LATIDIA · Investigación

La precisión del monitor no es la respuesta: un marco de valor de decisión para evaluar los monitores de reinicio de agentes de IA.

arXiv:2610.36544v1 Anuncio Tipo: nuevo Resumen: La precisión de la clasificación y las tasas de verdaderos positivos y falsos positivos por sí solas no determinan si reiniciar un agente de IA mejora el éxito. Usando un identificador exacto de alarma a acción

La noticia

arXiv:2610.36544v1 Announce Type: new Abstract: Classification accuracy and true-positive and false-positive rates alone do not determine whether restarting an AI agent improves success. Using an exact alarm-to-action identity for an independent clean restart with zero monitoring overhead, we show analytically that a monitor with fixed 90% accuracy can swing from a five-percentage-point loss to a 35-point gain in success rate, depending solely on the replacement attempt's residual-budget success probability. We introduce two techniques for evaluating monitors and progress signals in decision terms. First, a decision-sufficiency criterion, a binary-action specialization of Blackwell's comparison of experiments, identifies which progress representations or context compressions preserve the information needed to choose optimally between the