arXiv (All)
open access
OpenDiscoveryTrace: Process Traces for Evaluating AI Scientist Workflows
Existing benchmarks for autonomous AI scientists evaluate only final outputs---generated code, hypotheses, or papers---yet discard the reasoning process by which those outputs were obtained. This makes it impossible to audit scientific methodology, diagnose failure modes, or distinguish systematic r
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
- AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery#144352
- SCIRIGOR:Evaluating Open-Ended Scientific Analysis Beyond Final Scores#973792
- Notes2Skills: From Lab Notebooks to Certainty-Aware Scientific Agent Skills#1011135
- Closing the Empirical Loop: Autonomous AI Agents Conduct End-to-end Research With Human Participants.#923674
- Closing the Empirical Loop: Autonomous AI Agents Conduct End-to-end Research With Human Participants#922268
Record · ID 986976
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