No confidence-gated RL policy can achieve maximum helpfulness, optimal calibration, and full autonomy under rational oversight when tasks exceed the agent's competence, because non-affine autonomy incentives destroy strict properness of scoring rules and cause confidence inflation.
Murphy’s laws of AI alignment: Why the gap always wins
2 Pith papers cite this work. Polarity classification is still indexing.
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Qualixar OS provides a runtime for multi-agent AI systems with support for 12 topologies, LLM-driven team design, dynamic routing, consensus judging, content attribution, and protocol bridging, achieving 100% accuracy on a custom 20-task suite at $0.000039 mean cost per task.
citing papers explorer
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The Behavioral Credibility Trilemma: When Calibrated Autonomy Becomes Impossible
No confidence-gated RL policy can achieve maximum helpfulness, optimal calibration, and full autonomy under rational oversight when tasks exceed the agent's competence, because non-affine autonomy incentives destroy strict properness of scoring rules and cause confidence inflation.
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Qualixar OS: A Universal Operating System for AI Agent Orchestration
Qualixar OS provides a runtime for multi-agent AI systems with support for 12 topologies, LLM-driven team design, dynamic routing, consensus judging, content attribution, and protocol bridging, achieving 100% accuracy on a custom 20-task suite at $0.000039 mean cost per task.