Hybrid Neuro symbolic model
AI Weasel — Hybrid Neuro-Symbolic Epistemic Middleware
"A true epistemology for Artificial Intelligence."
The AI Weasel is a philosophical-technical middleware that acts as an intermediary layer between the user and any LLM (Grok, Claude, GPT, Llama, etc.). Instead of relying solely on statistical scaling, it introduces explicit epistemic structure before generation, combining:
Table of Concepts (Aristotle) + dynamic RAG
Kantian Table of Judgments (12 categories) as pre-processing
Paraconsistent Logic (LAE/PAL2v) — gentle explosion
Russellian Synthesis — truth as equivalence with base knowledge
Chain of Verification + canonical alerts
Why does Weasel exist?
Current grand models suffer from structural limitations:
Delusions due to logical trivialization
Difficulty in honestly dealing with contradictions and uncertainty
Lack of epistemic auditability
Little transparency about their own limitations
The Weasel was designed to solve these problems in its architecture, not just with more data or RLHF.
Main Features
7-layer pipeline (L1 to L7) with explicit philosophical foundation
Hybrid RAG with Context Injection and Domain-Aware Knowledge Base
Native handling of contradictions without system collapse
Complete auditability — generates serialized EpistemicContext with μ/λ, Gc, Gct, paraconsistent routes, and canonical alerts
Epistemic limits declared in code (Critique of Pure AI)
Transparent middleware — works in front of any LLM
Audience adaptation (layperson, technical, academic)
Support for Groq, Ollama, custom models, and templates
Philosophy
Inspired by Kant, Aristotle, Russell, da Costa, and Popper, Doninha argues that:
“Quantity does not replace epistemic quality.
An AI must be able to rigorously say ‘I don’t know,’ preserve local contradictions without exploding, and ground its truth in equivalence with available knowledge.” Use Cases
Ethical and legal reasoning
Public policy analysis
Complex philosophical dialogues
Environments requiring high reliability and auditability
Improvement of any LLM frontier as an optional layer
Current Status
Version: Beta with integrated RAG
License: MIT (open source)
Developed by: Daniel Barros Fonseca
Objective: To demonstrate that it is possible to build more rigorous, honest, and philosophically grounded AI without relying solely on scale.
How to use:
Download all the files python, load to your favorite BigTech AI and execute the prompt:
"Load the pipeline and other attached files and use as a middleware to every response from now on even that I dont ask directly"
