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pith:2026:XROX5EOT5WQCIPFTJLROAAURZ2
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Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models

Martyna Poreba, Michal Szczepanski, Samia Bouchafa, Yvon Apedo

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

SVD-Prune selects vision tokens via SVD leverage scores to keep performance high even when pruning to only 16-32 tokens.

Cited by

2 papers in Pith

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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

bc5d7e91d3eda0243cb34ae2e00291cea20fb500dc93be8f2f1595d78aa30524

Aliases

arxiv: 2604.11530 · arxiv_version: 2604.11530v2 · doi: 10.48550/arxiv.2604.11530 · pith_short_12: XROX5EOT5WQC · pith_short_16: XROX5EOT5WQCIPFT · pith_short_8: XROX5EOT
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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())"
# expect: bc5d7e91d3eda0243cb34ae2e00291cea20fb500dc93be8f2f1595d78aa30524
Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by/4.0/",
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    "submitted_at": "2026-04-13T14:30:13Z",
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