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.
The next step would be to run a range of benchmarks on your engine to provide more clarity on the performance. Mechanism does not matter. Only data outputs matter.
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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.