Empirical Phase Patterns in GPT-3.5: A 9.7% Transformation Bottleneck

NeurIPS 2025 Workshop NeurReps Submission10 Authors

Keywords: transformer models, attention mechanisms, geometric deep learning, phase transitions, mechanistic interpretability, GPT-3.5, neural geometry

TL;DR: We discover a statistically significant 9.7% "transformation bottleneck" in GPT-3.5 outputs and propose that semantic phases correspond to distinct geometric attention patterns (pentagonal, hexagonal, square, triangular).

Abstract: We report a statistically significant phase distribution in GPT-3.5 conversational outputs, with a notable 9.7% "transformation bottleneck" ($\chi^2 = 120.24, p < 0.0001$) discovered through semantic analysis of 1,000 responses. The model exhibits four distinct behavioral phases: transformation (9.7%), generation (21.8%), consumption (29.9%), and integration (38.6%). We propose that these phases may correspond to distinct geometric patterns in attention mechanisms—pentagonal, square, triangular, and hexagonal respectively—and present testable predictions for this hypothesis. If validated, this finding could reveal fundamental architectural constraints in transformer models and suggest that the 9.7% bottleneck represents an inherent limitation in processing novel or transformative content.

Submission Number: 10