Diagnostic models aim to precisely infer individuals’ cognitive or non-cognitive competencies from their response logs, such as mathematical or social-emotional skills. While deep learning shows success in cognitive diagnosis, it remains underexplored in the equally important area of non-cognitive trait diagnosis. Accurate non-cognitive trait estimation is critical for individuals’ development. Unlike cognitive assessments using right or wrong responses, non-cognitive trait assessments typically use subjective Likert-scale items with ordinal polytomous options to reflect latent trait levels. Furthermore, individual response styles, such as tendencies toward higher or lower options, introduce bias in trait inference, causing estimations that deviate from true trait levels. Thus, maintaining options ordinal semantic structure and mitigating the response style bias in trait estimation are two major challenges for accurate trait diagnosis. To address these issues, this paper proposes a Style-Aware Polytomous Diagnosis (SAPD) model. Specifically, to capture the ordinal semantics of response options, SAPD constructs an Ordinal Option Graph (OOG) that explicitly encodes the ordinal relationship among polytomous options, where higher options reflect higher latent trait levels. To mitigate the bias caused by individual response styles, we first design a Style-Aware Relational Graph (SARG), a heterogeneous graph that integrates multiple interactions among participants, items, options and traits, implicitly embedding response style information within node representations. We then propose a Response Style Corrector (RSC) that explicitly captures individual response tendencies and disentangles response style bias during trait diagnosis, allowing for dynamic and adaptive correction of trait levels. Extensive experiments on five real-world datasets show that SAPD improves accuracy by an average of 4% over competitive methods. Visualizations confirm SAPD effectively disentangles response style effects, leading to more accurate and interpretable trait diagnosis.

As the number of data center servers grows into the millions and due to the demand for more accurate, rapid and powerful network fault detection and location, the existing Pingmesh-centric monitoring and diagnostic system is not efficient enough. In this paper, we propose ByteTracker, the first agentless probing and diagnostic system for large-scale data center networks. It does not need to deploy probe processes or make any configurations on end hosts, and all probes are launched by a small number of centralized Probers. ByteTracker achieves accurate, real-time probe path tracking with packet mirroring on switches. By reducing end-host probe noise, precisely identifying network timeout probes, accurately tracking probe paths, and marking the failed switch with multiple network timeout probes, ByteTracker can locate network failures with nearly 100% accuracy. We have deployed ByteTracker in all of our data centers for over half a year. During deployment, ByteTracker can detect almost all network anomalies and locate them within 5 seconds with 100% accuracy.