The Problem

Have you ever seen an LLM repeat the same phrase over and over? It is a more common phenomenon than you might think. The model gets stuck in a narrow region of its latent space -- a kind of dead end where generation probabilities loop around.

This issue happens to me from time to time with all inference engines. It affects local models and large cloud services alike.

How Text Generation Works

Large Language Models (LLMs) work by predicting, at each step, the most likely token to append to a text, based on probabilities learned during training. This word-by-word generation mechanism is fundamental to understanding why loops and repetitions can occur.

The Main Causes

Inadequate Response to an Ambiguous Prompt

When an instruction is unclear or the provided context is not specific enough, the model may fail to produce a satisfactory answer and repeat the same pattern hoping to arrive at a solution.

Issues in the Generation Algorithm

The parameters used during inference -- temperature, top-k sampling, top-p -- influence the diversity and spontaneity of responses. Poorly tuned settings make the model more likely to repeat the same words or phrases, because it favors the most probable choices too strongly.

Training or Data Limitations

When an LLM lacks diversity in its training data, it can develop repetition biases: it endlessly recycles learned structures, especially on topics that are poorly covered or on frequently encountered instructions.

Cognitive Echo Loops

The model can be led to rephrase close variants of the same idea because it locks itself into a logical "echo chamber," recombining its knowledge in a loop rather than producing novelty.

Latent Space: The Key to the Problem

The latent space is an abstract, compressed representation of data within an AI model. It is a hidden space where complex information (texts, images, etc.) is transformed into a simplified form, often as vectors of numbers in a multi-dimensional space.

In the case of an LLM:

• Input words or sentences are converted into tokens, then projected into this latent space via embeddings (numerical vectors).
• This space represents semantic relationships: words or concepts that are close in meaning (e.g., "cat" and "feline") are close in latent space.

When an LLM generates text, it navigates this space to predict the next token. Sometimes, it gets stuck in a region where probabilities favor a repetitive sequence. This happens when:

• The model over-optimizes a certain trajectory in the latent space.
• The input context lacks diversity, limiting the options.
• Loops appear in conditional probabilities, often due to imperfect training or data biases.

The Role of Embeddings

Embeddings are the bridge between the human world (words, sentences) and the numerical world of LLMs. They transform tokens into vectors in a latent space, allowing the model to understand and generate text.

Key properties:

• Dimension: typically between 100 and 4096, depending on the model (768 for BERT, 4096 for some GPT variants).
• Similarity: the cosine distance between two embeddings measures their semantic similarity.
• Context: modern models (BERT, GPT) generate contextual embeddings -- the same word will have a different embedding depending on the sentence.
• Transferability: pre-trained embeddings can be reused for other tasks (classification, semantic search).

My Solution in mAIstrow

In my inference engine for mAIstrow (built in Rust and assembly), I added a simple, almost "low-tech" hack: I insert perturbation tokens into the input when my algorithm detects a loop.

These "bumpers" force the model out of the loop and onto a more varied trajectory -- without breaking the coherence of the response.

Technically, these tokens modify the input embedding vectors, which disrupts the attention computations and redirects the model toward more diverse regions of the latent space. It is a form of dynamic prompt engineering: adjusting the input to guide the model's behavior without touching its architecture.

It is an intuitive, temporary solution, but one that works surprisingly well.

The Importance of Logging

The problem with this kind of bug is that you need to be able to reproduce it. That is why I insist heavily on logging. You need to save the context and especially the seed so you can reproduce the problem. My solution is intuitive and I am not certain I will not encounter the bug again, but at least I am prepared for further testing.

Often, the Culprit Is the Sampler

The real culprit is not always as "neural" as you might think: it is often the sampler. Sampling parameters (temperature, top-k, top-p) have a direct impact on the model's tendency to loop. I will go into more detail on this in a future article.

Known Technical Solutions

Generation Parameter Tuning

Adjusting the temperature or top-p during inference can reduce the probability of repetition by diversifying the model's proposals.

Fine-Tuning

Fine-tuning on more varied or recent corpora allows the model to correct certain repetition biases and broaden its contextual capabilities.

Human Oversight and Reinforcement Learning

Including a human validation step or using reinforcement learning techniques can help break the model out of its repetitive patterns, especially on complex or poorly structured tasks.

Summary

LLMs sometimes get stuck in loops because of the combination of their probabilistic logic, poorly tuned settings, training data limitations, and the absence of genuine human contextual understanding. Supervision, continuous improvement, and fine-tuning inference parameters are all approaches to mitigating this phenomenon.

I prefer a simple solution that works now and allows me to dig deeper later, rather than premature complexity.