DDCL-INCRT: A Self-Organising Transformer with Hierarchical Prototype Structure (Theoretical Foundations)

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• DDCL-INCRT is a transformer architecture that determines its own structure during training rather than requiring pre-specified hyperparameters.method 95%
• DDCL replaces the feedforward block with a dictionary of learned prototype vectors that spread apart automatically without explicit regularization.method 90%
• INCRT controls the number of attention heads by starting from one and adding new heads only when uncaptured directional information exceeds a threshold.method 90%
• The DDCL and INCRT mechanisms reinforce each other, with new heads amplifying prototype separation which triggers further head additions.technical 85%
• At convergence, the network self-organizes into a hierarchy of heads ordered by representational granularity.result 85%
• The hierarchical structure is proved to be unique and minimal - the smallest architecture sufficient for the task under stated conditions.technical 90%
• Formal guarantees of stability, convergence, and pruning safety are established throughout the paper.technical 90%
• Standard transformer architectures are systematically larger than necessary because architectural decisions are made without knowledge of the task.limitation 80%

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