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pith:6JPDOEJO

pith:2026:6JPDOEJOYRA5FEWUO6ZI7PUYDL
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Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models

Bo Hui, Gen Li, Kaiyuan Deng, Xiaolong Ma, Yang Xiao

ScaPre gives diffusion models a closed-form way to unlearn many concepts at once by isolating relevant parameters and stabilizing updates.

arxiv:2601.06162 v4 · 2026-01-06 · cs.LG · cs.CV

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\pithnumber{6JPDOEJOYRA5FEWUO6ZI7PUYDL}

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1 Bitcoin timestamp
2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

ScaPre yields an efficient closed-form solution without requiring auxiliary data or sub-models. It forgets up to ×5 more concepts than the best baseline within acceptable quality limits, achieving state-of-the-art precision and efficiency for large-scale unlearning.

C2weakest assumption

That the Informax Decoupler can accurately identify concept-relevant parameters and strictly confine updates to the target subspace without collateral damage, and that spectral trace regularization plus geometry alignment fully resolve conflicting weight updates across large numbers of concepts.

C3one line summary

ScaPre delivers a closed-form method for precise large-scale multi-concept unlearning in diffusion models that removes up to five times more concepts than prior baselines while preserving image quality.

Cited by

1 paper in Pith

Receipt and verification
First computed 2026-05-20T00:04:20.754650Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

f25e37112ec441d292d477b28fbe981adf8a7d1ee6b2778c7e2f73f6bfc0a106

Aliases

arxiv: 2601.06162 · arxiv_version: 2601.06162v4 · doi: 10.48550/arxiv.2601.06162 · pith_short_12: 6JPDOEJOYRA5 · pith_short_16: 6JPDOEJOYRA5FEWU · pith_short_8: 6JPDOEJO
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/6JPDOEJOYRA5FEWUO6ZI7PUYDL \
  | 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: f25e37112ec441d292d477b28fbe981adf8a7d1ee6b2778c7e2f73f6bfc0a106
Canonical record JSON
{
  "metadata": {
    "abstract_canon_sha256": "1a465136b825769debdf97ba9de6b47e9d0cbfec3dd49e71583602ab791d87f9",
    "cross_cats_sorted": [
      "cs.CV"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-01-06T23:59:17Z",
    "title_canon_sha256": "b85f83a57e5451235104f6799118ead66deada61c9c148502c67ba2381756b11"
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  "source": {
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    "kind": "arxiv",
    "version": 4
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}