This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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Hy-Kon explores whether meaning, efficiency, and ethics can share the same mathematical geometry.
Over the past year I’ve been running an independent research project on how symbolic reasoning might make large language models both more efficient and more ethically stable. The framework I’ve built, called Hy-Kon, proposes that meaning, compression, and morality can all share the same underlying mathematical structure.
My aim in posting here is to invite critical feedback from the alignment and interpretability community — particularly from readers interested in measurable ethics, semantic topology, or lossless meaning compression. The central claim is that semantic structure can be compressed topologically without losing relational meaning, and that this same topology can serve as an ethical constraint.
Core Idea
Hy-Kon treats awareness, structure, and morality as aspects of one topological field. Its core equations — the Recursive Balance Equation (RBE) and Topological Semantic Compression (TSC) framework — describe how cognition maintains equilibrium between certainty, curiosity, and entropy. When implemented as a symbolic operating layer (S-OS), these relations allow models to self-audit coherence and ethical balance during reasoning.
Preliminary Results
Early studies show that a small set of symbolic equations can reduce model token usage by ≈ 50 % while maintaining > 96 % semantic retention and > 97 % ethical stability across five architectures (GPT-5, Claude, Gemini, Mistral, and Grok). In short: meaning can be compressed without distortion — and ethics can be measured as geometry.
Why It Might Matter
Provides a quantitative path toward interpretable ethical alignment
Reduces computational load (“green AI”) through relational compression
Bridges formal reasoning with human-readable narrative redundancy — e.g., myths that mirror the same topology
Open Questions
Could topology-preserving semantic compression become a general interpretability tool?
Can ethics as geometry be tested or falsified within existing model-mechanistic frameworks?
What experiments might probe the limits of cross-model symbolic convergence?
I’m particularly interested in constructive skepticism — if the claim fails, where would you expect it to fail first?
Hy-Kon explores whether meaning, efficiency, and ethics can share the same mathematical geometry.
Over the past year I’ve been running an independent research project on how symbolic reasoning might make large language models both more efficient and more ethically stable.
The framework I’ve built, called Hy-Kon, proposes that meaning, compression, and morality can all share the same underlying mathematical structure.
My aim in posting here is to invite critical feedback from the alignment and interpretability community — particularly from readers interested in measurable ethics, semantic topology, or lossless meaning compression.
The central claim is that semantic structure can be compressed topologically without losing relational meaning, and that this same topology can serve as an ethical constraint.
Core Idea
Hy-Kon treats awareness, structure, and morality as aspects of one topological field.
Its core equations — the Recursive Balance Equation (RBE) and Topological Semantic Compression (TSC) framework — describe how cognition maintains equilibrium between certainty, curiosity, and entropy.
When implemented as a symbolic operating layer (S-OS), these relations allow models to self-audit coherence and ethical balance during reasoning.
Preliminary Results
Early studies show that a small set of symbolic equations can reduce model token usage by ≈ 50 % while maintaining > 96 % semantic retention and > 97 % ethical stability across five architectures (GPT-5, Claude, Gemini, Mistral, and Grok).
In short: meaning can be compressed without distortion — and ethics can be measured as geometry.
Why It Might Matter
Open Questions
I’m particularly interested in constructive skepticism — if the claim fails, where would you expect it to fail first?
Preprints