Epistemic Filtering and Collective Hallucination: A Jury Theorem for Confidence-Calibrated Agents
#artificial intelligence #epistemic filtering #collective decision-making #Condorcet Jury Theorem #confidence calibration #AI safety #collective hallucination #abstention from voting
📌 Key Takeaways
- Jonas Karge published a new paper on AI collective decision-making on February 25, 2026
- The research introduces a framework where AI agents can abstain from voting when uncertain
- The approach generalizes the Condorcet Jury Theorem to confidence-gated settings
- The framework shows potential for improving AI safety by reducing collective hallucinations
📖 Full Retelling
Computer scientist Jonas Karge published a groundbreaking research paper on arXiv on February 25, 2026, introducing a new framework for improving collective decision-making by AI agents through confidence-calibrated abstention from voting. The paper addresses limitations in classical epistemic voting results like the Condorcet Jury Theorem (CJT), which assumes fixed participation from all agents. Karge proposes a probabilistic framework where AI agents first engage in a calibration phase to update their beliefs about their own competence, then face a confidence gate that determines whether to vote or abstain. This approach allows agents to effectively say "I don't know" when uncertain, potentially reducing collective hallucinations in AI systems. The research establishes non-asymptotic lower bounds on the group's success probability and proves that this selective participation approach generalizes the asymptotic guarantees of the CJT to sequential, confidence-gated settings. Karge validated these theoretical findings through Monte Carlo simulations. While the results have broad applications, the author specifically discusses their potential relevance to AI safety, suggesting this framework could mitigate issues in collective LLM (Large Language Model) decision-making.
🏷️ Themes
AI research, Collective intelligence, Epistemic filtering
📚 Related People & Topics
AI safety
Artificial intelligence field of study
AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses AI alignment (which aims to ensure AI systems behave as intended), monitoring AI systems for risks, and enhancing their rob...
Entity Intersection Graph
Connections for AI safety:
View full profileOriginal Source
--> Computer Science > Artificial Intelligence arXiv:2602.22413 [Submitted on 25 Feb 2026] Title: Epistemic Filtering and Collective Hallucination: A Jury Theorem for Confidence-Calibrated Agents Authors: Jonas Karge View a PDF of the paper titled Epistemic Filtering and Collective Hallucination: A Jury Theorem for Confidence-Calibrated Agents, by Jonas Karge View PDF HTML Abstract: We investigate the collective accuracy of heterogeneous agents who learn to estimate their own reliability over time and selectively abstain from voting. While classical epistemic voting results, such as the \textit{Condorcet Jury Theorem} , assume fixed participation, real-world aggregation often benefits from allowing agents to say ``I don't know.'' We propose a probabilistic framework where agents engage in a \textit phase, updating beliefs about their own fixed competence, before facing a final confidence gate that determines whether to vote or abstain. We derive a non-asymptotic lower bound on the group's success probability and prove that this \textit{selective participation} generalizes the asymptotic guarantees of the CJT to a sequential, confidence-gated setting. Empirically, we validate these bounds via Monte Carlo simulations. While our results are general, we discuss their potential application to AI safety, outlining how this framework can mitigate \textit in collective LLM decision-making. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22413 [cs.AI] (or arXiv:2602.22413v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.22413 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jonas Karge [ view email ] [v1] Wed, 25 Feb 2026 21:09:14 UTC (212 KB) Full-text links: Access Paper: View a PDF of the paper titled Epistemic Filtering and Collective Hallucination: A Jury Theorem for Confidence-Calibrated Agents, by Jonas Karge View PDF HTML TeX Source view license Current browse context: cs.AI < prev | next >...
Read full article at source