pith:6CTORDBG
Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting
Drift-AR uses per-position prediction entropy to drive both speculative AR drafting and anti-symmetric drift, achieving genuine single-step visual generation.
arxiv:2603.28049 v3 · 2026-03-30 · cs.CV
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Claims
Drift-AR leverages entropy signal to accelerate both stages... enabling single-step (1-NFE) decoding without iterative denoising or distillation... Experiments on MAR, TransDiff, and NextStep-1 demonstrate 3.8-5.5× speedup with genuine 1-NFE decoding, matching or surpassing original quality.
The per-position prediction entropy of continuous-space AR models naturally encodes spatially varying generation uncertainty, which simultaneously governing draft prediction quality in the AR stage and reflecting the corrective effort required by vision decoding stage.
Drift-AR achieves 3.8-5.5x speedup in AR-diffusion image models by using entropy to enable entropy-informed speculative decoding and single-step (1-NFE) anti-symmetric drifting decoding.
Receipt and verification
| First computed | 2026-06-30T02:17:19.488002Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f0a6e88c269405a141281f8f3e2ba90e92f56ed7fea29940606789346a829442
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/6CTORDBGSQC2CQJID6HT4K5JB2 \
| 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: f0a6e88c269405a141281f8f3e2ba90e92f56ed7fea29940606789346a829442
Canonical record JSON
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