SOURCE-LINKED INTELLIGENCE
MAWILE: Multi-Axis Workbench for Inspecting LLM Evaluators
Large language model (LLM) judges provide a flexible and scalable method for evaluating model and agent outputs, but their verdicts can be sensitive to incidental changes in the evaluated response, judge instructions, and scoring rubric. Existing systems examine important subsets of these failure modes, but auditing a configured judge requires testing both the judge instrument and the items it evaluates. We introduce MAWILE, a developer-facing workbench for auditing judge sensitivity across four surfaces: the judge prompt, judge rubric, target-system input, and target-system output. Given a us
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T21:34:22.000Z
First collected: 2026-09-23T12:01:45.602Z. This is not the publication date.