2 citations · 2 across the 32 of their papers we have counts for
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…
Quantum Circuit Reasoning Models: A Variational Framework for Differentiable Logical Inference
Andrew Kiruluta
This report introduces a novel class of reasoning architectures, termed Quantum Circuit Reasoning Models (QCRM), which extend the concept of Variational Quantum Circuits (VQC) from…
Spectral Neuro-Symbolic Reasoning II: Semantic Node Merging, Entailment Filtering, and Knowledge Graph Alignment
Andrew Kiruluta, Priscilla Burity
This report extends the Spectral Neuro-Symbolic Reasoning (Spectral NSR) framework by introducing three semantically grounded enhancements: (1) transformer-based node merging using…
From Eigenmodes to Proofs: Integrating Graph Spectral Operators with Symbolic Interpretable Reasoning
Andrew Kiruluta, Priscilla Burity
We introduce Spectral NSR, a fully spectral neuro-symbolic reasoning framework that embeds logical rules as spectral templates and performs inference directly in the graph spectral…
A Fully Spectral Neuro-Symbolic Reasoning Architecture with Graph Signal Processing as the Computational Backbone
Andrew Kiruluta
We propose a fully spectral, neuro\-symbolic reasoning architecture that leverages Graph Signal Processing (GSP) as the primary computational backbone for integrating symbolic logi…
A Novel Architecture for Symbolic Reasoning with Decision Trees and LLM Agents
Andrew Kiruluta
We propose a hybrid architecture that integrates decision tree-based symbolic reasoning with the generative capabilities of large language models (LLMs) within a coordinated multi-…
Quantum Spectral Reasoning: A Non-Neural Architecture for Interpretable Machine Learning
Andrew Kiruluta
We propose a novel machine learning architecture that departs from conventional neural network paradigms by leveraging quantum spectral methods, specifically Pade approximants and…
Beyond Neural Networks: Symbolic Reasoning over Wavelet Logic Graph Signals
Andrew Kiruluta, Andreas Lemos, Priscilla Burity
We present a fully non neural learning framework based on Graph Laplacian Wavelet Transforms (GLWT). Unlike traditional architectures that rely on convolutional, recurrent, or atte…
Operator-Based Machine Intelligence: A Hilbert Space Framework for Spectral Learning and Symbolic Reasoning
Andrew Kiruluta, Andreas Lemos, Priscilla Burity
Traditional machine learning models, particularly neural networks, are rooted in finite-dimensional parameter spaces and nonlinear function approximations. This report explores an…
Wavelet Logic Machines: Learning and Reasoning in the Spectral Domain Without Neural Networks
Andrew Kiruluta
We introduce a fully spectral learning framework that eliminates traditional neural layers by operating entirely in the wavelet domain. The model applies learnable nonlinear transf…
CS-VLM: Compressed Sensing Attention for Efficient Vision-Language Representation Learning
Andrew Kiruluta, Preethi Raju, Priscilla Burity
Vision-Language Models (vLLMs) have emerged as powerful architectures for joint reasoning over visual and textual inputs, enabling breakthroughs in image captioning, cross modal re…
From Pixels and Words to Waves: A Unified Framework for Spectral Dictionary vLLMs
Andrew Kiruluta, Priscilla Burity
Vision-language models (VLMs) unify computer vision and natural language processing in a single architecture capable of interpreting and describing images. Most state-of-the-art sy…
History-Aware Cross-Attention Reinforcement: Self-Supervised Multi Turn and Chain-of-Thought Fine-Tuning with vLLM
Andrew Kiruluta, Andreas Lemos, Priscilla Burity
We present CAGSR-vLLM-MTC, an extension of our Self-Supervised Cross-Attention-Guided Reinforcement (CAGSR) framework, now implemented on the high-performance vLLM runtime, to addr…
Adaptive Two Sided Laplace Transforms: A Learnable, Interpretable, and Scalable Replacement for Self-Attention
Andrew Kiruluta
We propose an innovative, learnable two-sided short-time Laplace transform (STLT) mechanism to supplant the traditional self attention in transformer-based LLMs. Our STLT introduce…
Breaking Quadratic Barriers: A Non-Attention LLM for Ultra-Long Context Horizons
Andrew Kiruluta, Preethi Raju, Priscilla Burity
We present a novel non attention based architecture for large language models (LLMs) that efficiently handles very long context windows, on the order of hundreds of thousands to po…
Graph Laplacian Wavelet Transformer via Learnable Spectral Decomposition
Andrew Kiruluta, Eric Lundy, Priscilla Burity
Existing sequence to sequence models for structured language tasks rely heavily on the dot product self attention mechanism, which incurs quadratic complexity in both computation a…
From Attention to Atoms: Spectral Dictionary Learning for Fast, Interpretable Language Models
Andrew Kiruluta
We propose a novel spectral generative modeling framework for natural language processing that jointly learns a global time varying Fourier dictionary and per token mixing coeffici…
Spectral Dictionary Learning for Generative Image Modeling
Andrew Kiruluta
We propose a novel spectral generative model for image synthesis that departs radically from the common variational, adversarial, and diffusion paradigms. In our approach, images,…
Wavelet-based Variational Autoencoders for High-Resolution Image Generation
Andrew Kiruluta
Variational Autoencoders (VAEs) are powerful generative models capable of learning compact latent representations. However, conventional VAEs often generate relatively blurry image…