Probabilistic State Space Enumeration in Neural Network Computation

arXiv:2611.15062v1 Announce Type: new Abstract: Deep neural networks typically realize dense computation by propagating information through all intermediate channels, while many approximation methods rank and retain individual computational components. We ask whether dense neural computation can instead be represented exactly as an expectation over a combinatorial state space, and approximated by evaluating only its most probable configurations. We introduce a novel probabilistic state-space framework for Transformer MLPs. The framework partitions the intermediate dimension into channel groups, defines an input-dependent distribution over binary inclusion states, and uses importance-corrected masked outputs such that the full expectation exactly recovers the dense MLP output.