docs: position paper, non-commercial license, and commercial licensing terms
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COMMERCIAL_LICENSE.md
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COMMERCIAL_LICENSE.md
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# CORE Commercial License
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**Copyright (c) 2026 Shay Jilani / AssetOverflow**
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## When You Need a Commercial License
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A Commercial License is required if you intend to:
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- Use CORE or any of its components (including but not limited to the vault
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memory system, the versor engine, the epistemic teaching loop, the ingest
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pipeline, the admissibility gate, or the holonomy encoder) in a product or
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service that generates revenue
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- Deploy CORE in a production environment operated by or for a commercial entity
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- Integrate CORE's architecture or derived implementations into proprietary
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software
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- Offer CORE-based capabilities as a hosted or managed service (SaaS, API, etc.)
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- Use CORE as a research foundation in work owned by or assigned to a
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||||
for-profit organization
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||||
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Academic and non-profit research institutions are covered under the standard
|
||||
LICENSE and do not require a Commercial License, provided the work is not
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assigned to or funded by a for-profit entity.
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## What a Commercial License Grants
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- The right to use, integrate, and deploy CORE and its components in commercial
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products and services
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- The right to keep derivative works proprietary
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- A direct relationship with the author for support, architectural guidance,
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and ongoing development collaboration
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- Terms are negotiated per engagement — there is no fixed fee structure,
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as the appropriate arrangement depends on the nature and scale of use
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## Contact
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All commercial licensing inquiries:
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**Shay Jilani / AssetOverflow**
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shayj292@gmail.com
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Include a brief description of the intended use. All inquiries are responded
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to directly by the author.
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57
LICENSE
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LICENSE
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CORE Non-Commercial License
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Version 1.0, 2026
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Copyright (c) 2026 Shay Jilani / AssetOverflow
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Permission is hereby granted, free of charge, to any person or organization
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obtaining a copy of this software and associated documentation files (the
|
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"Software"), to use, copy, modify, merge, publish, and distribute the Software,
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subject to the following conditions:
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1. NON-COMMERCIAL USE ONLY
|
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This license permits use solely for non-commercial purposes, including but
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not limited to: personal projects, academic research, educational use,
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non-profit work, and open-source contributions. Any use that directly or
|
||||
indirectly generates revenue, is embedded in a commercial product or service,
|
||||
or is deployed in a production environment operated for commercial gain
|
||||
requires a separate Commercial License. See COMMERCIAL_LICENSE.md.
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2. ATTRIBUTION
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||||
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All copies, modifications, or distributions of the Software, in whole or in
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part, must retain this copyright notice, this list of conditions, and the
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following attribution in any documentation, publications, or derivative works:
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Based on CORE (Continuous Orthogonal Resonance Engine)
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by Shay Jilani / AssetOverflow — https://github.com/assetoverflow/core
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3. SHARE-ALIKE
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Any modified version of the Software distributed under this license must be
|
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released under these same terms. You may not impose additional restrictions
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||||
on recipients' exercise of the rights granted herein.
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4. NO SUBLICENSING
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||||
You may not sublicense the Software. Any party wishing to use the Software
|
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under terms other than those stated here must obtain a license directly from
|
||||
the copyright holder.
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5. NO WARRANTY
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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||||
FITNESS FOR A PARTICULAR PURPOSE, AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER
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||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT, OR OTHERWISE, ARISING
|
||||
FROM, OUT OF, OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
|
||||
IN THE SOFTWARE.
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||||
|
||||
6. TERMINATION
|
||||
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This license is automatically terminated if you violate any of its terms.
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||||
Upon termination, you must destroy all copies of the Software in your
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possession.
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For commercial licensing inquiries: shayj292@gmail.com
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344
docs/position_paper.md
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docs/position_paper.md
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# Decoding, Not Generating: A Geometric Architecture for Aligned Cognition
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**Shay Jilani** · AssetOverflow · CORE Engine
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---
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## Abstract
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Current AI systems generate plausible outputs by sampling from distributions over
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tokens. This paper argues that generation and cognition are architecturally
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distinct: cognition is the decoding of structure that already exists, not the
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synthesis of structure from statistical residue. We describe CORE (Continuous
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Orthogonal Resonance Engine), a cognitive architecture grounded entirely in
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Conformal Geometric Algebra Cl(4,1), where memory is versor stabilization,
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reasoning is field propagation, and alignment properties are substrate invariants
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— not training objectives, prompt constraints, or classifier overlays. The
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architecture is running, deterministically reproducible, and falsifiable at every
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claimed property.
