What a Pooled Leaderboard Does Not Say: Auditing the Scope and Provenance of Method Comparisons in Microservice RCA Benchmarks
Lining HuTing LiuYuzhuo Fu
Oct 2026
Artificial Intelligence
Abstract
Offline benchmarks for microservice root cause analysis (RCA) rank methods by a score pooled over several applications, and a pooled lead leaves two things unstated: on which of the systems it holds, and how the score was produced. We audit both on three public benchmark families (OpenRCA, RCAEval, and PetShop), running the released implementations of 16 methods under recorded configurations on 11 subsystems and on a second release of three of the applications. Whether a pooled lead between published methods holds on every subsystem is decided by a quantity the leaderboard withholds, the spread of the lead across subsystems: of 15 leads whose margin exceeds this spread, 14 hold everywhere; of the 8 whose margin is below it, 6 reverse. The per-fault and per-scenario tables that the benchmark papers publish split the same way, and every error of a ranking computed without one subsystem falls on a lead of the second kind. On the second release a third of the comparisons change sign, most through one method's response to the metric kinds it records, and on several subsystems a simple probe or a shipped baseline beats every published method. How the score was produced matters as much: the input table that one released runner reads is not the one its documentation describes, and this choice alone reverses a comparison between published methods with intervals excluding zero both ways; an unpinned dependency and a duplicated column header each make a method return its input column order without an error. Of 23 RCA papers we examined, none releases the per-case outputs needed to check either premise. We recommend that a pooled comparison state the subsystems it covers and its spread across them, the input table, the handling of failed outputs, and the denominator, and be accompanied by per-case outputs, as ours are in our replication package.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9