Prototypes, notes, and experiments.

Xybern-Reasoning-7B addresses the reliability limitations of standard LLMs in high-stakes fields like law and finance by introducing a neuro-symbolic architecture that couples fast neural inference with a deterministic "System 2" constraint engine. Instead of relying solely on probabilistic token generation, the system validates candidate answers against explicit constraint graphs to ensure formal consistency and strict rule adherence. This hybrid approach eliminates silent compliance failures and provides the audit-ready traceability required for critical enterprise decision-making.

Xybern-Reasoning-7B is a 7B-parameter transformer model built from scratch and specialized for law and finance, while still achieving frontier-level performance on general reasoning, math, and coding benchmarks. It combines long-context, reasoning-optimized architecture with DeepSeek-generated synthetic data and process-aware RLHF, outperforming other 7B models and competing with much larger closed-source systems.