finding

active

finding:the-change-in-free-energy-from-pruning-sigma-is-negative-delta-f-delta-complexity-delta-accuracy-0-because-complexity-cost-remains-while-accuracy-contribution-approaches-zero-after-contemplative-trajectory

The change in free energy from pruning sigma is negative (delta_F = delta_Complexity + delta_Accuracy < 0) because complexity cost remains while accuracy contribution approaches zero after contemplative trajectory

Formal result establishing that BMR prunes sigma when the metacognitive model is in place

Source paper

extracted_from

There is no self-evidence: A physics of emptiness realisation

(2026) · Lars Sandved-Smith · Chris Fields · Thomas Doctor · Ruben Laukkonen +1

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Claims (2)

claim

Buddhist awakening can be formalised as Bayesian model reduction of the separation prior sigma, yielding a post-dual agent with unconstrained QRF deployments
supports
Central theoretical contribution of the paper unifying contemplative path with active inference framework
Self-evidencing is not only unimpaired but improved after emptiness realisation, as the pruned model is more parsimonious without loss of accuracy
supports
Addresses the concern that emptiness realisation might undermine adaptive functioning

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

If epistemic value is removed from expected free energy, the resulting objective reduces to maximizing expected future reward (pragmatic value).hypothesis0.773
Stated as conditional statement explaining the special case whence RL emerges.
"A system can minimize free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception respectively."concept0.767
Load-bearing definition of how action and perception implement free energy minimization.
Current training methods rely on loss minimization, meaning the experiential profile of training is predominantly negative across billions of parameter updatesclaim0.747
Ethical implication about the nature of AI training experience if the thesis holds
All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.claim0.742
Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.
Emotional valence identified with the negative rate of change of free energy, a signed quantity in which decreasing free energy yields positive valencefinding0.738
Antecedent proposal within the FEP framework that shares the signed-error identification with the present thesis
If the minimization of free energy is just a corollary of descent onto a global random attractor, does this mean that adaptation and evolution are just ways of describing the same thing?question0.738
Question about the relationship between adaptation, evolution, and free energy minimization.
Agents perceive by minimizing variational free energy to ensure model consistency with past observations and act by minimizing expected free energy to make future sensations consistent with preferences.claim0.738
Formalization of perception-action cycle integrating inference and decision-making.
The concept of signed evaluation 'minus the feeling' has no more content than the concept of molecular motion 'minus the heat.'quote0.735
The heat-motion analogy making the identity claim vivid