What question did this study set out to answer?

To investigate a new geometric approach to language encoding that moves beyond traditional linear token streams.

April 4, 2026Open Access

Applied ITT - A Pre-Emergence Geometric Tokenizer: Recasting Language as a Structured Manifold

Key Points

To investigate a new geometric approach to language encoding that moves beyond traditional linear token streams.
Proposed a geometric tokenizer that maps subword units into geometric objects
Developed a framework based on structured 2D code tiles
Leveraged field dynamics and boundary-constrained problem-solving techniques
Introduced a novel architecture for language processing using geometric principles
Demonstrated the potential for pre-symbolic treatment of language through geometric mappings

Abstract

Applied ITT - A Pre-Emergence Geometric Tokenizer: Recasting Language as a Structured Manifold Armstrong Knight (Sensei Intent Tensor) - intent-tensor-theory.com We propose a geometric tokenizer in which language is encoded not as a linear stream of opaque token IDs, but as a compositional manifold of structured 2D code tiles. The motivating hypothesis is that if ARC-style grid problems can be solved as boundary-constrained field problems, then language may also admit a pre-symbolic treatment in which subword units are mapped into writable geometric objects and interpreted through field dynamics rather than purely sequential token recursion. The resulting architecture is QR-inspired, but not standard-QR-based; it uses subword segmentation, structured code zones, similarity-preserving placement rules, and field operators adapted from `Φ, ∇Φ, σ, ρq`. The goal is not to replace learning, but to place learning on top of a mechanically constrained substrate. Running implementation: intent-tensor-theory.com/applied-itt

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