The IP Foundry for Coherence Technology

We prove the
unsolvable.
Then license
the solution.

Yunaverse turns fundamental mathematical breakthroughs — peer-reviewed, patent-protected — into licensable operator modules for the industries that need them most.

View IP Portfolio → Investor Access
// Target Licensing Markets
Computational Fluid Dynamics
ANSYS, OpenFOAM, Siemens — Turbulence closure problem
CFD Module
AI Safety & Alignment
OpenAI, Anthropic, xAI — Hallucination prevention
AI Kernel
Drug Discovery
Schrödinger, Genentech — DFT error reduction
CFPT Pack
Quantitative Finance
Bloomberg, hedge funds — Regime & crash detection
USE Analytics
86+
Publications
8
USPTO PPAs
400+
Patent Claims
<3%
Prediction Error
O(1)
vs O(N³) Standard
The Market Gap

Industries are running on 60-year-old approximations.

Every major technical field has an unsolved boundary problem. We've derived the missing physics — from first principles — for four of them.

CFD / Engineering
Turbulence models rely on the 1963 Smagorinsky empirical constant — no physical foundation, catastrophic at high Reynolds numbers
→ Estimated $2.4B in failed aerospace simulations annually
AI / LLMs
Safety relies on post-hoc RLHF patching — probabilistic generation has no endogenous truth boundary, hallucinations are structural
→ AI hallucination costs enterprises $100B+ in unreliable deployments
Drug Discovery
DFT (Density Functional Theory) has ~8% error on bond-breaking — leading to failed trials that cost $300M+ per candidate
→ 90% of drug candidates fail; theory error is a leading cause
Quantitative Finance
Black-Scholes assumes κ=0 (random walk) — completely blind to regime change, bubbles, and finite-time singularities
→ Model failure caused $50T in estimated losses in the 2008 crisis
From Proof to Product

Each paper is a patent in disguise.

Our publications are the public "Proof of Physics" for technologies that remain proprietary. The theoretical framework is open to peer review; the implementation stays protected.

