pith:XROX5EOT
Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models
SVD-Prune selects vision tokens via leverage scores from singular value decomposition to preserve essential content at extreme pruning ratios.
arxiv:2604.11530 v2 · 2026-04-13 · cs.CV · cs.AI
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Claims
Experiments show that SVD-Prune consistently outperforms prior pruning methods under extreme vision token budgets, maintaining strong performance even with 32 and 16 vision tokens.
That statistical leverage scores derived from the SVD of the vision token feature matrix reliably identify tokens containing essential visual content without introducing positional bias or losing fine details on complex images.
SVD-Prune selects vision tokens via SVD leverage scores to keep performance high even when pruning to only 16-32 tokens.
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| First computed | 2026-05-21T01:05:18.965457Z |
|---|---|
| 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/XROX5EOT5WQCIPFTJLROAAURZ2 \
| 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())"
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Canonical record JSON
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