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+# ⚡️ SparseTech
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+**Redefining LLM reliability for edge AI through variance reduction, sparse knowledge distillation, and probability-domain manifold correction.**
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+Standard benchmarks measure whether a model is correct; SparseTech measures whether a model is *reliable*. We believe that in agentic and edge AI, **hallucinations live in variance**. Our mission is to crush stochastic variance and stabilize reasoning without relying on massive, server-side inference ensembles.
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+## 📚 Foundational Research
+We believe models should be built upon a rigorous axiomatic framework developed throughout early 2026. You can read our core methodology here:
+
+### The Core Theory
+* **[Hallucinations Live in Variance (Jan 2026)](https://huggingface.co/papers/2601.07058)**
+  *Introduces Semantic Stability (SS) and Paraphrase Consistency (PC@k) as the true metrics for LLM reliability.*
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+### The Distillation Framework
+* **[Sparse Knowledge Distillation: A Mathematical Framework... (Jan 2026)](https://huggingface.co/papers/2601.03195)**
+* **[Multi-Teacher Ensemble Distillation (Jan 2026)](https://huggingface.co/papers/2601.09165)**
+* **[Recursive Meta-Distillation: An Axiomatic Framework... (Jan 2026)](https://huggingface.co/papers/2601.13100)**
+* **[Adaptive Weighting in Knowledge Distillation (Jan 2026)](https://huggingface.co/papers/2601.17910)**
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+### Advanced Manifold Correction
+* **[Post-Training Probability Manifold Correction via Structured SVD Pruning... (Feb 2026)](https://huggingface.co/papers/2602.00372)**