Pith Number

pith:JH45OEVI

pith:2026:JH45OEVIQGZ3U2DVFPQRQ4KIAK

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Observation-Aligned Mask Priors for Learning Physical Dynamics from Authentic Occlusions

Chiyuan Ma, Tianshu Yu, Zihan Zhou

Mask priors learned from authentic occlusions create context-query splits that give every observed dimension a positive chance of being queried.

arxiv:2605.16818 v1 · 2026-05-16 · cs.CV · cs.AI

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2 Internet Archive

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

This intersection-based partitioning gives every valid observed dimension a strictly positive probability of being queried, preventing zero-query dead zones and local generative collapse.

C2weakest assumption

The pretrained Bayesian Flow Network on binary observation masks from the target datasets accurately captures the true distribution of authentic occlusions and that the globally normalized cross-entropy guidance produces sample-specific masks without introducing systematic bias in the resulting context-query splits.

C3one line summary

A framework pretrained on authentic binary occlusion masks uses guided sampling and intersection-based partitioning to train diffusion models on incomplete physical observations without zero-query regions.

References

36 extracted · 36 resolved · 3 Pith anchors

[1] Alexander Barth, Julien Brajard, Aida Alvera-Azcárate, Bayoumy Mohamed, Charles Troupin, and Jean-Marie Beckers. Ensemble reconstruction of missing satellite data using a denoising diffusion model: ap 2024

[2] Generative data assimilation for surface ocean state estimation from multi-modal satellite observations.Journal of Advances in Modeling Earth Systems, 17(8):e2025MS005063, 2025 2025

[3] Accurate medium-range global weather forecasting with 3d neural networks.Nature, 619(7970):533–538 2023

[4] Learning skillful medium-range global weather forecasting.Science, 382(6677):1416–1421 2023

[5] Forecasting corporate financial performance using deep learning with environmental, social, and governance data 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:24.110010Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

49f9d712a881b3ba68752be118714802a3c096c949f3a77cc361c749f51babe2

Aliases

arxiv: 2605.16818 · arxiv_version: 2605.16818v1 · doi: 10.48550/arxiv.2605.16818 · pith_short_12: JH45OEVIQGZ3 · pith_short_16: JH45OEVIQGZ3U2DV · pith_short_8: JH45OEVI

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JH45OEVIQGZ3U2DVFPQRQ4KIAK \
  | 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: 49f9d712a881b3ba68752be118714802a3c096c949f3a77cc361c749f51babe2

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "614c860d5a539587de24d4569e4ea2a118b9f1efe4462058df222a8213cac853",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-16T05:23:49Z",
    "title_canon_sha256": "4efddd94f0aaa4740b97f24d8c12b42a170afce4d1691fb1313b902a82ef7d24"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16818",
    "kind": "arxiv",
    "version": 1
  }
}