REPORT-OPS: Hybrid Evidence-Guided Reporting with Human-Supervised Refinement for HPC

Abstract

Operational reporting for high-performance computing (HPC) facilities requires combining information from monitoring services, schedulers, incident and maintenance records, storage measurements, periodic reports, and operator investigations. Producing a reliable report requires more than retrieving relevant records: the evidence must refer to the correct system, metric definition, time period, and source of authority. We present REPORT-OPS, a hybrid reporting agent in which an LLM interprets requests, plans evidence collection, evaluates possible explanations, and drafts reports, while author-seeded operational policies and automated checks determine whether the available evidence supports the requested conclusion. Depending on the evidence state, the system answers the request, reports a qualified conclusion, or withholds an unsupported claim. During offline refinement, an LLM critic proposes bounded policy changes that a human approves or rejects without editing; accepted candidates are promoted only after automated target, held-out, and regression testing. We evaluate REPORT-OPS on 68 sanitized reporting queries constructed from 50 base scenarios in a frozen HPC operational corpus, using three agent models and two LLM judges. REPORT-OPS achieves 68.63% Pass@80, compared with 7.02% for the strongest retrieval baseline. Evidence gating increases claim-support precision from 40.58% to 90.06%. On the 13-query explanatory subset, explanation accuracy reaches 55.54%. Across five rounds of human-supervised refinement, localized policy updates correct 91.17% of targeted failures and improve performance on related held-out cases by 9.33 percentage points, with no observed regression breaks under release gating. These results demonstrate improved evidence-grounded reporting and within-environment policy reuse; they do not establish unrestricted causal discovery or cross-facility generalization.