arXiv (All)
open access
Capabilities Ain't All You Need: Measuring Propensities in AI
AI evaluation has primarily focused on measuring capabilities, with formal approaches inspired from Item Response Theory (IRT) being increasingly applied. Yet propensities - the tendencies of models to exhibit particular behaviours - play a central role in determining both performance and safety out
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
- Models That Know How Evaluations Are Designed Score Safer#984168
- OSCAR: Order-aware Scoring and Calibration for AI Rankings#1050313
- MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization#1118430
- AgentAudit: An Open, Extensible Framework for Full-Lifecycle Trust Evaluation of AI Agents#996690
- Self-Indexed Control as a Testable Hypothesis about Self-Specific Control Asymmetry#713428
Record · ID 1157262
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