Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models
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. We show how LIR unifies and generalizes a wide variety of important algorithms in the literature, including the Expectation-Maximization (EM) algorithm, belief propagation, adversarial training, GANs, and GFlowNets. In the last case, LIR actually suggests a m...
| Authors | Oliver E. Richardson, Mandana Samiei, Mehran Shakerinava et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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