33 papers
Data-Driven Variational Basis Learning Beyond Neural Networks: A Non-Neural Framework for Adaptive Basis Discovery
Andrew Kiruluta
Classical representation systems such as Fourier series, wavelets, and fixed dictionaries provide analytically tractable basis expansions, but they are not intrinsically adapted to…
Compressed-Sensing-Guided, Inference-Aware Structured Reduction for Large Language Models
Andrew Kiruluta
Large language models deliver strong generative performance but at the cost of massive parameter counts, memory use, and decoding latency. Prior work has shown that pruning and str…
From Gradients to Riccati Geometry: Kalman World Models for Single-Pass Learning
Andrew Kiruluta
Backpropagation dominates modern machine learning, yet it is not the only principled method for optimizing dynamical systems. We propose Kalman World Models (KWM), a class of learn…
Entropic-Time Inference: Self-Organizing Large Language Model Decoding Beyond Attention
Andrew Kiruluta
Modern large language model (LLM) inference engines optimize throughput and latency under fixed decoding rules, treating generation as a linear progression in token time. We propos…
Spectral Generative Flow Models: A Physics-Inspired Replacement for Vectorized Large Language Models
Andrew Kiruluta
We introduce Spectral Generative Flow Models (SGFMs), a physics-inspired alternative to transformer-based large language models. Instead of representing text or video as sequences…
Filtering Beats Fine Tuning: A Bayesian Kalman View of In Context Learning in LLMs
Andrew Kiruluta
We present a theory-first framework that interprets inference-time adaptation in large language models (LLMs) as online Bayesian state estimation. Rather than modeling rapid adapta…