# Where Quality Breaks in Compressed Short-Text Generation: Staged Bottleneck Localization — key results

| Evaluation point | n | Mean PPL | Median PPL | p95 PPL |
| --- | --- | --- | --- | --- |
| Original texts | 256 | 16.24 | 15.17 | 25.1 |
| Codec reconstructions | 256 | 37.26 | 27.36 | 98.91 |
| AR baseline | 251 | 30.98 | 23.27 | 56.11 |
| Token-space MDLM | 256 | 44.74 | 38.42 | 93.6 |
| Code-space MDLM | 256 | 30.01 | 26.55 | 59.36 |

Most of the observed quality loss is introduced before generation; code-space diffusion still reduces median perplexity by 30.9% versus token-space diffusion.

Dataset: TinyStories
External mirror: https://huggingface.co/datasets/aogavrilov/where-quality-breaks-results
External collection: https://huggingface.co/collections/aogavrilov/publication-result-summaries-6a6b733b3116a87befb37ec2
Sample size: 256 paired reconstruction samples; 251–256 generated samples per mode; four matched geometry settings.
Uncertainty: The reported comparisons are descriptive single runs; confidence intervals and multi-seed significance estimates were not computed.
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.