Aurora STEM AI - Sovereign Reasoning Core

General-purpose AI systems encounter a critical reasoning wall when dealing with formal STEM disciplines: hallucinating non-synthesizable silicon RTL latches, proposing thermodynamically unfeasible retrosynthetic chemical pathways, and failing formal multi-step mathematical proofs. Aurora STEM AI is a sovereign multi-agent reasoning infrastructure purpose-built to eliminate stochastic hallucination in high-consequence engineering. Built upon a 32B teacher model expanded via surgical Depth Up-Scaling (DUS) and continual pre-training, Aurora distills specialized capabilities into four domain-adapted 7B foundation models: 1. Silicon RTL/EDA (olmo2-7b-silicon-rtl-eda): Synthesizable Verilog, UVM testbenches, and clock-domain crossing verification. 2. Biomed & Chemistry (olmo2-7b-biomed-chem): SMILES retrosynthesis, stereochemical validation, and binding affinity analytics. 3. Aerospace & Orbital Dynamics (olmo2-7b-astro-logic): GNC control laws, trajectory optimization, and radiative flux models. 4. PhD Pure Mathematics (olmo2-7b-phd-pure-math): Formal theorem proving (Lean 4/Coq) and stochastic differential equation solvers. The platform features an OpenAI-compatible inference gateway and provides Bring-Your-Own-Cloud (BYOC) deployment directly inside customer private VPCs, guaranteeing 100% data custody with zero IP leakage.