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#document-understanding

2 curated events
papersTODAY 04:00 UTC

TestHallVQA benchmark probes document-level reasoning in vision-language models

A new arXiv paper introduces TestHallVQA, a benchmark built from scientific exam material for evaluating large vision-language models on visual question answering over long, multi-page documents. The authors argue that current planar VQA benchmarks tend to test isolated skills rather than document-level reasoning amid redundant context. The benchmark is intended to expose where such models fail when relevant information is buried in lengthy inputs.

benchmarks/vision-language/document-understanding/evaluation
papersSEP 12 04:00 UTC

VikingRAG: Token-efficient retrieval-augmented generation for structured documents

A new arXiv paper introduces VikingRAG, a retrieval-augmented generation approach aimed at cutting the number of tokens spent on structural context when working with structured documents. The authors note that current state-of-the-art RAG systems use document structure to gather better evidence but pay a high token cost for it, and their method targets that trade-off. The abstract excerpt provided does not detail the full technique or report benchmark results.

rag/efficiency/research/document-understanding