pith:RPLG2DVK
Eradicating Negative Transfer in Multi-Physics Foundation Models via Sparse Mixture-of-Experts Routing
Shodh-MoE uses Top-1 routing on compressed latents from a divergence-free autoencoder to let distinct physics regimes train separate experts, yielding low MSE and autonomous domain separation in mixed pretraining.
arxiv:2605.15179 v1 · 2026-05-14 · cs.LG · cs.AI · physics.comp-ph
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Claims
These results support sparse expert routing as a practical architectural mechanism for mitigating multi-physics interference in universal neural operators.
That a Top-1 soft-semantic router will reliably produce autonomous domain bifurcation and specialized parameter paths for incompatible PDE regimes without losing shared symmetries or requiring extensive hyperparameter tuning.
Shodh-MoE uses Top-1 routing on compressed latents from a divergence-free autoencoder to let distinct physics regimes train separate experts, yielding low MSE and autonomous domain separation in mixed pretraining.
Receipt and verification
| First computed | 2026-05-17T21:40:25.187828Z |
|---|---|
| Last reissued | 2026-05-17T21:57:18.547039Z |
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | unsigned_v0 |
| Schema | pith-number/v1.0 |
Canonical hash
8bd66d0eaa91b0c184ab44525bee5a4b6979d0de4233ced412911af3d7d5d732
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/RPLG2DVKSGYMDBFLIRJFX3S2JN \
| 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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