Cognitive Simulation·Physics·Information·Learning
A running cognitive simulation engine with real-time physics, information-theoretic entropy tracking, and live machine learning. HAKARI simulates, detects collapse cascades, and learns from every tick of its internal clock.
System State · Live
Three Pillars
HAKARI v3 is built on three interlocking layers — each a complete subsystem, each feeding the others. Physics, information theory, and machine learning running simultaneously in a single tick-loop, every second the engine is alive.
01 — Physics Layer
Every node in HAKARI has mass, velocity, and force interactions. The engine runs a tick-loop updating positions, computing network strength (meanH), and detecting structural collapse in real time.
02 — Information
HAKARI continuously measures Shannon entropy across its node graph. Entropy spikes signal critical state transitions. CSD detection via AR1 autocorrelation and variance acceleration identifies approaching collapse bifurcations.
03 — Learning
HAKARI tracks its own learning curve in real time. The J-metric (cost function) is monitored tick-by-tick for regression, plateau, and convergence. The engine flags when learning is declining and auto-adjusts update rules.
Live Console
HAKARI exposes a full live console API accessible from the browser devtools. Every function below runs against the live simulation state — no mocking, no static data.
Cognitive Simulation Engine
"HAKARI v3 is not just another chatbot. It's a running cognitive simulation engine — with physics, entropy, cascade detection, and live learning regression, all ticking in real time." — HAKARI v3 · Achieved Architecture
The engine runs a tick-loop that synchronizes three subsystems in parallel. Physics updates propagate through the node graph, entropy is sampled from the state distribution, and the learning cost function is updated — all within a single scheduler cycle.
Unlike agent frameworks that orchestrate API calls, HAKARI's simulation state is continuous and persistent. The system doesn't reset between conversations — it remembers where it was, what entropy looked like, and how its learning curve has evolved.
01 — Physics
Physics LayerNodes have mass, velocity, and inter-node force fields. The tick scheduler runs physics updates computing meanH, detecting structural weakening, and propagating cascade failures through the network topology.
02 — Information
EntropyHAKARI measures Shannon entropy S across the node-state distribution each tick. CSD detection uses AR1 autocorrelation and variance acceleration — two leading indicators of approaching bifurcation tipping points borrowed from ecology.
03 — Learning
Online MLThe engine monitors its own learning cost function J in real time. Regression events are detected when J consistently declines over multiple ticks. Rate tracking at multiple scales reveals the learning deceleration curve.
Physics Signals
Information Signals
Learning Signals
Overview
HAKARI v3 is a research-grade cognitive simulation engine that goes far beyond a standard AI assistant. At its core is a running physics simulation — nodes with mass, velocity, and force interactions, a tick-loop scheduler, and real-time network health monitoring.
Built on three interlocking layers — physics, information theory, and machine learning — HAKARI models the dynamics of a cognitive system as it runs, measures its own entropy, detects tipping points before they happen, and tracks its own learning regression in real time.
"HAKARI v3 is not just another chatbot. It's a running cognitive simulation engine — with physics, entropy, cascade detection, and live learning regression, all ticking in real time."
— HAKARI v3 · Achieved ArchitectureThe system exposes a full console API: causal trace for dead nodes, n-step prediction, semantic maps, and full JSON session export — making it a live scientific instrument, not just a product.
System State · Live
Active Modules
Core Principles
The architectural principles that make HAKARI different from any AI system built before it. Each pillar is implemented, running, and observable in the live engine.
