Pith Number

pith:AQHORQN4

pith:2026:AQHORQN46JYQNGTYVOEY62MJ72

not attested not anchored not stored refs pending

Learning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data

Chuanbin Liu, Jie Chen, Xi Peng, Yuanbiao Gou, Zhu Wang

A sparse attention graph learning method recovers subspace structures from heterogeneous multiview data using bilinear factorization and entmax projections.

arxiv:2605.11881 v2 · 2026-05-12 · cs.CV

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\pithnumber{AQHORQN46JYQNGTYVOEY62MJ72}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

we propose a sparse attention graph learning (SAGL) method that learns subspace-preserving sparse attention graphs from heterogeneous multiview data... SAGL consistently outperforms the state-of-the-art unsupervised transfer learning approaches.

C2weakest assumption

That the bilinear attention factorization combined with α-entmax projection and dynamic sparsity gating will faithfully recover intrinsic subspace structures across heterogeneous views without introducing artifacts that harm semantic alignment.

C3one line summary

SAGL learns subspace-preserving sparse attention graphs from heterogeneous multiview data via bilinear attention factorization, dynamic sparsity gating, and α-entmax projection.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:05:47.213177Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

040ee8c1bcf271069a78ab898f6989fe8832d084ad7ff66a4901d6d480b26900

Aliases

arxiv: 2605.11881 · arxiv_version: 2605.11881v2 · doi: 10.48550/arxiv.2605.11881 · pith_short_12: AQHORQN46JYQ · pith_short_16: AQHORQN46JYQNGTY · pith_short_8: AQHORQN4

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/AQHORQN46JYQNGTYVOEY62MJ72 \
  | 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: 040ee8c1bcf271069a78ab898f6989fe8832d084ad7ff66a4901d6d480b26900

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "bd2b66c6b9e9e0f9500198de18b8ef6cea8552c1c873ace5d2bf88b4d5eecb28",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-12T09:56:28Z",
    "title_canon_sha256": "b6cb4f4d1f1bb1e3ffb59631138121b64b3199ee780b48f2662b0a61396eff43"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.11881",
    "kind": "arxiv",
    "version": 2
  }
}