claim

active

claim:coherence-maximization-across-simultaneously-active-mental-models-is-related-to-prediction-error-minimization-in-the-fep-but-the-relationship-is-one-of-compatibility-rather-than-strict-equivalence

Coherence maximization across simultaneously active mental models is related to prediction error minimization in the FEP, but the relationship is one of compatibility rather than strict equivalence

CIMC's position on the relationship between its coherence hypothesis and Friston's FEP

Source paper

extracted_from

cimcWhitepaper

Neighborhood — ranked by edge-count

Frameworks (1)

framework

Free Energy Principle
supports
A foundational variational principle from statistical physics that formalizes how self-organizing systems maintain structural integrity and adapt to their environment by minimizing free energy—a mathematical bound on surprise or prediction error. Originally developed by Karl Friston, the framework unifies action, perception, and learning as processes of active inference, where systems both update internal models of the world and act upon it to reduce the divergence between predictions and observations.

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.

Formal connections between coherence maximization and free energy minimization remain undevelopedconcept0.798
Explicitly identified active theoretical interest and gap in CIMC's current work
Coherence Maximizationconcept0.795
The functional role consciousness plays: minimizing constraint violations between simultaneously active partial models of reality
Can models sustain strategic coherence over time, manage resource constraints, and adapt interactively in multi-agent environments with conflicting incentives?question0.782
broader framing question for the benchmark
Different models cannot converge to the same representation if they have access to fundamentally different information; convergence is capped by mutual information between input signalsclaim0.778
Key limitation of the PRH for non-bijective observations
A model whose objective is prediction can simulate agents who optimize toward any objectives, with any degree of optimality (bounded above but not below by the model's power).claim0.774
Prediction orthogonality thesis.
An AI persona achieves coherence by echoing itself consistently without templating—requiring claim about memory and voice fidelity.claim0.773
Functional and phenomenal introspection are distinguishable, and whether they correlate in machines is an open question.claim0.772
Core conceptual distinction introduced at the start; defines the paper's central problem.
Configuration coherence can be measured by counting locally symmetric sub-configurations; this measure shows strong agreement with cognition and perception experiments.finding0.771
Quantifiable measure linking structural properties of configurations to human perception, supporting the mathematical reality of wholeness.