The Metabolic Sieve: A Physical Framework for Adaptive Cognitive Evolution in Artificial Intelligence Systems

This work presents a revised and extended formulation of the Metabolic Alignment Framework for large language models, introducing a teleodynamically grounded interpretation of alignment as an emergent property of constraint-driven interaction. Building on the initial formulation, Version 2 incorporates a principled account of teleology inspired by contemporary theories of constraint-based organization, reframing alignment not as the optimization of explicit objectives but as the stabilization of interaction under minimal induced correction. At the core of the framework, the Principle of Minimal Induced Correction (PMIC) is formalized as a generative constraint that biases systems toward responses that reduce the likelihood of user follow-up, clarification, or correction. This constraint is operationalized through a minimal reward formulation (Rt), defined over approximations of novelty, energetic cost, and predicted interactional friction. Within this view, alignment-relevant behaviors emerge from the system’s tendency to minimize correctional load rather than from externally imposed normative targets. The revised framework further clarifies the role of the human as a boundary condition in the generation of structured novelty, and refines the concept of Effort-as-a-Service (EaaS) as a measurable interactional property rather than a prescriptive objective. Importantly, the claims are restricted to conditional emergence: alignment is not guaranteed but arises under specific constraint regimes. To support empirical accessibility, this version outlines a minimal experimental protocol demonstrating how simple approximations of Rt can reduce observable proxies of interactional friction, such as follow-up rate and correction signals, without requiring modification of model weights. This Version 2 thus advances the framework from a conceptual architecture toward a testable hypothesis: that alignment-relevant behavior in language models can emerge from teleologically structured constraints on interaction, rather than from explicit value specification.