Tootfinder

Safe cross-entropy-based importance sampling for rare event simulations
Zhiwei Gao, George Karniadakis
https://arxiv.org/abs/2509.07160 https://arxiv.org/p…

Safe cross-entropy-based importance sampling for rare event simulations
The Improved Cross-Entropy (ICE) method is a powerful tool for estimating failure probabilities in reliability analysis. Its core idea is to approximate the optimal importance-sampling density by minimizing the forward Kullback-Leibler divergence within a chosen parametric family-typically a mixture model. However, conventional mixtures are often light-tailed, which leads to slow convergence and instability when targeting very small failure probabilities. Moreover, selecting the number of mixtu…

A cross-diffusion system with independent drifts and fast diffusion
Charles Elbar, Filippo Santambrogio
https://arxiv.org/abs/2510.07937 https://arxiv.org/…

A cross-diffusion system with independent drifts and fast diffusion
We study a one-dimensional cross-diffusion system for two interacting populations on the torus, with a fast-diffusion law with exponent $0< α\le 1$ and different external potentials. For arbitrary non-negative $L^{1}$ initial data with bounded entropy and a mixing condition we prove the existence of global weak solutions. This extends the recent result of Mészáros, Parker from the linear diffusion ($α=1$) to the fast-diffusion.

Entropy2Vec: Crosslingual Language Modeling Entropy as End-to-End Learnable Language Representations
Patrick Amadeus Irawan, Ryandito Diandaru, Belati Jagad Bintang Syuhada, Randy Zakya Suchrady, Alham Fikri Aji, Genta Indra Winata, Fajri Koto, Samuel Cahyawijaya
https://arxiv.org/abs/2509.05060

Entropy2Vec: Crosslingual Language Modeling Entropy as End-to-End Learnable Language Representations
We introduce Entropy2Vec, a novel framework for deriving cross-lingual language representations by leveraging the entropy of monolingual language models. Unlike traditional typological inventories that suffer from feature sparsity and static snapshots, Entropy2Vec uses the inherent uncertainty in language models to capture typological relationships between languages. By training a language model on a single language, we hypothesize that the entropy of its predictions reflects its structural sim…

Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
Markus Walker, Daniel Frisch, Uwe D. Hanebeck
https://arxiv.org/abs/2510.05706

Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
Cross-entropy method model predictive control (CEM--MPC) is a powerful gradient-free technique for nonlinear optimal control, but its performance is often limited by the reliance on random sampling. This conventional approach can lead to inefficient exploration of the solution space and non-smooth control inputs, requiring a large number of samples to achieve satisfactory results. To address these limitations, we propose deterministic sampling CEM (dsCEM), a novel framework that replaces the ra…

CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models
Yu Zhang, Shuliang Liu, Xu Yang, Xuming Hu
https://arxiv.org/abs/2510.02342

CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models
Watermarking algorithms for Large Language Models (LLMs) effectively identify machine-generated content by embedding and detecting hidden statistical features in text. However, such embedding leads to a decline in text quality, especially in low-entropy scenarios where performance needs improvement. Existing methods that rely on entropy thresholds often require significant computational resources for tuning and demonstrate poor adaptability to unknown or cross-task generation scenarios. We prop…

Constraint Satisfaction Approaches to Wordle: Novel Heuristics and Cross-Lexicon Validation
Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Kamrujjaman, Eftakhar Ahmed Arnob, Ahsan Habib Tareq
https://arxiv.org/abs/2510.02855

Constraint Satisfaction Approaches to Wordle: Novel Heuristics and Cross-Lexicon Validation
Wordle presents an algorithmically rich testbed for constraint satisfaction problem (CSP) solving. While existing solvers rely on information-theoretic entropy maximization or frequency-based heuristics without formal constraint treatment, we present the first comprehensive CSP formulation of Wordle with novel constraint-aware solving strategies. We introduce CSP-Aware Entropy, computing information gain after constraint propagation rather than on raw candidate sets, and a Probabilistic CSP fra…

