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All Journal Journal of Technology Informatics and Engineering
Chenyu Li
Applied Analytics, Columbia University, NY, USA
Computer Science & IT

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Cost-Aware LLM-Style Routing for AIOps Log Analysis: Log Parsing, Anomaly Detection, Fault Diagnosis, and Incident Summarization on LogEval Task Files Chenyu Li; Ge Liu; Zoe Zhao
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.538

Abstract

This study investigates a local and cost-aware routing framework for AIOps log analysis using the LogEval benchmark. The evaluation covers four tasks: log parsing, anomaly detection, fault diagnosis, and incident summarization. Instead of relying on external large language model APIs, the experiment implements deterministic local policies that simulate zero-shot and few-shot LLM-style inference under controlled token-cost and latency assumptions. Six approaches were compared: regex normalization, TF-IDF with machine learning, a local character-based classifier, zero-shot policy, few-shot retrieval policy, and a routing cascade. At a risk threshold of 0.20, the router directed only 12.9% of queries to the few-shot retrieval policy while achieving parsing accuracy of 0.991, anomaly F1-score of 1.000, diagnosis accuracy of 1.000, ROUGE-L of 0.743, and BLEU-1 of 0.814. The routing strategy reduced simulated token cost by 80.1% compared with always using few-shot retrieval. Additional unseen-template evaluation revealed limited generalization for closed-label classifiers and retrieval methods when encountering unseen patterns. The findings indicate that routing can effectively reduce AIOps inference costs, while further validation with real LLMs and stronger generalization testing are required before production deployment.
Trajectory Reliability Prediction for Generalist AI Agents: Tool-Use Failure Analysis and Success Forecasting on ZClawBench Ziliang Samuel Zhong; Chenyu Li; Hengning Rao
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.539

Abstract

Generalist AI agents increasingly perform complex tasks through planning, tool execution, action revision, and artifact generation rather than isolated response generation. This study empirically investigates trajectory reliability prediction on ZClawBench, a public OpenClaw-style agent benchmark containing 696 model-task trajectories across 116 tasks, six model families, and six scenario categories. The study evaluates whether operational trajectory signals—including tool-call volume, replanning behavior, tool errors, invalid-action ratio, recovery patterns, looping indicators, trajectory length, and task category—can predict task success before manual evaluation. Since the dataset provides trajectories and identifiers but lacks explicit per-instance success labels, the binary target is reconstructed from official model-by-category score distributions and treated as a modeling assumption. Five-fold cross-validation with task-level splitting was applied to prevent task leakage. Logistic Regression, Random Forest, XGBoost, sequence TF-IDF classification, and a rubric-based trajectory judge were compared. Logistic Regression achieved the strongest calibrated performance, obtaining ROC-AUC of 0.970, F1-score of 0.913, Brier score of 0.066, and expected calibration error of 0.018. Feature analysis indicated that task difficulty, no-progress behavior, response size, tool errors, and invalid actions contributed most to reliability prediction. The findings suggest that lightweight trajectory diagnostics can support agent monitoring, failure triage, and routing decisions, while further validation with direct case-level evaluation labels is required for deployment claims.
Co-Authors Ge Liu Hengning Rao Ziliang Samuel Zhong Zoe Zhao