LATIDIA · Robótica

Garantizar sistemas ciberfísicos con capacidad de aprendizaje mediante modelos fundamentales: una agenda neurosimbólica para pruebas, verificación y garantía en tiempo de ejecución.

arXiv:2610.39547v1 Anuncio Tipo: nuevo Resumen: Los sistemas ciberfísicos (CPS) habilitados para el aprendizaje, incluidos los vehículos autónomos y los UAV, incorporan cada vez más modelos fundamentales, pero las prácticas de garantía fragmentadas proporcionan

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arXiv:2610.39547v1 Announce Type: new Abstract: Learning-enabled cyber-physical systems (CPS), including autonomous vehicles and UAVs, increasingly incorporate foundation models, yet fragmented assurance practices provide insufficient support for system-level guarantees. We argue that restoring engineering control across stochastic neural, symbolic, and physical boundaries requires a shift from model-centric evaluation to system-level assurance. We introduce the Foundation Model Assurance Stack, connecting three directions: (i) neuro-symbolic architectures that constrain foundation models through explicit symbolic world models, reasoning layers, and interfaces; (ii) semantically grounded, conformance-based testing that combines formal specifications and symbolic world models with foundation-model-assisted specification mining, scenario generation, and test synthesis; and (iii) compositional verification and runtime assurance that target formally tractable components