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README.md

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+Koinic Labs: Central Compliance & Transparency Report
+Date: April 2026
+
+Status: SME Provider (Research & Development Phase)
+
+1. Copyright Policy (EU 2019/790)
+Koinic Labs respects the rights of content creators. In accordance with Article 4(3) of Directive (EU) 2019/790, we honor all machine-readable reservations of rights (TDM opt-outs). Our training pipelines are designed to exclude data from sources that have explicitly opted out of AI training.
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+2. Training Data Summary (Synthetic-First)
+The AXL Architecture models are trained using a Synthetic-First Methodology.
+
+Source: Data is primarily generated through high-fidelity AI-driven instruction sets and code-generation pipelines.
+
+Categories: Programming logic (Python, C++, Rust, Go), multi-scale reasoning, and cybersecurity defense patterns.
+
+Curation: Automated filters and human-in-the-loop (HITL) checks are used to ensure data quality and architectural alignment.
+
+3. Intended Use & Boundaries (Liability Protection)
+To ensure safety and compliance, use of Koinic Labs models is subject to the following boundaries:
+
+AXL-Secure & AXL-Debugger Series:
+Intended Use: Defensive cybersecurity augmentation, code auditing, and vulnerability patching assistance.
+
+Human-in-the-Loop: These models are designed to assist human experts. They are NOT intended for autonomous deployment in critical infrastructure (e.g., power grids, healthcare, transport) without human verification.
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+Forbidden Use: Any offensive cyber-operations or unauthorized intrusion testing.
+
+4. Environmental Impact
+Koinic Labs prioritizes sustainability. By optimizing for CPU-first inference, our models significantly reduce the carbon footprint compared to standard GPU-intensive LLMs.
+
+Training Efficiency: Typical runs average 0.0070 kg CO2.