arXiv (All)
open access
Who Gets Unmasked? Sampler-Induced Bias in Diffusion Language Models
Diffusion language models (dLLMs) generate by gradually unmasking a canvas, so their samplers decide not only which token to write but also when to commit it. We show that this second decision changes who appears in generated text, a sampler-induced bias on top of the model's own. Common commit rule
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Noise Out, Bias In: Targeted Bias Injection in Diffusion Language Models via Closed-Loop Activation Steering#1202313
- TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering#1387087
- IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models#1188352
- IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models#1181632
- On the SPOT: Enhancing Speculative Decoding for Diffusion Language Models via Commit-Order Prediction#1386518
Record · ID 1389741
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive