Cognitive Reasoning
by Design.
An autonomous backend architecture exploring internal heuristics, meta-level control, and local language models.
Motivation
Shifting from reactive instruction-following to signal-driven autonomous exploration.
Research Target
Studying how curiosity-driven cycles can lead to emergence in modular AI systems without external dependencies.
Local First
Built to run entirely on local infrastructure, ensuring data privacy and architectural transparency.
Modular Flow
Every decision stage is isolated, allowing for granular evaluation of the reasoning process.
System Architecture
A multi-stage pipeline where signals evolve into hypotheses and decisions.
Core Modules
Each component handles a specific cognitive function.
Signal Processor
Analyzes raw numerical context to identify novelty and uncertainty thresholds.
Reasoning Stage
Leverages local GGUF models to generate structured explanations based on curiosities.
Meta-Controller
The system's executive function, deciding whether to explore further or stabilize results.
System Simulator
Experiment with the internal reasoning pipeline in real-time.
API Specification
Standardized interfaces for internal research orchestration.
// POST /api/pipeline/run
{
"uncertainty": 0.9,
"novelty": 0.3
}
// Response
{
"curiosity_score": 0.86,
"hypothesis": "System identifies high entropy in signals...",
"analysis_decision": "accept",
"meta_decision": "explore"
}