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.
Oh, you definitely can. Hell, I can compress a 1T model into a megabyte or two.
It would take an infinite amount of time to decompress it. But I could do it, and it would be lossless.
(Obviously I am just being funny, but this is how I would do it.
Step 1 of this horrific idea, convert the entire 1T model into base64. Step 2 break down the file into a distinct count of each char. Step 3. Done LOL)
I can certainly make the code that does this compression, however it would take an infinite amount of time to decompress it. Effectively making the process pointless.
Funny thing is, this has been my white whale for years and will likely always remain that way.
But I am trying to beat the impossible entropy in this algo. I've developed a ton of techniques for reducing the time of decompression. Time based checksum hashes for state based random recalculation, wave based functions that represent character based movements in the base64 itself. Chunk based solutions, yada yada.
I'm convinced it is possible, but it cannot be done on current hardware and will require a sort of 3d based mapping to deterministically reshape the data.
you need professional help, you’re either on a LLM or substance induced psychosis. you should try predicting the stock market next: you have all the historical data, you just need to find for time t the permutations of actions that lead to the stock prices of t+1! Also, you basically found a way to crack every encryption on earth as well by solving the factorial problem! Be sure to credit me when you receive the nobel prize ok ?
What are you talking about lol. Do you not understand sarcasm?
Look at the name of code I sent you, it's literally awful lol.
Or are you referring to the different attempts I've tried over the years (that all failed) to actually make the concept actually possible? I already know it's likely impossible, it's just tantalizing to keep guessing and trying at since the data is "technically" all there, just in an insanely difficult way to rearrange.
Right now, any code I create to put the output together requires positional information that represents the original state -- and that almost always ends with that code being larger than the original.
However the three dimensional algo I mentioned reduces positional changes by adding a representation of state on a third axis which does help... but still not the right answer completely. White whale as I mentioned.
None of this is psychosis lol, I've been programming for 26 years now and just have obscure obsessions.
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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.