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README.md

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+# AETHER: A Self-Evolving Neuro-Symbolic Architecture for AGI
+
+AETHER (Adaptive Evolving Towards Higher-order Reasoning) is a unified self-evolving neuro-symbolic architecture that integrates symbolic and sub-symbolic computation within a dynamically self-modifying framework.
+
+## Architecture Integration
+
+AETHER synthesizes cutting-edge research from:
+
+| Component | Source | Key Contribution |
+|-----------|--------|-----------------|
+| **Evolutionary Core** | AlphaEvolve (DeepMind, 2025) | MAP-Elites + island model + LLM code diffs for algorithm discovery |
+| **Hierarchical Reasoning** | HiMAC (2026) | Macro-Policy / Micro-Policy co-evolution with iterative optimization |
+| **Group Self-Evolution** | GEA (2026) | Performance-Novelty selection with experience sharing |
+| **Tool Evolution** | Yunjue Agent (2026) | Manager/Executor/Developer/Integrator role decomposition + tool absorption |
+| **AI Research Agent** | ASI-Evolve (2026) | 4-stage Researcher→Engineer→Analyzer→Database loop |
+| **Co-Evolution** | CoMAS (2025) | Decentralized multi-agent co-evolution via interaction rewards |
+| **Cognitive Architecture** | CoALA (2023) | Working/Episodic/Semantic/Procedural memory taxonomy |
+| **Agentic Neural Networks** | ANN (2025) | Textual backpropagation across multi-agent layers |
+| **Leader Training** | MLPO (2025) | Train single leader, peers untrained - efficient multi-agent RL |
+| **Task-Driven Agents** | BabyAGI | Task creation / prioritization / execution loop |
+
+## System Components
+
+```
+AETHER
+├── Core (AetherCore)
+│   ├── Neuro-Symbolic Fusion Gate (learned attention weights)
+│   ├── Recursive Evolution Loop
+│   └── Safety Sandbox
+├── Memory (CoALA-inspired)
+│   ├── Working Memory (attention-based retrieval)
+│   ├── Episodic Memory (experience buffer)
+│   ├── Semantic Memory (world knowledge via KG)
+│   └── Procedural Memory (learned tools/skills)
+├── Knowledge (PyG-style)
+│   ├── RGCN Encoder (relational graph convolution)
+│   ├── ComplEx Scorer (link prediction)
+│   └── Symbolic Rule Engine (forward chaining)
+├── Agents
+│   ├── Hierarchical Agent (HiMAC: Macro + Micro policy)
+│   ├── Agent Orchestrator (MLPO leader + dynamic routing)
+│   ├── BabyAGI Loop (task-driven autonomy)
+│   └── Textual Backpropagation (Agentic NN update)
+└── Evolution
+    ├── MAP-Elites Archive (quality-diversity)
+    ├── Performance-Novelty Selection (GEA)
+    ├── Constrained Mutation (AlphaEvolve)
+    └── Experience Sharing (group evolution)
+```
+
+## Installation
+
+```bash
+pip install torch transformers trl datasets accelerate peft networkx
+# Optional: pip install torch-geometric pyribs smolagents
+```
+
+## Quick Start
+
+```python
+from aether.core import AetherCore, AetherConfig
+
+# Initialize AETHER
+config = AetherConfig(
+    population_size=8,
+    mutation_rate=0.15,
+    num_agents=4,
+    enable_self_modification=True,
+)
+aether = AetherCore(config, model_name="Qwen/Qwen2.5-0.5B-Instruct")
+
+# Execute task with neuro-symbolic fusion
+result = aether.forward("What is the relationship between learning and reasoning?")
+print(f"Symbolic weight: {result['symbolic_weight']:.3f}")
+print(f"Neural weight: {result['neural_weight']:.3f}")
+
+# Self-reflection
+reflection = aether.self_reflect()
+print(json.dumps(reflection, indent=2))
+```
+
+## Training with TRL GRPO
+
+```bash
+python aether_train.py \
+    --model_name Qwen/Qwen2.5-0.5B-Instruct \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 8 \
+    --num_agents 4 \
+    --enable_evolution
+```
+
+## Demo
+
+```bash
+python -c "import aether_demo; aether_demo.main()"
+```
+
+## Design Principles
+
+1. **Neuro-Symbolic Fluidity**: Dynamic translation between symbolic and sub-symbolic representations
+2. **Architectural Evolvability**: Structural components are subject to learning and refinement
+3. **Parallel Agent Intelligence**: Intelligence emerges through coordinated multi-agent interaction
+4. **Constrained Self-Modification**: All self-changes are sandboxed and validated
+5. **Responsible Development**: Interpretability, auditability, and safety are first-class constraints
+
+## Citation
+
+```bibtex
+@article{aether2026,
+  title={AETHER: A Self-Evolving Neuro-Symbolic Architecture for Artificial General Intelligence},
+  author={Anonymous},
+  year={2026}
+}
+```
+
+## License
+
+MIT License - Open for research and development toward responsible AGI.