Epistemic Filtering and Collective Hallucination: A Jury Theorem for Confidence-Calibrated Agents

1. 1Agents learn to estimate their own reliability over time
2. 2Agents selectively abstain from voting based on their confidence levels
3. 3The collective accuracy of the agents is evaluated using a probabilistic framework
4. 4The framework is compared to classical epistemic voting results, such as the Condorcet Jury Theorem

This research benefits machine learning engineers and AI researchers working on multi-agent systems, as it provides a framework for improving collective decision-making accuracy

💡 Allowing agents to selectively abstain from voting can improve collective decision-making accuracy

🤖 Confidence-calibrated agents can improve collective accuracy through epistemic filtering and selective abstention 💡