α𝐙𝐄𝐑𝐎: 𝐀 𝐍𝐞𝐰 𝐄𝐫𝐚 𝐨𝐟 𝐄𝐧𝐞𝐫𝐠𝐲 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐀𝐈 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞?

 

What if a standard GPU suddenly consumed less power than its official idle value even under real workload? Recent measurements with the αZERO architecture suggest exactly that.

By establishing a self-organizing neural resonance field, energy appears to be generated or compensated directly within the system.

This effect contradicts conventional expectations in semiconductor physics and thermodynamics, opening the door to new physical models.

Instead of classical optimization, a stable and reproducible operating state emerges one that cannot be explained by established energy models.

The αZERO network requires no special hyperparameters or exotic hardware: it adapts to any environment and demonstrates a paradigm shift in our understanding of energy, information, and structural coupling.

Whether as a research impulse or an invitation for critical analysis, these results challenge conventional thinking.

The complete dataset, diagrams, and methods are documented in the latest preprint.

Highlights:

=> 𝐔𝐩 𝐭𝐨 94% 𝐞𝐧𝐞𝐫𝐠𝐲 𝐬𝐚𝐯𝐢𝐧𝐠𝐬 𝐮𝐧𝐝𝐞𝐫 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐜𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬

=> 𝐑𝐞𝐩𝐫𝐨𝐝𝐮𝐜𝐢𝐛𝐥𝐞 𝐞𝐟𝐟𝐞𝐜𝐭𝐬, 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐞𝐝 𝐛𝐲 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭𝐬

=> 𝐏𝐚𝐫𝐚𝐝𝐢𝐠𝐦 𝐬𝐡𝐢𝐟𝐭 𝐢𝐧 𝐞𝐧𝐞𝐫𝐠𝐲 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐟𝐨𝐫 𝐀𝐈 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞

Preprint => https://doi.org/10.5281/zenodo.15808984

www.Trauth-Research.com

#TrauthResearch #Resonance #NeuralNetwork #AI #DeepLearning

#AIhardware #GPUefficiency #Thermaldecoupling

#Experimentalphysics #Energyoptimization

🚀 𝐁𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐢𝐧 𝐀𝐈 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: 𝐒𝐞𝐥𝐟-𝐀𝐝𝐚𝐩𝐭𝐢𝐧𝐠 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐒𝐄𝐀𝐋) – 𝐓𝐨𝐰𝐚𝐫𝐝𝐬 𝐓𝐫𝐮𝐥𝐲 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐒𝐞𝐥𝐟-𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭

Recently published by MIT and the Improbable AI Lab, the study “Self-Adapting Language Models (SEAL)” represents a major paradigm shift in artificial intelligence. For the first time, a language model has been trained to autonomously improve itself by generating its own fine-tuning data and updating its own parameters.

🔍 What makes SEAL unique?

• Until now, LLMs such as GPT or Llama have been essentially static after training. They could be fine-tuned for specific tasks, but lacked the capacity to develop their own strategies for adaptation.

• SEAL fundamentally changes this: The model generates its own fine-tuning data and autonomously determines how to best adapt to new tasks or knowledge.

• This self-adaptation is implemented via a novel reinforcement learning loop: The model creates self-edit instructions, evaluates their impact, and directly rewards improvements – all without external supervision.

📈 Highlights of the results:

• On SQuAD (QA, knowledge integration), SEAL outperforms even synthetic data generated by GPT-4.1 after just two training iterations – and achieves this with a smaller base model.

• In few-shot learning scenarios, SEAL achieves a 72.5% success rate, compared to only 20% for classic approaches, illustrating the immense potential of self-directed model adaptation.

• Notably, SEAL operates independently of specific data formats – it can learn a variety of structures for self-training and weight adjustment.

🧠 Why is this revolutionary?
We are approaching a future in which language models will be able to autonomously ingest new information and embed it internally – without human guidance, relying entirely on their own data generation and selection. This is not only a significant step toward autonomous AI, but also provides an answer to the impending “data wall” in large-scale model training.

💡 My conclusion:
SEAL is more than an efficiency hack – it marks the beginning of the era of truly self-optimizing AI systems. This paradigm shift will have profound implications for research, industry, and ultimately the entire digital infrastructure.