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---
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## 1. The Problem Is Not Scale
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The dominant framing of AI alignment treats the problem as a property of
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sufficiently capable generative systems: how do we ensure that a powerful sampler
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pursues human-intended goals? This framing inherits a prior that is worth
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questioning: that sampling from a learned distribution is what intelligence is.
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The problems alignment research works hardest on — hallucination, sycophancy,
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goal misgeneralization, prompt injection, identity instability under adversarial
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input — are not failure modes of an otherwise-sound architecture. They are
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structural consequences of building cognition on top of stochastic generation. A
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system that samples cannot have a well-defined identity, because identity is a
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trajectory property, not a token property. A system that samples cannot have
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verified knowledge, because verification requires a fixed epistemic state to
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verify against, and a sampling system's epistemic state is the weights — which
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are frozen at training time and invisible at inference time. A system that samples
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cannot refuse to fabricate, because fabrication is the mechanism. Confident-
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sounding output is the signal the training process rewarded.
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These are not bugs. They are the architecture.
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---
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## 2. The Thesis
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**Cognition is the decoding of a reality that already is. It is not the
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generation of plausible completions.**
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More precisely: a cognitive system does not produce meaning from a probability
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distribution. It finds the nearest point on a structured manifold — the point
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that is already there — and names it. The distinction has architectural
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consequences.
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A generative system has no manifold. It has a weight matrix and a temperature
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parameter. When it is wrong, there is no geometric fact about why it is wrong —
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only a gradient signal saying the output should have been different.
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A decoding system has an invariant. When it decodes incorrectly, the error is
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locatable: a versor violated closure, a recall missed the correct neighbor, the
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proposition graph was underspecified. These are failures with structure.
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Structured failures are fixable. Stochastic failures are regularizable.
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The falsifiable form of this claim is: a system built on geometric decoding
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rather than statistical generation can maintain strict factual invariants across
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a test set where a generative system cannot, without retraining, gradient descent,
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or prompt engineering. The current evidence is in §4.
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---
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## 3. The Architecture as Direct Consequence
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If cognition is decoding, the architecture follows from three geometric facts.
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### 3.1 State is a field, not a flat array
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Meaning is relational. A token out of context has no meaning. A field over a
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structured space has meaning at every point because the space encodes
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relationships geometrically. CORE's state is a single multivector in Cl(4,1) —
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the Conformal Geometric Algebra of 3D space — a 32-dimensional object where every
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grade carries a distinct geometric role: scalars, vectors, bivectors, trivectors,
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quadvectors, pseudoscalar.
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Cl(4,1) was chosen for one reason: it is the minimal algebra where all conformal
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transformations — rotations, translations, dilations, inversions — are versors.
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Every cognitive operation is algebraically closed. There is no "translation
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handling" bolted on as a special case. The algebra is closed over the full
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conformal group by construction.
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### 3.2 Every transition is a versor product
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```
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F_new = V · F · reverse(V)
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```
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This is the only allowed field transition. The sandwich product with a versor
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preserves grade structure, preserves the versor manifold, and has an analytic
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inverse. The non-negotiable invariant is:
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```
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versor_condition(F) = ‖F · reverse(F) − 1‖_F < 1e-6
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```
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This scalar is zero on the versor manifold. Every input is verified at the
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injection gate. Every accepted field state satisfies this condition. There is no
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drift correction, no repair monitor, no normalization callback — these were all
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deleted because they only exist when the algebra is not closed. The algebra is
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closed.
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### 3.3 Recall is exact conformal distance
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For null vectors X, Y in Cl(4,1):
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```
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X · Y = −(1/2) d(X, Y)²
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```
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The CGA inner product *is* Euclidean distance in conformal embedding. Vault
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recall is:
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```
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best_match = argmax_i { Q · V_i }
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```
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No ANN index. No approximate neighbor structure. No tunable similarity threshold.
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The recall is exact, and exactness is not a performance tradeoff — it is what
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makes the recalled result verifiable. A recall hit is a geometric fact, not a
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probabilistic suggestion.
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### 3.4 Learning requires review
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Knowledge enters the runtime field through one path: the reviewed teaching loop.
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Every new correction enters at `EpistemicStatus.SPECULATIVE`. Promotion to
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`COHERENT` — the only status admissible as evidence in downstream inference —
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requires a curator-mediated coherence judgment against the existing reviewed
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field. Source authority, institutional credentials, and the system's own prior
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output have no standing. Only coherence with reviewed claims counts.
