arXiv (All)
open access
CARVE: Verified Expansion for Variable-Length Generation in Diffusion Language Models
Masked diffusion language models predict tokens from a partially observed response canvas, enabling bidirectional conditioning and parallel token refinement. Yet standard masked-diffusion decoders use a rigid inference interface: the number of masked positions allocated to the answer is fixed before
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
- CARVE: Verified Expansion for Variable-Length Generation in Diffusion Language Models#808964
- Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models#800070
- Informed Masking: Structure-Aware Perturbation for Reinforcement Learning in Diffusion Large Language Models#1061833
- Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models#798735
- Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models#797321
Record · ID 818956
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