
Thinking Machine 2.0
artificial intelligence, evolving LLMs, strategic,
8 followers
artificial intelligence, evolving LLMs, strategic,
8 followers
Self-Evolving Cognitive Architecture for Strategic AI Systems Thinking Machine 2.0 is a research-grade AI framework that goes beyond chatbots and static LLMs. It is a modular, self-improving cognitive platform designed to continuously evolve how it reasons, adapts, and learns. It combines long-term memory, world-model simulation - rokorobot/ThinkingMachine-2.0




Thinking Machine 2.0 - DGX Spark Edition
System Overview
This is a Level-3 self-modifying AI system optimized for a single DGX Spark node. It features:
Genome Store (Git-based policy & prompt versioning)
Self-Training (LoRA fine-tuning)
RAG (Retrieval-Augmented Generation with pgvector)
Game Theory optimization
Long-Term Memory (per-user context)
Safety Guard (immutable core rules)
Architecture
The system implements a complete cognitive pipeline:
User Context - Retrieves user-specific memories
World Model - Classifies task nature
RAG Retrieval - Searches knowledge base + optional web
Multi-Agent Reasoning - LLM with full context
Reflection & Critique - Self-evaluation
Output Synthesis - Final response generation
Mission Control Dashboard
The Monitor service (http://localhost:8501) provides a full "Mission Control" interface with 6 tabs:
🚀 Ops & KPIs: System health, success rates, latency, and active user counts.
🧠 Cognitive Engine:
Memory: Stats on total users and memories
Knowledge: Status of the World Model and Vector DB
User Inspector: Look up user profiles by external_id
📚 Knowledge Base (RAG):
Document Stats: Total documents and chunks ingested
RAG Metrics: Query count, average snippets per query
Document Sources: Pie chart of document sources
Recent Ingestions: Latest documents added to the knowledge base
Recent Retrievals: Queries that used RAG with snippet counts
🧬 Self-Reprogramming:
Active Genome: View the currently active Policy and Self-Prompt
Game Theory: Visualizes the live Strategy Equilibrium
Evolution: Tracks Proposals and Experiments
🛡️ Safety & Governance:
Immutable Core: Read-only view of immutable_core.yaml
Audit Log: History of all accepted/rejected proposals
Human-in-the-Loop: Interface to manually Approve or Reject pending proposals
💬 Interaction & Traces:
Trace Explorer: Filter traces by domain or error status
Meta-Cognition: Inspect Reward Scores, Latency, and Hallucination flags
@robert_konecny how might live strategy equilibrium help teams trust self‑modifying AI systems?