pith:VJVFICHM
OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting
OceanCBM routes forecasts of ocean heat content through prescribed physical concepts plus one free concept to deliver both skill and mechanistic insight.
arxiv:2605.12639 v1 · 2026-05-12 · cs.LG
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Claims
OceanCBM achieves interpretable, physically grounded representations without sacrificing skill, explicitly characterizing the interpretability-performance trade-off. Across ensemble initializations, mixed supervision yields consistent mechanistic representations, whereas prediction-only and prescription-only baselines learn highly variable latent structures despite similar predictive performance.
That the prescribed concepts derived from geophysical fluid dynamics are the right set to capture the key drivers of mixed layer heat content, and that adding one free concept plus mixed supervision will reliably produce consistent mechanistic representations rather than artifacts of the training process.
OceanCBM is the first concept bottleneck model for spatiotemporal ocean prediction that uses mixed supervision on physical concepts and a free concept to deliver consistent mechanistic representations for mixed layer heat content forecasts.
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Receipt and verification
| First computed | 2026-05-18T03:10:00.029913Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
aa6a5408ec026636e8a8925978a12100770771800ce7d2378656752c0f43e3b3
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/VJVFICHMAJTDN2FISJMXRIJBAB \
| 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())"
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Canonical record JSON
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