claim

active

claim:once-visible-the-prior-is-eliminated-via-bayesian-model-reduction-resulting-in-a-post-dual-agent-with-unconstrained-qrf-deployments

Once visible, the prior is eliminated via Bayesian model reduction resulting in a post-dual agent with unconstrained QRF deployments.

Formal description of the transition to post-duality.

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

Neighborhood — ranked by edge-count

Papers (1)

paper

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

Concepts (1)

concept

prior eliminated by Bayesian model reduction
about
Bayesian model reduction removes the structural partition prior once evidence shows it is unnecessary, yielding a post-dual agent.

Methods (1)

method

Bayesian model reduction (method)
about
A method for simplifying models by removing parameters that don't contribute; applied to eliminate the self-boundary prior.

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.

post-dual agent with unconstrained QRF deploymentsconcept0.812
An agent that no longer imposes a fixed self-environment partition; its reference frame choices are free from dualistic constraints.
Bayesian model reduction after 12 trials correctly removes off-diagonal (redundant) parameters from the likelihood array, recovering the true contingency structure.finding0.795
Validation that BMR correctly identifies and prunes wrong connections in the likelihood mapping
If sigma is pruned, the agent gains access to QRF deployments and predictive strategies that were structurally excluded under dualistic fixationhypothesis0.791
Formal consequence of moving from constrained (Eq. 13) to unconstrained (Eq. 14) optimisation over QRF space
The results of abductive reasoning (reduced model priors) can be communicated to other agents as prior beliefs, provided all agents share the same model lexicon or hypothesis space.claim0.786
Explanation of how knowledge (not just parameters) is shared between agents; links to pre-Cartesian consciousness
We formalise the belief in separation as a structural prior over an agent's quantum reference frame (QRF) deployments, constraining all measurement frames to respect a self/environment partition.claim0.778
Formalisation of the dualistic belief.
Buddhist awakening can be formalised as Bayesian model reduction of the separation prior sigma, yielding a post-dual agent with unconstrained QRF deploymentsclaim0.777
Central theoretical contribution of the paper unifying contemplative path with active inference framework
In simulations, positive evidence threshold for Bayesian model reduction corresponds to ΔF ≤ −3, equivalent to odds ratio of exp(−3) ≈ 0.05 (reduced model ~20 times more likely than full model).finding0.768
Quantitative threshold used for accepting reduced models; linked to Bayes factor of ~20
A naive agent equipped with reduced priors from an experienced agent performs perfectly with maximum confidence from the first trial.finding0.766
Demonstration that model-level priors (not parameter-level knowledge) suffice for immediate transfer