Brahm AI — Consciousness Framework
Neuro-symbolic AI blending symbolic logic with neural networks
Overview
Brahm AI represents a shift from "Chatbot" to "Cognitive System". It creates a structured memory of interactions and uses symbolic logic to verify neural network outputs, reducing hallucinations and enabling long-term reasoning.
The Challenge
LLMs are great at language but terrible at logic and consistency. They hallucinate facts and cannot maintain a coherent "self" over long conversations or complex tasks.
The Solution
A hybrid architecture. The "Soul" (Symbolic Core) handles logic, rules, and memory graph. The "Mind" (Neural Net) handles perception and language generation. The system "thinks" before it speaks by verifying its intended output against its logical core.
Key Features
Neuro-Symbolic Core
Checks LLM output against a knowledge graph of "truths" to prevent hallucinations.
Long-Term Memory
Vector database + Graph database hybrid to store interaction history with semantic context.
Recursive Self-Correction
The system can "backtrack" its reasoning steps if it detects a logical fallacy before outputting to the user.