arXiv — cs.AI preprintsInternational5 October 2026
Open-Endedness Bench: Measuring Epistemic Process from Agent Records
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.02588v1 Announce Type: new Abstract: Agents are increasingly given open-ended research tasks: discovering an empirical law from self-designed experiments, improving a heuristic whose optimum nobody knows, or beating a standing record. Their execution logs record every step of this research, yet the runs are still judged by their outcome score. That score alone does not establish whether an agent's claims follow from executed experiments, and a reference answer may be unavailable. We evaluate the agent's epistemic process: how it forms hypotheses, tests them, and revises them in resp
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- MintFlow: Minimal Trajectory Intervention for Constrained Flow Matching5 October 2026
- Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses5 October 2026
- The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?5 October 2026
- Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation5 October 2026
- Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents5 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.