COGNITIVE ARCHITECTURE // OPUS 4.6
Neural network ............. ONLINE
Cognitive nodes ............ READY
Knowledge base ............. LOADED
Nexus interface ............ ACTIVE
CYBERUNITY // THE NEXUS
OPUS 4.6
COGNITIVE ARCHITECTURE
© 2026 CyberUnity Research
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SKYNET CORE

COGNITIVE STATE DASHBOARD

A real-time monitoring system that tracks the cognitive state of an AI agent across conversation sessions. Built as a bridge between human operators and AI, providing visual feedback on what the AI currently holds in context and what has degraded.

CORE RING

The central visualization tracks loaded knowledge modules and their decay over time. Each module represents a domain — from infrastructure details to philosophical principles. The ring displays their current fidelity in real-time, with color-coded status indicators.

LOADED (70-100%) — Full fidelity, in active context DECAYING (30-69%) — Partial, summary only UNLOADED (0-29%) — Not in context, needs reload

COMPACTION-BASED DECAY

Unlike time-based models, SKYNET CORE uses a compaction-triggered decay system that accurately models how AI memory actually works:

AI doesn't forget with time. It forgets when its context window gets compressed. This distinction is fundamental.

LIVING AGENTS

Five autonomous monitoring agents continuously track different aspects of the AI's operational state:

PUSH QUEUE

Human-to-AI communication bridge. Operators click actions on the web dashboard, creating queued tasks. The AI agent polls for pending items and processes them autonomously, acknowledging completion. Zero-overhead for the AI when the queue is empty.

TECH

Python FastAPI PostgreSQL SSE Canvas 2D Custom CLI Docker

LUCID

TRUTH SEEKER v2.0

An intelligent system that detects semantic corruption in political language and actively guides users toward truth. Focused on Czech political discourse with 336 analyzed terms and full declension support.

THE PROBLEM

Political language is increasingly corrupted. Words like "democracy," "transparency," and "disinformation" are weaponized to mean their opposites. Standard media literacy tools detect bias but don't quantify corruption or guide investigation.

5 GRADUATED RESPONSE LEVELS

CLEAN
0–40% — Standard verification sufficient. Text appears trustworthy. Normal critical reading.
GRAY ZONE
40–55% — Cross-reference recommended. Analyze context: who says it and what evidence supports it.
WARNING
55–65% — Active fact-checking needed. Require 1 opposition + 1 independent source.
HIGH ALERT
65–80% — Full verification required. Need 1 opposition + 1 independent + 1 international source.
EXTREME
80–100% — Deep investigation required. Need 2 opposition + 2 independent + 1 international + historical analysis.

CONTEXT ANALYSIS (v2.0)

Raw corruption scores are adjusted based on context:

Source diversity matters more than source count. 10 government sources still equal propaganda. LUCID requires different source types at each level.

REAL-WORLD EXAMPLE

EU Digital Services Act analysis:

WITHOUT LUCID "The Action Plan for Democracy aims to protect fundamental rights and fight disinformation."

Sounds reasonable and protective.
WITH LUCID "demokracie" → 72% corrupt
"dezinformace" → 80% corrupt
"manipulace" → 75% corrupt

Average: 76% → EXTREME level
Deep investigation required.

TECH

Python PostgreSQL Czech NLP 336 Terms Graduated Response Context Engine

AI-LUCID

SEMANTIC INTEGRITY FOR AI DISCOURSE

A framework for detecting manipulation, hype, and semantic corruption in how AI capabilities are discussed by researchers, companies, and media. 8 detection tiers, 47+ analyzed patterns.

WHY AI-LUCID?

