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

We tend to think of neural networks as digital linguists that greedily devour libraries in order to learn to express thoughts coherently. But if you look under the hood of a transformer from first principles, you will find neither grammar nor syntax in the usual sense. Instead, you will find an extremely complex signal-processing system. That is the central irony of today's AI industry: we built systems that talk like humans using methods once applied to cleaning noise out of audio or transmitting data over satellite links. Understanding this changes everything, from how we train models to why they suddenly start hallucinating.

For a model, any text begins with discretization. When we split a sentence into tokens, we are in effect discretizing the continuous flow of human thought. Think of it as turning an analog recording into an MP3 file. Each token becomes a vector in a multidimensional space, but it is not just a point. In modern architectures it is a signal with its own frequency and phase. This is where the secret of transformers' success over the older recurrent networks lies. We used to pass information along a chain, like a game of telephone; now we work with the entire spectrum of data at once, applying filters to it.

Special attention should go to how models understand word order. In early versions this was a crutch, but rotary positional embeddings (RoPE) changed everything. Engineers effectively built the principles of trigonometry into neural networks, encoding a word's position in a sentence through the rotation of a vector. This is pure physics: we shift the phase of the signal so the model can grasp 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 follows the context of a long novel. This is not the magic of association; it is mathematical wave interference in the model's latent space.

In this paradigm, the attention mechanism is not a "focus" in the human sense but a dynamic filter. When the model generates the next token, it passes the entire preceding 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 picks the needed harmonics out of 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 and let through excess noise, which we interpret as silly mistakes.

Why does this matter right now? Because we have hit the ceiling of pure data scaling. The industry is starting to realize that simply feeding models more text is a path of diminishing returns. The future belongs to optimizing the signal side. New architectures are appearing, such as Mamba or hybrid designs, that try to process information even more efficiently than standard Attention. They treat data as continuous signals, which lets them "remember" arbitrarily long sequences without choking on the computational load. If we learn to control this signal as deftly as radio engineers control radio waves, the hallucination problem may be solved at the physical level.

Ultimately, the success of LLMs confirms an old truth: mathematics is one. Whether you analyze seismic activity, encode video, or try to teach a machine to write poetry, the laws of information propagation and filtering stay the same. We stopped teaching machines language and started teaching them the physics of the information field. And judging by the latest benchmarks, it was the right decision in the entire history of computer science. Ahead lies a transition from discrete tokens to fully continuous systems, where the boundary between text, sound, and video will finally blur, because all of it will become a single signal.

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