OpenAlex
metadata only
Scaling Laws for Neural Language Models
This paper develops a transport-validity theory for agentic AI interventions that are first screened on small systems and later considered for frontier-scale deployment. Rather than predicting absolute frontier performance, it asks when a comparative gain observed at small scale can be carried forwa
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
- The Double Measurement Confound in Agent Benchmarks: De-Scaffolding, Ground-Truth Scoring, and Reliability Beyond the Mean#986988
- Who Bridges Safety? Identifying and Targeting Cross-Lingual Shared Safety Pathways#1144864
- Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights#139146
- OTROPE: Optimal Transport-based Robust Off-policy Evaluation for Large Language Models#1131106
- The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems#271722
Record · ID 964387
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