arXiv (All)
open access
Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling
A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing inference at test time using only examples provided in the prompt, without any parameter updates. Prior theoretical work has shown that this capabilit
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
- DIP: Dynamic In-Context Planner For Diffusion Language Models#792685
- DIP: Dynamic In-Context Planner For Diffusion Language Models#788685
- Long-Context Demonstration Selection Using State Space Models#1011603
- Large Language Models as Statistical Decision-Makers at Inference Time#634803
- Large Language Models Develop Belief State Geometry In-Context#1123741
Record · ID 999895
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