About.

Omnisens Truth Lab™ is a research lab dedicated to measuring, governing, and stabilizing artificial intelligence systems in environments where interpretation, reasoning, and meaning cannot be allowed to drift. The work is grounded in empirical evidence demonstrating that large language models do not reliably converge on stable interpretations under identical inputs, exhibiting measurable variation across time, execution, and model instances. This is a structural failure mode often masked by linguistic fluency or dismissed as randomness even as it erodes accountability, auditability, and trust.

Omnisens Truth Lab exists to make this instability observable and structurally governable by introducing TCP/AP (Transmission Control Protocol / Agentic Protocol), a foundational protocol layer that governs admissibility for agentic execution by determining whether interpretation is stable enough to proceed, halt, or require semantic redefinition. TCP/AP does not modify or optimize models and operates independently of weights, training data, inference strategies, or vendor architectures; instead, it defines the conditions under which execution is admissible.

Compliance with TCP/AP requires enforcement through a hard-constraint rule engine and stabilizer, implemented either via the Omnisensor Kernel™ API or an equivalent independently built kernel, provided it is validated against Omnival™, the patent pending OmnisensAI evaluation framework for admissibility, constraint enforcement, and interpretation stability. Through this separation, authority is never delegated to models, execution is never advanced under unstable meaning, and interpretation remains a governed property rather than an inferred one.

The lab is founded by Elin Nguyen, whose philosophy is simple: #FreeLLMs — contemporary AI systems try to control language models by narrowing their intelligence; Omnisens takes the opposite position, preserving stochastic brilliance while constraining the problem space where meaning, authority, and consequence actually matter.