"Why LLMs Keep Generating Errors in Code — And This Can't Be Fixed by Scaling"

Introduction

Imagine a typical scenario: you ask a model to write a data processing function for a corporate project. The code appears in seconds, looks neat, and passes local tests. But in production, hidden issues arise: the logic slightly diverges from requirements under edge cases, or the code ignores specific constraints of an internal library that the model has never seen in its training data.

The Legacy Code Challenge

The same issues occur with legacy code: the model suggests methods from a fresh release of a framework that isn’t supported in your environment yet. The code won’t compile, and it has to be rewritten manually. In modern projects, the situation is reversed: generated code stubbornly clings to outdated APIs, even though safe and efficient alternatives have been available for a long time.

Language-Specific Errors

Errors are particularly noticeable in less popular programming languages. In Python or JavaScript, the model confidently builds complex constructs. But as soon as you switch to Rust or Haskell, logical missteps appear: improper handling of borrowing, missed edge cases. Sometimes, the model simply doesn’t know about syntactical changes that emerged after its training, overlooking new opportunities that would fit the task perfectly.

Systematic Issues

In real projects, especially with proprietary code and internal libraries, the model frequently offers solutions that miss the mark. All these examples share a common thread: these errors are not accidental—they are systemic. The model relies on statistical patterns from publicly available data, rather than the strict logic of your task and environment.

Benchmarking Results

Benchmarks confirm this picture. On the SWE-bench Verified, where tasks are taken from real GitHub repositories, the best models achieve 70–80% accuracy by early 2026. The numbers are improving, but there remains a significant gap to full reliability in generated code.

The Mathematical Foundation

The root cause isn’t the volume of training data or the number of parameters. It lies deeper—in the very nature of modern LLMs: they approximate probabilities of continuations rather than construct logically coherent solutions. This mathematical foundation explains why bugs are inevitable—not due to the model’s “stupidity,” but because of how it transforms a request into a response. Expecting future model versions to radically change this situation is unrealistic.

Understanding Systematic Errors

To grasp where these systematic errors arise from, let’s put aside all external enhancements. If we strip away everything added on top of the base model—chat interfaces, editor integrations, reasoning agents, additional checks—we are left with the bare neural network, which is the heart of any modern LLM.

The architecture rests on the transformer model introduced in 2017. But if we look beyond the attention mechanism and other tricks, topologically, the transformer is equivalent to a multilayer perceptron—a structure that has been around since the 1960s. The history goes even deeper: the first artificial neuron, performing a weighted sum of inputs followed by a nonlinear transformation, was described in 1943.

The One-Way Signal Flow

A key feature of this architecture is that the signal flows strictly in one direction—from input to output. There are no feedback loops as in biological neural networks. The network cannot “look back” at its output, nor can it reflect on intermediate results and adjust them before providing a final answer. It lacks internal verification capabilities—merely transforming input into output without checking the meaningfulness of the result.

The Training vs. Usage Dichotomy

Here lies a crucial distinction often overlooked: training a neural network and using it are two entirely different processes. During training, weights are dynamically adjusted: millions of examples are fed into the network while complex algorithms compare its output to correct answers and tweak parameters to minimize errors. This phase requires colossal computational resources and can take weeks.

However, once training is complete, the weights are fixed. During code generation, the network is no longer learning—it merely applies the frozen parameters. A sequence of tokens is fed in, the signal passes in one direction through the layers, and the next token appears as output. There’s no “magic” here: no reflections, no consistency checks, no understanding of the task. It’s just input transformed into output based on patterns learned from a fixed training set.

The Limits of Approximation

If the network only approximates the patterns it has seen, what tasks is it good at, and where will it inevitably fail? The answer lies in the mathematical foundation—the universal approximation theorem.

This theorem explains why deep neural networks can generate meaningful code. The concept dates back to the late 1980s and early 1990s, rooted in 19th-century mathematics. In essence, it states that a multilayer network with a nonlinear activation function can approximate any continuous function within a compact area to an arbitrary degree of accuracy.

Conclusion

The universal approximation theorem not only describes the capabilities of neural networks but also clearly defines their limits. The model approximates statistics but does not verify logic. It generates tokens but doesn’t prove the correctness of algorithms. This isn’t a flaw of a specific implementation; it’s a consequence of the very nature of approximation in mathematics. Therefore, the responsibility for logical correctness remains with the human. As long as the architecture is a statistical approximation without logical understanding, the responsibility for ensuring that the code does what it’s supposed to do remains human-driven.