arXiv — cs.AI preprintsInternational21 October 2026
Humans Correct Their Judgments Through Debate, but Weaker AI Models Do Not
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:2610.28539v1 Announce Type: new Abstract: How can we effectively oversee AI systems that surpass human intelligence? AI Safety via Debate proposes that two AI systems argue opposing positions and a judge, either a human or a weaker model, decides which is correct. By facilitating supervisors in providing high-quality training signals, debate aims to encourage truthful and safe behavior from AI systems whose capabilities exceed our own. However, human judgment is not neutral: people often rely on biases which advanced AI systems may learn to exploit. Drawing on insights from collective in
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training21 October 2026
- Large language model and reinforcement learning based autonomous construction and progress updates21 October 2026
- Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models21 October 2026
- MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring21 October 2026
- DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution21 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.