2 citations · 2 across the 32 of their papers we have counts for
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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…
FourierNAT: A Fourier-Mixing-Based Non-Autoregressive Transformer for Parallel Sequence Generation
Andrew Kiruluta, Eric Lundy, Andreas Lemos
We present FourierNAT, a novel non-autoregressive Transformer (NAT) architecture that employs Fourier-based mixing in the decoder to generate output sequences in parallel. While tr…
Learnable Multi-Scale Wavelet Transformer: A Novel Alternative to Self-Attention
Andrew Kiruluta, Priscilla Burity, Samantha Williams
Transformer architectures, underpinned by the self-attention mechanism, have achieved state-of-the-art results across numerous natural language processing (NLP) tasks by effectivel…
A Hybrid Wavelet-Fourier Method for Next-Generation Conditional Diffusion Models
Andrew Kiruluta, Andreas Lemos
We present a novel generative modeling framework,Wavelet-Fourier-Diffusion, which adapts the diffusion paradigm to hybrid frequency representations in order to synthesize high-qual…
State Fourier Diffusion Language Model (SFDLM): A Scalable, Novel Iterative Approach to Language Modeling
Andrew Kiruluta, Andreas Lemos
In recent years, diffusion based methods have emerged as a powerful paradigm for generative modeling. Although discrete diffusion for natural language processing has been explored…
A Self-Supervised Reinforcement Learning Approach for Fine-Tuning Large Language Models Using Cross-Attention Signals
Andrew Kiruluta, Andreas Lemos, Priscilla Burity
We propose a novel reinforcement learning framework for post training large language models that does not rely on human in the loop feedback. Instead, our approach uses cross atten…