𝐖𝐡𝐚𝐭 𝐢𝐟 𝐀𝐈 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐧𝐨𝐭 𝐚𝐛𝐨𝐮𝐭 𝐩𝐮𝐬𝐡𝐢𝐧𝐠 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞 𝐡𝐚𝐫𝐝𝐞𝐫, 𝐛𝐮𝐭 𝐚𝐛𝐨𝐮𝐭 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐠𝐞𝐨𝐦𝐞𝐭𝐫𝐲 𝐨𝐟 𝐜𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧 𝐢𝐭𝐬𝐞𝐥𝐟?

Today I’m sharing the full Prime platform pitch deck from Trauth Research LLC, combining two tightly linked tracks:

𝐏𝐫𝐢𝐦𝐞 𝐂𝐨𝐫𝐞

A software-only AI system that delivers 40–60% GPU energy reduction at production workloads

– 𝘯𝘰 𝘧𝘪𝘳𝘮𝘸𝘢𝘳𝘦 𝘤𝘩𝘢𝘯𝘨𝘦𝘴

– 𝘯𝘰 𝘵𝘩𝘳𝘰𝘵𝘵𝘭𝘪𝘯𝘨

– 𝘯𝘰 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘭𝘰𝘴𝘴

Validated with 1,300,000+ telemetry samples, 1,000+ hours of continuous runtime, multiple NVIDIA architectures, and peer-reviewed, DOI-verified publications. This is not a simulation. It runs. At scale.

𝐏𝐫𝐢𝐦𝐞 𝐅.𝐀.𝐓.𝐇.𝐄.𝐑.

The research layer behind it.

Here we move beyond brute-force computation and classical assumptions of hardness. Using information geometry and deterministic neural architectures, we show how certain exponential problems collapse structurally rather than computationally.

They learn the geometry. We use it.

Why this matters:

• 𝘋𝘢𝘵𝘢 𝘤𝘦𝘯𝘵𝘦𝘳𝘴 𝘸𝘢𝘴𝘵𝘦 𝘶𝘱 𝘵𝘰 60% 𝘰𝘧 𝘎𝘗𝘜 𝘦𝘯𝘦𝘳𝘨𝘺 𝘢𝘴 𝘩𝘦𝘢𝘵

• 𝘎𝘭𝘰𝘣𝘢𝘭 𝘎𝘗𝘜 𝘦𝘯𝘦𝘳𝘨𝘺 𝘴𝘱𝘦𝘯𝘥 𝘪𝘴 𝘩𝘦𝘢𝘥𝘪𝘯𝘨 𝘵𝘰𝘸𝘢𝘳𝘥 $120𝘉/𝘺𝘦𝘢𝘳

• 𝘐𝘯𝘤𝘳𝘦𝘮𝘦𝘯𝘵𝘢𝘭 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘢𝘵𝘪𝘰𝘯𝘴 𝘢𝘳𝘦 𝘯𝘰 𝘭𝘰𝘯𝘨𝘦𝘳 𝘦𝘯𝘰𝘶𝘨𝘩

𝐏𝐫𝐢𝐦𝐞 𝐂𝐨𝐫e delivers immediate OPEX reduction.

𝐏𝐫𝐢𝐦𝐞 𝐅.𝐀.𝐓.𝐇.𝐄.𝐑. defines the long-term security and computation paradigm.

This is where science becomes infrastructure.

Full pitch deck and open data are available via Zenodo.

Pitch Deck Prime Core => https://zenodo.org/records/17923831

Pitch Deck Prime Core & Prime F.A.T.H.E.R. => https://zenodo.org/records/17923811

www.Trauth-Research.com

Stefan Trauth #GreenAI #GPUOptimization

#DeepTech #EnergyEfficiency #InformationGeometry

#PostQuantumSecurity #DataCenters #AppliedAI #SustainableComputing

#Cryptography #ClassicalCryptography #KeyManagement #CyberSecurity #QKD #QuantumCryptography #PostQuantumCryptography #CryptographicResilience #TrauthResearch

𝐆𝐄𝐎𝐌𝐄𝐓𝐑𝐈𝐂 𝐂𝐎𝐋𝐋𝐀𝐏𝐒𝐄 𝐂𝐎𝐍𝐅𝐈𝐑𝐌𝐄𝐃: 𝐍𝐏-𝐇𝐚𝐫𝐝 𝐒𝐩𝐢𝐧-𝐆𝐥𝐚𝐬𝐬 𝐜𝐨𝐥𝐥𝐚𝐩𝐬𝐞𝐝.

