LLM as a Radio Receiver: Why Signal Processing Matters More Than Linguistics

We are accustomed to thinking of neural networks as digital linguists that greedily consume libraries to learn how to express thoughts coherently. But if you look under the hood of a transformer from a first-principles perspective, you will find neither grammar nor syntax in the conventional sense. Instead, you will discover an extraordinarily complex system of signal processing. Herein lies the great irony of the modern AI industry: we built systems that speak like humans using methods that were previously applied to clean audio from noise or transmit data via satellite communication. Understanding this fact changes everything — from how we train models to why they suddenly start hallucinating.

Any text for a model begins with discretization. When we break a sentence into tokens, we are essentially discretizing the continuous stream of human thought. Imagine this as converting an analog recording into an MP3 file. Each token becomes a vector in multidimensional space, but it is not simply a point. In modern architecture, it is a signal with its own frequency and phase. Herein lies the secret to transformers' success over old recurrent networks. Previously, we tried to transmit information through a chain, like a game of telephone, but now we work with the entire spectrum of data simultaneously, applying filters to it.

Special attention should be paid to how models understand word order. In early versions, this was a workaround, but with the advent of Rotary Positional Embeddings (RoPE), everything changed. Engineers effectively embedded trigonometric principles into neural networks, where a word's position in a sentence is encoded through vector rotation. This is pure physics: we shift the phase of the signal so the model understands the distance between concepts. If you understand how phase modulation works in your Wi-Fi router, you are already halfway to understanding how GPT-4 grasps the context of a long novel. This is not the magic of associations, but mathematical wave interference in the model's latent space.

The Attention mechanism in this paradigm is not "focus" in the human sense, but a dynamic filter. When the model generates the next token, it passes all previous context through a set of learnable filters that suppress noise and amplify the useful signal. We call this "understanding context," but for the processor, it is a dot-product operation that extracts the relevant harmonics from the overall stream. The more parameters a model has, the narrower and more precise the filters it can tune. This explains why small models often "drift" in their logic: their filters are too coarse, they pass extra noise that we interpret as silly mistakes.

Why does this matter right now? Because we have hit a ceiling on pure data scaling. The industry is beginning to realize that simply feeding models more text is a path of diminishing returns. The future lies in optimizing the signal component itself. We see new architectures emerging, like Mamba or hybrid solutions, that try to process information even more efficiently than standard Attention. They work with data as continuous signals, which allows them to "remember" infinitely long sequences without choking on computational volume. If we learn to manage this signal as finely as radio engineers manage radio waves, the problem of hallucinations could be solved at the physical level.

Ultimately, the success of LLMs confirms one old truth: mathematics is universal. Whether you are analyzing seismic activity, encoding video, or trying to teach a machine to write poetry — the laws of information propagation and filtering remain unchanged. We stopped teaching machines language and started teaching them the physics of the information field. And judging by the results of recent benchmarks, this was the most correct decision in the entire history of computer science.

Ahead of us lies a transition from discrete tokens to fully continuous systems, where the boundary between text, sound, and video finally blurs, because all of it will become a single signal.

The key point: LLMs are not digital philologists, but supercharged signal processors. If you want to understand where AI is heading, read textbooks on radio engineering and information theory, not linguistics.