[Submitted on 15 Oct 2026]
Title:AuditKIT: A Unified Evaluation Spine for Benchmarking, Judging, and Comparing Large Language Models and Agentic Systems
View a PDF of the paper titled AuditKIT: A Unified Evaluation Spine for Benchmarking, Judging, and Comparing Large Language Models and Agentic Systems, by Shreeyans Arora and 6 other authors
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Abstract:Evaluating language models and agents requires combining benchmark accuracy, judge-based assessment, runtime performance, tool-use behavior, and retrieval quality. Differences in interfaces, metric semantics, and trace formats complicate these comparisons and can hide failures in agentic workflows. We present AuditKIT, a source-available evaluation library that records these measurements in one result representation, with direction-aware metric contracts, configuration fingerprints, and extensible metrics and backends. For agents, it provides turn-aware tool-call matching, separates parallel from sequential execution, and distinguishes verified task outcomes from trajectory-level diagnostics. Missing or unobservable evidence is marked unscored rather than counted as failure. For retrieval-augmented generation, it combines ranked retrieval metrics, judged grounding, and deterministic evidence checks. Seven case studies spanning model comparison, serving runtimes, retrieval, tool calling, and interoperability show how answer extraction, runtime behavior, and metric selection change evaluation outcomes.
Comments: 25 pages, 2 figures, 13 tables. Code: this https URL. Shreeyans Arora and Utsav Avaiya contributed equally; work done while at Lexsi Labs
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Software Engineering (cs.SE)
Cite as: arXiv:2610.22511 [cs.CL]
(or arXiv:2610.22511v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.22511
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hem Gosalia [view email] [v1] Thu, 15 Oct 2026 18:05:10 UTC (241 KB)
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