---
pretty_name: "Where Quality Breaks in Compressed Short-Text Generation: Staged Bottleneck Localization — reported result summary"
language:
- en
tags:
- "tabular"
- "research-results"
- "academic-paper"
- "where-quality-breaks"
- "compressed-text-generation"
- "discrete-latent-diffusion"
- "hierarchical-vq-vae-2"
- "bottleneck-localization"
- "tinystories"
- "masked-diffusion-language-modeling"
- "compressed-short-text-generation"
- "codec-bottleneck-diagnosis"
- "staged-bottleneck-localization"
- "discrete-latent-text-generation"
size_categories:
- n<1K
configs:
- config_name: default
  default: true
  data_files:
  - split: summary
    path: results.csv
---

# Where Quality Breaks in Compressed Short-Text Generation: Staged Bottleneck Localization — reported result summary

This repository contains an author-maintained, machine-readable summary of the key quantitative values reported in **Where Quality Breaks in Compressed Short-Text Generation: Staged Bottleneck Localization**.

> Scope: this is a small table-level result summary. It is not the underlying training corpus, evaluation corpus, model code, checkpoint, benchmark release, or a new experimental run.

## Publication

- Canonical publication page: https://aogavrilov.com/publications/where-quality-breaks/
- DOI: https://doi.org/10.23919/FRUCT70069.2026.11506553
- Authors: Alexey Gavrilov, Alan-Barsag Gazzaev, Sergey Muravyov
- Venue: 2026 39th Conference of Open Innovations Association (FRUCT)
- Open preprint record: https://arxiv.org/abs/2607.24176
- Searchable full-text HTML: https://aogavrilov.com/publications/where-quality-breaks/full-text/
- Source-derived full-text Markdown: https://aogavrilov.com/publications/where-quality-breaks/full-text/index.md
- Source-derived NISO JATS 1.4 XML: https://aogavrilov.com/publications/where-quality-breaks/full-text/article.jats.xml
- Open result section: https://aogavrilov.com/publications/where-quality-breaks/#results
- Hugging Face collection: https://huggingface.co/collections/aogavrilov/publication-result-summaries-6a6b733b3116a87befb37ec2

## Reader guides

- English paper guide: https://aogavrilov.com/publications/where-quality-breaks/
- Руководство на русском: https://aogavrilov.com/ru/publications/where-quality-breaks/
- 简体中文论文导读: https://aogavrilov.com/zh/publications/where-quality-breaks/
- 한국어 논문 가이드: https://aogavrilov.com/ko/publications/where-quality-breaks/
- Problem-first guide — How to tell whether a compressed text generator fails in the codec or the generator: [English](https://aogavrilov.com/projects/codec-bottleneck-diagnosis/) · [Русский](https://aogavrilov.com/ru/projects/codec-bottleneck-diagnosis/) · [简体中文](https://aogavrilov.com/zh/projects/codec-bottleneck-diagnosis/) · [한국어](https://aogavrilov.com/ko/projects/codec-bottleneck-diagnosis/)
- Machine-readable research indexes: [English](https://aogavrilov.com/llms.txt) · [Русский](https://aogavrilov.com/ru/llms.txt) · [简体中文](https://aogavrilov.com/zh/llms.txt) · [한국어](https://aogavrilov.com/ko/llms.txt)

## Focused evidence notes

These maintained notes answer narrower problem-first questions and keep the paper’s evidence boundary explicit:

- [Code-space vs token-space masked diffusion: how to compare them](https://aogavrilov.com/research-notes/code-space-vs-token-space-masked-diffusion/) — A stage-consistent comparison protocol for code-space and token-space masked diffusion language models when the discrete codec is lossy. [Markdown](https://aogavrilov.com/research-notes/code-space-vs-token-space-masked-diffusion/index.md)

## Files

- `results.csv` — table shown in the Dataset Viewer.
- `results.json` — table plus DOI, metric, sample-size, condition, uncertainty, and takeaway metadata.
- `results.md` — human-readable result summary.
- `citation.bib` — BibTeX record for the paper.
- `manifest.json` and `SHA256SUMS` — source links and integrity metadata for this export.

## Experimental scope recorded by the paper

- Source data: TinyStories, as described in the paper.
- Reported sample size: 256 paired reconstruction samples; 251–256 generated samples per mode; four matched geometry settings.
- Conditions: GPT-2 token sequences of length 64 compressed to 16 top-level codes with a hierarchical VQ-VAE-2; all generation modes use the shared external scorer.
- Metrics: External GPT-2 perplexity: mean, median, p95, and maximum; Codebook usage and support size; SBERT, BERTScore, MAUVE, and an LLM-judge summary for geometry runs
- Main reported takeaway: Most of the observed quality loss is introduced before generation; code-space diffusion still reduces median perplexity by 30.9% versus token-space diffusion.

## Limitations

- Statistical uncertainty: The reported comparisons are descriptive single runs; confidence intervals and multi-seed significance estimates were not computed.
- Data boundary: The evidence covers short synthetic stories and should not be treated as a benchmark for unrestricted natural-language generation.
- Version boundary: A dataset checksum or immutable TinyStories snapshot identifier is not reported in the paper.
- Reproducibility boundary: A public installation recipe is not yet available; no inactive Code button is shown.

Do not treat absent values as zero, infer functional correctness from structural proxies, or transfer the reported ranking beyond the stated experimental setting.

## Rights and provenance

No separate reuse license is asserted for the source paper or upstream data by this export. The paper PDF remains subject to [the stated IEEE rights](https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/), and the upstream data retain their own terms.

Use of TinyStories remains subject to the dataset's own terms; no dataset files are redistributed by this site.

The authoritative context and current rights statement are maintained at https://aogavrilov.com/publications/where-quality-breaks/#data.

## Citation

```bibtex
@inproceedings{Gavrilov2026WhereQuality,
  title      = {Where Quality Breaks in Compressed Short-Text Generation: Staged Bottleneck Localization},
  author     = {Gavrilov, Alexey and Gazzaev, Alan-Barsag and Muravyov, Sergey},
  booktitle  = {2026 39th Conference of Open Innovations Association (FRUCT)},
  publisher  = {IEEE},
  year       = {2026},
  pages      = {69--76},
  doi        = {10.23919/FRUCT70069.2026.11506553},
  url        = {https://doi.org/10.23919/FRUCT70069.2026.11506553},
  isbn       = {978-952-65246-5-8},
}
```