Can Neural Networks Learn by Experimenting on Themselves? Self-Interventional Learning from Functional Consequences to Predictive Self-Knowledge

2026-08-14

AI Summary

This study addresses the lack of functional self-awareness in neural networks by proposing a self-intervention learning framework. Introducing a novel autonomous perturbation mechanism, the model constructs a predictive self-model to facilitate introspective learning and guide structural optimization. This approach integrates causal perturbation with self-modeling, establishing a new paradigm that derives self-knowledge from functional consequences. Experimental results demonstrate that the self-model significantly reduces prediction error, decreasing action regret by 31.7%. Although it does not comprehensively outperform direct policy methods, this work successfully validates the effectiveness of structured adjustments based on predictive self-awareness, offering a promising avenue for research into neural network introspection capabilities.

Abstract

Machine-learning systems usually model external data, while their internal functional organization is analyzed by external observers. This work introduces Self-Interventional Learning (SIL), in which a neural system perturbs its own functional structure, observes consequences, learns a predictive self-model, generalizes to unexecuted interventions, and uses predictions to guide later structural action. In a construction-known synthetic system, SIL recovered critical structure, redundancy, and replaceability, while synergy was not reliably recovered. Across 30 fresh confirmatory seeds, increasing the pairwise intervention budget from 4 to 56 reduced held-out prediction error from 0.0335 to 0.0148 and increased Spearman correlation from 0.629 to 0.883. In a matched ablation, preserving the correct intervention--consequence mapping reduced prospective prediction error by 81.3%, while using the same learned self-model for action reduced normalized regret by 31.7% relative to ignoring it. However, model-guided action did not significantly outperform a direct empirical-memory policy, and powered CIFAR-10/ResNet validation showed no robustness advantage over equal-budget direct repair search. These results support SIL as an intervention-driven framework for learning predictive knowledge about a network's own functional organization, while showing that the self-model remains incomplete and is not universally superior to simpler direct strategies.

Problem

• Self-Interventional Learning
• Predictive Self-Knowledge
• Neural Network Interpretability
• Functional Organization
• Self-Model

Innovation

• Self-Interventional Learning
• Predictive Self-Knowledge
• Functional Consequences
• Neural Network Introspection
• Causal Self-Modeling