arXiv subjects

MingXue Wang

Publications and source records attributed to MingXue Wang.

6 recordsLinked to original sources

arXiv2026

Large Causal Models for Temporal Causal Discovery

Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset. Such an approach limits the potential of multi-dataset pretraining. The concept of large causal models (LCMs) envisions a class of pre-trained neural architectures specifically designed for temporal causal discovery. Prior approaches are constrained to small variable counts, degrade with larger inputs, and rely heavily on synthetic data, limiting generalization. We propose a principled framework for LCMs, combining diverse synthetic generators with realistic time-series datasets, allowing learning at scale. Extensive experiments on synthetic, semi-synthetic and realistic benchmarks show that LCMs scale effectively to higher variable counts and deeper architectures while maintaining strong performance. Trained models achieve competitive or superior accuracy compared to classical and neural baselines, particularly in out-of-distribution settings, while enabling fast, single-pass inference. Results demonstrate LCMs as a promising foundation-model paradigm for temporal causal discovery. Experiments and model weights are available at https://github.com/kougioulis/LCM/.

cs.LG↗

arXiv2025

CrossTrace: Efficient Cross-Thread and Cross-Service Span Correlation in Distributed Tracing for Microservices

Distributed tracing has become an essential technique for debugging and troubleshooting modern microservice-based applications, enabling software engineers to detect performance bottlenecks, identify failures, and gain insights into system behavior. However, implementing distributed tracing in large-scale applications remains challenging due to the need for extensive instrumentation. To reduce this burden, zero-code instrumentation solutions, such as those based on eBPF, have emerged, allowing span data to be collected without modifying application code. Despite this promise, span correlation, the process of establishing causal relationships between spans, remains a critical challenge in zero-code approaches. Existing solutions often rely on thread affinity, compromise system security by requiring the kernel integrity mode to be disabled, or incur significant computational overhead due to complex inference algorithms. This paper presents CrossTrace, a practical and efficient distributed tracing solution designed to support the debugging of microservice applications without requiring source code modifications. CrossTrace employs a greedy algorithm to infer intra-service span relationships from delay patterns, eliminating reliance on thread identifiers. For inter-service correlation, CrossTrace embeds span identifiers into TCP packet headers via eBPF, enabling secure and efficient correlation compromising system security policies. Evaluation results show that CrossTrace can correlate thousands of spans within seconds with over 90% accuracy, making it suitable for production deployment and valuable for microservice observability and diagnosis.

cs.NI↗

arXiv2025

Adversarial Causal Tuning for Realistic Time-series Generation

We address the problem of generating simulated, yet realistic, time-series data from a causal model with the same observational and interventional distributions as a given real dataset (probabilistic causal digital twin). While non-causal models (e.g., GANs) also strive to simulate realistic data, causal models are fundamentally more powerful, able to simulate the effect of interventions (what-if scenarios), optimize decisions, perform root-cause analysis, and counterfactual causal reasoning. We introduce the Adversarial Causal Tuning (ACT) methodology, which outputs the optimal causal model that fits the data, along with a quantification of the goodness-of-fit. The returned causal model can then be employed to simulate new data or to perform other causal reasoning tasks. ACT adopts ideas from Generative Adversarial Network training and AutoML to search for optimal causal pipelines and discriminators that detect deviations between the distributions of real and simulated data. It also adapts a permutation testing procedure from established causal tuning methods to penalize models for complexity. Through extensive experiments on real, semi-synthetic, and synthetic datasets, we show that (a) employing multiple optimized discriminators is paramount for selecting the optimal causal models and quantifying goodness-of-fit, (b) ACT selects the optimal causal model in synthetic datasets while avoiding overfitting, generating data indistinguishable from the true data distribution (c) all state-of-the-art generative and causal simulation methods, exhibit room for improvement in reproducing real data distributions; generating realistic temporal data is still an open research challenge.

cs.LG↗

arXiv2025

RADICE: Causal Graph Based Root Cause Analysis for System Performance Diagnostic

Root cause analysis is one of the most crucial operations in software reliability regarding system performance diagnostic. It aims to identify the root causes of system performance anomalies, allowing the resolution or the future prevention of issues that can cause millions of dollars in losses. Common existing approaches relying on data correlation or full domain expert knowledge are inaccurate or infeasible in most industrial cases, since correlation does not imply causation, and domain experts may not have full knowledge of complex and real-time systems. In this work, we define a novel causal domain knowledge model representing causal relations about the underlying system components to allow domain experts to contribute partial domain knowledge for root cause analysis. We then introduce RADICE, an algorithm that through the causal graph discovery, enhancement, refinement, and subtraction processes is able to output a root cause causal sub-graph showing the causal relations between the system components affected by the anomaly. We evaluated RADICE with simulated data and reported a real data use case, sharing the lessons we learned. The experiments show that RADICE provides better results than other baseline methods, including causal discovery algorithms and correlation based approaches for root cause analysis.

cs.SE↗

arXiv2024

SHREC: a SRE Behaviour Knowledge Graph Model for Shell Command Recommendations

In IT system operations, shell commands are common command line tools used by site reliability engineers (SREs) for daily tasks, such as system configuration, package deployment, and performance optimization. The efficiency in their execution has a crucial business impact since shell commands very often aim to execute critical operations, such as the resolution of system faults. However, many shell commands involve long parameters that make them hard to remember and type. Additionally, the experience and knowledge of SREs using these commands are almost always not preserved. In this work, we propose SHREC, a SRE behaviour knowledge graph model for shell command recommendations. We model the SRE shell behaviour knowledge as a knowledge graph and propose a strategy to directly extract such a knowledge from SRE historical shell operations. The knowledge graph is then used to provide shell command recommendations in real-time to improve the SRE operation efficiency. Our empirical study based on real shell commands executed in our company demonstrates that SHREC can improve the SRE operation efficiency, allowing to share and re-utilize the SRE knowledge.

cs.SE↗

arXiv2022

AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.

cs.CL↗