Research · White Papers
Research & Publications
Exploring the mathematical foundations of efficient signal processing and AI model compression. Join the waitlist for updates on our latest research.
Research Focus Areas
Our research explores fundamental challenges in sparse signal processing, deterministic algorithm design, and certification frameworks for production systems.
Efficient Spectral Analysis
Investigating efficient algorithms for identifying and reconstructing sparse frequency components in high-dimensional signals across various domains.
Deterministic Algorithms
Developing verification-ready signal processing frameworks with guaranteed reproducibility for production AI and high-reliability applications.
Computational Optimization
Advancing computational efficiency for frequency domain analysis through optimized processing and intelligent resource allocation.
Validation & Benchmarking
Establishing comprehensive benchmarking methodologies and validation frameworks for comparing signal processing implementations across diverse use cases.
Technical Papers
SparseTech technical papers covering deterministic sparse FFT engines, sublinear data discovery, and memory-compute workload modeling. PDF available for each.
Sparse FFT as a Memory-Compute Workload: FFTW Benchmarking and Traffic/Energy Modeling
Aaron R. Flouro, Shawn P. Chadwick
Published: May 12, 2026
eess.SPcs.ARcs.PF
We benchmark a production Rust implementation of a Four-View GATED CRT sparse FFT against FFTW and evaluate its suitability for near-memory sparse spectral processing. The sparse arithmetic core scales as $O(k \log k)$, while input acquisition remains streaming $O(N)$. On synthetic on-grid sparse signals, the…
SparseDSP: System-Level Evaluation of Deterministic Sparse FFT Engine Routing across Synthetic, Impaired, and Curated Real-Payload Workloads
Aaron R. Flouro, Shawn P. Chadwick
Published: May 10, 2026
eess.SPcs.DScs.AR
We present SparseDSP, a regime-adaptive deterministic sparse FFT engine routing system evaluated against Dense FFT across on-grid, off-grid, and curated real-payload workloads. SparseDSP estimates input sparsity internally, then dispatches to an exact engine drawn from a complexity-class family spanning $O(k \log k)$,…
SparseDSP: System-Level Evaluation of Deterministic Sparse FFT for 5G/6G-Relevant Wideband Spectrum Sensing
Aaron R. Flouro, Shawn P. Chadwick
Published: May 11, 2026
eess.SPcs.ITcs.NI
We present SparseDSP, a regime-adaptive deterministic sparse FFT system evaluated against dense FFT baselines for transform-stage bin identification in 5G/6G-relevant wideband sensing regimes. SparseDSP estimates effective sparsity internally and dispatches among deterministic sparse recovery engines spanning…
SparseDSP: System-Level Evaluation of Deterministic Sparse FFT for Radar, Sonar, and LiDAR
Aaron R. Flouro, Shawn P. Chadwick
Published: April 19, 2026
eess.SPcs.AR
We present SparseDSP, a regime-adaptive deterministic sparse FFT system evaluated against Dense FFT across radar, sonar, electronic warfare, and LiDAR operating points. SparseDSP estimates signal sparsity internally, then dispatches to an exact engine selected by its internal dispatch policy from an internal family of…
SparseDSP: Sublinear Data Discovery for Large-Scale Computational Pipelines
Aaron R. Flouro, Shawn P. Chadwick
Published: April 19, 2026
cs.DScs.LGcs.IR
Large-scale data processing pipelines spend substantial time on discovery: selecting relevant subsets from large data stores before downstream computation begins. This discovery stage, which includes dense scans, FFT-based analysis, and exhaustive top-$k$ selection, scales linearly with data size regardless of…
SparseTech Publications
Deterministic Sparse FFT via Keyed Multi-View Gating with $O(\sqrt{N} \log k)$ Expected Time
Aaron R. Flouro, Shawn P. Chadwick
Published: May 5, 2026
eess.SPcs.DScs.IT
