pith:QFEQI5BY
PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting
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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Claims
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.
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.
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.
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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
· · · · ·Agent API
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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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