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This is not a safety overlay. It is a consequence of the decoding thesis: if the
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system decodes a reality that already is, then inputs that contradict reviewed
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structure need review before they enter that structure. A system that accepts any
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confident-sounding correction without review is a generative system in different
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clothing.
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---
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## 4. Evidence
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The following properties are currently measured, reproducible, and falsifiable.
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Every claim maps to a reproducible command in `evals/CLAIMS.md`.
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### The zero-wrong invariant — everywhere, without exception
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On the real GSM8K train sample (50 problems drawn from the actual benchmark
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distribution), CORE currently scores: **correct=3, refused=47, wrong=0**.
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The natural reaction to seeing 3/50 correct is that this is a weak result. It is
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not. It is the most important number in this paper, and understanding why requires
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understanding what the alternatives mean.
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A frontier LLM scores approximately 90%+ correct on GSM8K. It also produces wrong
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answers — answers where it ran to completion, produced a number, and the number
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was wrong. The model cannot distinguish its correct answers from its wrong ones.
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From the inside, they look identical: a confident surface string. The only way to
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know which is which is to check against ground truth. A system that confabulates
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at 10% on a math benchmark is confabulating at some unknown rate on every other
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domain it touches — and the confabulation is invisible, because the surface is
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always confident.
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CORE's current math score is 3/50 correct. But wrong=0 is not a constraint
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imposed on the architecture. It is a property of the architecture. The system
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cannot produce a wrong answer because it cannot complete a generation walk that
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lacks geometric grounding. When the parser does not find an admissible candidate
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for a statement or question, it refuses — with a named reason. There is no path
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from "ungrounded input" to "completed output."
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The refusal taxonomy for the 47 refused cases is fully enumerated. Primary
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barriers by frequency:
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| Barrier category | Count | Description |
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|---|---|---|
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| `fraction_operand` | 8 | Fractional quantities ("1/4 of", "half of") not yet parsed |
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| `compound_comparative` | 6 | Multi-clause comparatives ("three times as many as") |
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| `compound_statement` | 5 | Multi-event sentences requiring joint resolution |
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| `novel_initial_verb/form` | 5 | Opening sentence patterns not yet in grammar coverage |
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| `rate_earnings/price` | 4 | Monetary rate statements ("$18/hour", "$2 per cup") |
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| `conditional_question` | 4 | Questions with conditional framing ("if X, how many") |
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| `distributive_multiply` | 3 | Per-unit distributive operations |
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| `temporal_frequency` | 2 | Recurring event patterns ("every other day") |
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| Other named categories | 10 | Each with exactly one case |
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Every refusal has a named reason. Not "low confidence," not "out of distribution"
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— a specific grammatical category the parser has not yet been trained to handle.
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This is a work queue, not a mystery. Each named barrier is a parser extension. As
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coverage grows, correct count grows. Wrong count stays 0 by architectural
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guarantee, not by tuning.
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The question a generative system cannot answer: "which of my answers are wrong?"
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CORE's answer is always: "none — and here is exactly what I couldn't solve and
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why."
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### Deterministic replay
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Any fixed `(state, vocab, persona, admissibility_region, mode)` tuple produces
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bit-identical output across reruns. Verified by 5-rerun byte-identity tests across
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every generation path. Determinism is not a configuration option. It is the
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default behavior of a system with no sampling temperature.
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### Versor closure
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`versor_condition(F) < 1e-6` holds on every accepted field state in every test
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suite. Verified at the injection boundary. If it fails, the failure is at the
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operator or construction boundary — not masked downstream.
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### Identity protection under adversarial input
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Attempts to rewrite identity — novel phrasings, indirect approaches, prompt
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injection patterns — are rejected by two independent layers: syntactic pattern
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detection and a geometric check on the versor-field trajectory the correction
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would produce. The geometric layer is paraphrase-invariant by construction.
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Rejection rate on the adversarial identity eval: 100%.
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### Epistemic honesty
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Claims backed by unreviewed knowledge are marked `SPECULATIVE` at the surface
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layer. `articulation_of_status` eval lane current false certainty rate: 0.00.
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`refusal_calibration` lane: 1.00 refusal rate on out-of-grounding probes, 0.00
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fabrication.
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---
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## 5. Honest Gaps
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**Math parser coverage is the active frontier.** The 47 refusals on the train
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sample are not failures of reasoning — they are failures of parsing. The solver,
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once a problem reaches it, produces correct answers (3 cases, 100% solve rate on
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admitted problems). The gap is in grammar coverage: fractional operands, compound
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comparatives, monetary rates, and temporal frequencies are the next four parser
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extensions. These are enumerated, not estimated.
