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cs.LG2026
A Transport-Based Geometry of Belief-Cost
Laurent Caraffa
A finite agent, a machine's digital twin or any bounded reasoner, infers a fixed and noisy world through finite sensors, so its coherent output is a belief: a probability density o…
cs.LG2026
Dissipative Learning: A Framework for Viable Adaptive Systems
Laurent Caraffa
We propose a perspective in which learning is an intrinsically dissipative process. Forgetting and regularization are not heuristic add-ons but structural requirements for adaptive…
cs.LG2026
Thermodynamically Optimal Regularization under Information-Geometric Constraints
Laurent Caraffa
Modern machine learning relies on a collection of empirically successful but theoretically heterogeneous regularization techniques, such as weight decay, dropout, and exponential m…