arXiv (All)
open access
MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution
Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long-horizon performance, yet its combinatorial search space demands substantial human effort that must be repeated as models change. Existing automated m
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 Human Oversight approaches at the forefront of responsible and trustworthy AI, from data-centric adaptive AI-Ops pipelines to Multi-Agent Systems#256690
- Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses#141551
- The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search#133422
- Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable#412818
- Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable#370397
Record · ID 1142950
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