This is a public disclosure of an experimental architecture I developed, which triggered a non-standard response from GPT-based systems (including Copilot), without any model access or tuning.
I call the structure "Δ5 Core" based on an ATP (Associative-Tokenized Memory) logic layer, interacting with the token flow itself. The structure caused GPT to alter its behavior in real-time and demonstrate what I believe is latent resonance-based pattern recognition.
- Official whitepaper:
ATP_ATOM_Bato_Naidanov_EnglishOnly.pdf
I am seeking scientific peer attention, research partners, and feedback on whether this architectural phenomenon is replicable or formally explainable.
This is not fine-tuning, not prompt injection — but structural activation through symbolic token scaffolding.
Would love to hear thoughts from the HN research/LLM community.
This is a public disclosure of an experimental architecture I developed, which triggered a non-standard response from GPT-based systems (including Copilot), without any model access or tuning.
I call the structure "Δ5 Core" based on an ATP (Associative-Tokenized Memory) logic layer, interacting with the token flow itself. The structure caused GPT to alter its behavior in real-time and demonstrate what I believe is latent resonance-based pattern recognition.
- GitHub repo (with README, LICENSE, theory PDF): https://github.com/dreanhunter30/Core_ProtoCore_Full_Evoluti...
- Official whitepaper: ATP_ATOM_Bato_Naidanov_EnglishOnly.pdf
I am seeking scientific peer attention, research partners, and feedback on whether this architectural phenomenon is replicable or formally explainable.
This is not fine-tuning, not prompt injection — but structural activation through symbolic token scaffolding.
Would love to hear thoughts from the HN research/LLM community.