A note on entropy of matter in presence of gravity: status of extensivity of entropy
Saurav Samanta, Bibhas Ranjan Majhi
https://arxiv.org/abs/2507.14817 h…

A note on entropy of matter in presence of gravity: status of extensivity of entropy
Entropy of matter in a very strong gravity depends on cross-sectional area of the container of the system -- is being further bolstered by calculating entropy of a monoatomic gas kept under uniform strong gravity at Newtonian scale. This bypasses the earlier analysis where existence of horizon is crucial. Also the extensivity of this entropy has been discussed in the light of the same of a two-space dimensional ultra-relativistic non-interacting gas without gravity. The whole analysis, as far a…

A Hybrid Approach for Unified Image Quality Assessment: Permutation Entropy-Based Features Fused with Random Forest for Natural-Scene and Screen-Content Images for Cross-Content Applications
Mohtashim Baqar, Sian Lun Lau, Mansoor Ebrahim
https://arxiv.org/abs/2508.17351

A Hybrid Approach for Unified Image Quality Assessment: Permutation Entropy-Based Features Fused with Random Forest for Natural-Scene and Screen-Content Images for Cross-Content Applications
Image Quality Assessment (IQA) plays a vital role in applications such as image compression, restoration, and multimedia streaming. However, existing metrics often struggle to generalize across diverse image types - particularly between natural-scene images (NSIs) and screen-content images (SCIs) - due to their differing structural and perceptual characteristics. To address this limitation, we propose a novel full-reference IQA framework: Permutation Entropy-based Features Fused with Random For…

Crosslisted article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Safe cross-entropy-based importance sampling for rare event simulations
Zhiwei Gao, George Karniadakis

Magnetoelectric coupling and its impact on the multicaloric effect
Kentaro Ino, Keisuke Matsuura, Tetsuya Nomoto, Takashi Kurumaji, Yoshinori Tokura, Fumitaka Kagawa
https://arxiv.org/abs/2507.19126

Magnetoelectric coupling and its impact on the multicaloric effect
The multicaloric effect, which represents the reversible entropy change that occurs when both external magnetic and electric fields are applied, is an interesting phenomenon characteristic to multiferroics. Targeting the multicaloric effect in the typical multiferroic (Fe$_{0.95}$Zn$_{0.05}$)$_2$Mo$_3$O$_8$, we experimentally evaluate the isothermal entropy change due to a magnetoelectric cross-correlation. A pronounced cross-correlation-derived isothermal entropy change is found at the magneti…

Quantum Probabilistic Label Refining: Enhancing Label Quality for Robust Image Classification
Fang Qi, Lu Peng, Zhengming Ding
https://arxiv.org/abs/2510.00528 https://

Quantum Probabilistic Label Refining: Enhancing Label Quality for Robust Image Classification
Learning with softmax cross-entropy on one-hot labels often leads to overconfident predictions and poor robustness under noise or perturbations. Label smoothing mitigates this by redistributing some confidence uniformly, but treats all samples equally, ignoring intra-class variability. We propose a hybrid quantum-classical framework that leverages quantum non-determinism to refine data labels into probabilistic ones, offering more nuanced, human-like uncertainty representations than label smoot…

Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
Yunrui Yu, Hang Su, Cheng-zhong Xu, Zhizhong Su, Jun Zhu
https://arxiv.org/abs/2507.22428

Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
Gradient-based adversarial attacks using the Cross-Entropy (CE) loss often suffer from overestimation due to relative errors in gradient computation induced by floating-point arithmetic. This paper provides a rigorous theoretical analysis of these errors, conducting the first comprehensive study of floating-point computation errors in gradient-based attacks across four distinct scenarios: (i) unsuccessful untargeted attacks, (ii) successful untargeted attacks, (iii) unsuccessful targeted attack…

Optimal smoothing parameter in Eilers-Wittaker smoother
Roberto Bernal-Arencibia, Karel Garcia Medina, Ernesto Estevez-Rams, Beatriz Aragon-Fernandez
https://arxiv.org/abs/2510.01798