When I published my NP-Hardness preprint, the obvious question was: Is this a property of my specific architecture – or something deeper?

Now I have the answer.

In a new technical appendix, I applied a completely different system to the same data: a classical GMDH network based on the work of Alexei Ivakhnenko (1968) – the forgotten "Father of Deep Learning" who developed self-organizing polynomial networks decades before backpropagation existed.

The result is striking:

❌ R² = -0.0055 across all 98 neurons (zero predictive power)

✅ Full structural connectivity (9 layers, complete pairwise coupling)

✅ Invariant activation (49-50 neurons active per iteration, near-zero variance)

✅ Uniform switching (mean: 48.0 switches per neuron)

Translation: The GMDH network finds nothing to learn – yet the system is fully coupled, perfectly stable, and globally coordinated.

This is the signature of geometric collapse: Structure without function. Coupling without causation. Order without computation.

The information-geometric manifold is not an artifact of my architecture. It is a property of the informational substrate itself.

📄 Appendix DOI: 10.5281/zenodo.17849616

📄 Appendix Link: https://doi.org/10.5281/zenodo.17849616

#Physics #ArtificialIntelligence #InformationGeometry #ComplexityTheory #Research #SpinGlass #TrauthResearch #GMDH #Ivakhnenko #DeepLearning #NPHardness

𝐍𝐏-𝐇𝐀𝐑𝐃𝐍𝐄𝐒𝐒 𝐂𝐎𝐋𝐋𝐀𝐏𝐒𝐄𝐃: 𝐍=100 𝐢𝐧 90 𝐌𝐢𝐧𝐮𝐭𝐞𝐬.

 

The P = NP problem is considered the Holy Grail of computer science. The dogmatic assumption: NP-hard problems require exponential search times.

My new data proves: The problem lies not in complexity, but in representation.

In my new preprint "NP-Hardness Collapsed: Deterministic Resolution of Spin-Glass Ground States via Information-Geometric Manifolds (Scaling from N=8 to N=100)" I demonstrate a mechanism that allows Spin-Glass state spaces to deterministically collapse.

The Performance Benchmark:

Total runtime for N=100: 90 minutes.

But here is the sensation: In this single 90-minute run, every intermediate solution from N=2 to N=100 was solved simultaneously.

The Facts:
✅ Speed: 90 Minutes for the full spectrum (N=2...100).
✅ Exactness: Deterministic verification, not probabilistic approximation.
✅ Scalability: Information-geometric field dynamics that encompass all sub-solutions instantly.

It needs no searching algorithms. It only needs information and geometry.

Anyone claiming I have 'solved P=NP' is arguing against a straw man; I am demonstrating that specific NP-hard representations can be bypassed geometrically.

As explicitly stated in my paper 'NP-Hardness Collapsed: Deterministic Resolution of Spin-Glass Ground States via Information-Geometric Manifolds' on Page 32: '𝘛𝘩𝘪𝘴 𝘥𝘰𝘦𝘴 𝘯𝘰𝘵 𝘳𝘦𝘴𝘰𝘭𝘷𝘦 𝘵𝘩𝘦 𝘗=𝘕𝘗 𝘱𝘳𝘰𝘣𝘭𝘦𝘮...' – but it opens a perspective that may lead to a new understanding. Perhaps the P = NP or P ≠ NP question cannot be resolved in its classical formulation, but the underlying structure can be addressed geometrically.

Information is all it needs – geometry follows.

📄 Read the Preprint here:

DOI:10.20944/preprints202512.0207.v2
Link: NP-Hardness Collapsed: Deterministic Resolution of Spin-Glass Ground States via Information-Geometric Manifolds (Scaling from N=8 to N=100)[v2] | Preprints.org

Hashtag#Physics Hashtag#ArtificialIntelligence Hashtag#InformationGeometry

Hashtag#ComplexityTheory

Hashtag#Research Hashtag#SpinGlass Hashtag#TrauthResearch Hashtag#ExactComputing

Hashtag#InformationTheory Hashtag#PvsNP Hashtag#NPHardness