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pith:3MQ3NOMB

pith:2026:3MQ3NOMBXVOFH2ZP2NQ3JVYXBE
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Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching

Jian Wang, Jiarui Xing, Song Wang

For any pre-trained flow matching velocity field the trace of the posterior covariance equals the divergence of that field up to a time-dependent factor.

arxiv:2605.00941 v3 · 2026-05-01 · cs.LG · cs.CV

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4 Citations open
5 Replications open
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Claims

C1strongest claim

For any pre-trained flow matching velocity field, the trace of the posterior covariance over the clean data given the current state equals, in closed form, the divergence of the velocity field, up to a known time-dependent prefactor and an additive constant.

C2weakest assumption

The flow matching velocity field is assumed to be the exact conditional expectation under the posterior; any deviation from this (e.g., due to optimization error or model misspecification) would break the exact equality.

C3one line summary

In flow matching, the uncertainty of the clean data given the current state is exactly the divergence of the velocity field (up to a known scalar).

Receipt and verification
First computed 2026-05-22T01:04:03.975063Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

db21b6b981bd5c53eb2fd361b4d7170906a3097620b8cc7d1d705f69a69475fa

Aliases

arxiv: 2605.00941 · arxiv_version: 2605.00941v3 · doi: 10.48550/arxiv.2605.00941 · pith_short_12: 3MQ3NOMBXVOF · pith_short_16: 3MQ3NOMBXVOFH2ZP · pith_short_8: 3MQ3NOMB
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/3MQ3NOMBXVOFH2ZP2NQ3JVYXBE \
  | 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: db21b6b981bd5c53eb2fd361b4d7170906a3097620b8cc7d1d705f69a69475fa
Canonical record JSON
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      "cs.CV"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-01T04:25:00Z",
    "title_canon_sha256": "42b9a8ad6a05da7006e5e45c2beebc4a3a4008e900cd05de1d7ecab210201160"
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