Pooled Leaderboards Hide System-Specific Winners: A Reporting-Protocol Audit of Offline Root-Cause Analysis Benchmarks
arXiv:2606.29159v1 Announce Type: new Abstract: Offline root-cause-analysis (RCA) benchmarks commonly rank methods by a single pooled top-1 accuracy across multiple subsystems, and engineers often read the pooled winner as a recommendation for their own subsystem. We audit that reading on three public RCA benchmark families -- OpenRCA, RCAEval, and PetShop -- covering 11 subsystems and 778 matched scoring units. To keep pairwise comparisons on identical cases, the main analysis retains four methods or comparators with complete coverage: BARO, a CD-1min adapter, max-$|Z|$, and per-service alert
This research is published as the field of AI, particularly in anomaly detection and root-cause analysis, matures, requiring more rigorous benchmarking standards.
A strategic reader should care because flawed benchmarking can misguide investment and development efforts in critical enterprise AI systems, leading to suboptimal solutions.
The understanding of AI model performance for specific applications changes, moving away from aggregated metrics towards more granular, subsystem-specific evaluations.
- · AI developers focused on niche applications
- · Enterprises with complex IT infrastructures
- · Benchmarking protocol designers
- · Developers of generalist AI solutions
- · Organizations relying solely on pooled benchmark results
Increased scrutiny on the methodology and reporting protocols for AI performance benchmarks across various domains.
Development of more specialized and transparent benchmarking frameworks tailored to specific operational contexts and subsystems.
A potential fragmentation of the AI solutions market as vendors optimize for performance in distinct, well-benchmarked niches rather than broad 'pooled' superiority.
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Read at arXiv cs.AI