⚡️ SparseTech

Redefining LLM reliability for edge AI through variance reduction, sparse knowledge distillation, and probability-domain manifold correction.

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.

📚 Foundational Research

We believe models should be built upon a rigorous axiomatic framework recently published in early 2026. You can read our core methodology here:

The Core Theory

• Hallucinations Live in Variance (Jan 2026) Introduces Semantic Stability (SS) and Paraphrase Consistency (PC@k) as the true metrics for LLM reliability.

The Distillation Framework

• Sparse Knowledge Distillation: A Mathematical Framework... (Jan 2026)
• Multi-Teacher Ensemble Distillation (Jan 2026)
• Recursive Meta-Distillation: An Axiomatic Framework... (Jan 2026)
• Adaptive Weighting in Knowledge Distillation (Jan 2026)

Advanced Manifold Correction

• Post-Training Probability Manifold Correction via Structured SVD Pruning... (Jan 2026)