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Modern oncology has achieved significant success in describing the molecular mechanisms of uncontrolled cellular growth. However, the dominant paradigm still treats cancer primarily as a problem of acceleration, hyperactivation, and excessive signaling stimulation. This work proposes an alternative conceptual framework in which the core of oncogenesis is not so much the amplification of growth signals as the systemic loss of a cell’s ability to perceive and execute stop signals (STOP signals). We consider cancer as a state of acquired STOP resistance and discuss the implications of this view for the interpretation of remission and the prospects of therapeutic strategies. 1. Introduction: an asymmetry of attention Over the past decades, the cell biology of growth and division has been studied in great detail. Oncogenes, growth factors, proliferative signaling cascades, metabolic shifts—all of these form a dense and well-mapped landscape. At the same time, systems of biological sto...

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TRON: Technology for Predicting the Magnitude, Location, and Time of Earthquakes

The TRON technology (Technology Real-time Online Nucleus) was created in 2011, immediately after the Tohoku earthquake in Japan on March 11, 2011. It is based on the ability of animals to sense earthquakes in advance. By collecting data on changes in animal behavior via the internet and analyzing it, it is possible to predict in advance the magnitude, location, and time of a specific earthquake. In May 2026, the TRON technology reached full automation. As a result of a pilot experiment based on the multimodal AI model Gemma 4, it became possible to completely eliminate humans from the technological chain of collecting the necessary information. The data-processing chain is automated. The next step — a working end-to-end prototype — is intended to be funded through investment and validation. Current status : seeking investment (≈ €200,000) to test the pilot prototype and scientifically validate the hypothesis. Full...

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TRON: технология прогноза силы, места и времени землетрясений

Технология TRON - точный прогноз силы, места и времени землетрясений была разработана 15 лет назад, в 2011 году . В её основе - лежит способность животных за дни и часы предчувствовать приближение конкретного землетрясения. В мае 2026 года, в результате пилотного эксперимента на основе ИИ модели Gemma 4 удалось протестировать автоматизированную систему, которая полностью исключает участие людей в цепочке по обработки информации. Текущее состояние : поиск инвестиций (≈ €200 000) для тестирования пилотного прототипа и научной валидации гипотезы . Фулстек фаундер: Борис Д. Яровой Ежегодное количество жертв землетрясений XX век — 33.000 человек. ТАНШАНЬ 1976 . Китай. Магнитуда 7,8. Погибло 242 тысячи человек. СПИТАК 1988 . Армения. Магнитуда 7,2. Погибло 25 тысяч людей. НЕФТЕГОРСК 1995 . Россия. Магнитуда 7,6. Погибло 2040 человек. XXI век — 45.000 человек. ...

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From Ideal Fusion to Working Nuclear Energy Systems

1. The Structural Failure of Big Fusion Projects The current fusion ecosystem is fragmented by design: Plasma physicists optimize reaction conditions (temperature, confinement time, Q-factor ). Materials scientists optimize isolated samples for peak performance under narrow conditions. System engineers extrapolate optimistic assumptions into plant-scale renderings. Each layer is locally successful. The system as a whole is not. The missing role is a system owner responsible for lifetime operation, maintenance, fuel logistics, radiation damage, and cost of ownership. As a result: Physics does not translate into engineering tolerances. Material properties do not translate into predictable service life. Subsystems do not assemble into an autonomous, economically stable energy platform. This is not a failure of science. It is a failure of architecture. 2. Radiation Is Not the Enemy — Unmanaged Degradation Is Neutron damage , swelling, helium embrittlement , and activation are fundamental...

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The Physics and Chemistry of Artificial Intelligence — A Comparison with the Human Brain

The Human Brain from the Point of View of Physics and Chemistry The human brain is a system that maintains a complex order within itself as long as energy continuously flows through it: it comes in with the blood, which carries oxygen and nutrients, and leaves into the surrounding world as heat. A candle flame and a whirlpool in a river work the same way: their form persists while the flow continues, and disappears when it stops. In physics, such systems are called non-equilibrium dissipative systems. The brain weighs about 1.4 kilograms and consists of roughly 10 26 atoms (a one followed by twenty-six zeros). Foundation: long chains of carbon-based molecules (called polymers) that operate in a water environment. Chemistry: about 97% of its mass comes from four elements — oxygen, carbon, hydrogen, and nitrogen. The rest is phosphorus, sodium, potassium, sulfur, chlorine, calcium, and ti...

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AI That Learns to Generalize Later: The Mystery of Grokking

Research note on Xu, Vardi & Safran, “To Grok Grokking: Provable Grokking in Ridge Regression” , ICML 2026. A neural network is trained on a task. On the training data it performs well almost immediately. On new, unseen data it fails. Then, after a long period during which nothing appears to change, it begins to produce correct answers on data it has never seen. This phenomenon is known in machine learning as grokking . The core finding: a team of researchers has rigorously proven all three stages of grokking in a simple linear model and shown how the effect can be controlled through training parameters. The problem The common assumption is that longer training leads to better generalization. Grokking contradicts this assumption. The process typically unfolds in three stages: Overfitting. The model memorizes the training data. Plateau. Performance on new data remains poor for a long period, with little visible change. Generalization. The model begins to pro...

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Alternative to QKV Architecture — STO (Semantics, Time, Operator)

Author: Boris D. Yarovoy Status: Conceptual framework with comprehensive empirical validation — including negative results that corrected earlier claims. Abstract The Transformer architecture has dominated sequence modeling through its Query-Key-Value (QKV) attention mechanism. I propose an alternative attention architecture called Attentime , based on three orthogonal components: a symmetric Query-Key matrix capturing pure semantic affinity, an asymmetric Time matrix implementing causal physics through learnable decay, and a dynamically activated Dynamic Value matrix computed as a function of interacting keys. Through targeted micro-experiments, I demonstrate: (1) numerical stability, (2) 100% accuracy on syntactic role resolution across active, passive, and relative clause structures, (3) architectural prevention of catastrophic forgetting for new tokens — a capability standard Transformers lack without external methods, and (4) separable caching that preserves 100% accu...

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A2A Is Not a Feature. It Is a High-Stakes System

We are entering a phase where AI systems no longer operate only in a human-to-machine loop, but increasingly in agent-to-agent (A2A) interactions. This is not a product upgrade. It is a structural shift. When autonomous systems negotiate, optimize, coordinate, or escalate without direct human mediation, the system boundary changes. Responsibility diffuses. Latency shrinks. Error propagation accelerates. In low-stakes environments, this is manageable. In high-stakes domains , it is not. What “High-Stakes” Means Here High-stakes systems are not defined by intent, but by consequence.

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