pith:HILXOGON
Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models
3D medical vision-language models struggle with semantic-spatial reasoning in CT volumes, averaging just 34% accuracy on a new benchmark.
arxiv:2605.08787 v2 · 2026-05-09 · cs.CV
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Claims
We introduce CT-SpatialVQA, a benchmark designed to evaluate semantic-spatial reasoning in 3D CT data. [...] finding severe degradation on semantic-spatial reasoning tasks, averaging 34% accuracy and often below random.
The constructed QA pairs require and test explicit 3D volumetric spatial reasoning rather than being solvable through 2D projections, language correlations, or learned priors alone.
CT-SpatialVQA benchmark shows 3D medical VLMs achieve only 34% average accuracy on semantic-spatial reasoning tasks in CT volumes, often below random chance.
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| First computed | 2026-06-23T01:12:07.993098Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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