Pith Number

pith:AVH43REN

pith:2026:AVH43RENQKKVLTOUNTY3ZLZSMZ

not attested not anchored not stored refs resolved

Proximal-Based Generative Modeling for Bayesian Inverse Problems

Boyang Zhang, Ya-Feng Liu, Zhiguo Wang

PGM replaces the intractable likelihood score in diffusion models with a closed-form Moreau score computed via proximal operators, enabling non-asymptotic sampling for inverse problems trained only on prior data.

arxiv:2605.13278 v1 · 2026-05-13 · math.OC · cs.LG

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

PGM eliminates the early-stopping bias inherent in the score-based diffusion model and achieves non-asymptotic convergence.

C2weakest assumption

The theoretical equivalence between Gaussian convolution in diffusion processes and Moreau-Yosida regularization holds rigorously and directly yields a closed-form Moreau score via proximal operators that can be learned from prior samples alone.

C3one line summary

References

132 extracted · 132 resolved · 5 Pith anchors

[1] Proceedings of the 28th International Conference on Machine Learning , pages=

[2] Score-Based Generative Modeling through Stochastic Differential Equations 2011 · arXiv:2011.13456

[3] SIAM Review , volume= 2022

[4] Advances in Neural Information Processing Systems , volume=

[5] Statistical Physics: Volume 5 , author=. 2013 , publisher= 2013

Receipt and verification

First computed	2026-05-18T02:44:49.221200Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

054fcdc48d829555cdd46cf1bcaf32667ea6eb885b41b3e72f0de933db7b04f4

Aliases

arxiv: 2605.13278 · arxiv_version: 2605.13278v1 · doi: 10.48550/arxiv.2605.13278 · pith_short_12: AVH43RENQKKV · pith_short_16: AVH43RENQKKVLTOU · pith_short_8: AVH43REN

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/AVH43RENQKKVLTOUNTY3ZLZSMZ \
  | 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: 054fcdc48d829555cdd46cf1bcaf32667ea6eb885b41b3e72f0de933db7b04f4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "25869610d6d1dd0007a927c4d438c105019e8135f02a985cd33559cd0b4bd17e",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "math.OC",
    "submitted_at": "2026-05-13T09:55:51Z",
    "title_canon_sha256": "3886e83adf6382b8de95b0dda8f2b27372762ab17ebf6676948ffa2005fc7481"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13278",
    "kind": "arxiv",
    "version": 1
  }
}