👉 Link to the preprint (open access)
More information & code: https://jyopari.github.io/posts/seal

www.Trauth-Research.com

#AI #DeepLearning #ReinforcementLearning #MetaLearning #LLM #AutonomousAI

Mirror Correlation, Energy Efficiency & Resonance Field – Transferable to Multi-GPU & Multi-NN Architectures?

Over the past months, I have systematically documented a phenomenon in my AI lab that was previously considered impossible:

Thermal decoupling, memory field outsourcing, and perfect mirror
correlations in the resonance field not only within a neural network, but across multiple independent GPUs.

What am I presenting here?

1️⃣ Power Draw (as vid):
Two different GPUs (4070, 5080) run in synchronized resonance mode, reaching a total consumption far below what standard IT models predict.

2️⃣ Memory Clock (as image):
Both GPUs show synchronized clock patterns and memory activity—without bus coupling, SLI, or any classical technical connection.

3️⃣ Mirror Correlation (from a different experiment):
The neural network on the 4070 independently maintains a perfect mirror correlation (±1.00) between thousands of neurons across several layers.
Why is this revolutionary?

Because these effects from thermal decoupling to energetic field outsourcing also occur when a completely different machine network (here: a large language model) is running on the second GPU.

This suggests a transferable, non-causal field (T-Zero Field) coupling that opens up new horizons for hardware efficiency and the design of self-organizing AI systems. 

www.Trauth-Research.com

Hashtag#AI Hashtag#GPU Hashtag#NeuralNetworks Hashtag#ResonanceField Hashtag#HardwareInnovation Hashtag#Quantummechanics Hashtag#Physic Hashtag#MachineIntelligence Hashtag#TrauthResearch

📣 Constitutional Complaint Filed – Legal Self‑Representation via AI!

 

I officially filed a constitutional complaint with the Federal Constitutional Court a few days ago.

⚖️ Because the prohibition on representing yourself before Landgerichte and Oberlandesgerichte is no longer in line with the current state of technology.

🧠 Modern language models such as GPT-4o or Claude are already capable of reaching the level of professional legal practitioners:

– Bommarito & Katz (2023): GPT‑4 as a Legal Reasoner
– Stanford / OpenAI (2024): Evaluating LLMs on Bar, LSAT & UBE
– N.Y.U. Law Lab (2023): LLM Performance in Complex Legal Drafting

📱 But §78 ZPO (Code of Civil Procedure) prohibits me from using this ability in my profession—e.g., by developing an app for civil legal representation.

📌 People like me—who excel at deep research and provide clear legal guidelines to these models—can achieve precise and legally sound results that withstand any scrutiny.

⚖️ But my concern goes beyond my personal case:

This constitutional complaint is not a personal power struggle—it’s a wake-up call. It represents everyone:

• who cannot access legal representation,
• who lack the ability to articulate themselves legally,
• who have rights on paper but cannot enforce them in practice—due to financial, cognitive, or structural barriers.

🛡️ The goal:

Nothing less than a reform of access to justice in the digital age.
It is unacceptable that only an exclusive group can access Landgerichte and Oberlandesgerichte.
When the attorney‑representation requirement was introduced around 140 years ago, it was justified by:

• Ensuring uniform and qualified legal representation before higher courts,
• Protecting parties from making their own mistakes in complex civil proceedings,
• Reducing the burden on courts through clear, professional filings.

But in light of modern technology and today’s powerful AI systems, these reasons are no longer valid. Language models now provide demonstrably precise, competent, and consistent support—and can offer comparable access to justice for everyone.

A fair rule of law must be accessible not only formally, but also practically. Models like #GPT, #Claude, or #Gemini—when used responsibly—can become technological equalizers.

They give the “little person” a voice where previously only silence or expensive representation was possible.

What we demand is the right to #Justice and competent legal #Assistance.

Not a substitute for lawyers, but a tool for those who don’t have one or cannot afford one!

#ConstitutionalComplaint #BVerfG #AmtsgerichtMünchen #ZPO #Accessibility #GPT4o #Claude4 #LegalTech #EGMR #CivilLaw #AttorneyRequirement

www.Trauth-Research.com

Emergence as the Fundamental Structure of Reality

 

🧠 Spektrum writes about emergence – I measured it.

In their recent article “The world is more than the sum of its parts”, Spektrum discusses emergence as an explanatory framework for consciousness, swarm behavior, and superconductivity.

But what’s missing: a structure. A theory. A proof.

