pith:TXN3N6B2
On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins
Three design principles using a density-preserving intermediate representation enable resilient LLM-assisted workflows for digital twins.
arxiv:2603.25898 v3 · 2026-03-26 · eess.SY · cs.AI · cs.SE · cs.SY
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Claims
We present three critical design principles for integrating resilience and oversight into such workflows... A key contribution is detailed characterization of LLM-induced errors across model descriptions of varying detail and complexity, revealing how IR choice critically impacts error rates.
The assumption that the error characterization and design principles observed in FactoryFlow generalize beyond the specific manufacturing examples and LLM versions tested.
Three design principles for LLM-assisted digital twin modeling emphasize separating structure from parameters, using library components, and a density-preserving Python IR to limit hallucination error growth.
Receipt and verification
| First computed | 2026-05-21T01:05:18.227747Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/TXN3N6B2ZCSPO4P4J2BCZYW6WO \
| jq -c '.canonical_record' \
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# expect: 9ddbb6f83ac8a4f771fc4e822ce2deb38a9fabe173e82f43178accdadbaa11a0
Canonical record JSON
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