Research
Technical Papers
Public research, technical notes, and defensible explanations from Newport Resonance.
Research That Informs the Product
Newport Resonance is working at the frontier of governed artificial intelligence: systems that need continuity, evidence discipline, and auditability rather than generic automation. Our research programme tests the mechanics behind that work, from structural reasoning to context stability in long-horizon model use.
The papers below share public technical findings that inform our product decisions and give customers, partners, and reviewers a clearer view of how we evaluate intelligence infrastructure. They are written to offer value without exposing protected ToM implementation details.
2026-05-12
Multi-Step General Reasoning Without an LLM: A Structural-Mechanics Architecture Outperforms GPT-5.2 on a Seven-Family Reasoning Battery
A technical paper on structured multi-step reasoning, benchmark discipline, and what governed AI systems can learn from non-prompt-only evaluation methods.
2026-04-28
Eliminating Context Rot in Frozen LLMs: A Three-Mode Structural State-Coupling Architecture
A technical paper on context degradation in frozen LLM workflows and the design implications for stateful, governed AI systems.