A representation can be an observation.
Hidden tensors, glyphs, patches, and call-time vocabularies can be evidence for this computation without sharing one permanent token identity.
Literal · Runtime vocabulary · PulseARTI / CONCEPT
ARTI is a PyTorch-first research architecture. It lets variable observations enter fixed-capacity latent workspaces while coordinates, provenance, visibility, and call-time output spaces remain explicit tensor contracts.
EDITORIAL FRAME · NOT A PROVEN OBJECT THEORY
Observation, Perspective, and Boundary are not a proven object theory. They organize the public tensor contracts and the questions behind them. Task behavior still depends on training, data, and caller-supplied structure.
Hidden tensors, glyphs, patches, and call-time vocabularies can be evidence for this computation without sharing one permanent token identity.
Literal · Runtime vocabulary · PulseCoordinates, frame operators, and observer coordinates are supplied explicitly. ARTI does not discover sources or physical reference frames.
coord · observer frame · PhaseMixerVisibility, model context, and public emission can be modeled separately. Membrane is not encryption or an independent security boundary.
visibility · context · emit01 / SHARED STRUCTURAL PROBLEM
hidden states · glyphs · coordinates · masks[B,N,D] → [B,K,D]explicit state · compact multi-source workspacesdynamic layout · call-time local slots02 / ADOPTION
Attach ARTI to existing PyTorch hidden tensors to add structure, visibility, dynamic state, or fixed-capacity workspaces.
tokenizer → hidden tensor → ARTILet visible glyphs, layout, coordinates, and the current output vocabulary enter the model. These Alpha interfaces still require task-specific training.
literal input → layout → runtime vocabulary03 / RESEARCH DISCIPLINE
Fixed shapes, value-preserving gathers, and visibility routing can be proven by interface tests. Cross-segmentation stability, shared object representations, or security robustness require trained tasks, baselines, and ablations.