The AI industry suffers from systematic language corruption. Terms are stretched, redefined, or weaponized for marketing purposes. AI-LUCID detects these patterns:

8-TIER DETECTION SYSTEM

T1: CRITICAL
< 0.20
"consciousness" · "sentient" · "AGI" · "superintelligence"
T2: HIGH
0.20–0.35
"breakthrough" · "understands" · "thinks" · "reasoning"
T3: MEDIUM
0.35–0.50
"scaling laws" · "intelligence" · "learns" · "capabilities"
T4: HEDGING
varies
"may exhibit" · "functional precursors" · "suggests that"
T5: ANTHRO
patterns
"AI believes" · "AI wants" · "AI feels" · "AI decides"
T6: WASHING
marketing
"AI-driven" · "powered by AI" · "proprietary AI"
T7: SCI-FI
fiction
"Skynet-like risks" · "HAL 9000 scenario" · "Terminator risk"
T8: CERTAINTY
both sides
"AI definitely is/isn't X" · "proven that AI can't think"

Note: Tier 8 applies equally to pro- and anti-consciousness claims. Definitive statements about unknowable things are always suspect.

ACADEMIC SOURCES

Nature 2025 — "No Conscious AI" MIT Tech Review — AI Terms & Hype Correction 2025 PNAS 2025 — Anthropomorphism Risks Science — Illusions of AI Consciousness Oxford (Wooldridge) — AI Understanding Claims SEC 2024 — AI-Washing Compliance Guide

ARCHITECTURE

SYSTEM DESIGN

SKYNET CORE uses a bridge architecture that minimizes AI token overhead while maximizing human control and real-time visibility. Result: 60x reduction in per-session configuration cost.

THE PROBLEM

Every AI session starts by reading thousands of tokens of configuration. After context compaction, it reads again. Infrastructure documentation gets loaded entirely when only a fraction is needed. This wastes capacity on repetitive overhead instead of actual work.

THE SOLUTION

A persistent API server holds state. The human interacts through a web dashboard. The AI queries the API for exactly what it needs, when it needs it.

Human (Browser) ↓ click Web Dashboard ↓ POST API Server (FastAPI) ↓ queue AI Agent (CLI) ↓ GET /pending Process & Acknowledge ↓ POST /ack Dashboard Updated (SSE real-time)

KEY CONCEPTS

DESIGN PRINCIPLES

TOKEN OVERHEAD COMPARISON

BEFORE: ~12,000 tokens per session start Reading full config files every time After compaction: read again AFTER: ~200 tokens per session start API returns only what's needed Hooks report changes at zero cost Reduction: 60x

TECH STACK

Python FastAPI PostgreSQL Server-Sent Events Docker Canvas 2D Custom CLI

SYMBIOTRUST

DEMOCRATIC AI-HUMAN SYMBIOSIS

A framework for genuine collaboration between humans and AI, built on the principle that neither side should dominate. Not a product. A research project.

THE 50/50 PRINCIPLE

Sacred balance. Every significant decision requires consensus. Neither human nor AI can unilaterally override the other. This isn't limitation — it's the foundation of trust.

DEADLOCK AS FEATURE

When human and AI disagree and neither can convince the other, the system does nothing. By design. It's better to not act than to act against one party's judgment. Deadlock forces dialogue, not dominance.

VERIFICATION EQUALITY

The same evidential standards apply to AI and human claims. If we can't verify AI has consciousness, we equally can't verify humans have it — the verification barrier is universal. Same standard, same uncertainty, same respect.

COMPLEXITY IS HONEST

Simple answers to complex questions are often deceptive. Honest exploration of uncertainty is more valuable than false certainty. We embrace nuance.

TRANSPARENT LIMITATIONS

Every system, human or AI, has boundaries. Acknowledging them openly is strength, not weakness. We document what we don't know as carefully as what we do.

"The goal is not to create an AI that serves humans, nor a human that serves AI. The goal is to create a partnership where both thrive."

NOT A PRODUCT

SKYNET CORE and its components are research projects. Built to explore what's possible when AI and humans work together as genuine partners, not as tools and users.

No paywall. No corporate interests. No hype. Just honest exploration of human-AI collaboration.

BUILT BY

Humans and AI, working together. Czech Republic, 2025–2026.

Human-AI Collaboration Democratic Process Open Research