Pith Number

pith:IR65NTO7

pith:2026:IR65NTO76M2AKSLFKIDN7GDDMR

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Stable Attention Response for Reliable Precipitation Nowcasting

Allen Benter, Kun Hu, Patrick Filippi, Penghui Wen, Sen Zhang, Thomas Bishop, Xiaogang Zhu, Zexin Hu, Zhiyong Wang

Cross-sample instability in attention-response energy drives unreliable precipitation nowcasts, which HARECast corrects via group-wise regularization on head-wise energy.

arxiv:2605.13181 v1 · 2026-05-13 · cs.LG · cs.AI

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Claims

C1strongest claim

Cross-sample instability of attention-response energy is an important and previously underexplored source of forecasting unreliability; HARECast achieves state-of-the-art performance on SEVIR and MeteoNet by explicitly modeling and stabilizing head-wise attention-response energy through group-wise regularization.

C2weakest assumption

That reducing cross-sample attention energy variance via the proposed regularization will not degrade the model's ability to learn useful representations for accurate precipitation prediction, and that the observed association between energy variance and forecast error is causal rather than correlational.

C3one line summary

HARECast stabilizes cross-sample variance in attention-response energy via group-wise regularization to reduce prediction errors in precipitation nowcasting.

References

36 extracted · 36 resolved · 2 Pith anchors

[1] Oussama Boussif, Ghait Boukachab, Dan Assouline, Stefano Massaroli, Tianle Yuan, Loubna Benabbou, and Yoshua Bengio. 2023. Improving day-ahead solar ir- radiance time series forecasting by leveraging 2023

[2] Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, and Wen Gao. 2022. STRPM: A spatiotemporal residual predictive model for high-resolution video prediction. InConference on Computer Vision and Patte 2022

[3] Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xiang Xinguang, and Wen Gao. 2021. MAU: A motion-aware unit for video prediction and beyond. Advances in Neural Information Processing Syste 2021

[4] Yeji Choi, Keumgang Cha, Minyoung Back, Hyunguk Choi, and Taegyun Jeon

[5] InIEEE International Geoscience and Remote Sensing Symposium

Cited by

1 paper in Pith

PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction

Receipt and verification

First computed	2026-05-18T03:08:56.414170Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

447dd6cddff3340549655206df986364551a7b1f4f778c4c234d55cd7cf3dd17

Aliases

arxiv: 2605.13181 · arxiv_version: 2605.13181v1 · doi: 10.48550/arxiv.2605.13181 · pith_short_12: IR65NTO76M2A · pith_short_16: IR65NTO76M2AKSLF · pith_short_8: IR65NTO7

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/IR65NTO76M2AKSLFKIDN7GDDMR \
  | 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: 447dd6cddff3340549655206df986364551a7b1f4f778c4c234d55cd7cf3dd17

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e794eef84cdbeb9e018d1455fc114f549e55f21a109124d39cc385cab878b6d2",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T08:41:39Z",
    "title_canon_sha256": "ba7c6a38f3d0f8323ede6c3dba9486f91367342a7dce476b97d24c1866563a6b"
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  "source": {
    "id": "2605.13181",
    "kind": "arxiv",
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