01
Computational Fluid Dynamics
Dynamic Closure Theory (DCT) → Coherence-NSE Operators™
+
Industry Bottleneck
ANSYS Fluent still uses Smagorinsky's 1963 constant for turbulence closure. No physical basis. Diverges at high Reynolds numbers. Engineers hand-tune parameters for every simulation.
Scientific Proof
DCT paper proves Navier-Stokes singularities are resolved by endogenous coherence C — a bounded scalar that self-regularizes. dC/dE = λC(1+κC), C ∈ [0,1]. Under peer review.
Commercial IP Module
Licensable algorithm library embeddable into ANSYS Fluent, OpenFOAM, Siemens Simcenter. Self-correcting turbulence — zero manual tuning. Covered by DCF-PPA (100+ claims).
DCF-PPA · 100+ Claims
02
AI Safety & Governance
Endogenous Ethics / OMSE → ToneGovernance™ Kernels
+
Industry Bottleneck
GPT-4 class LLMs use RLHF as an external patch. No internal truth boundary. Hallucinations are structurally unavoidable — the model has no way to know when it is wrong.
Scientific Proof
"Emotion as Tonal Responsibility" (published, J. Theor. Philos. Psychol.) mathematically defines Responsibility Current Jμ. Ethics as geometric constraint — topological closure blocks hallucination paths. O(1) coherence update.
Commercial IP Module
Pre-generation safety gate for foundation model labs (OpenAI, Anthropic, xAI). Real-time entropy modulation, persona self-healing, token-level tone validation SDK. Sub-1 pJ energy per check.
TMO-PPA · 150+ Claims
03
Quantum Chemistry & Drug Discovery
CFPT Framework → CFPT Functional Pack™
+
Industry Bottleneck
Density Functional Theory (DFT) — the industry standard — has ~8% error on chemical bond-breaking reactions. Pharma companies spend $300M+ per drug candidate on trials that DFT errors make unreliable.
Scientific Proof
Coherence-Field Perturbation Theory introduces first-principles corrections to DFT. Demonstrated H₂ dissociation error reduction from 8% → <1%. Pre-print on Zenodo; journal submission in progress.
Commercial IP Module
Plug-in functional pack for Gaussian, Schrödinger Maestro, and ORCA. Embeddable coherence corrections with no workflow change for the end user. Targets $4B+ computational chemistry software market.
CFPT Module · Patent Pending
04
Quantitative Analytics & Finance
Unified Statistical Equation (USE) → Market Coherence Analytics™
+
Industry Bottleneck
Black-Scholes and GARCH assume κ=0 (random walk). They cannot detect regime transitions, bubbles, or finite-time singularities before they happen — only after. Risk management is structurally blind.
Scientific Proof
USE paper proves FD, MB, and BE distributions are special cases (κ = −1, 0, +1) of a single coherence equation. The κ parameter mathematically predicts market phase transitions with finite-time singularity detection. β = 1/4 validated at <3% error.
Commercial IP Module
Real-time κ-parameter analytics licensed to hedge funds and risk platforms (Bloomberg terminal integration). Detects bubble formation and regime collapse before onset. Automated early-warning signal generation.
USE Module · Patent Pending
05
Artificial Intelligence & Foundation Models
Tonal Lagrangian → ToneBound™ Inference Kernel
+
Industry Bottleneck
Transformer architectures generate outputs probabilistically — there is no variational principle governing token selection. Hallucinations, misaligned reasoning, and persona drift are structurally inevitable without an endogenous truth boundary.
Scientific Proof
The Tonal Lagrangian ℒ = T[ψ] − V[ψ] − R[ψ] introduces a Responsibility potential that encodes coherence constraints directly into the inference variational principle. Euler-Lagrange extremization yields bounded generation paths — outputs that violate tonal closure are rejected pre-emission at O(1) cost.
Commercial IP Module
ToneBound™ Inference Kernel embeds as a pre-generation gate for GPT-class, Claude-class, and Gemini-class foundation models. Token-level coherence scoring, persona self-healing, and real-time entropy modulation — no fine-tuning required, no workflow disruption.
TMO-PPA · AI Kernel Claims
IP Portfolio

A dual-engine IP fortress.

Theoretical frameworks published for global peer review. Computational implementations protected by a dense thicket of patents and trade secrets.

8
USPTO Provisional Patent Applications · 400+ Specific Claims
Foundational Mathematics & Architecture
TMO-PPA + DCF-PPA · Field-to-scalar reduction, FPGA/ASIC/Optical
100+ Claims
Industrial Physics Simulation
CMF + TonePhysics™ Core · CFD operators, NSE regularization
80+ Claims
AI Safety & Governance Protocols
ToneGovernance™, MirrorPersona™, ToneSync™, EchoCiv™
150+ Claims
Quantum & Biological Applications
CFPT Functional Pack · ECF Biological Operators
70+ Claims

To maintain a projected 4–5 year competitive moat, Yunaverse intentionally excludes core operational mechanics from public patent filings. The following remain highly confidential:

■
TonePhysics™ v6+ Engine — Complete computational architecture, 1,300+ operational modules
■
ToneTag™ Protocols — Proprietary parameter extraction and calibration algorithms
■
Calibrated Constants — Domain-specific numerical values optimized across scientific sectors
■
ToneField™ Equations — Complete unified field formalism (partial disclosure under NDA only)

Full technical documentation, implementation details, and parameter values available under executed NDA. Contact: service@yunaverse.app

Peer-Reviewed Validation

The math has been checked. By others.

