Adaptive Learning and Collaborative Self-Adaptive Learning Between Self-and-Interaction Models: A Unified Framework for Adaptive Mode Switching

Abstract

Learning systems often face a fundamental design choice between adapting independently and adapting through collaboration. Independent adaptation allows a model to preserve its autonomy, exploit its own experience, and avoid unnecessary coordination costs. Collaborative adaptation allows multiple models to exchange information, learn from complementary experiences, coordinate their adaptation strategies, and respond to problems that exceed the capabilities of individual models. Relying exclusively on either mode may be inefficient in dynamic environments, particularly when the relative value of independent and collaborative learning changes over time. This paper proposes a unified framework for adaptive learning and collaborative self-adaptive learning between Self-and-Interaction Models (SIMs). In this framework, a Self-and-Interaction Model is conceptualized as a model-level computational entity that supports self-directed processing and interaction with other models. It is not defined as a large language model, nor does it require any particular neural architecture. The proposed framework enables a model to operate in a self-adaptive learning mode, a collaborative self-adaptive learning mode, or a transition process between the two. Mode selection is governed by an adaptive controller that considers learning progress, uncertainty, prediction error, knowledge complementarity, interaction cost, resource availability, and the expected value of collaboration. The framework formalizes model states, local adaptation, collaborative adaptation, mode selection, information exchange, and adaptive switching through a unified mathematical formulation. It also introduces mechanisms for selective collaboration, confidence-aware information integration, adaptive interaction frequency, and safeguards against harmful information transfer. A research methodology is outlined to evaluate the framework against fixed-mode and non-adaptive switching baselines across heterogeneous and changing environments. Because the framework has not yet been experimentally validated in this paper, its expected benefits are presented as testable hypotheses rather than established results. The proposed approach provides a general research foundation for investigating how self-directed and collaborative adaptation can coexist within a unified model-level learning architecture.