arXiv (All)
open access
Exploring the Design Space of Permutation Diffusion LLMs
Permutation generation with language models requires both text-conditioned ranking and a global one-to-one assignment between candidates and output positions. We study this problem through three diffusion-LLM parameterizations: hard-assignment prediction over shuffled candidate-index sequences, soft
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
- How Much Can a LoRA Adapter Memorize? Measuring Adapter Capacity in Bits#1201020
- Pair Difficulty Matters: Rethinking Pairwise LLM-as-a-Judge Evaluation and Consistency#1141894
- Predicting Steering Vectors and Adapter Weights for Few-Shot Author-Style Transfer#1187841
- StrategyBench: Evaluating Explicit Strategy Induction in Large Language Models#482170
- Nonnesting permutations avoiding 123#973323
Record · ID 1355624
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