LATIDIA · Robótica

Políticas de pensamiento condicionado por la situación de aprendizaje para agentes de LLM a largo plazo

arXiv: 2610.09590v1Tipo de anuncio: cross Resumen: Los agentes autónomos de larga duración deben reutilizar la experiencia de razonamiento acumulada sin permitir que la memoria histórica explícita y el contexto LLM crezcan indefinidamente. Sin embargo, exis

La noticia

arXiv:2610.09590v1 Announce Type: cross Abstract: Long-running autonomous agents must reuse accumulated reasoning experience without allowing explicit historical memory and LLM context to grow indefinitely. However, existing memory mechanisms mainly retrieve, summarize, or compress past content and do not directly learn when particular kinds of thinking should be activated or discover new thinking knowledge from temporally dispersed experiences. This paper proposes a situation-conditioned thinking memory framework that transforms historical reasoning experience into a lightweight policy for predicting what should be thought about in the current situation, while leaving detailed reasoning to a large language model. Situations may represent temporal or spatiotemporal evolution rather than only current states. Temporary experiences are also