AI RESEARCH
Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models
arXiv CS.LG
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ArXi:2604.17140v1 Announce Type: cross We present a generic algorithm for learning and approximate inference with an intuitive epistemic interpretation: iteratively focus on a subset of the model and resolve inconsistencies using the parameters under control. This framework, which we call Local Inconsistency Resolution (LIR) is built upon Probabilistic Dependency Graphs (PDGs), which provide a flexible representational foundation capable of capturing inconsistent beliefs.