arXiv (All)
open access
Recursive Criticality of AI Self-Improvement
AI is increasingly used in the R\&D process that produces future AI systems. We study the conditions under which this feedback becomes self-amplifying. Our model describes how the rate of AI capability growth depends on baseline research productivity, recursive feedback, and the increasing difficult
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
- Recursive Criticality of AI Self-Improvement#809611
- Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning#381773
- Frontier Lag: A Bibliometric Audit of Capability Misrepresentation in Academic AI Evaluation#1077886
- Collaboration in the third stage between Artificial Research by Application and Artificial Research by Deduction#74859
- From Authorial Validation to Autonomous Truth Production: Artificial Intelligence and the Future of the Academic Paper#690638
Record · ID 819607
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