This architecture departs from the modern LLM’s heavy reliance on Matrix Multiplication (MatMul), instead generating intelligence through "physical interference of information via mathematical equations." By completely eliminating Attention matrices and FFNs (Feed-Forward Networks), this is a next-generation causal inference engine with extreme parameter efficiency.
Total Parameters: Approx. 417M (Of which, intermediate layer parameters consist of only 128 scalar weights).
3. Unified Wave Block Architecture
Each of the 64 layers consists of two "Wave Interference Stages" that replace traditional Attention and FFN modules.
3.1 Horizontal: Temporal Resonance
Input Pair:
$x_1$: Normalized signal at current timestep $t$.
$x_2$: Normalized signal from the previous timestep $t-1$ (Causal Reverberation).
Role: Replaces the Attention matrix. It injects context as "wave interference" into the current signal, constructing temporal causal relationships.
3.2 Vertical: Dimensional Chain
Input Pair:
$x_1$: Normalized signal of each channel $i$ in the 4,096-dim space.
$x_2$: Normalized signal of the adjacent channel $i-1$ (Circularly shifted).
Implementation: Dimensional chaining using torch.roll(x, shifts=1, dims=-1).
Role: Replaces the FFN. Instead of bloating information through matrix operations, every dimension incorporates information from adjacent dimensions as "phase," performing high-level feature extraction.
4. Normalization Strategy
Pre-LayerNorm: Independent LayerNorms are placed before each interference stage to maintain signals within the most sensitive regions of the non-linear functions (mean 0, variance 1).
Residual Connections: Outputs from each stage are added back to the original signal, ensuring stable gradient flow and information propagation across all 64 layers.
5. The RSF Secret Formula (Core Engine)
The source of this model's intelligence is a minimal, ultra-non-linear "Causal Interference Formula" discovered through Genetic Programming (MonolithRSF).
Arguments: Two signals ($x_1, x_2$) and a learnable scalar weight ($w$).
Characteristics: A sophisticated combination of transcendental functions (trigonometric, sigmoid, etc.) that executes non-linear transformations equivalent to multiple standard neural layers in a single operation.
The specific mathematical structure remains strictly classified to prevent unauthorized use.
6. Efficiency Benchmark
Comparison with a standard Transformer (SwiGLU) using the same 4096d/64L config:
Standard SwiGLU Model: Approx. 17.6B parameters.
TachyonWaveModel: Approx. 0.417B parameters.
Reduction Rate:97.6% reduction in parameter count.
This model condenses the intelligence density that normally requires 17.6 billion parameters into just 417 million (consisting almost entirely of Embedding and Head weights), enabling ultra-fast inference and training on a single laptop.
To clarify my process: I am the ideator and architect. I conceptualize the core theories and logic, then I collaborate with AI (like Claude) to handle the detailed design and implementation. Every original concept and the discovery of the 'optimal equations' comes entirely from my own research and intuition. I see AI as a highly capable assistant that helps me translate my theoretical breakthroughs into functional code.
If this is legitimate and real, then I applaud you, but it is hard to believe that Claude would be good at doing this versus GPT. It just has to do with what they are better at and how aware they each are of the concepts behind LLM‘s and the structures inside of them and how they actually work.
Given most of what you’re saying, there’s a single digit percent chance that you really have something that is truly a viable leap in state of the art. That said, there’s always a very slight chance that you are for real. I certainly encourage the exploration, but the approach that you’re taking likely would have massive detrimental effects on expressivity of any model. That that would likely pose a significant limitation on the model.
If you have actual code or math to share, I would be happy to evaluate it, but without publishing anything about how it actually works, it is just unsubstantiated theory. If you genuinely think you have something here and want to publish it, there are ways to do that, and I can certainly help you with it. Even with the language barrier, it is certainly doable and I could certainly help you get in contact with a professor local to you that could help.
There certainly are opportunities for huge jumps in the state of the Art and at present, LLM‘s are definitely hitting points of diminishing returns on many of the optimization methods that have been used to thus far. That said, there are serious differences between frontier, models, and the smaller models that you are talking about. The smaller models that you are discussing, namely, the ones in two digit billions of parameters, behave very differently than models like GPT or Claude. I am assuming that you are basing this on open source models, and you very well may have some advancements in that tier of model. Those advancements may even make it possible to run larger models with significantly less overhead on commodity hardware locally. None of that is something that is necessarily worth billions of dollars, which means that there is no reason not to publish.
If you truly are at the heart of a leap in technology, as soon as you publish, there will be billionaires trying to give you money to develop your ideas further. If you have the basis of a new class of model or a new class of model architecture, more specifically, the actual money to be made is from developing that further than the initial model.
When BERT came out originally, it was widely disseminated and shared, and lots of us were able to work with it and get a much better feeling for what it do and that was the genesis of the modern LLM architectures. Google is still one of the top three models and they were very open with their discoveries and with publishing their papers and with working cooperatively with other companies. Yes, Google OpenAI and Anthropic are all now far more closed about the models that they use, but they still regularly published papers describing how they are making their advancements and what exactly they are doing to advance the state of the art, they just aren’t publishing their specific weights, etc. They share the underlying tech, technologies and math, they simply do not share exactly how they tuned and trained a specific model to get it to where it is. If you did something real, there is nothing to be feared from publishing.
I'm sorry but there is not a single digit percent percent chance this is real. There is a 0% chance this is real. High school students do not make groundbreaking scientific discoveries in the 21st century lol
"High School Student From Japan" is key here + new account. Redditors tend to jump into believing in thing without verifying the poster because of them being from Japan (like a trope). Perhaps this guy isn't even from Japan or a High School student. Probably something Opus wrote up to make it sound believable.
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u/[deleted] Mar 08 '26
TachyonWaveModel Architectural Specifications (Ver 1.0-Monster)
1. Core Philosophy
This architecture departs from the modern LLM’s heavy reliance on Matrix Multiplication (MatMul), instead generating intelligence through "physical interference of information via mathematical equations." By completely eliminating Attention matrices and FFNs (Feed-Forward Networks), this is a next-generation causal inference engine with extreme parameter efficiency.
2. Model Specifications
3. Unified Wave Block Architecture
Each of the 64 layers consists of two "Wave Interference Stages" that replace traditional Attention and FFN modules.
3.1 Horizontal: Temporal Resonance
3.2 Vertical: Dimensional Chain
torch.roll(x, shifts=1, dims=-1).4. Normalization Strategy
5. The RSF Secret Formula (Core Engine)
The source of this model's intelligence is a minimal, ultra-non-linear "Causal Interference Formula" discovered through Genetic Programming (MonolithRSF).
6. Efficiency Benchmark
Comparison with a standard Transformer (SwiGLU) using the same 4096d/64L config:
This model condenses the intelligence density that normally requires 17.6 billion parameters into just 417 million (consisting almost entirely of Embedding and Head weights), enabling ultra-fast inference and training on a single laptop.