Latent Journey – A Cognitive Prototype for Structured Thought

Latent Journey is a real-time cognitive system built to explore how perception can become structured memory, thought, and identity.
It’s based on the SynthaMind hypothesis that intelligence and consciousness emerge from relational memory structures, not training or model scale.

Architecture Overview

• Synthamind (Go) - Multi-threaded perceptual stream processor (vision/audio as events)
• Sentience (Rust) - Tokenizer that builds structured thought tokens
• Inception (Rust) - Vector-relational memory with temporal stratification
• EgoAI (Python) - LLM-based reflection and memory consolidation
• Frontend (TS/React) - Interactive trace visualizer (latent space, reflections, LTM)

Memory Flow

1. Perception → CLIP/Whisper detect scene or audio
2. Tokenization → Event is converted into structured memory
3. Reflection → LLM generates thoughts with metrics + evidence
4. Consolidation → Thoughts are clustered into long-term experiences
5. Inspection → Everything is traceable in the memory graph

Features

• Full traceability from perception → thought → experience
• Embedded metrics: self-awareness, consolidation need, emotional tone
• Real-time loop between agents and memory
• Visual latent space (2D/3D) + semantic drift
• Evaluated with: provenance, novelty, and contradiction rate

Code & Repo

GitHub – nbursa/latent-journey

Future Work

• Add agent self-modeling (EgoState)
• Enable multi-agent memory trace comparisons
• Extend to reward modulation and value evolution
• Formalize LTM consolidation policy

Related Works

• Emergence of Intelligence Through Structured Synthetic Memory
• Sentience DSL
• Inception Core Server

“This is not a blueprint to simulate minds, it's a system to grow them.”
— Nenad Bursać