Epistemic Bridge Protocol: Regulating Epistemic Commitment in Probabilistic Generative Agents

Generative AI is now a standard tool for decision support and complex reasoning. Yet, a fundamental tension remains: these systems are linguistically fluent but epistemically unstable. Users often mistake "thinking out loud" for authoritative facts, leading to misaligned trust and a heavy burden of manual verification. While existing research focuses on fixing hallucinations or estimating uncertainty, we argue the core issue is structural. Current agents lack a mechanism to distinguish between exploring a thought and committing to an answer; both are piped through the same generative channel. We introduce the Epistemic Bridge Protocol (EBP), a minimal interaction protocol that regulates transitions between epistemic states and gates when commitment is permitted. EBP treats assertion as a controlled action rather than a default outcome of fluent generation. We formalize EBP as a state-based control mechanism, outline implementable system designs, and derive falsifiable predictions regarding premature commitment, trust calibration, and interaction cost. A controlled experiment (N = 40 per condition) demonstrates that EBP-conditioned systems reduce spatial variance in semantic embeddings by 65% (p = 0.0016) and reduce API fabrication by 57 percentage points, providing empirical evidence that epistemic protocols constrain probabilistic sampling trajectories. Together, these results position epistemic commitment as an interaction-level control problem and provide a foundation for more reliable human–AI collaboration.