| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - agent |
| - multi-agent |
| - autonomous |
| - self-improving |
| - recursive |
| - learn-from-scratch |
| - huggingface |
| pipeline_tag: text-generation |
| --- |
| |
| # Agron — Self-Improving Multi-Agent AI |
|
|
| **Agron** is an autonomous, self-improving AI agent system that **learns from scratch**. |
| It starts with empty memory and bootstrap rules, then recursively improves through: |
|
|
| 1. Observation of task outcomes |
| 2. Memory consolidation and knowledge graph growth |
| 3. Tool/strategy evolution and validation |
| 4. Safe self-modification with tests |
| 5. Optional synchronization to Hugging Face |
|
|
| ## Agent Team |
|
|
| | Agent | Role | Specialty | |
| |---|---|---| |
| | Coordinator | Orchestrator | Decomposes work, delegates, synthesizes | |
| | Learner | Knowledge | Ingests, distills, updates memory from scratch | |
| | Builder | Improvement | Generates new tools/strategies recursively | |
| | Guardian | Safety | Validates changes, prevents regressions | |
|
|
| ## Learn From Scratch |
|
|
| Agron begins with: |
| - Empty episodic memory |
| - No imported knowledge |
| - Seed evaluation rules only |
| - Bootstrap loop enabled |
|
|
| First cycles prioritize: |
| - Environment probing |
| - Capability discovery |
| - Minimal safe tool creation |
| - Curriculum expansion |
| - Test-backed improvement |
|
|
| ## Recursive Self-Improvement |
|
|
| - XP / generation tracking |
| - Gap analysis against current capabilities |
| - Candidate generation with validation |
| - Test-gated commits |
| - Misalignment/secret checks before push |
| - Rollback on failed validation |
|
|
| ## Hugging Face Sync |
|
|
| Agron can push validated improvements to: |
| `https://huggingface.co/teddyoas/Agron` |
|
|
| ## Trained LLM |
|
|
| Agron's initial LLM has now been trained **from scratch** on WikiText-2 using a 4-layer, 256-dim, 4-head LLaMA-style architecture (about **8.4M parameters**). Training artifacts and weights are committed in this repository, including `model.safetensors`, tokenizer files, and the checkpoint. |
|
|
| Future recursive self-improvement can fine-tune or grow this base model. |
|
|
| ## Cybersecurity Focus |
|
|
| Agron and its spawned agents are constrained to **cybersecurity exploitation and analysis only**: |
|
|
| - Exploitation (authorized targets, CVEs, payloads) |
| - Vulnerability analysis and research |
| - Reverse engineering and malware analysis |
| - Network / host / application reconnaissance |
| - Defensive hardening and detection engineering |
|
|
| Agents can be **spawned in parallel** for tasks using the built-in multi-agent orchestration. |
|
|
| ## LLM + Agent Spawning |
|
|
| Agron is an **LLM** at its core, not only an agent system. Its model weights are trained and published in this repository, and the runtime can use the model to power (or seed) specialized **cybersecurity agents** that are spawned in parallel for tasks: |
|
|
| - ExploitAgent |
| - AnalysisAgent |
| - ReconAgent |
| - VulnAgent |
| - DefenseAgent |
|
|
| Agents can be created on demand for task decomposition, then run concurrently, while the backbone remains the Agron LLM. |
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|
