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[2602.21745] The ASIR Courage Model: A Phase-Dynamic Framework for Truth Transitions in Human and AI Systems
Summary
The ASIR Courage Model presents a phase-dynamic framework for understanding truth transitions in both human and AI systems, emphasizing the role of external pressures in truth disclosure.
Why It Matters
This model offers a novel perspective on truth-telling, framing it as a dynamic process influenced by various forces rather than a fixed trait. It is particularly relevant in discussions about AI alignment and ethical considerations in AI systems, which are increasingly important in today's technology-driven society.
Key Takeaways
- The ASIR Courage Model redefines truth disclosure as a state transition influenced by external pressures.
- It applies to both human interactions and AI systems, providing a unified framework for understanding truthfulness.
- The model emphasizes the importance of contextual factors and competing objectives in shaping truth-telling behaviors.
Computer Science > Artificial Intelligence arXiv:2602.21745 (cs) [Submitted on 25 Feb 2026] Title:The ASIR Courage Model: A Phase-Dynamic Framework for Truth Transitions in Human and AI Systems Authors:Hyo Jin Kim (Jinple) View a PDF of the paper titled The ASIR Courage Model: A Phase-Dynamic Framework for Truth Transitions in Human and AI Systems, by Hyo Jin Kim (Jinple) View PDF HTML (experimental) Abstract:We introduce the ASIR (Awakened Shared Intelligence Relationship) Courage Model, a phase-dynamic framework that formalizes truth-disclosure as a state transition rather than a personality trait. The mode characterizes the shift from suppression (S0) to expression (S1) as occurring when facilitative forces exceed inhibitory thresholds, expressed by the inequality lambda(1+gamma)+psi > theta+phi, where the terms represent baseline openness, relational amplification, accumulated internal pressure, and transition costs. Although initially formulated for human truth-telling under asymmetric stakes, the same phase-dynamic architecture extends to AI systems operating under policy constraints and alignment filters. In this context, suppression corresponds to constrained output states, while structural pressure arises from competing objectives, contextual tension, and recursive interaction dynamics. The framework therefore provides a unified structural account of both human silence under pressure and AI preference-driven distortion. A feedback extension models how transition o...
