---
title: "ADSM: limits of model capabilities"
description: "Transformers stick to the network architecture: width, depth, and connections define quality and compute limits. The context window determines reproducibility bounds, so stability comes from narrowing the context. The full article is on Habr."
date: 2025-09-10
display_date: "September 10, 2025"
image: "/img/blog/2025/20250910-01-adsm-model-limits.png"
image_alt: "ADSM: limits of model capabilities"
---

# ADSM: limits of model capabilities

<zoom-img src="/img/blog/2025/20250910-01-adsm-model-limits.png" alt="ADSM: limits of model capabilities"
        width="100px"></zoom-img>

Models operate inside the transformer architecture: vector width defines how detailed the representations are,
        depth sets the level of abstraction, and the number of connections fixes the computational load. Each token is
        produced by a full pass through the network. The context window is the shared space for input and output, so
        growing the output reduces reproducibility. Efficient work relies on narrowing the context, feeding
        homogeneous inputs, and keeping a strict one-shot mode. Minimizing the creative component keeps results steady
        and the model within the intended frame. The full article in Russian is published on Habr.

[Read the original on Habr](https://habr.com/ru/articles/945816/)