AuditKIT provides unified evaluation framework for LLMs and agents
A new arXiv paper introduces AuditKIT, a source‑available library that consolidates multiple evaluation dimensions—benchmark accuracy, judge‑based scoring, runtime performance, tool‑use behavior, and retrieval quality—into a single result representation. The library adds direction‑aware metric contracts, configuration fingerprints, and extensible back‑ends, allowing researchers to track and…
Key points
- AuditKIT records benchmark, judge, runtime, tool‑use, and retrieval metrics in one result format.
- It provides turn‑aware tool‑call matching and separates parallel from sequential execution for agents.
- Seven case studies demonstrate that metric choice and runtime behavior alter model comparison results.
For agent evaluation, AuditKIT offers 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 as unscored rather than a failure, reducing false negatives. In retrieval‑augmented generation, it combines ranked retrieval metrics, judged grounding, and deterministic evidence checks. The authors illustrate the impact of these features through seven case studies covering model comparison, serving runtimes, retrieval, tool calling, and interoperability, showing how answer extraction, runtime behavior, and metric selection can change evaluation outcomes.
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