EFE-based planning is formulated as variational free energy minimization with epistemic priors, decomposing into expected plan costs plus a complexity term.
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EFE-based active inference planning is characterized as VFE on an augmented model plus entropy and planning corrections, with a derived message-passing implementation and grid-world validation.
Introduces distribution-level unsupervised feature discovery for LLMs by clustering continuations using semantic embeddings and prefix-to-continuation attribution signatures via rate-distortion optimization.
Combined CMS result gives α^{Hττ} = 7 ± 16° for the CP mixing angle in H→ττ, consistent with SM expectation of 0 ± 14°.
Thesis uses statistical mechanics to study DAM and RBM models for understanding memorization, low-dimensional learning, and adversarial robustness in neural networks.
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