Patent-Pending AI Infrastructure
Grammar-Aware AI
Grammatical Orthogonality for Large Language Models · Inventor: Michael Malgeri · 2026
The Challenge & The Approach
The Problem
Current LLMs treat every word identically — whether it's a subject, a verb, or an adjective. Grammatical roles are entangled in a single embedding space, making models harder to interpret, control, or improve. There is no way to surgically modify one aspect of generated text without re-generating everything.
The Solution
We add seven grammatically orthogonal embedding subspaces — one per grammatical role — plus a three-part dependency system that detects when orthogonality is violated and corrects for it automatically. The result is a more accurate, interpretable, and controllable language model.
Measured Results — Reproducible Prototype
51.4%
Language model quality improvement over identical-architecture baseline
361.34 → 175.68 perplexity
20/21
Grammatical role pairs achieving orthogonality through training
95.2% — without explicit supervision
31.9%
Pairwise role correlation reduction after dependency correction
NS↔NO: the one detected dependency
6/8
Patent claims fully demonstrated with measured, reproducible experimental evidence
The Three-Component System
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1. Grammatical Embeddings
Seven role-specific subspaces (subject noun, verb, object noun, modifiers, prepositions). Each token receives a full-dimension base embedding plus a small, learnable role offset. No information loss — every dimension is used.
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2. Dependency Detection
Measures pairwise Pearson correlation between role activations across a corpus. Builds a dependency graph of any role pairs exceeding the orthogonality threshold. Identifies linguistically meaningful violations.
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3. Dependency Correction
Trains linear correction modules per detected dependency. Factors out dependent components, leaving only the independent signal. Assigns adaptive weights to roles based on their dependency scores.
Customer Use Cases
- Semantic Search: Role-separated document indexing with dependency-corrected similarity matching and explainable results
- Controllable Generation: Fix certain grammatical roles while varying others — change the verb without changing the subject
IP Status & Next Steps
Intellectual Property
- Provisional patent filed — priority date established
- Defensive publication protects broad concepts
- Non-provisional filing in progress (attorney engaged)
- Concept traces to MyXEngine paper (Malgeri, 2006)
- All code, checkpoints and logs preserved for enablement
Immediate Opportunities
🚀Pilot customer conversations — open now
💡Licensing discussions — AI labs & platforms
🔬Technology Readiness Level: TRL 4 (lab validated)
📈Target TRL 5–6 with seed funding within 6 months