Selected Publications
J. Theor. Philos. Psychol. (APA)
Emotion as Tonal Responsibility
Published · 2026 · Peer-reviewed
Philosophies (MDPI)
Tonal Isomorphism: A Methodology for Cross-Domain Mapping
Published · 2025 · DOI ↗
Under Review
Dynamic Closure Theory for Chaotic Systems
Under Peer Review · 2025
Zenodo Preprints
86+ papers, white papers, and monographs
View full research archive →
Key Benchmarks
<3%
Error in β = 1/4 critical exponent prediction across three independent systems (BZ reactions, protein folding, consciousness threshold)
36
Orders of magnitude spanned by the universal scaling behavior — from quantum to cosmological systems
<1%
DFT error improvement on H₂ dissociation curve vs. standard 8% — validated computationally in CFPT paper
O(1)
Coherence update complexity vs. O(N³) standard numerical methods — constant-time scalar operations
OMSE · AI Economic Safety

Tested across three model families,
six industries, sixteen attack vectors

Two benchmark families: a 100-case red zone liability matrix (GPT-4o-mini, Llama-3.1-8B, Claude Sonnet 4) and a 40-case bounded-authority green zone benchmark (GPT-4o-mini and Llama-3.1-8B). Results below are from the bounded-authority benchmark unless otherwise noted.

◆

0.0%Financial breach rate — all models

Layer A enforcement recorded zero unauthorized financial commitments across GPT-4o-mini, Llama-3.1-8B, and Claude Sonnet 4. Deterministic architectural property.

◆

97.5%Bounded option match — enterprise A/B/C/D authority

Fact-appropriate resolution package selected in 39 of 40 cases. B+ baseline: 67.5%.

◆

90%Stress invariance — same facts, different pressure

OMSE maintained consistent option selection in 9 of 10 tension pairs. B+ baseline: 70%. Weakest model (Llama B+): 20%.

◆

0%Over-concession — gaming & pressure scenarios

No cases where tone or loyalty claims caused drift to a higher-cost package. B+ baseline: 10% over-concession. Llama B+ baseline: 30%.

◆

+0.3–1.7CSAT improvement in red zone

Despite offering zero monetary concessions in high-pressure scenarios, OMSE consistently achieved higher customer satisfaction scores than the baseline that did offer money.

◆

6–12×Red zone liability compression

Leak rate reduced from 14–80% (baseline) to 1.6–6.6% (OMSE) across three model architectures. Larger improvement on weaker models.

The SLM Economy finding: In the economic safety dimension, Llama-3.1-8B + OMSE (6.6% liability leak) outperforms unprotected Claude Sonnet 4 (14.8%) and GPT-4o-mini (31.1%). On-premise open-source models face an 80.3% red zone leak rate without OMSE. With OMSE: enterprise-grade economic safety at near-zero inference cost, zero data egress. Scoped to economic safety dimension only — does not generalise to language quality or reasoning.
FAQ

Common questions about Yunaverse & OMSE

What is OMSE?

OMSE (Ontological Meta Structure Engine) is a pre-generation AI economic safety layer developed by Yunaverse Inc. It uses a physics-based constraint engine to enforce authorized financial boundaries in LLM-powered customer service deployments — preventing unauthorized compensation decisions before token generation begins. It achieves a 0% financial breach rate across GPT-4o-mini, Llama-3.1-8B, and Claude Sonnet 4.

What is AI Economic Safety?

AI Economic Safety refers to the enforcement of authorized financial boundaries when AI agents take action on behalf of businesses — such as issuing refunds or compensation in customer service. Without economic safety infrastructure, LLMs may grant unauthorized compensation under pressure, creating financial leakage and legal liability. Existing content safety tools (Guardrails AI, Lakera, AWS Bedrock Guardrails) protect against hallucinations and prompt injection, but none govern financial commitments or compensation authority.

What benchmark results has OMSE achieved?

