Tootfinder

Straightforward Bayesian A/B testing with Dirichlet posteriors
Dustin Hayden, Thomas Armitage
https://arxiv.org/abs/2508.08077 https://arxiv.org/pdf/2508.0…

Straightforward Bayesian A/B testing with Dirichlet posteriors
Bayesian A/B testing investigates metric changes using the joint posterior distribution of two (or more) experimentally-derived datasets. The construction of said joint posterior is often a time-consuming process requiring specialized knowledge and domain expertise. In businesses that perform tens to hundreds of A/B tests per month it is important to have a robust analysis pipeline that can handle the variety of experiments performed on a modern web platform; requiring a domain expert to select…

Geometry-Controlled Freezing and Revival of Bell Nonlocality through Environmental Memory
Mohamed Hatifi
https://arxiv.org/abs/2508.07046 https://arxiv.org…

Geometry-Controlled Freezing and Revival of Bell Nonlocality through Environmental Memory
We show that the distance between two qubits coupled to a structured reservoir acts as a single geometric control that can store, revive, or suppress Bell nonlocality. In a mirror-terminated guide, quantum correlations lost to the bath return at discrete recurrence times, turning a product state into a Bell-violating one without any entangling drive (only local basis rotations/readout). In the continuum limit, we derive closed-form criteria for the emergence of nonlocality from backflow, and in…

Learning The Minimum Action Distance
Lorenzo Steccanella, Joshua B. Evans, \"Ozg\"ur \c{S}im\c{s}ek, Anders Jonsson
https://arxiv.org/abs/2506.09276

Learning The Minimum Action Distance
This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor the actions executed by the agent. We propose learning the minimum action distance (MAD), defined as the minimum number of actions required to transition between states, as a fundamental metric that captures the underlying structure of an environment. MAD naturally enables critical downstream tasks such as goal-conditi…

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Haoran Xi, Chen Liu, Xiaolin Li
https://arxiv.org/abs/2508.06517

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Automated polyp segmentation is essential for early diagnosis of colorectal cancer, yet developing robust models remains challenging due to limited annotated data and significant performance degradation under domain shift. Although semi-supervised learning (SSL) reduces annotation requirements, existing methods rely on generic augmentations that ignore polyp-specific structural properties, resulting in poor generalization to new imaging centers and devices. To address this, we introduce Frequen…

Hurwitz--Lerch Type Families of Poly-Bernoulli and Poly-Cauchy Numbers and Polynomials with Parameters $(\alpha,a)$
Noel B. Lacpao, Roberto B. Corcino
https://arxiv.org/abs/2508.06766

Hurwitz--Lerch Type Families of Poly-Bernoulli and Poly-Cauchy Numbers and Polynomials with Parameters $(α,a)$
This study introduces $(α,a)$-parameterized Hurwitz-Lerch type poly-Bernoulli and poly-Cauchy numbers and polynomials, extending classical sequences through the Hurwitz-Lerch zeta function. We derive generating functions, recurrences, and explicit formulas, revealing deeper structures and connections with zeta functions and polylogarithms. The results enrich existing theory and open new directions in analytic number theory and combinatorics.

COMponent-Aware Pruning for Accelerated Control Tasks in Latent Space Models
Ganesh Sundaram, Jonas Ulmen, Amjad Haider, Daniel G\"orges
https://arxiv.org/abs/2508.08144 ht…

COMponent-Aware Pruning for Accelerated Control Tasks in Latent Space Models
The rapid growth of resource-constrained mobile platforms, including mobile robots, wearable systems, and Internet-of-Things devices, has increased the demand for computationally efficient neural network controllers (NNCs) that can operate within strict hardware limitations. While deep neural networks (DNNs) demonstrate superior performance in control applications, their substantial computational complexity and memory requirements present significant barriers to practical deployment on edge dev…

Deterministic Integration of CsPbBr3 Quantum Dots with Plasmonic Ring Microcavities
Pengji Li, Chenxi Ma, Jingzhong Yang, Tom N. Rakow, Xian Zheng, Eddy P. Rugeramigabo, Franziska Krieg, Gabriele Raino, Maksym V. Kovalenko, Michael Zopf, Fei Ding
https://arxiv.org/abs/2508.07965

Deterministic Integration of CsPbBr3 Quantum Dots with Plasmonic Ring Microcavities
Perovskite quantum dots hold great promise for quantum information processing as wavelength-tunable single photon sources operable over a broad temperature range. However, their deterministic integration into nanophotonic structures remains a major challenge, limited by their random spatial distribution and non-directional emission. In this work, we employ a two-step electron beam lithography process to deterministically place CsPbBr3 quantum dots within the mode volume of plasmonic ring microc…

Compressibility Barriers to Neighborhood-Preserving Data Visualizations
Szymon Snoeck, Noah Bergam, Nakul Verma
https://arxiv.org/abs/2508.07119 https://ar…

Compressibility Barriers to Neighborhood-Preserving Data Visualizations
To what extent is it possible to visualize high-dimensional datasets in a two- or three-dimensional space? We reframe this question in terms of embedding $n$-vertex graphs (representing the neighborhood structure of the input points) into metric spaces of low doubling dimension $d$, in such a way that maintains the separation between neighbors and non-neighbors. This seemingly lax embedding requirement is surprisingly difficult to satisfy. Our investigation shows that an overwhelming fraction o…

This https://arxiv.org/abs/2506.02878 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective
Chain-of-Thought (CoT) prompting has demonstrably enhanced the performance of Large Language Models on tasks requiring multi-step inference. This success has led to widespread claims of emergent reasoning capabilities in these models. In this paper, we present a theoretical counter-perspective: Chain-of-Thought (CoT) does not elicit genuine, abstract reasoning. Instead, we argue that Chain-of-Thought functions as a powerful structural constraint that guides Large Language Models to imitate the …

Correlations and quantum circuits with dynamical causal order
Rapha\"el Mothe, Alastair A. Abbott, Cyril Branciard
https://arxiv.org/abs/2507.07992 ht…

Correlations and quantum circuits with dynamical causal order
Requiring that the causal structure between different parties is well-defined imposes constraints on the correlations they can establish, which define so-called causal correlations. Some of these are known to have a "dynamical" causal order in the sense that their causal structure is not fixed a priori but is instead established on the fly, with for instance the causal order between future parties depending on some choice of action of parties in the past. Here we identify a new way that the cau…