01
SimulationHAKARI runs a real tick-loop physics simulation. Nodes have mass and velocity, network health (meanH) is computed each tick, and structural collapse is detected as it propagates.
physics_first02
EntropyEntropy S is measured across the node-state distribution continuously. Spikes (S=+2.840) signal critical transitions. The system distinguishes normal fluctuation from genuine regime change.
information_theory03
CSDCritical Slowing Down detection via AR1 autocorrelation and variance acceleration identifies bifurcation tipping points before they occur — a technique from complex systems science.
early_warning04
LearningThe cost function J is tracked tick-by-tick. Multi-rate regression detection (−0.02246 → −0.00161) reveals the learning deceleration curve and triggers adaptive rule updates in real time.
self_monitoring05
CausalitywhyDidNodeDie(id) reconstructs the exact causal chain behind any node collapse — tracing entropy propagation, force interactions, and learning state at time of failure.
explainability06
SemanticsemanticMap() renders the live concept-space topology — node clusters, semantic distances, and information density. The map evolves in real time as the simulation runs.
concept_space07
MemoryThe LLM layer is informed not just by conversation history (MongoDB) but by the live simulation state — entropy level, network health, learning regression — giving HAKARI responses grounded in the actual dynamic state of the system.
simulation_grounded_intelligence08
ExportdownloadLog() exports every tick, every diagnostic, every metric as structured JSON. Scientific reproducibility built into the core — not bolted on.
reproducibility09
PredictpredictNext(n) runs n-step lookahead simulations — returning predicted entropy trajectories, collapse probabilities, and expected J-curve evolution.
predictive_intelligenceArchitecture
Core primitives — tensors, probability distributions, Bayesian priors, and information-theoretic operators.
Force fields, velocity updates, Shannon entropy computation. The physics tick-loop lives here.
Node lifecycle — birth, health decay, death. Each node tracks strength and causal history.
Edge formation, weight dynamics, topology changes. Detects structural weakening.
Temperature analogs, heat diffusion, phase transitions. Models information heat under stress.
Bayesian inference engine, decision routing. Drives predictNext(n) and semanticMap().
Genetic-algorithm mutation applied to node rules. J-metric learning regression detection.
Information propagation, persistent memory. Integrates with MongoDB for session retention.
Higher-order concept formation. Feeds LLM context injection, grounding GPT in live state.
window.__hakari, window.__scheduler, Diagnostics.js. BLOCK_15_UPGRADE — hot-swap without restart.
Backend
Language Breakdown
Quick Start
Simulation Pipeline — Per Tick
Research Significance
01
Most AI projects are static models. HAKARI is a running system — a tick-loop cognitive simulation where physics, information theory, and learning interact dynamically. The console diagnostics are not test outputs — they are live signals from a working simulation engine.
Primary Achievement02
CSD detection via AR1 autocorrelation and variance acceleration is a technique from ecology and climate science for detecting regime shifts before they occur. Applying it to an AI cognitive simulation is a novel cross-domain contribution.
Novel Contribution03
importantMoments(), semanticMap(), and downloadLog() turn HAKARI into a scientific instrument. Every run produces analyzable data about cognitive dynamics under stress.
Scientific Platform04
Unlike RAG systems that inject documents, HAKARI injects live simulation state — entropy, health, learning regression — into LLM context. Dynamic-state-augmented generation.
New Paradigm05
Full JSON export via downloadLog(), Ctrl+Shift+D shortcut, and session logging mean every HAKARI run is fully reproducible — meeting computational research standards.
ReproducibilityTech Stack
Simulation Core · JS 94%
BLOCK1–9 + BLOCK_10
Backend · Node.js
hakari-backend/server.js
Memory + Persistence
MongoDB + Snapshots
Frontend + Console API
Controls.js · main.js
Roadmap
Real-time WebGL rendering of the node graph — visualizing physics forces, entropy gradients as heat maps, cascade propagation waves, and the live semantic topology as the simulation runs.
Next TargetSpawn multiple HAKARI instances with distinct simulation states. Study inter-agent information exchange, emergent consensus, and how entropy propagates across agent boundaries.
Next TargetWhen CSD signals tipping point risk, automatically propose intervention strategies to steer the system away from collapse — applying control theory to cognitive simulation stabilization.
PlannedExplore More
Check the GitHub for the simulation engine source, or reach out to discuss cognitive simulation research.