The Context-Indexed Challenge-Reversal and Self-Undermining Principle (CICRSP)
Year: 2026
Type: preprint
Abstract: This paper proposes the Context-Indexed Challenge Reversal and Self-Undermining Principle (CICRSP), a theoretical framework for analysing situations in which an agent challenges the necessity, justification, or validity of another agent's question or action, but contextual evidence may weaken the challenge while making the challenged action more understandable or better justified.The framework originates in a two-agent conversational case. Agent A asks for information. Agent B questions A's need to ask. The interaction may appear to turn against B's objection when A's lack of the requested information becomes evident. However, the result depends on whether B's objection presupposes that A already knows, has already received, or ought to possess the information. If the relevant answer was previously supplied and was accessible to A, the same challenge may instead be justified.The theory therefore treats context and event history as central analytical components rather than reducing the mechanism to question-answer sequences or temporal order alone. It distinguishes objective system history from agent-accessible history, literal questions from their argumentative functions, challenge self-undermining from strict logical self-refutation, and action reinforcement from contextual reversal.A mathematical architecture is proposed using state-transition functions, context- dependent action-justification scores, challenge- support scores, change measures, and a conditional reversal index. The paper also provides a multi-agent generalization, operational rules, domain-transfer criteria, research hypotheses, experimental conditions, alternative explanations, and falsifiability tests.The mathematical results established within the paper follow from their stated definitions and assumptions. They do not independently establish that the proposed mechanism operates in every real-world domain. Empirical validation and a comprehensive comparison with existing theories are required before stronger claims can be made.
Author: Shan, Shamiul Hoque
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225598490