HAI / BIS: A Finite, Shared, Structured Basis of Semantic Primitives Extractable from Large Language Models

Computational test of the Brain Instruction Set (BIS) hypothesis: that humanconceptual knowledge can be represented over a finite, shared, internallystructured basis of semantic primitives recoverable from large language models.This release contains the BIS Release bundle (152 verified terminals across ninemodalities with per-terminal test scores and provenance; schema bis-schema/1.0),the decomposition graph (13,534 concepts / 44,682 weighted edges, GraphML), thecross-model comparison (16-terminal model-invariant core), the sub-qualia dataset(3,684 stable / 6,928 extended named sub-types with per-terminal dimensionality),the saturation curve, the preprint (SK + EN), and figures. Mandatory declarations: results characterize the structure of textualrepresentations, not phenomenal experience (qualia), not perceptualdiscriminability, and — without external anchors — not calibrated truth.