We introduce a deterministic sparse Fourier transform framework based on a keyed multi-view gating mechanism that leverages 2-of-3 Chinese Remainder Theorem (CRT) agreement to reduce candidate frequency pairs from $O(k^2)$ to $Θ(k)$ under sparse-regime assumptions. Unlike prior approaches that rely on randomized…
Safety-Certified CRT Sparse FFT: $Ω(k^2)$ Lower Bound and $O(N \log N)$ Worst-Case
Aaron R. Flouro, Shawn P. Chadwick
Published: April 20, 2026
eess.SPcs.DScs.IT
Computing Fourier transforms of k-sparse signals, where only k of N frequencies are non-zero, is fundamental in compressed sensing, radar, and medical imaging. While the Fast Fourier Transform (FFT) evaluates all N frequencies in $O(N \log N)$ time, sufficiently sparse signals should admit sub-linear complexity in N.…
Post-Training Probability Manifold Correction via Structured SVD Pruning and Self-Referential Distillation
Aaron R. Flouro, Shawn P. Chadwick
Published: January 30, 2026
cs.LGcs.AIcs.CL
Large language models are expensive to deploy. We introduce Sparse Knowledge Distillation (SparseKD), a post-training method that compresses transformer models by combining structured SVD pruning with self-referential knowledge distillation. The key insight is simple: instead of using an external teacher, the model…
Adaptive Weighting in Knowledge Distillation: An Axiomatic Framework for Multi-Scale Teacher Ensemble Optimization
Aaron R. Flouro, Shawn P. Chadwick
Published: January 25, 2026
cs.LG
Knowledge distillation with multiple teachers is increasingly used to improve robustness, efficiency, and safety, yet existing approaches rely largely on heuristic or implementation-specific weighting schemes. This paper develops an operator-agnostic axiomatic framework for adaptive weighting in multi-teacher knowledge…
Recursive Meta-Distillation: An Axiomatic Framework for Iterative Knowledge Refinement
Aaron R. Flouro, Shawn P. Chadwick
Published: January 19, 2026
cs.LG
Recent work in probability-domain knowledge distillation has established axiomatic frameworks for temperature scaling, multi-teacher aggregation, and bias-variance trade-offs in single-stage settings. However, the mathematical behavior of recursive or multi-generation distillation remains poorly understood, with prior…
Multi-Teacher Ensemble Distillation: A Mathematical Framework for Probability-Domain Knowledge Aggregation
Aaron R. Flouro, Shawn P. Chadwick
Published: January 14, 2026
cs.LG
Building on the probability-domain distillation framework of Sparse-KD, we develop an axiomatic, operator-theoretic framework for multi-teacher ensemble knowledge distillation. Rather than prescribing a specific aggregation formula, we define five core axioms governing valid knowledge aggregation operators,…
Hallucinations Live in Variance
Aaron R. Flouro, Shawn P. Chadwick
Published: January 11, 2026
cs.LGcs.AI
Benchmarks measure whether a model is correct. They do not measure whether a model is reliable. This distinction is largely academic for single-shot inference, but becomes critical for agentic AI systems, where a single rephrased prompt can trigger cascading failures in multi-step execution. Yet this form of…
Sparse Knowledge Distillation: A Mathematical Framework for Probability-Domain Temperature Scaling and Multi-Stage Compression
Aaron R. Flouro, Shawn P. Chadwick
Published: January 6, 2026
cs.LG
We develop a unified theoretical framework for sparse knowledge distillation based on probability-domain softening operators. While the equivalence $p^{1/T} \propto \mathrm{softmax}(z/T)$ is well known, our contribution is an operator-level analytical framework built on this foundation rather than the equivalence…
Foundational Readings
Curated academic papers that inform our research directions.
Note: These are external publications from arXiv, not SparseTech publications. We share them as context for the mathematical foundations underlying our work.