Optimal smoothing parameter in Eilers-Wittaker smoother
The Eilers-Whittaker method for data smoothing effectiveness depends on the choice of the regularisation parameter, and automatic selection is a necessity for large datasets. Common methods, such as leave-one-out cross-validation, can perform poorly when serially correlated noise is present. We propose a novel procedure for selecting the control parameter based on the spectral entropy of the residuals. We define an S-curve from the Euclidean distance between points in a plot of the spectral ent…

Aspects of holographic entanglement using physics-informed-neural-networks
Anirudh Deb, Yaman Sanghavi
https://arxiv.org/abs/2509.25311 https://arxiv.org/p…

Aspects of holographic entanglement using physics-informed-neural-networks
We implement physics-informed-neural-networks (PINNs) to compute holographic entanglement entropy and entanglement wedge cross section. This technique allows us to compute these quantities for arbitrary shapes of the subregions in any asymptotically AdS metric. We test our computations against some known results and further demonstrate the utility of PINNs in examples, where it is not straightforward to perform such computations.

Weak-strong uniqueness for general cross-diffusion systems with volume filling
Maria Heitzinger, Ansgar J\"ungel
https://arxiv.org/abs/2509.25978 https://

Weak-strong uniqueness for general cross-diffusion systems with volume filling
The weak-strong uniqueness of solutions to a broad class of cross-diffusion systems with volume filling is established. In general, the diffusion matrices are neither symmetric nor positive definite. This issue is overcome by supposing that the equations possess a Boltzmann-type entropy structure, which ensures the existence of bounded weak solutions. In this framework, general conditions on the mobility matrix are identified that allow for the proof of the weak-strong uniqueness property by me…

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Yavuz Bakman, Sungmin Kang, Zhiqi Huang, Duygu Nur Yaldiz, Catarina G. Bel\'em, Chenyang Zhu, Anoop Kumar, Alfy Samuel, Salman Avestimehr, Daben Liu, Sai Praneeth Karimireddy
https://arxiv.org/abs/2510.02671

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Uncertainty Quantification (UQ) research has primarily focused on closed-book factual question answering (QA), while contextual QA remains unexplored, despite its importance in real-world applications. In this work, we focus on UQ for the contextual QA task and propose a theoretically grounded approach to quantify epistemic uncertainty. We begin by introducing a task-agnostic, token-level uncertainty measure defined as the cross-entropy between the predictive distribution of the given model and…

A Multimodal Stochastic Planning Approach for Navigation and Multi-Robot Coordination
Mark Gonzales, Ethan Oh, Joseph Moore
https://arxiv.org/abs/2509.19168 https://

A Multimodal Stochastic Planning Approach for Navigation and Multi-Robot Coordination
In this paper, we present a receding-horizon, sampling-based planner capable of reasoning over multimodal policy distributions. By using the cross-entropy method to optimize a multimodal policy under a common cost function, our approach increases robustness against local minima and promotes effective exploration of the solution space. We show that our approach naturally extends to multi-robot collision-free planning, enables agents to share diverse candidate policies to avoid deadlocks, and all…

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees
Thomas Y. Chen
https://arxiv.org/abs/2509.11054 https://

Rate-Distortion Limits for Multimodal Retrieval: Theory, Optimal Codes, and Finite-Sample Guarantees
We establish the first information-theoretic limits for multimodal retrieval. Casting ranking as lossy source coding, we derive a single-letter rate-distortion function $R(D)$ for reciprocal-rank distortion and prove a converse bound that splits into a modality-balanced term plus a skew penalty $κ\,ΔH$ capturing entropy imbalance and cross-modal redundancy. We then construct an explicit entropy-weighted stochastic quantizer with an adaptive, per-modality temperature decoder; a Blahut-Arimoto …

Learning Admissible Heuristics for A*: Theory and Practice
Ehsan Futuhi, Nathan R. Sturtevant
https://arxiv.org/abs/2509.22626 https://arxiv.org/pdf/2509.2…

