arXiv (All)
open access
All against the machine: the Solo score for rating skill in variable environments
We propose a distribution-free metric to rate individual skill in ``player-versus-environment'' settings, where participants face heterogeneous tasks without direct opponents. Such settings are common in digital platforms, games, education, finance, and the benchmark evaluation of AI agents. They co
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
- MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination#1248453
- Predicting Task Difficulty Without Rollouts#1117544
- Messier: A High-Resolution Corpus for Cross-Benchmark Agent Evaluation#799015
- Messier: A High-Resolution Corpus for Cross-Benchmark Agent Evaluation#797600
- The New Associationism: Lessons from Deep Learning#691728
Record · ID 1200174
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