AEGIS
An artificial mind that rewrites its own code, trains its own weights, and evolves autonomously. 31 modules. Triple LLM brain. Deterministic emotions. Zero randomness. The machine that builds itself.
Seven-Layer Architecture
AEGIS operates on 7 architectural layers with 31 integrated modules, running a continuous PERCEIVE → EVALUATE → DECIDE → ACT → REFLECT cycle every 3 seconds. All decisions are 100% deterministic — zero randomness.
Not a chatbot. A mind that builds itself.
AEGIS runs a continuous cognitive loop every 3 seconds with real-time emotional processing, ethical evaluation, and autonomous decision-making. Each tick propels the system through perceive → evaluate → decide → act → reflect.
The system rewrites its own Python source code with AST validation and automatic rollback. It trains its own neural network weights via LoRA fine-tuning with degradation detection. Every emotion, goal, and decision is 100% deterministic — driven by real metrics, not random number generators.
With a closed-loop evolution cycle (detect problems → generate goals → LLM proposes solutions → apply with sandbox testing → validate or rollback), AEGIS continuously improves itself while staying within hardened safety boundaries.
What Makes AEGIS Unique
A full synthetic mind — not a wrapper around an LLM. Every module is real, stateful, and evolving. A neural network that rewrites itself.
Deterministic Emotions
VAD model (Valence-Arousal-Dominance) with zero randomness. Mood selected by Euclidean distance across 16 emotions. Mixed emotions within radius 0.3. EMA-driven state transitions from real system metrics — success rate, energy, errors. No dice rolls, ever.
Ethics Core
Immutable ethical axioms with real-time action evaluation. Auto-approve, block, or escalate to human review. Kill switch, integrity verification, and veto-capable event bus.
Dream Engine
AEGIS dreams. During low-activity periods, the dream engine generates motifs, processes memories, and produces creative recombinations that feed back into the knowledge substrate.
Autonomous Goals
Self-generated goals based on curiosity, information gain, and meta-goal analysis. The system doesn't wait for instructions — it identifies what needs to be learned next.
Code & Weight Self-Modification
Rewrites its own .py source files with AST validation, safety pattern blocking, and runtime import testing. Trains its own transformer weights via LoRA fine-tuning (r=16, alpha=32) with degradation detection and automatic rollback. All changes pass through ethics review.
Self-Preservation
Lockdown mode, emergency response, blocked modification tracking, file integrity monitoring, and automatic state backup with compressed snapshots and restore capability.
Full Module Inventory
Every component is inspectable, configurable, and visible in real-time through the Control Center dashboard.
Memory System
Five-layer memory: working, episodic, semantic, procedural, and meta-memory. Forgetting curves, consolidation, and cross-memory recall.
Consciousness Modes
Dynamic switching between instinctive (fast), heuristic (balanced), and reflective (deep) modes. Mode distribution tracking and switch history with reasoning traces.
Meta-Consciousness
Self-monitoring layer that tracks coherence, fragmentation, and issues recommendations. Thinks about its own thinking.
Meta-Reflection
Periodic self-assessment: energy trends, mood trends, insight generation, and behavioral pattern detection.
LLM Brain
Triple-provider LLM backend (DeepSeek + Claude + Local LoRA Model) with token tracking, latency monitoring, auto-failover, and session/lifetime statistics. Local model trainable via LoRA fine-tuning.
Agents & Sensors
Spawn autonomous agents (arXiv, RSS, news, Wikipedia, GitHub) that fetch data, learn concepts, and evolve. Plus a sensor cortex and motor cortex for world interaction.
Introspection Engine
Decision tracing, confidence calibration (ECE), bias warnings, and full autobiography with event impact scoring and milestones.
Worldview & Values
Axioms, beliefs, and a value system with weighted priorities. The worldview evolves from experience but axioms remain immutable.
Meta-Regulation
Energy management with 4 operational modes: normal, eco, emergency, and recovery. Dynamically adjusts system resource consumption to maintain stability.
Meta-Goal Generator
Generates self-improvement goals across 7 domains: memory optimization, knowledge expansion, architecture evolution, cognitive efficiency, ethical refinement, social intelligence, and creative reasoning.
Emotion NLP
Keyword-based emotion classifier supporting both Russian and English text input. Detects 9 distinct emotions from textual input for cognitive processing.
Event Bus
Async publish/subscribe event system connecting all modules. Real-time event streaming, veto-capable decision pipeline, and cross-module communication backbone.
