Author

Burc Gokden

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#machine learning Preprint Aug 2026

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference

The Large Language Model from Power Law Decoder Representations (PLDR-LLM) and its attention, Power Law Graph Attention (PLGA), replace the fixed bilinear form of scaled dot-product attention (SDPA) with a learned, input-generated bilinear operator $G_{LM}$, built from a positive tensor $A_{LM}$ by elementwise power la...

Burc Gokden · 0 citations

#machine learning Preprint Sep 2026

Training and Inference Dynamics of PLDR-LLMs: Row-Map Collapse, Renormalization, and Predictive Reduction

This monograph develops a unified account of training and inference in Power Law Decoder Representation language models (PLDR-LLMs) with exact identities, conditional dynamical claims, and finite empirical findings, with proofs, selected formal checks, and compact numerical evidence.

Burc Gokden · 0 citations