Showing 729,520 results
VideoRAE: Taming Video Foundation Models for Generative Modeling via Representation Autoencoders
Zhihao Xie, Junfeng Wu, Xinting Hu +2 more
Published: July 15, 2026
cs.CV
Video generative models commonly rely on latent spaces learned by 3D Variational Autoencoders (3D-VAEs). However, conventional 3D-VAEs are mainly optimized for pixel-level reconstruction, which can limit the semantic and spatio-temporal structure captured by their latents. Meanwhile, Video Foundation Models (VFMs) such…
Stochastic Domination of Gaussian Maxima: A Resolution to the Weak Simplex Conjecture
Abhijeet Mulgund
Published: July 15, 2026
math.PRcs.ITmath.MG
We prove a stochastic comparison for Gaussian maxima. Let $R$ be an $m\times m$ correlation matrix satisfying $R-\mathbf{1} \mathbf{1}^{\mathsf T}/m\succeq0$, let $X\sim\mathcal{N}(0,R)$, and let $Z_1,\ldots,Z_m$ be independent standard Gaussian random variables. Then…
Leveraging unlabelled data for generalizable neural population decoding
Ximeng Mao, Nanda H. Krishna, Avery Hee-Woon Ryoo +2 more
Published: July 15, 2026
cs.LGq-bio.NC
Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments. Recent work has shown that tokenizing neural data at the spike level facilitates multi-session pretraining and delivers state-of-the-art decoding performance. However, current spike-based…
Linear Independent Component Analysis via Optimal Transport
Ashutosh Jha, Michel Besserve, Simon Buchholz
Published: July 15, 2026
cs.LGstat.ML
Linear Independent Component Analysis (ICA) recovers jointly independent source signals from their linear mixtures. To achieve this, classical ICA algorithms attempt to maximize non-Gaussianity, measured by negentropy, which is linked to independence by information theory. Because exact negentropy optimization is…
From Pixels to States: Rethinking Interactive World Models as Game Engines
Zhen Li, Zian Meng, Shuwei Shi +4 more
Published: July 15, 2026
cs.CV
Building interactive worlds that respond coherently to player actions has long been a shared goal of computer graphics, games, and artificial intelligence. Recent video generative models provide a data-driven route toward this goal by predicting future observations conditioned on user actions, and are increasingly…
MetaPerch: Learning from metadata for bioacoustics foundation models
Mustafa Chasmai, Vincent Dumoulin, Jenny Hamer
Published: July 15, 2026
cs.LGcs.SD
Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data. Recent work has shown that supervision alone can produce SotA species detection models when trained on this large-scale data -- however, there remains unutilized potential in the…
Screening of Biosecurity Features in Metagenomic Data with Evo 2 Probes
Jeremy Guntoro, Alexander Dack, Dylan Danno +3 more
Published: July 15, 2026
q-bio.GNcs.LG
Genomic foundation models such as Evo 2 learn rich sequence representations, but their value for biosecurity screening is largely unexplored. We ask how much biosecurity-relevant signal is linearly accessible in these representations by training minimal linear and attention probes on frozen Evo 2 layer-26 activations,…
The even-uniform hypergraph Moore bound
Afonso S. Bandeira, Dmitriy Kunisky, Petar Nizić-Nikolac +2 more
Published: July 15, 2026
math.COcs.DMcs.DS
The hypergraph Moore bound conjectured by Feige (2008) controls the size of the smallest even cover in a $k$-uniform hypergraph in terms of the average density of hyperedges. An even cover is a set of hyperedges covering each vertex an even number of times, generalizing the notion of a cycle in a graph, so the size of…
Minimax Theory of Likelihood-Based Deep Learning for Speckle Regression
Soham Jana
Published: July 15, 2026
math.STstat.ML
Speckle noise is a multiplicative noise commonly encountered in coherent imaging modalities such as synthetic aperture radar, optical coherence tomography, and digital holography. Although deep learning methods, in practice, have achieved state-of-the-art performance for speckle denoising, their fundamental statistical…
Adaptivity in Local Kernel Based Methods for Approximating Solutions to the Poisson Equation
Jonah A. Reeger, Anders R. Johnson, Shelby W. Woodrum
Published: July 15, 2026
math.NA
Expanding on the recent development of adaptive local kernel methods for approximating the action of linear operators, a local estimate of the error and an adaptive procedure for approximating solutions to the Poisson equation is developed. The error estimate is used in the midst of the adaptive procedure to determine…
Fast Cascaded Recursive Filtering via a Block-Matrix Reformulation
Haotian Zhai, Bernd-Peter Paris
Published: July 15, 2026
eess.SP
Recursive (IIR) filters realized as cascaded second-order sections (biquads) offer both design generality and robustness against coefficient quantization. However, their inherent sample-to-sample feedback dependency poses a fundamental obstacle to parallel computation. This paper reformulates the biquad difference…
Hindcast: Replaying Prediction Markets to Evaluate LLM Forecasters
Xiao Ye, Jacob Dineen, Evan Zhu +3 more
Published: July 15, 2026
cs.CL
Forecasters are evaluated by backtesting, which replays resolved questions and grades the probability the system would have assigned before the outcome was known. For LLMs, two channels leak the answer into this test. A model that retrieves can surface reports written after the event, turning forecasting into a lookup,…
Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models
Hefeng Zhou, Jinxuan Zhang, Jiong Lou +4 more
Published: July 15, 2026
cs.AI
The emergence of Chain-of-Thought (CoT) reasoning has significantly enhanced the ability of large language models (LLMs) to tackle complex, multi-step tasks. However, when errors occur, current interaction approaches typically involve re-generating another response that may make mistakes again, or users laboriously…
Earthquaker-AI: A Retrieval-Augmented Generation Framework with Rubric-Based Assessment for Primary School Earthquake Education
Xanthi Kokkinou, Chaido Mizeli, Nafsika Koulaxidou +2 more
Published: July 15, 2026
cs.AI
This paper presents Earthquaker-AI, a hybrid educational framework building upon a previously implemented educational robotics project by integrating a conversational AI assistant based on Retrieval-Augmented Generation. It aims to enhance earthquake preparedness and conscious action among primary-school students. The…
AI-accelerated End-to-End Framework for Rapid Professional Upskilling
Tam Nguyen, Hung Nguyen, Robert Ogburn
Published: July 15, 2026
cs.AI
By 2030, 59 of every 100 workers will need reskilling or upskilling, yet the average time to close an enterprise skills gap grew from roughly 3 days in 2014 to 36 days in 2018. Most current frameworks accelerate single stages of upskilling programs and generally lack industry validation. We present an end-to-end…
Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study
Zhan Chen, Jiqiao Ma, Chih-wen Kuo
Published: July 15, 2026
cs.CVcs.AIcs.LG
Historical Manchu OCR must accommodate various visually distinct writing styles, including regular script, running script, and the semi-cursive chancery hand used in palace memorials, despite limited labeled data. We study a multi-expert system that reuses checkpoints from an iterative fine-tuning process as domain…
Can an Old Dog Be Taught New Tricks? Taking LLMs Beyond Sentence Level Translation
Alaina Brandt
Published: July 15, 2026
cs.CL
Automatic translation systems, from CAT tools to MT, overwhelmingly treat translation as a sentence-by-sentence act. This paper asks whether LLMs can be moved beyond that paradigm through whole-document, corpus-informed translation. We present PAT (Pragmatic Auto-Translator), a RAG-based system that pairs…
Early Adoption of Agentic Coding Tools by GitHub Projects
Maliha Noushin Raida, Daqing Hou
Published: July 15, 2026
cs.SEcs.AIcs.CY
Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development. While prior studies have examined PR-level outcomes of agent-generated contributions, less is known about how agentic coding…
Exploiting Graph Structure for Near-Optimal Broadcasting
Rudranarayan Kar, Praneet Kumar Patra, Diya Roy +1 more
Published: July 15, 2026
cs.DS
Telephone broadcasting is a classical model for spreading information in a network. Given a connected graph $G(V,E)$ with source vertex $s$, each informed vertex may inform exactly one uninformed neighbor in every time step. The \textsc{Broadcasting} problem asks whether all vertices can be informed within $t$ steps;…
SPECS: Speciated Evolutionary Circuit Synthesis
Yağız Gençer, Stefan Uhlich, Andrea Bonetti +3 more
Published: July 15, 2026
cs.NE
We propose SPECS, a genetic algorithm for automated analog circuit synthesis with joint topology and sizing optimization. SPECS is inspired by NeuroEvolution of Augmenting Topologies (NEAT), an evolutionary algorithm originally developed to synthesize neural networks. By reformulating the genome representation and…
Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
Daniel Grillmeyer, Marius Hadry, Michael Stenger +3 more
Published: July 15, 2026
cs.LGcs.AI
With rising global energy demand and growing awareness of climate change and its impacts, the share of renewable energies in the global energy mix continues to grow. Unlike conventional power generation, the output of renewable energy sources cannot be controlled as consistently due to their dependence on environmental…
Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth
Katie Everett
Published: July 15, 2026
cs.LGcs.AI
We investigate how each component of the Transformer feedforward block architecture design determines how much rank survives across depth at initialization. We reinterpret skip connections and normalization, long understood as controlling magnitude, as mechanisms for preserving gradient rank across depth, since the…
Square-Root Law for Covert Communication with Warden-Favorable Side Information
Hossein Ahmadi, Christian Deppe, Boulat A. Bash +1 more
Published: July 15, 2026
cs.IT
Covert communication enables Alice to transmit to Bob while making the transmission difficult for Willie to detect. We study a scalar Gaussian covert-overlay model in which Alice's low-power covert signal is superimposed on an aggregate public component generated by Alice or other trackable sources. Willie is given all…
Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points
Mustafa Emre Gürsoy, Stefan Uhlich, Ryoga Matsuo +6 more
Published: July 15, 2026
cs.LGcs.AR
In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing. Traditional methods lack generalization across different performance targets, while standard RL approaches waste resources exploring unpromising regions. Our method addresses these…