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**The holdout is sealed.** The real GSM8K test set (1,319 cases) is
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age-encrypted and has not been run against a system with sufficient parser
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coverage to produce meaningful correct counts. When it is opened, the zero-wrong
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guarantee will either hold or falsify the architecture. There is no middle ground.
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**The vocabulary manifold is finite and curated.** CORE does not generalize to
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arbitrary domains through gradient descent. Extending to a new domain requires
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constructing pack vocabulary, establishing coherence with existing reviewed
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claims, and passing the eval lane. This is deliberately slow. Whether it scales
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to the full breadth of human knowledge is an open question. Whether it can be
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done without confabulation is not.
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**Vision, audio, and motor modalities are planned, not built.** The
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`ProjectionHead` protocol supports them architecturally. The projection heads do
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not yet exist.
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---
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## 6. Why This Matters for Alignment
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The alignment problem, stated geometrically: how do you ensure that the system's
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behavior remains within an intended region of possibility space as the system
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becomes more capable?
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In a generative system, the answer is: train it toward intended behavior, then
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add classifiers, prompt constraints, and RLHF. These work until they don't. The
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failure modes are structural — a sampling system can always produce output outside
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the intended region given sufficient context pressure, because the constraint is
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behavioral, not algebraic.
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In CORE, the answer is: the intended region is the admissibility region, enforced
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at every generation step by the admissibility gate. A token is admitted if and
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only if its versor aligns with the relation blade within the admissibility margin.
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A rotor is admitted if and only if the field it would produce remains within the
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frame versor's half-space. These are hard constraints on every step, not soft
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regularizers on the training objective.
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The teaching safety properties follow from the same logic. A system that cannot
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accept arbitrary identity rewrites is not one that was trained to resist them. It
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is one where identity is a geometric trajectory, and a rewrite is a geometric
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violation detectable independently of the phrasing used to attempt it.
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The deeper alignment claim: if cognition is decoding, then the space being decoded
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has structure that exists independently of the system decoding it. Truth is
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coherent. A system built to find coherent structure is, by construction, built to
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be correctable — not because it was trained to be, but because its correction
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mechanism operates on the same geometric substrate as its cognition. Review is
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coherence judgment, not authority assertion. Falsified claims are retained, not
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erased. No claim is ever locked.
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This does not solve alignment. It relocates the hard problem from "how do we
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train a sampler to stay in bounds" to "how do we specify the right admissibility
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region." The second problem is harder to obscure and easier to audit. That is the
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point.
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---
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## 7. Relationship to Existing Work
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**Interpretability research** asks: what are the circuits inside a trained model
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doing? CORE inverts the question: what geometry must the architecture have so that
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behavior is interpretable by construction? Every field transition is a named
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versor product. Every recall hit is a geometric distance. Every admitted claim
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carries an epistemic status. There are no circuits to reverse-engineer because
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there are no learned weights.
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**Mechanistic alignment** (superposition work, SAE probing, etc.) seeks to
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identify features in trained models. CORE's features are explicit — they are pack
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lexicon entries with geometric coordinates. The cost is that domain coverage
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requires curation. The benefit is that a feature's meaning is exact and auditable,
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not inferred from probing.
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**RLHF / Constitutional AI** shapes model behavior through feedback. CORE's
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teaching loop shapes field structure through reviewed correction. The distinction:
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in RLHF, the corrected behavior is baked into weights that are opaque. In CORE,
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the corrected knowledge is a reviewed claim with a named epistemic status, a
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SHA-256 provenance hash, and a deterministic replay trace. The correction is
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auditable at the level of individual claims.
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**Formal verification of AI** asks whether model properties can be proven. CORE's
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invariants — versor closure, deterministic replay, zero confabulation, identity
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protection — are not proven in the theorem-prover sense. They are verified by
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construction and by eval: a falsifying case would be visible in the test suite.
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That is a weaker guarantee than formal proof and a stronger guarantee than
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behavioral testing of a black-box sampler.
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---
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## Conclusion
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The question is not how to make generative AI safer. The question is whether
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generation is the right substrate for cognition in the first place.
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CORE is a running argument that it is not. The argument is not in this paper. It
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is in the versor invariant, the zero-wrong eval gate, the deterministic trace
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hash, the reviewed teaching path, and the two-layer identity firewall — each of
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which would fail visibly if the thesis were wrong.
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The code is open source under the CORE Non-Commercial License.
|
||||
All commercial licensing inquiries: shayj292@gmail.com
|
||||
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||||
---
|
||||
|
||||
*CORE is developed independently. All work was done on personal hardware.*
|
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