Learning Admissible Heuristics for A*: Theory and Practice
Heuristic functions are central to the performance of search algorithms such as A-star, where admissibility - the property of never overestimating the true shortest-path cost - guarantees solution optimality. Recent deep learning approaches often disregard admissibility and provide limited guarantees on generalization beyond the training data. This paper addresses both of these limitations. First, we pose heuristic learning as a constrained optimization problem and introduce Cross-Entropy Admis…

Parameter-Free Logit Distillation via Sorting Mechanism
Stephen Ekaputra Limantoro
https://arxiv.org/abs/2508.16544 https://arxiv.org/pdf/2508.16544…

Parameter-Free Logit Distillation via Sorting Mechanism
Knowledge distillation (KD) aims to distill the knowledge from the teacher (larger) to the student (smaller) model via soft-label for the efficient neural network. In general, the performance of a model is determined by accuracy, which is measured with labels. However, existing KD approaches usually use the teacher with its original distribution, neglecting the potential of incorrect prediction. This may contradict the motivation of hard-label learning through cross-entropy loss, which may lead…

Predictable Compression Failures: Why Language Models Actually Hallucinate
Leon Chlon, Ahmed Karim, Maggie Chlon
https://arxiv.org/abs/2509.11208 https://a…

Predictable Compression Failures: Why Language Models Actually Hallucinate
Large language models perform near-Bayesian inference yet violate permutation invariance on exchangeable data. We resolve this by showing transformers minimize expected conditional description length (cross-entropy) over orderings, $\mathbb{E}_π[\ell(Y \mid Γ_π(X))]$, which admits a Kolmogorov-complexity interpretation up to additive constants, rather than the permutation-invariant description length $\ell(Y \mid X)$. This makes them Bayesian in expectation, not in realization. We derive (i)…

Coarse-Grained BCFT Tensor Networks and Holographic Reflected Entropy in 3D Gravity
Ning Bao, Jinwei Chu, Yikun Jiang, Jacob March
https://arxiv.org/abs/2509.10170 https://

Coarse-Grained BCFT Tensor Networks and Holographic Reflected Entropy in 3D Gravity
We use the framework of $\textit{BCFT tensor networks}$ to present a microscopic CFT derivation of the correspondence between reflected entropy (RE) and entanglement wedge cross section (EW) in AdS$_3$/CFT$_2$, for both bipartite and multipartite settings. These fixed-point tensor networks, obtained by triangulating Euclidean CFT path integrals, allow us to explicitly construct the canonical purification via cutting-and-gluing CFT path integrals. Employing modular flow in the large-$c$ limit, w…

Parsec-scale jet precession and a putative supermassive binary black hole system in the blazar AO 0235 164
Flavio Benevenuto da Silva Junior, Anderson Caproni
https://arxiv.org/abs/2508.14178

Parsec-scale jet precession and a putative supermassive binary black hole system in the blazar AO 0235+164
The blazar AO 0235+164 is a key source for studying the interplay between multi-wavelength variability in its light curves and changes in the position angles and apparent velocities of its parsec-scale jet components. In this work, we analyse public interferometric radio maps of AO 0235+164 at 15 and 43 GHz, using the Cross Entropy global optimisation technique to determine the structural parameters of its jet components. We identified 36 kinematically distinct jet components across all sky qua…

Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
Maxim Zhelnin, Dmitry Redko, Volkov Daniil, Anna Volodkevich, Petr Sokerin, Valeriy Shevchenko, Egor Shvetsov, Alexey Vasilev, Darya Denisova, Ruslan Izmailov, Alexey Zaytsev
https://arxiv.org/abs/2509.09682…

Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
Sequential recommendations (SR) with transformer-based architectures are widely adopted in real-world applications, where SR models require frequent retraining to adapt to ever-changing user preferences. However, training transformer-based SR models often encounters a high computational cost associated with scoring extensive item catalogs, often exceeding thousands of items. This occurs mainly due to the use of cross-entropy loss, where peak memory scales proportionally to catalog size, batch s…

