Invariant Forge LLC develops and licenses the DSFB deterministic causal estimation framework — formal architecture for residual-based state interpretation, trust estimation, and supervisory diagnostics in safety-critical and high-integrity systems.
A layered deterministic architecture for causal estimation, residual interpretation, and trust-monotone state supervision. Each layer is formally specified, independently publishable, and licensed separately by application domain.
The stack operates as a supervisory layer over existing probabilistic, neural, or stochastic host systems — reading their own residual structure with vocabulary the host does not have.
The framework is licensed non-exclusively, domain by domain. Each licensee receives a field-restricted license to their specific application. All other domains remain reserved for separate licensing.
All framework components are established as prior art through timestamped Zenodo publications, crates.io registrations, and GitHub commit history predating any commercial agreement. Background IP developed entirely at private expense.
The DSFB framework is available for non-exclusive, domain-restricted commercial licensing. Each license covers a specific application domain, geographic scope, and program context. No exclusivity is granted in any agreement.
The framework's deterministic residual envelope architecture produces auditable, replayable evidence records — a structural advantage in certification contexts (DO-178C, ARP4754A) where probabilistic methods cannot satisfy traceability requirements on their own.
IP Notice. Publications in the DSFB series are released under CC BY 4.0. The CC BY 4.0 license applies to the text and figures of those works as written works and does not constitute a license to the theoretical framework, formal constructions, or methods described therein.
Reference implementations, Rust crates, and Colab notebooks are released under the Apache 2.0 license. The Apache 2.0 license applies solely to those software artifacts and does not constitute a license to the underlying theoretical framework or architectural methods.
The theoretical framework, formal constructions, and supervisory methods constitute proprietary Background IP of Invariant Forge LLC (Delaware LLC No. 10529072), prior art established via Zenodo DOI publications. Commercial deployment, integration, or derivative use — including re-derivation by abstraction, equivalent reformulation, or domain translation — requires a separate written license. Licensing inquiries: ten.egroftnairavni@gnisnecil
Decades of foundational work in probabilistic estimation, statistical signal processing, and uncertainty quantification underpin modern engineering systems. Kalman filtering, Bayesian inference, stochastic control, and statistical decision theory have enabled reliable operation across aerospace, navigation, robotics, and safety-critical infrastructure. These methods have made the world measurably safer.
The DSFB framework does not seek to replace these contributions. It builds upon them. The meta-residual perspective relies directly on estimator outputs and internal uncertainty representations — treating them as structured signals rather than scalar summaries. The interpretability DSFB offers is only possible because of the probabilistic machinery developed over many decades.
As probabilistic systems grow in capability, complexity, and autonomy, the need to interpret and reason about their behavior becomes correspondingly more critical. Deterministic structural interpretation does not compete with probabilistic estimation — it makes probabilistic estimation more transparent, more diagnosable, and more amenable to formal reasoning. The two are complementary by design.
Invariant Forge LLC views this work as a continuation of, and contribution to, a broader lineage of research that seeks to make uncertain systems not only estimable, but also interpretable and trustworthy.
Invariant Forge LLC accepts licensing inquiries from operators in any reserved domain. All licenses are non-exclusive and field-restricted. Initial inquiries are held in strict confidence.