arXiv (All)
open access
ROAR: Unifying Runs across Heterogeneous AI-Driven Research Systems
Each run of an AI-driven research system (ADRS) is an expensive search over a vast solution space, and dependable evaluation requires many runs, making run data both costly to produce and valuable to retain for large-scale analysis. Yet this data remains fragmented: teams operate in isolation, ADRS
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
- ResearchFlow-Eval: Tracing the Execution Gap in End-to-End Multi-Claim Autoresearch#1344709
- OpenDiscoveryTrace: Process Traces for Evaluating AI Scientist Workflows#986976
- AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration#214319
- AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration#204828
- Multimodal AI Detection for Math-Heavy Scientific Text#1356398
Record · ID 1199240
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