AI RESEARCH
Learning to Trust: Bayesian Adaptation to Varying Suggester Reliability in Sequential Decision Making
arXiv CS.AI
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ArXi:2511.12378v2 Announce Type: replace Autonomous agents operating in sequential decision-making tasks under uncertainty can benefit from external action suggestions, which provide valuable guidance but inherently vary in reliability. Existing methods for incorporating such advice typically assume static and known suggester quality parameters, limiting practical deployment. We