AI RESEARCH
BALAR : A Bayesian Agentic Loop for Active Reasoning
arXiv CS.LG
•
ArXi:2605.05386v1 Announce Type: cross Large language models increasingly operate in interactive settings where solving a task requires multiple rounds of information exchange with a user. However, most current systems treat dialogue reactively and lack a principled mechanism to reason about what information is missing and which question should be asked next. We propose BALAR (Bayesian Agentic Loop for Active Reasoning), a task-agnostic outer-loop algorithm that requires no fine-tuning and enables structured multi-turn interaction between an LLM agent and a user.