffic patterns and varying system loads. This paper proposes an adaptive rate limiting strategy based on deep reinforcement learning that dynamically balances system throughput and service latency. We design a hybrid architecture combining Deep Q-Network (DQN) and Asynchronous Advantage Actor-Critic (A3C) algorithms, modeling the rate limiting decision process as a Markov Decision Process. The system continuously monitors microservice states and learns optimal rate limiting policies through environmental interaction. Extensive experiments conducted in a Kubernetes cluster environment demonstrate that our approach achieves 23.7% throughput improvement and 31.4% P99 latency reduction compared to traditional fixed-threshold strategies under high-load scenarios. Results from a 90-day production deployment handling 500 million daily requests validate the practical effectiveness of the proposed method, with 82% reduction in service degradation incidents and 68% decrease in manual interventions.

cs.LG↗

arXiv2025

Graph Neural AI with Temporal Dynamics for Comprehensive Anomaly Detection in Microservices

This study addresses the problem of anomaly detection and root cause tracing in microservice architectures and proposes a unified framework that combines graph neural networks with temporal modeling. The microservice call chain is abstracted as a directed graph, where multidimensional features of nodes and edges are used to construct a service topology representation, and graph convolution is applied to aggregate features across nodes and model dependencies, capturing complex structural relationships among services. On this basis, gated recurrent units are introduced to model the temporal evolution of call chains, and multi-layer stacking and concatenation operations are used to jointly obtain structural and temporal representations, improving the ability to identify anomaly patterns. Furthermore, anomaly scoring functions at both the node and path levels are defined to achieve unified modeling from local anomaly detection to global call chain tracing, which enables the identification of abnormal service nodes and the reconstruction of potential anomaly propagation paths. Sensitivity experiments are then designed from multiple dimensions, including hyperparameters, environmental disturbances, and data distribution, to evaluate the framework, and results show that it outperforms baseline methods in key metrics such as AUC, ACC, Recall, and F1-Score, maintaining high accuracy and stability under dynamic topologies and complex environments. This research not only provides a new technical path for anomaly detection in microservices but also lays a methodological foundation for intelligent operations in distributed systems.

cs.LG↗

arXiv2025

Structural Generalization for Microservice Routing Using Graph Neural Networks

This paper focuses on intelligent routing in microservice systems and proposes an end-to-end optimization framework based on graph neural networks. The goal is to improve routing decision efficiency and overall system performance under complex topologies. The method models invocation relationships among microservices as a graph. In this graph, service nodes and communication links are treated as graph nodes and edges. Multi-dimensional features such as node states, link latency, and call frequency are used as input. A multi-layer graph neural network is employed to perform high-order information aggregation and structural modeling. The model outputs a score for each candidate service path. These scores are then used to guide dynamic routing decisions. To improve the model's ability to assess path quality, an edge-aware attention mechanism is introduced. This mechanism helps the model capture instability and bottleneck risks in service communications more accurately. The paper also conducts a systematic analysis of the model's performance under different network depths, topology densities, and service scales. It evaluates the effectiveness of the method in terms of routing accuracy, prediction error, and system stability. Experimental results show that the proposed method outperforms existing mainstream strategies across multiple key metrics. It handles highly dynamic and concurrent microservice environments effectively and demonstrates strong performance, robustness, and structural generalization.

cs.NI↗

arXiv2025

Spatiotemporal Traffic Prediction in Distributed Backend Systems via Graph Neural Networks

This paper addresses the problem of traffic prediction in distributed backend systems and proposes a graph neural network based modeling approach to overcome the limitations of traditional models in capturing complex dependencies and dynamic features. The system is abstracted as a graph with nodes and edges, where node features represent traffic and resource states, and adjacency relations describe service interactions. A graph convolution mechanism enables multi order propagation and aggregation of node features, while a gated recurrent structure models historical sequences dynamically, thus integrating spatial structures with temporal evolution. A spatiotemporal joint modeling module further fuses graph representation with temporal dependency, and a decoder generates future traffic predictions. The model is trained with mean squared error to minimize deviations from actual values. Experiments based on public distributed system logs construct combined inputs of node features, topology, and sequences, and compare the proposed method with mainstream baselines using MSE, RMSE, MAE, and MAPE. Results show that the proposed method achieves stable performance and low error across different prediction horizons and model depths, significantly improving the accuracy and robustness of traffic forecasting in distributed backend systems and verifying the potential of graph neural networks in complex system modeling.

cs.DC↗

arXiv2025

HOID-R1: Reinforcement Learning for Open-World Human-Object Interaction Detection Reasoning with Multimodal Large Language Model

Understanding and recognizing human-object interaction (HOI) is a pivotal application in AR/VR and robotics. Recent open-vocabulary HOI detection approaches depend exclusively on large language models for richer textual prompts, neglecting their inherent 3D spatial understanding capabilities. To address this shortcoming, we introduce HOID-R1, the first HOI detection framework that integrates chain-of-thought (CoT) guided supervised fine-tuning (SFT) with group relative policy optimization (GRPO) within a reinforcement learning (RL) paradigm. Specifically, we initially apply SFT to imbue the model with essential reasoning capabilities, forcing the model to articulate its thought process in the output. Subsequently, we integrate GRPO to leverage multi-reward signals for policy optimization, thereby enhancing alignment across diverse modalities. To mitigate hallucinations in the CoT reasoning, we introduce an "MLLM-as-a-judge" mechanism that supervises the CoT outputs, further improving generalization. Extensive experiments show that HOID-R1 achieves state-of-the-art performance on HOI detection benchmarks and outperforms existing methods in open-world generalization to novel scenarios.

cs.CV↗