Nexa-gauge – LLM evaluation framework with per-node scoring controls
Nexa-gauge is a graph-based evaluation framework designed for assessing outputs from LLM and LVLM applications. It streamlines the evaluation process by normalizing records and providing a consistent reporting mechanism. The framework supports iterative development and allows teams to estimate costs before execution, enhancing efficiency and reproducibility.
- ▪Nexa-gauge replaces manual checks with a repeatable evaluation pipeline for LLM outputs.
- ▪It offers two operational modes: run for executing branches and estimate for cost computation.
- ▪The framework enables scalable semantic evaluation by scoring outputs against explicit criteria.
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Introduction Overview nexa-gauge is a graph-based evaluation system for LLM and LVLM application outputs. It replaces ad-hoc manual checks with a repeatable pipeline that can be run on local datasets or hosted datasets. At a high level, nexa-gauge: Normalizes raw records into a typed evaluation state. Executes only the nodes required for the selected target. Reuses prior node outputs through deterministic caching. Produces a consistent per-case report for downstream tooling. This architecture supports day-to-day prompt iteration, benchmark runs, and release gating with measurable quality and safety signals. Why LLM-As-A-Judge Is Necessary Exact-match metrics are useful but limited for modern generative systems.
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