[2603.02934] On the Structural Limitations of Weight-Based Neural Adaptation and the Role of Reversible Behavioral Learning

Computer Science > Machine Learning arXiv:2603.02934 (cs) [Submitted on 3 Mar 2026] Title:On the Structural Limitations of Weight-Based Neural Adaptation and the Role of Reversible Behavioral Learning Authors:Pardhu Sri Rushi Varma Konduru View a PDF of the paper titled On the Structural Limitations of Weight-Based Neural Adaptation and the Role of Reversible Behavioral Learning, by Pardhu Sri Rushi Varma Konduru View PDF HTML (experimental) Abstract:Neural models are usually adapted through changes in parameters shared among model components via fine-tuning, alignment-based training, and reinforcement learning. These changes have been found effective in short-term optimization. However, they result in long-term alterations in the model's base behavior. In this study, we introduce the concept of structural irreversibility as a characteristic of shared-parameter model adaptation. This concept refers to the intertwining of task-specific objectives with the representational identity of the model. We show that when parameters are directly mutated, the resulting model behaves divergently from the original model. This divergence cannot be reversed deterministically without an explicit parameter snapshot. We introduce reversible behavioral learning, in which model behaviors are structurally dissociated from identity parameters and can be deterministically unloaded through an explicit unload process. We also introduce the Recoverability Factor as a normalized measure of behavioral...