Binary Generation Constraint

Implementation technique zeroing all logits except Yes/No tokens to convert steering into binary cross-entropy

Neighborhood — ranked by edge-count

method

Loss-Guided Concept Cone Discovery
uses
Optimization procedure that learns orthonormal basis vectors satisfying causal truth and retention constraints via composite loss

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.

Binary Relationconcept0.721
Fundamental structure (G, M, R) modeling objects with attributes; gives rise to polar maps and concept lattices.
Fluency Constraintconcept0.719
Early stopping in alpha sweeps when mean fluency falls below 95% of baseline or >5% of responses fall below 90% baseline
Binary Logic Gatesconcept0.718
Fundamental discrete computation units used as neurons in DLGN and DiffLogic CA
Metabolic Constraints as Architectural Driverconcept0.708
Behaviorally Binary Subspaceconcept0.707
A vector subspace that causally impacts outputs only through the sign of its values, enabling harmless magnitude divergence
Implicitly Mimetic Generationconcept0.703
Alternative explanation: models produce first-person experiential language by extending predictive text modeling of human-authored introspective writing without encoding it as roleplay
resource constraintsconcept0.703
Discrete money and no-change payments limiting spending flexibility.
Constraints as Causesconcept0.702
Key reframing: contextual constraints (both bottom-up and top-down) replace mechanistic causation in explaining action.