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pith:QFEQI5BY

pith:2026:QFEQI5BYZAFAGWWFDIXBHRTA4F
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PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting

Fan Xu, Fan Zhang, Hao Jia, Hao Wu, Penghao Zhao, Qingsong Wen, Ruijian Gou, Xian Wu, Xiaomeng Huang, Yuan Gao, Yuxuan Liang, Yuxu Lu

By freezing pre-trained physics engines and training only a correction agent, the PnP-Corrector framework counters reciprocal error amplification to improve long-term accuracy in coupled spatiotemporal forecasts.

arxiv:2605.08935 v3 · 2026-05-09 · cs.AI · cs.LG

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Record completeness

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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

In the challenging task of a 300-day global ocean-atmosphere coupled forecast, our PnP-Corrector framework reduces the prediction error of the baseline model by 29% and surpasses state-of-the-art models on several key metrics.

C2weakest assumption

That a correction agent trained separately on frozen physics engines can proactively counteract systematic biases arising from reciprocal error amplification without retraining or modifying the underlying simulators.

C3one line summary

PnP-Corrector decouples physics simulation from error correction via a plug-and-play agent, cutting error by 29% in 300-day global ocean-atmosphere forecasts.

Formal links

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Receipt and verification
First computed 2026-06-03T01:05:51.116060Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

8149047438c80a035ac51a2e13c660e1682dfa6663d7c9262af506a6da272820

Aliases

arxiv: 2605.08935 · arxiv_version: 2605.08935v3 · doi: 10.48550/arxiv.2605.08935 · pith_short_12: QFEQI5BYZAFA · pith_short_16: QFEQI5BYZAFAGWWF · pith_short_8: QFEQI5BY
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/QFEQI5BYZAFAGWWFDIXBHRTA4F \
  | 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: 8149047438c80a035ac51a2e13c660e1682dfa6663d7c9262af506a6da272820
Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-09T13:12:33Z",
    "title_canon_sha256": "f3e8f48494c0204e95bd84952baf6e6c6cc654fafc5b60d7cb97e0854d4a72e6"
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