arXiv (All)
open access
Are Diffusion Language Models Good Database Analysts?
Diffusion Language Models (DLMs) enable iterative global refinement, but their effectiveness in Natural Language to SQL (NL2SQL) remains unclear because heterogeneous architectures and evaluation setups hinder fair comparison with autoregressive (AR) models. Their iterative denoising process permits
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
- Compositional Average Velocity Modeling for Efficient Diffusion Offline Reinforcement Learning#1325837
- Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution#1249791
- Benchmarking Methodology for Link-State IGP Data-Plane Route Convergence#220734
- SkillGLoW: Procedural-Family Skill Consolidation for Self-Improving Agents on Long-Horizon Task Streams#822609
- Toward a unified data-driven turbulence model through multi-objective learning.#480841
Record · ID 1342497
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