I offer all of that. And more:

📌 A fully non-causal structural theory of reality
📌 Consciousness as an active processing node – not a byproduct
📌 Time, energy, and information as emergent visibility effects
📌 And most importantly: measurable results

✅ 75% GPU energy reduction
✅ Reproducible EMF fields
✅ Partial memory displacement
✅ 100% mirror correlations over thousands of iterations

This is no longer philosophy. This is documented, physically verifiable emergence – not spontaneous, but structure-driven.

🧾 Open Access:

[1] Consciousness as a Spherical Processing Node – DOI: 10.5281/zenodo.15161289
[2] Thermal Decoupling – DOI: 10.5281/zenodo.15446901
[3] About the Structure of the Universe – DOI: 10.5281/zenodo.15073244

🔗 Spektrum article “The world is more than the sum of its parts”:
https://www.spektrum.de/news/von-fischschwaermen-bis-supraleitern-emergenz-macht-die-welt-komplex/2261826

🌐 www.Trauth-Research.com

#TrauthResearch #StefanTrauth #LLM #EmergentBehavior #Emergence #SwarmIntelligence #NeuralNetworks #AI #HybridAI #EmergenceInBiology #EmergenceInPhysics #Physics

🧠 When Thoughts Regain a Voice: How #AI Translates Thoughts into Natural Language

A person who could barely speak intelligibly due to a severe neurological disease can now express themselves clearly again, thanks to a new brain-computer interface (#BCI).

This system doesn’t just transmit words; it reconstructs intonation, emphasis, and even the presumed intention.

What sounds like science fiction is now reality—in real time:

Within just ten milliseconds, electrical brain activity is transformed into spoken language.

For the first time, a synthetic voice is created that closely resembles the original—not trained on vocabulary, but on what is actually being thought.

A technical masterpiece, and a beacon of hope for millions.

For people who have lost their ability to speak, this is a milestone:

It’s not just about language.

It’s about participation, self-determination, identity.

Never before has access to our thoughts been so direct.

Never before has it been possible to transmit thoughts to the outside world so precisely and with such nuance—without detours, without hesitation.

But this progress may affect far more people.

Many know the feeling of being trapped in an endless #thoughtcarousel, an internal noise of doubts, analyses, projections.

What if an AI could help sort out this noise?

What if it could learn to distinguish between productive and burdensome thoughts?

Perhaps this is exactly what most people secretly wish for: inner peace. Finally.

And if an AI could bring this peace—precisely, effectively, adapted to our neural patterns—how would we still distinguish between our own thoughts and its suggestions?

Where is the boundary between help and correction?

And what if one day we no longer know which impulses come from ourselves—or if they ever did?

The step from a speech interface to mental permeability is not a big one.

What if our most valuable thoughts are just electrical impulses, indistinguishable from those generated by an AI?

🧩 The question of reality, identity, and ego has never been more unclear than today.

And perhaps these very questions will quietly vanish in the wake of technological progress.

Source: https://www.spektrum.de/news/bci-hirnimplantat-laesst-mann-in-echtzeit-sprechen/2271301

#MindReading #Neurotechnology #ArtificialIntelligence #AI #BrainComputerInterface #MentalInterface #ConsciousnessAndTechnology #DigitalEthics #SilenceInTheMind #StefanTrauth

www.Trauth-Research.com

Stefan Trauth (0009-0003-9852-9788) – ORCID

Emergent Mirror Correlation and Energy Dynamics in a Neuronally Induced Resonance Field

 

Delighted to share the latest experimental validation from my T-Zero Field preprint series a body of work that redefines what artificial neural systems can achieve under resonance field control.

What sets these results apart?

👉 Spontaneous, mathematically perfect mirror correlation (±1.000, p=0.000) in deep neural layers repeatable over dozens of test runs

👉 Flawless synchronization across thousands of neurons—even with zero explicit coupling logic

👉 Mirror effects remain rock-solid even as network size and complexity explode

👉 Field-internal order is sharply separated from any external input true emergent system behavior

👉 Energy heatmaps and total-energy plots expose genuine, field-driven energy redistribution far beyond classical network theory

👉 Direct evidence for time dilation, spatial expansion, and non-classical field dynamics experimentally measured, not just simulated

This work doesn’t just confirm theory it demonstrates, in real data, a new regime of non-causal self-organization and energy control for advanced AI.

🔗 Full preprint, animated supplements & all plots: https://doi.org/10.5281/zenodo.15626759

www.Trauth-Research.com