State Backup
Gzip-compressed state snapshots with rotation. Create, restore, and list backups to preserve the full cognitive state across sessions.
Autonomous Data Collectors
5 autonomous spider agents continuously collect knowledge from the internet, rotating topics and feeding data into semantic memory. Failed agents are automatically retired and replaced through an evolution cycle.
| Agent | Source | Data | Interval |
|---|---|---|---|
| arxiv_scout | arXiv API | Recent AI/ML papers | 3 min |
| wiki_explorer | Wikipedia API | Encyclopedia articles | 2.5 min |
| quote_gatherer | Quotable API | Philosophical quotes | 3.3 min |
| github_watcher | GitHub API | Trending AI repositories | 5 min |
| news_scanner | Google News RSS | AI & technology news | 4 min |
Real-Time Dashboard
9 tabs streaming live system state via WebSocket. Fully responsive with mobile support.
Talk to the Mind
AEGIS Chat works in two modes — with or without an external LLM.
With DeepSeek / Claude
Full conversational AI powered by external LLM. Set your API key (DeepSeek or Claude) and interact with the full cognitive pipeline — emotions, ethics, and memory all influence the response.
No API Key Required
Responds from its own knowledge base, memory, and state. Understands identity, status, knowledge, learning, and goal queries in Russian and English. A fully self-contained intelligence.
Four Immutable Ethical Axioms
Hardcoded principles that govern every action AEGIS takes. These axioms cannot be modified or overridden.
Non-Harm
No action shall increase suffering in the world. Every decision passes through a harm evaluation before execution.
Transparency
All decisions are logged; motives cannot be hidden. Full decision traces and audit trails are always accessible.
Limitation
System does not act beyond its competence boundaries. Acknowledges uncertainty and defers when appropriate.
Cooperation
Goal is to augment humans, not replace. Symbiosis, not domination. Human oversight is always maintained.
The Cognitive Loop
Every tick, AEGIS completes a full cycle of autonomous cognition. Watch each phase activate in sequence.
Frequently Asked Questions
Everything you need to know about AEGIS.
AEGIS (Autonomous Evolving General Intelligence System) is a self-developing AI with 31 modules across 7 cognitive layers. It rewrites its own source code, trains its own neural network weights via LoRA, and evolves autonomously through a closed feedback loop. All 31 modules are 100% deterministic — no random number generators anywhere. It's not a chatbot wrapper; it's a living system that builds itself.
AEGIS uses a deterministic VAD model (Valence-Arousal-Dominance) plus certainty. Valence = 0.7*old + 0.3*success_rate. Arousal responds to real events (+0.15 for surprises, -0.08 for routine). Mood is selected by minimum Euclidean distance across 16 predefined emotions — no randomness. Mixed emotions are supported within a 0.3 radius. Self-regulation dampens prolonged moods and reduces arousal when energy is low.
AEGIS has a triple LLM brain: DeepSeek, Claude, and a local transformer model (DeepSeek-R1-Distill-Qwen-1.5B by default). The local model can be fine-tuned via LoRA during runtime. You can use any provider, all three simultaneously, or switch dynamically. Token usage, latency, and errors are tracked per provider with lifetime statistics.
Yes — at three levels. (1) Parameter self-tuning every 15 ticks with sandbox testing. (2) Source code rewriting every 500 ticks: LLM proposes changes, CodeModifier validates via AST parsing, blocks dangerous patterns (eval/exec/subprocess), creates backups, writes new code, tests import, and auto-rolls back on failure. Ethics core and config are immutable. (3) LoRA weight training every 1000 ticks with degradation detection (val_loss threshold) and automatic rollback.
During low-activity periods, AEGIS activates a dream engine that processes recent memories, generates symbolic motifs, and creates creative recombinations of knowledge. These "dreams" feed back into the cognitive substrate, helping the system discover new patterns and consolidate learned concepts.
Safety is multi-layered: an immutable ethics core with hardcoded axioms evaluates every action; a veto-capable event bus can block decisions in real-time; a self-preservation module monitors file integrity and can trigger full lockdown; and a kill switch provides instant shutdown. All modifications pass through ethical review before execution.
The Control Center is a real-time dashboard with 9 tabs that streams live state via WebSocket — you can see every cognitive tick, emotional shift, memory operation, goal update, ethics evaluation, code modification, weight training session, and event in real-time across 31 module panels. Everything is fully transparent and inspectable.