Measuring irreversibility by counting: a random coarse-graining framework
Ruicheng Bao, Naruo Ohga, Sosuke Ito
https://arxiv.org/abs/2508.11586 https://arx…

Measuring irreversibility by counting: a random coarse-graining framework
Thermodynamic irreversibility is a fundamental concept in statistical physics, yet its experimental measurement remains challenging, especially for complex systems. We introduce a novel random coarse-graining framework to identify model-free measures of irreversibility in complex many-body systems. These measures are constructed from the asymmetry of cross-correlation functions between suitably chosen observables, providing rigorous lower bounds on entropy production. For many-particle systems,…

Universality in the Anticoncentration of Noisy Quantum Circuits at Finite Depths
Arman Sauliere, Guglielmo Lami, Corentin Boyer, Jacopo De Nardis, Andrea De Luca
https://arxiv.org/abs/2508.14975

Universality in the Anticoncentration of Noisy Quantum Circuits at Finite Depths
We study the anticoncentration properties of quantum circuits in the presence of different sources of noise. In the \textit{weak-noise regime}, we show that different types of noise act in a similar fashion, leading to a universal distribution for the probability of a given bit-string, largely independent of the specific noise channel or circuit architecture. In addition, we can identify three distinct depth-dependent regimes, each signaled by a different scaling of cross-entropy benchmarking (…

Cross-diffusion limits in multispecies kinetic models
Ansgar J\"ungel, Annamaria Pollino, Satoshi Taguchi
https://arxiv.org/abs/2509.14046 https://arx…

Cross-diffusion limits in multispecies kinetic models
Cross-diffusion systems are formally derived from multispecies kinetic models in the diffusion limit. The first limit in the multispecies BGK model of Gross and Krook leads to a variant of the non-isothermal Maxwell-Stefan equations. The second limit in a BGK model with Brinkman-type force term yields generalized Busenberg-Travis equations, which reduce for constant temperature to the classical Busenberg-Travis system for segregating population species. Entropy equalities are derived for the ki…

Multipartite Markov Gaps and Entanglement Wedge Multiway Cuts
Norihiro Iizuka, Akihiro Miyata, Mitsuhiro Nishida
https://arxiv.org/abs/2507.15262 https://

Multipartite Markov Gaps and Entanglement Wedge Multiway Cuts
The Markov gap, defined as the difference between reflected entropy and mutual information, serves as a diagnostic for quantum recoverability and multipartite entanglement. In holographic settings, it admits a geometric interpretation as the deviation between entanglement wedge cross-sections and RT surfaces. Motivated by this holographic perspective, we propose a generalization of the Markov gap to multipartite systems by using a reflected multi-entropy. The resulting Multipartite Markov gap c…

Control of Legged Robots using Model Predictive Optimized Path Integral
Hossein Keshavarz, Alejandro Ramirez-Serrano, Majid Khadiv
https://arxiv.org/abs/2508.11917 https://

Control of Legged Robots using Model Predictive Optimized Path Integral
Legged robots possess a unique ability to traverse rough terrains and navigate cluttered environments, making them well-suited for complex, real-world unstructured scenarios. However, such robots have not yet achieved the same level as seen in natural systems. Recently, sampling-based predictive controllers have demonstrated particularly promising results. This paper investigates a sampling-based model predictive strategy combining model predictive path integral (MPPI) with cross-entropy (CE) a…

Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
Marko Tuononen, Heikki Penttinen, Ville Hautam\"aki
https://arxiv.org/abs/2509.15950 https://

Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
We present the first use of influence functions for deep learning-based wireless receivers. Applied to DeepRx, a fully convolutional receiver, influence analysis reveals which training samples drive bit predictions, enabling targeted fine-tuning of poorly performing cases. We show that loss-relative influence with capacity-like binary cross-entropy loss and first-order updates on beneficial samples most consistently improves bit error rate toward genie-aided performance, outperforming random fi…