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README.md

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+## Inference Labs  
+### Auditable Autonomy for ML Engineers
+
+Autonomous models make real-world decisions, but their inferences are not verifiable.
+
+Humans rely on cryptographic identity to secure access, payments, and accountability. ML systems lack a standard way to prove what a model computed, how it was executed, or whether an output has been tampered with.
+
+**Inference Labs builds verification primitives for machine learning.**
+
+We pioneered **Auditable Autonomy**, a research-driven framework that brings cryptographic verification to model inference. Our **Proof of Inference** approach makes every forward pass, agent decision, and workflow execution independently verifiable, while preserving IP through **veiled model weights and biases**.
+
+On this page you will find:
+- Research on verifiable and auditable inference
+- Benchmarks for integrity, performance, and overhead
+- Models and reference implementations with built-in verification
+- Tools for protecting proprietary weights and biases
+- Experiments for deploying ML in adversarial and regulated environments
+
+Verification becomes a first-class primitive for ML systems, enabling trustworthy deployment at scale.