From Retry to Intent: A Conceptual Framework for Self-Correcting Information Processing Systems

Many current AI systems address failure by repeatedly retrying the same or slightly
modified processes. While this approach can occasionally produce successful outcomes, it
does not guarantee convergence, nor does it systematically reuse information obtained from
failure.
This paper proposes a conceptual framework in which post-failure correction is treated
not as a sequence of operations, but as an abstract corrective intent. A corrective intent
represents the semantic purpose of a modification required to resolve a failure, independent
of specific implementations or execution mechanisms.
Within this framework, failures, corrective intents, and re-executions are considered as
structurally related elements of a single process, rather than isolated retry attempts. This
perspective distinguishes retry-based exploration from intent-guided correction, where repeated executions are conceptually interpreted as adjustments toward predefined success
conditions.
The contribution of this paper is not an algorithm, implementation, or empirical evaluation. Instead, it provides a clear problem formulation, introduces the notion of corrective
intent, and outlines a high-level structural view of self-correcting information processing systems. The framework is intended to serve as a conceptual foundation for future discussions
on reliability, convergence, and design principles of autonomous and generative systems.