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
Certificate-Governed CRT Sparse FFT: Verified Global-Label Candidate Construction under Explicit Decoding Models
Aaron R. Flouro, Shawn P. Chadwick
Published: September 28, 2026
eess.SPcs.DS
We present a deterministic CRT-based sparse Fourier architecture for exact recovery of noiseless, on-grid, at-most-\(k\)-sparse spectra with an engineered transform length. The transform length \(N\) is selected as an exact product of small pairwise-coprime stage primes, allowing each stage to use transforms whose size…
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 775,050 results
LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation
Zhixu Li, Keqian Tang, Litian Gong +7 more
Published: October 15, 2026
cs.ROcs.AIcs.LG
Personalization is essential for general-purpose robots to transition into household environments and achieve true practicality. While general robotic manipulation has been extensively studied, personalized robotic manipulation (PeRM) lacks a controlled benchmark for separating task completion from preference…
Gen4P: Video Generators are 4D Perceivers
Yitong Xia, Zeren Jiang, Chuanxia Zheng
Published: October 15, 2026
cs.CV
We introduce Gen4P, a unified framework for recovering 4D geometry and motion from monocular video. We show that a pretrained video generator can be repurposed into a feed-forward 4D perceiver without iterative denoising. Building on on-demand querying, Gen4P processes clean video latents in a single encoder pass and…
Which Tokens to Merge? Diffusion Dynamics for Efficient Image Generation
SeungJu Cha, Ye-Chan Kim, HyunGee Kim +2 more
Published: October 15, 2026
cs.CV
Token merging has emerged as an effective training-free strategy for accelerating diffusion models by reducing redundant token computation while minimizing generation degradation. A key challenge is determining which tokens can be safely merged and which should be preserved for subsequent computation. Existing method…
SoftCache: Adaptive Step Size Caching for Diffusion Transformers
Shimon Malnick, Nadav Orenstein, Ohad Fried +1 more
Published: October 15, 2026
cs.CV
Diffusion and flow models generate a sample over iterative steps, each a full pass through a large network, and that pass dominates generation cost. Training-free caching accelerates them by reusing activations from recent evaluations instead of recomputing them. Dynamic caching methods decide when to refresh by…
Discovering state-of-the-art quantum circuits with large language models
Andi Gu, Alek H. Kemeny, Susanne F. Yelin +1 more
Published: October 15, 2026
quant-phcs.AI
Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms. While for many practical problems the dominant resource costs are associated with a small set of…
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models
Chenyuan Zhou, Yining Hong, Huang Huang +9 more
Published: October 15, 2026
cs.ROcs.AIcs.CV
Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitly specified, from physical properties such as friction and mass to human-defined environmental rules. We introduce T2-Z for test-time…
Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation
Zirui Wu, Chen Guo, Tianjian Jiang +11 more
Published: October 15, 2026
cs.CVcs.RO
We introduce P$^{3}$-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and goal-directed planning through three complementary modes: Perceive, which reconstructs articulated hand motion and camera trajectories from video; Predict, which…
CoDA: Co-Evolution of Data Engines and Action Policies
Tianjiao Yu, Zhilin Ou, Yifan Shen +5 more
Published: October 15, 2026
cs.ROcs.AI
Generative simulation can automate robot environment construction, but physically valid environments are not necessarily useful for training. The challenge is to identify environments that address a policy's failures and supervision that helps it overcome them. We introduce CoDA, a framework in which a simulation data…
Grounding Generative Verification in 3D Content
Shuanglin Li, Jiaying Lin, Shuquan Ye
Published: October 15, 2026
cs.CV
Recent advances in multimodal large language models (MLLMs) and 3D generation have expanded 3D understanding and synthesis. However, beyond visual fidelity, task-specific correctness grounded in 3D evidence remains a major challenge. We argue that building reliable 3D systems requires not only stronger generation and…
PathFM: Fast Generative Forecasting of Multivariate Trajectories
Jiachen Yao, Xiyuan Zhang, Annan Yu +5 more
Published: October 15, 2026
cs.LG
Time series foundation models are becoming increasingly popular for general-purpose forecasting. However, most leading models predict marginal quantiles, leaving the dependence structure of future trajectories unspecified. This distinction matters for trajectory-dependent quantities such as cumulative demand, peak…
DK-VLM: Geometry as Position for Spatial Reasoning in Vision-Language Models
Han Su, Meng Tian, Tianyu Huang +7 more
Published: October 15, 2026
cs.CV
Vision-language models still struggle to understand 3D spatial relations across views and update spatial judgments as the observer moves. A common approach fuses 3D reconstruction features into visual tokens, but aligning these features with pretrained semantic representations remains challenging under limited spatial…
OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generative World Modeling
Bowei Liu, Xinchen Zhang, Xuhuan Li +10 more
Published: October 15, 2026
cs.CL
Generative visual verifiers play a crucial role in advancing multimodal intelligence. To enhance the robustness of visual verifiers in complex multimodal scenarios involving compositional requirements, we move beyond conventional binary verifiers that produce only True/False discrete judgments and introduce a verifier…
Rethinking Routing Exploration in Modern Mixture-of-Experts
Bingcong Li, Hao Ma, Michael Muehlebach +2 more
Published: October 15, 2026
cs.LGcs.AI
Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts. The former increases expressiveness by enabling more expert combinations at a fixed activation ratio, and…
SUPRA-TEX: 3D Generation with Super-Aligned Texture
Chen Yang, Jiemin Fang, Fanpeng Meng +5 more
Published: October 15, 2026
cs.CVcs.GR
Single image 3D generation aims to dress a given 3D mesh with relightable materials while faithfully preserving the visual details of a reference image. Existing methods either introduce artifacts when fusing inconsistent multi-view predictions or struggle to preserve fine details through implicit image-feature…
FDE-Gym: The Last Mile of AI Delivery
Dongxu Yang, Hongda Yan, Renxin Wang +23 more
Published: October 15, 2026
cs.AIcs.CLcs.SE
Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets. Real-world customer delivery is different: forward-deployed engineers (FDEs) must clarify ambiguous requirements, work in live systems, and uncover unstated constraints. We introduce…
Speculative Forcing for Efficient Autoregressive Video Generation
Hyunwook Choi, Seungryong Kim, Dongyoon Han +1 more
Published: October 15, 2026
cs.CV
Few-step autoregressive video generators enable streaming generation, yet they run every token through the final denoising step, regardless of how much refinement it needs. We find this uniform computation is often unnecessary: many tokens already closely match their eventual full-step outputs in the initial steps,…
MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
Shangheng Du, Jinxin Shi, Shuaiyu Zhang +7 more
Published: October 15, 2026
cs.AIcs.CLcs.LG
Large language models (LLMs) are expanding from assisting scientific research and engineering to autonomously developing and improving AI systems, advancing AI for AI (AI4AI) as a pathway toward recursive self-improvement (RSI). Workflow-driven machine learning engineering (MLE) systems provide a practical path toward…
GeoState: Learning View-Decoupled Geometry States for 3D World Modeling
Hongchang Liu, Jian Liu, Yujuan Ding +5 more
Published: October 15, 2026
cs.CVcs.AIcs.LG
Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometric priors. However, the view dependence of these features entangles scene dynamics with camera-induced variation, making the resulting state evolution harder to predict.…
3DCodeVerse: Exploring 3D Worlds as Code
Yipeng Gao, Ziyao Zeng, Jinfa Huang +16 more
Published: October 15, 2026
cs.CVcs.AI
We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises large-scale, high-quality datasets of executable 3D code, a coding harness for spatial reasoning and automated verification, and practical guidance for 3D coding. First,…
Dexterity from Supervised Play
Botao He, Yiming Dou, Yiannis Aloimonos +3 more
Published: October 15, 2026
cs.ROcs.AI
How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play, a framework in which a robot learns a play policy from minutes of human play through behavior cloning, then deploys it autonomously to collect hours of interaction data…
Lean Graph Search for Formal Theorem Proving
Yihan Xu, Jia-Chen Gu, Junyi Zhang +7 more
Published: October 15, 2026
cs.LG
Retrieving relevant lemmas is crucial for formal theorem proving, enabling provers to build machine-checkable proofs from established mathematical results. Recent retrieval systems match library declarations to a target theorem or its intermediate proof steps, but make limited use of multi-hop dependencies among…
Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty
Chengze Xie, Xuan Wang, Ying Ding +2 more
Published: October 15, 2026
cs.LG
We develop a robust reinforcement learning (RL) framework for learning adaptive treatment policies that generalize across patients and remain reliable under errors in the measured biomarker and shifts in the disease dynamics. By modeling the patient as a hidden context, we address generalization and robustness in a…
Evaluation Awareness Beyond English: Do LLMs Act Differently When Tested in Indic Languages?
Trizal Garg, Sibayan Mitra, Arya Karanjkar +6 more
Published: October 15, 2026
cs.AIcs.CL
Large language models (LLMs) can often tell when they are being tested. This ability, known as evaluation awareness, can lead models to act more cautiously during safety evaluations than in real use. Existing work has studied evaluation awareness almost entirely in English, although models serve users in many…
KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents
Siqi Chen, Zhengxi Lu, Yuchen Yan +10 more
Published: October 15, 2026
cs.AI
Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions is fundamentally difficult. Outcome-based reinforcement learning provides only a single terminal reward shared across an entire trajectory. Recent process-supervision…