pith:U2GPRN34
CurveBench: A Benchmark for Exact Topological Reasoning over Nested Jordan Curves
Vision-language models recover exact containment trees from nested Jordan curves at only 71 percent accuracy on easy cases and 19 percent on hard cases.
arxiv:2605.14068 v1 · 2026-05-13 · cs.CV · cs.LG
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Claims
Despite the visual simplicity of the task, the strongest evaluated model, Gemini 3.1 Pro, achieves only 71.1% tree-generation accuracy on CurveBench-Easy and 19.1% on CurveBench-Hard.
That the generated images and their tree annotations faithfully isolate topological containment reasoning without providing unintended low-level visual shortcuts or dataset-specific biases that models could exploit.
CurveBench benchmark reveals that even leading VLMs like Gemini 3.1 Pro reach only 71.1% accuracy recovering containment trees on easy nested-curve images and 19.1% on hard versions, while fine-tuning lifts an open 8B model to 33.3% on easy cases.
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Receipt and verification
| First computed | 2026-05-17T23:39:12.452745Z |
|---|---|
| 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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curl -sH 'Accept: application/ld+json' https://pith.science/pith/U2GPRN34O2GYARZJ26TNK44DDO \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: a68cf8b77c768d804729d7a6d573831b8f3f6eae26abd474e5d76efecae0380b
Canonical record JSON
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