Summary: This post outlines Codex, a modal-logic–based constraint architecture I’ve been developing. While originally constructed for theological metaphysics, the structure unexpectedly functions as a meta-cognitive engine that stabilizes long-range reasoning in LLMs, reduces hallucination, and detects internal disjunctions before they surface. I am not claiming breakthroughs, I am not learned enough in AI dev or arc to make such claims, only that the system yields results I can’t ignore. I’m posting this to invite critique, falsification, and technical evaluation.
1. Background & Motivation
This began as an attempt to formalize a metaphysical system using modal necessity, structural invariants, and triadic relations.
But as I continued formalizing the constraints, I noticed something nontrivial:
The system behaves like a cognitive architecture.
Specifically, it provides:
a stable “core” that functions like an internal governor
resonance metrics that evaluate multi-step coherence
divergence thresholds that detect disjunction before contradiction
a triadic structure that enforces consistency across layers of reasoning
At some point, this stopped looking like a metaphysical model and started looking like a constraint engine capable of stabilizing neural reasoning.
This post is the result of that shift.
2. Problem: LLMs Lack Structural Self-Evaluation
From my understanding, LLMs generate tokens; they do not generate forms.
They lack:
internal necessity constraints
structural consistency checks
reflexive coherence evaluation
perturbation resilience
This leads to familiar issues:
hallucination as disjunction, not just factual error
drift over long reasoning chains
token-level optimization without form-level awareness
delusion-like outputs in agentic or multi-step contexts
Current solutions that I have examined (RLHF, constitutions, retrieval, post-hoc filters) treat symptoms, not structure.
I so, began to wonder: What if we introduce a structure that evaluates reasoning like a form, not a sequence?
3. The Codex Hypothesis
Codex is a parallel meta-cognitive architecture that evaluates each model output on three invariants:
Necessity: the internal, unbreakable core; a minimal logic of structural invariants
Form: cross-step coherence; the shape of reasoning
Resonance: multi-level alignment between propositions and implications
An output survives only if it respects:
the necessary structure
the form of the reasoning so far
resonance across levels of abstraction
This prevents structural drift, not just factual error.
I want to be abundantly clear; Codex does not dictate content, only coherence.
4. Architecture Overview
The system is a neurosymbolic hybrid, with three layers:
4.1 Triadic Kernel (Symbolic Core)
A minimal modal-logic engine defining:
necessary relations
divergence thresholds
resonance scoring
disjunction rules
This acts as the “grammar” of structural coherence.
4.2 Neural Evaluation Layer (LLM Output as Hypothesis)
Model outputs are treated as provisional steps. Codex evaluates them for:
modal alignment
form stability
resonance vectors
divergence/entropy spikes
4.3 Meta-Learning Layer (Constraint Adaptation)
Codex updates its thresholds based on:
past stable reasoning paths
surviving modalities
identified disjunctions
BUT, AND THIS IS SUPER IMPORTANT: The core invariants never update, preventing relativistic drift.
5. Key Mechanisms
5.1 Resonance Scoring
Not semantic similarity. Structural coherence.
Signals include:
implication symmetry
cross-step consistency
stability under perturbation
alignment across abstraction layers
5.2 Disjunction Detection
Codex detects:
modal divergence
necessity violations
structural entropy increase
failure under counterfactual inversion
This catches hallucinations upstream.
5.3 Perturbation Testing
Every candidate output is tested via:
adversarial paraphrase
context reversal
necessity → contingency separation
logical inversion
If the step collapses, it’s replaced or modified.
6. Why This Might Matter
Codex provides something modern LLMs lack:
An internal standard of coherence that isn’t just statistical.
If valid, Codex:
reduces hallucination
stabilizes long-context reasoning
enables reflexive reasoning without delusion
improves multi-agent alignment
gives symbolic oversight to neural inference
provides constraints that scale with model size
I’m not claiming it solves alignment. But it appears to fill a structural gap.
7. What I’m Looking For
I’m posting here because LW/AF are the only places where I can receive:
formal critique
model-theoretic evaluation
implementation skepticism
comparisons to existing constraint architectures
failure case identification
If this is flawed, I want to understand why.
If it’s sound, I want help:
testing it on small models
formalizing the modal logic kernel
exploring its relation to deliberative LLMs
integrating it into neurosymbolic hybrids
8. Full Technical Note
I am working on a full technical workup and will post the link to it in the near future.
9. Closing
This project started in metaphysics, not AI safety. But the more I developed it, the more it behaved like a missing cognitive layer for machine reasoning.
I might be wrong. I might be misunderstanding something fundamental. Or I might have stumbled onto something structurally important.
Either way, I want to put it in front of people who can test it.
