Project GlassBox: Structure Over Scale in Neural Reasoning — Breaking the Black Box Through Architectural Transparency

Project GlassBox is a systematic 33-phase experimental campaign demonstrating that small, structurally constrained neural architectures can simultaneously achieve superior task performance and unprecedented interpretability compared to large unconstrained models. Using ARC-AGI as a benchmark for abstract visual reasoning, a 77K-parameter Graph Neural Network with Pointer attention (the "GlassBox Agent") outperforms a 1.45M-parameter Transformer baseline (56.8% vs 43.9% full match accuracy). Through test-time gradient adaptation with geometric data augmentation, accuracy reaches 87.4%, breaking through a previously observed 85% performance ceiling. Key Results: Structure > Scale: 77K structured parameters outperform 1.45M unstructured parameters (19× smaller, higher accuracy) Hydra Self-Repair: First quantitative characterization of neural self-repair — after destroying 50% of model neurons, few-shot adaptation recovers 95.8% of original performance 82.8% Attribution: Full causal path tracing for 82.8% of predictions, exceeding by 3.3× the 25% attribution coverage reported for large language models Adaptation Supremacy: Test-time gradient adaptation is strictly superior to symbolic program search (+34.5pp improvement) 85% Ceiling Breakthrough: D8 geometric augmentation during adaptation pushes accuracy from 85.1% to 87.4% Source code: https://github.com/hafufu-stack/glassbox Acknowledgments This research was conducted entirely independently, without institutional affiliation or corporate funding. The author currently faces financial constraints that make it increasingly difficult to maintain subscriptions to AI services essential for this line of research. To sustain and improve the quality of future work, the author is actively seeking community sponsorship. Details are available at https://github.com/sponsors/hafufu-stack.