arXiv (All)
open access
ELF-REG: Scaling Continuous Diffusion Language Models to Reasoning Tasks
Fully continuous diffusion language models (dLMs) denoise continuous representations without intermediate discretization, then decode all response tokens in parallel at the final step. Their performance on challenging reasoning tasks remains less established than that of autoregressive (AR) LLMs and
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
- Hierarchical Continuous Diffusion Language Models#1167697
- Hierarchical Continuous Diffusion Language Models#1161016
- Hierarchical Continuous Diffusion Language Models#1157541
- Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting#1142986
- Reasoning with Continuous Latent Diffusion#1108793
Record · ID 1077031
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