7 papers
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…
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…
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…
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…