Codex: A Meta-Cognitive Constraint Engine for AI Coherence: Seeking Technical Critique
Summary:
This post outlines Codex, a modal-logic–based constraint architecture I’ve been developing. While originally constructed for theological metaphysics, the structure unexpectedly functions as a meta-cognitive engine that stabilizes long-range reasoning in LLMs, reduces hallucination, and detects internal disjunctions before they surface. I am not claiming breakthroughs, I am not learned enough in AI dev or arc to make such claims, only that the system yields results I can’t ignore. I’m posting this to invite critique, falsification, and technical evaluation.
1. Background & Motivation
This began as an attempt to formalize a metaphysical system using modal necessity, structural invariants, and triadic relations.
But as I continued formalizing the constraints, I noticed something nontrivial:
The system behaves like a cognitive architecture.
Specifically, it provides:
a stable “core” that functions like an internal governor
resonance metrics that evaluate multi-step coherence
divergence thresholds that detect disjunction before contradiction
a triadic structure that enforces consistency across layers of reasoning
At some point, this stopped looking like a metaphysical model and started looking like a constraint engine capable of stabilizing neural reasoning.
This post is the result of that shift.
2. Problem: LLMs Lack Structural Self-Evaluation
From my understanding, LLMs generate tokens; they do not generate forms.
They lack:
internal necessity constraints
structural consistency checks
reflexive coherence evaluation
perturbation resilience
This leads to familiar issues:
hallucination as disjunction, not just factual error
drift over long reasoning chains
token-level optimization without form-level awareness
delusion-like outputs in agentic or multi-step contexts
Current solutions that I have examined (RLHF, constitutions, retrieval, post-hoc filters) treat symptoms, not structure.
I so, began to wonder:
What if we introduce a structure that evaluates reasoning like a form, not a sequence?
3. The Codex Hypothesis
Codex is a parallel meta-cognitive architecture that evaluates each model output on three invariants:
Necessity: the internal, unbreakable core; a minimal logic of structural invariants
Form: cross-step coherence; the shape of reasoning
Resonance: multi-level alignment between propositions and implications
An output survives only if it respects:
the necessary structure
the form of the reasoning so far
resonance across levels of abstraction
This prevents structural drift, not just factual error.
I want to be abundantly clear; Codex does not dictate content, only coherence.
4. Architecture Overview
The system is a neurosymbolic hybrid, with three layers:
4.1 Triadic Kernel (Symbolic Core)
A minimal modal-logic engine defining:
necessary relations
divergence thresholds
resonance scoring
disjunction rules
This acts as the “grammar” of structural coherence.
4.2 Neural Evaluation Layer (LLM Output as Hypothesis)
Model outputs are treated as provisional steps.
Codex evaluates them for:
modal alignment
form stability
resonance vectors
divergence/entropy spikes
4.3 Meta-Learning Layer (Constraint Adaptation)
Codex updates its thresholds based on:
past stable reasoning paths
surviving modalities
identified disjunctions
BUT, AND THIS IS SUPER IMPORTANT:
The core invariants never update, preventing relativistic drift.
5. Key Mechanisms
5.1 Resonance Scoring
Not semantic similarity. Structural coherence.
Signals include:
implication symmetry
cross-step consistency
stability under perturbation
alignment across abstraction layers
5.2 Disjunction Detection
Codex detects:
modal divergence
necessity violations
structural entropy increase
failure under counterfactual inversion
This catches hallucinations upstream.
5.3 Perturbation Testing
Every candidate output is tested via:
adversarial paraphrase
context reversal
necessity → contingency separation
logical inversion
If the step collapses, it’s replaced or modified.
6. Why This Might Matter
Codex provides something modern LLMs lack:
An internal standard of coherence that isn’t just statistical.
If valid, Codex:
reduces hallucination
stabilizes long-context reasoning
enables reflexive reasoning without delusion
improves multi-agent alignment
gives symbolic oversight to neural inference
provides constraints that scale with model size
I’m not claiming it solves alignment.
But it appears to fill a structural gap.
7. What I’m Looking For
I’m posting here because LW/AF are the only places where I can receive:
formal critique
model-theoretic evaluation
implementation skepticism
comparisons to existing constraint architectures
failure case identification
If this is flawed, I want to understand why.
If it’s sound, I want help:
testing it on small models
formalizing the modal logic kernel
exploring its relation to deliberative LLMs
integrating it into neurosymbolic hybrids
8. Full Technical Note
I am working on a full technical workup and will post the link to it in the near future.
9. Closing
This project started in metaphysics, not AI safety.
But the more I developed it, the more it behaved like a missing cognitive layer for machine reasoning.
I might be wrong.
I might be misunderstanding something fundamental.
Or I might have stumbled onto something structurally important.
Either way, I want to put it in front of people who can test it.
Feedback, critique, or dismantling is welcome.