pith:MHPVHFN3
VRAG: Learning World Models for Interactive Video Generation
Video retrieval augmented generation with explicit global state conditioning reduces compounding errors and improves consistency in interactive video world models.
arxiv:2505.21996 v4 · 2025-05-28 · cs.CV · cs.AI
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\pithnumber{MHPVHFN3475547EDVCFGBT3TIT}
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Record completeness
Claims
We propose video retrieval augmented generation (VRAG) with explicit global state conditioning, which significantly reduces long-term compounding errors and increases spatiotemporal consistency of world models.
The paper assumes that insufficient memory mechanisms are the primary cause of incoherence in current video world models and that retrieval of past clips plus explicit global state can overcome this without introducing new inconsistencies or requiring full retraining.
The work introduces video retrieval augmented generation (VRAG) with explicit global state conditioning to reduce compounding errors and improve spatiotemporal consistency in interactive video world models.
Formal links
Receipt and verification
| First computed | 2026-05-29T01:04:53.476410Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
61df5395bbe7fbde7c83a88a60cf7344e9d43c097bf2dc4f759fc9c2b09394a6
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/MHPVHFN3475547EDVCFGBT3TIT \
| 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: 61df5395bbe7fbde7c83a88a60cf7344e9d43c097bf2dc4f759fc9c2b09394a6
Canonical record JSON
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