Inference landscape

The combination of SOHMs and vascular tension that defines the brain's inference process.

Neighborhood — ranked by edge-count

concept

Vascular Smooth Muscle Cells (VSMCs)
associated_with
Proposed as the brain's compression/prediction infrastructure where tanha physically manifests; central mechanism in vasocomputation theory.
Self-Organized Harmonic Modes (SOHMs)
associated_with
Adam Safron's framework for neural building blocks functioning as symmetry detectors/autoencoders; tanha-free awareness theorized as direct feeling of undoctored SOHMs.

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.

Learning landscapeconcept0.785
The combination of neural weights and SOHMs that defines the brain's learning space.
Fitness Landscapeconcept0.756
Kauffman's concept of the coupling between an evolving genotype and its fitness landscape as generating internal ordering tendencies in evolution
Inference-Time Steeringconcept0.752
Subtracting a scaled persona vector from hidden states at each decoding step to reduce trait expression after finetuning
perceptual inferenceconcept0.752
The process of inferring causes of sensory inputs, a key aspect of the free-energy minimization scheme.
Stages of Inferenceframework0.751
The perspective that LLM inference decomposes into distinct computational stages, which the paper extends to looped models
Natural Language Inferenceconcept0.748
Three-way classification task (entailment, contradiction, neutral) leveraged to measure dialogue diversity
Bayesian Inferencemethod0.746
Inference-Time Scalingconcept0.743
Paradigm of improving model outputs through more computation at inference time; VS is presented as a diversity-oriented alternative