pith:GZF7TYGS
Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective
Sequential Bayesian Flow Matching reuses the previous posterior as a source distribution to accelerate sampling from streaming observations.
arxiv:2602.05319 v3 · 2026-02-05 · cs.LG
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Claims
By using the previous belief as an informative source distribution, it enables substantially faster sampling than naive resampling from scratch while achieving performance competitive with full-step diffusion on distributional metrics.
That a learned flow can reliably transport the full posterior distribution (including multimodality) from one time step to the next without accumulating approximation error or requiring retraining when the observation model changes.
Sequential Bayesian Flow Matching accelerates flow-based sampling for streaming probabilistic inference by transporting posteriors recursively like Bayesian filters rather than restarting from noise each step.
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Receipt and verification
| First computed | 2026-05-17T23:39:16.305129Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
364bf9e0d208c7c4b2dab904eeab9350ef3251e760a701d13fd335c8761efe5b
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/GZF7TYGSBDD4JMW2XECO5K4TKD \
| 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: 364bf9e0d208c7c4b2dab904eeab9350ef3251e760a701d13fd335c8761efe5b
Canonical record JSON
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