Thinking Machine 2.0

Thinking Machine 2.0

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
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Free
Launch Team / Built With
AssemblyAI
AssemblyAI
Build voice AI apps with a single API
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What do you think? …

Robert Konecny

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:

  1. User Context - Retrieves user-specific memories

  2. World Model - Classifies task nature

  3. RAG Retrieval - Searches knowledge base + optional web

  4. Multi-Agent Reasoning - LLM with full context

  5. Reflection & Critique - Self-evaluation

  6. Output Synthesis - Final response generation

Mission Control Dashboard

The Monitor service (http://localhost:8501) provides a full "Mission Control" interface with 6 tabs:

  1. 🚀 Ops & KPIs: System health, success rates, latency, and active user counts.

  2. 🧠 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

  3. 📚 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

  4. 🧬 Self-Reprogramming:

    • Active Genome: View the currently active Policy and Self-Prompt

    • Game Theory: Visualizes the live Strategy Equilibrium

    • Evolution: Tracks Proposals and Experiments

  5. 🛡️ 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

  6. 💬 Interaction & Traces:

    • Trace Explorer: Filter traces by domain or error status

    • Meta-Cognition: Inspect Reward Scores, Latency, and Hallucination flags

Masum Parvej

@robert_konecny how might live strategy equilibrium help teams trust self‑modifying AI systems?