AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

Claire Radar

Visual RAG Benchmark

Visual RAG Benchmark evaluates end-to-end retrieval-augmented generation on reference tables from three EU Water Framework Directive guidance documents, scoring answers on 103 and 154 question sets split into text and table question types via LLM-as-a-judge correctness, ROUGE-1, and retrieval accuracy across seven indexing and retrieval pipelines.

Evidence available for this benchmark

Numeric results have not been collected for this source record. Use the paper, code or dataset below to check the task and experiments; this does not imply that the original work contains no results.

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Cited documents and dates (1)

Catalog collected on 2026-10-14; resources and fields reflect that saved source.

Recorded attention

Observed on: 2026-10-14

GitHub stars
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james-a-watson/visual-rag-benchmark · View source snapshot

Dated attention observations, not live counts or benchmark scores.

Evaluation context and evidence

Source
Claire Radar
Publisher
Not recorded
Benchmark release
2026-09-10
Collected
2026-10-14
Modality
Not recorded

These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.

Reported results 0

This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.