GlassBox Telemetry Framework: Architectural Transparency and Autonomous Structural Self-Repair via Three-Signal Learning

Sequential task learning causes catastrophic forgetting of prior knowledge, and because standard error signals propagate holistically, mid-training representational collapse remains invisible—leaving two unsolved problems in neural optimiza- tion: monitoring failures and sequential task forgetting. In this work, we present a surprising discovery: a self-correcting neu- ral architecture, trained with no explicit modularity constraint, spontaneously develops factorized internal representations, enabling zero-shot compositional generalization. This emer- gent behavior was made possible by our proposed infrastruc- ture, the GlassBox Telemetry Framework, which breaks the historical global backpropagation loop. Standard neural net- works adhere to opaque optimization loops, rendering them structurally blind to internal failures. Current post-hoc inter- pretability methods evaluate finished models; they can neither detect mid-training representational collapse nor execute lo- calized repair. We introduce GlassBox Telemetry Framework, a transparent architecture that replaces solitary backpropaga- tion with Three-Signal Learning. By structurally isolating the backward pass to a 7-operation gradient chain via a bounded 0.3 attenuation floor, GlassBox Telemetry Framework sev- ers gradient entanglement, making exact causal functional attribution mathematically possible. Within this transparent topology, our implementation (ORMAS) functions as an au- tonomous control system. It utilizes continuous spatial teleme- try to execute autonomous structural self-corrections mid- training. In a 430-run empirical evaluation, ORMAS demon- strates that robustness is the structurally verified byproduct of architectural transparency, autonomously recovering from total representational collapse to 71.5% accuracy where a parameter-matched baseline collapses to 10.0% (n = 3). Furthermore, telemetry proves ORMAS autonomously iso- lates conflicting concepts into decoupled linear bases under sequential task shift, granting emergent zero-shot composi- tional generalization as a byproduct (58.8% vs 25% chance, n = 3). By transforming neural architectures into verifi- able, continuous self-maintaining systems, GlassBox Teleme- try Framework replaces correlational approximations with un- compressed, causally-grounded telemetry, establishing foun- dational infrastructure for lifelong neural adaptation.