arXiv (All)
open access
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing
Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for sequence synthesis rather than edit prediction. This setting is poorly matched to modern AI-assisted IDEs, where edit prediction must infer the next c
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
- Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing#1319086
- How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks#1186450
- JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting#1117472
- Beyond Reproducibility: Token Probabilities Expose Large Language Model Nondeterminism#971744
- AI as a High-Bandwidth Unreliable Source: Why It Is Neither Tool Nor Agent#731142
Record · ID 1343036
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