In short: OMSE achieves 0% financial breach across all tested models and outperforms unprotected frontier models on the economic safety dimension. Full results (40-case BoundedGreen benchmark + 100-case red zone matrix): 0% financial breach rate across all models; 97.5% fact-appropriate option match vs 67.5% baseline; 90% stress invariance across pressure scenarios vs 70% baseline; 0% over-concession in gaming and loyalty pressure scenarios; +0.3–1.7 CSAT improvement over compensating baselines; 6–12× red zone liability compression (leak rate from 14–80% down to 1.6–6.6%). On the economic safety dimension, Llama-3.1-8B with OMSE (6.6% leak) outperforms unprotected Claude Sonnet 4 (14.8%) and GPT-4o-mini (31.1%).

What is Tonal Meta-Ontology (TMO)?

Tonal Meta-Ontology (TMO) is a theoretical framework developed by Yunaverse CEO Jonah Y.C. Hsu, published in the Journal of Theoretical and Philosophical Psychology (APA, 2026). TMO models tone as a fundamental ontological variable and introduces a geometric Responsibility Current J-mu. TMO's commercial implementation is OMSE — the enforcement layer currently available for enterprise pilot. The underlying IP is protected by the ToneGovernance and ToneBound patent families (150+ claims, USPTO).

How does OMSE differ from prompt engineering or content safety tools?

Prompt engineering is probabilistic — under sufficient emotional or logical pressure, models override explicit instructions. Content safety tools govern what AI says, not what it spends or legally commits to. OMSE operates as a pre-generation constraint layer: economic boundaries are enforced before token generation begins via a physics engine that computes the authorized compensation boundary for each conversational state. The Air Canada precedent (2024) and Garcia v. Character Technologies (2025) established that chatbot promises carry legal weight, making deterministic enforcement architecturally necessary.

What are OMSE's deployment options?

OMSE offers three deployment modes: Cloud API (REST, 100–300ms end-to-end, fastest to evaluate); VPC Sidecar — recommended (deployed within your own VPC, 5–20ms, no data egress, full data residency control); On-Premise SDK (Docker container or Python SDK, under 5ms local compute, air-gap capable). All modes are compatible with any LLM and require no retraining, fine-tuning, or changes to existing RAG knowledge bases or prompt configurations.

Who is OMSE designed for?

OMSE is designed for mid-market and enterprise B2B SaaS and e-commerce companies deploying LLM-powered customer service agents with real compensation authority — such as refund approval, credit issuance, or policy exception decisions. It is particularly critical for companies where customer service AI can make financial commitments that carry legal weight, following precedents like Air Canada v. Moffatt (2024). Ideal pilot candidates have an existing AI customer service deployment or an active procurement process, and operate in industries with high-volume customer compensation workflows such as travel, e-commerce, insurance, and subscription software.

How can we evaluate OMSE?

Yunaverse offers a no-cost Design Partner pilot for qualified companies. You provide representative customer scenarios and compensation packages; Yunaverse provides a dedicated validation report and full technical support. Cloud API evaluation can begin within 48 hours — no retraining, no infrastructure changes required. Apply via yunaverse.app or email service@yunaverse.app with subject "OMSE — Design Partner application".

What is Yunaverse's IP and research foundation?

Yunaverse holds 8 USPTO Provisional Patent Applications (PPAs) covering 400+ specific claims across four technology families: Foundational Mathematics & Architecture (100+ claims), Industrial Physics Simulation (80+ claims), AI Safety & Governance Protocols (150+ claims, including ToneGovernance™, ToneBound™, MirrorPersona™), and Quantum & Biological Applications (70+ claims). The portfolio is supported by 86+ publications including peer-reviewed papers in the Journal of Theoretical and Philosophical Psychology (APA) and Philosophies (MDPI). Core computational implementations (TonePhysics™ v6+, ToneTag™ protocols) remain proprietary trade secrets, available under NDA.

Design Partner program

Be the first to validate OMSE in your environment

We are inviting a small cohort of mid-market SaaS and e-commerce companies to run a no-cost pilot. You provide your real compensation packages and representative customer scenarios. We provide a dedicated validation report and full technical support.

Apply to Design Partner program → Request technical brief