pith:E7QWGGV6
Long-Context Autoregressive Video Modeling with Next-Frame Prediction
Asymmetric patchify kernels enable efficient long-context autoregressive video modeling by exploiting context redundancy.
arxiv:2503.19325 v3 · 2025-03-25 · cs.CV
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Claims
Our method achieves state-of-the-art results on both short and long video generation, providing an effective baseline for long-context autoregressive video modeling.
The assumption that video autoregression exhibits exploitable context redundancy where distant frames can safely use large asymmetric patchify kernels without losing critical temporal information needed for coherence.
FAR baseline plus asymmetric kernels for long short-term context modeling achieves SOTA short and long video generation in autoregressive setups.
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| First computed | 2026-05-17T23:38:46.305077Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
27e1631abe15d805a4686088ed5489e62adfb3e99fe8837159d27d3fe5014963
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/E7QWGGV6CXMALJDIMCEO2VEJ4Y \
| 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: 27e1631abe15d805a4686088ed5489e62adfb3e99fe8837159d27d3fe5014963
Canonical record JSON
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