LATIDIA · Robótica

SAGE: Control y edición simbólicos de acciones para planificadores de tareas de LLM

arXiv:2609.34268v1 Tipo de anuncio: nuevo Resumen: Los modelos de lenguaje grandes (LLM) ahora son el núcleo cognitivo predeterminado de los agentes domésticos incorporados, sin embargo, los planes que emiten rara vez se comparan con un modelo fundamentado del env

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arXiv:2609.34268v1 Announce Type: new Abstract: Large language models (LLMs) are now the default cognitive core of embodied household agents, yet the plans they emit are rarely checked against a grounded model of the environment before execution, and the task-success they report is often measured on benchmarks so saturated that no method can be separated from another. We present SAGE (Symbolic Action-Gating and Editing), a single-LLM planner built from two lightweight mechanisms: a domain-agnostic symbolic gate (~250 lines of Python, zero tokens, $O(|\pi|)$) that blocks precondition-violating actions with typed reasons as a runtime safety monitor, and a local edit that regenerates only the failed sub-goal's suffix, keeping completed and untouched work