Pith Number

pith:GHNBNG4U

pith:2026:GHNBNG4UEWLWPVU2NRB2RO2EY3

not attested not anchored not stored refs resolved

SANA-WM: Efficient Minute-Scale World Modeling with Hybrid Linear Diffusion Transformer

Enze Xie, Haoyi Zhu, Haozhe Liu, Jincheng Yu, Junsong Chen, Song Han, Tian Ye, Tong He, Yuyang Zhao

SANA-WM generates minute-scale 720p videos with camera control at 36 times higher throughput than prior open-source models.

arxiv:2605.15178 v1 · 2026-05-14 · cs.CV

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\usepackage{pith}
\pithnumber{GHNBNG4UEWLWPVU2NRB2RO2EY3}

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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

SANA-WM achieves visual quality comparable to large-scale industrial baselines such as LingBot-World and HY-WorldPlay, while significantly improving efficiency... On our one-minute world-model benchmark, SANA-WM demonstrates stronger action-following accuracy than prior open-source baselines and achieves comparable visual quality at 36× higher throughput for scalable world modeling.

C2weakest assumption

The robust annotation pipeline extracts accurate metric-scale 6-DoF camera poses from public videos to yield high-quality, spatiotemporally consistent action labels that enable effective training of the world model.

C3one line summary

SANA-WM is a 2.6B-parameter efficient world model that synthesizes minute-scale 720p videos with 6-DoF camera control, trained on 213K public clips in 15 days on 64 H100s and runnable on single GPUs at 36x higher throughput than prior open baselines.

References

102 extracted · 102 resolved · 46 Pith anchors

[1] World Models 2018 · arXiv:1803.10122

[2] Genie 3: A new frontier for world models 2025

[3] GAIA-1: A Generative World Model for Autonomous Driving 2023 · arXiv:2309.17080

[4] DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos 2026 · arXiv:2602.06949

[5] Aether: Geometric-aware unified world modeling 2025

Receipt and verification

First computed	2026-05-17T21:40:25.203865Z
Last reissued	2026-05-17T21:57:18.558439Z
Builder	pith-number-builder-2026-05-17-v1
Signature	unsigned_v0
Schema	pith-number/v1.0

Canonical hash

31da169b94259767d69a6c43a8bb44c6d0d47bdb22323b83a8eea4144ede5b37

Aliases

arxiv: 2605.15178 · arxiv_version: 2605.15178v1 · pith_short_12: GHNBNG4UEWLW · pith_short_16: GHNBNG4UEWLWPVU2 · pith_short_8: GHNBNG4U

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/GHNBNG4UEWLWPVU2NRB2RO2EY3 \
  | 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: 31da169b94259767d69a6c43a8bb44c6d0d47bdb22323b83a8eea4144ede5b37

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "eef6e0529d268c02237d963884733f3ac70a5b621928b74e9f4c12eaa73e4cd1",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T17:58:03Z",
    "title_canon_sha256": "ac7f02e2b426f77376b0a94085db9f39f21a7eac26efeb50d349b13165daa19a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15178",
    "kind": "arxiv",
    "version": 1
  }
}