Tootfinder

With moving my calendar away from Google I was looking for a Caldev solution, but ...
Interesting take on CalDev and encryption on Cryptpad's github page.
No chance that that will happen encrypted.
It's waiting for an solution in the Caldev protocol. 😇
[Feature request] Possibility to have CalDAV for the Calendar · Issue #823 · cryptpad/cryptpad

edit_wiktionary: Wiktionary edits (2010)
Three bipartite user-page networks extracted from Wiktionary, for French, German, and English. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 28802 nodes and 66112 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

SAFE: Multitask Failure Detection for Vision-Language-Action Models
Qiao Gu, Yuanliang Ju, Shengxiang Sun, Igor Gilitschenski, Haruki Nishimura, Masha Itkina, Florian Shkurti
https://arxiv.org/abs/2506.09937

SAFE: Multitask Failure Detection for Vision-Language-Action Models
While vision-language-action models (VLAs) have shown promising robotic behaviors across a diverse set of manipulation tasks, they achieve limited success rates when deployed on novel tasks out-of-the-box. To allow these policies to safely interact with their environments, we need a failure detector that gives a timely alert such that the robot can stop, backtrack, or ask for help. However, existing failure detectors are trained and tested only on one or a few specific tasks, while VLAs require…

What Causes Errors in Wang-Sheeley-Arge Solar Wind Modeling at L1 ?
Satabdwa Majumdar, Martin Reiss, Karin Muglach, Charles N. Arge
https://arxiv.org/abs/2506.09676

What Causes Errors in Wang-Sheeley-Arge Solar Wind Modeling at L1 ?
Previous ambient solar wind (SW) validation studies have reported on discrepancies between modeled and observed SW conditions at L1. They indicated that a major source of discrepancies stems from how we model the solar corona. Thus, enhancing predictive capabilities demands a thorough examination of coronal modeling. The Wang-Sheeley-Arge (WSA) model has been a workhorse model that provides the near-Sun SW conditions. An important component of it is the Potential Field Source Surface (PFSS) mod…

Magnetic Burial in Millisecond Magnetars and Late GRB Afterglow Signatures
Nissim Fraija, Cristian Giovanni Bernal, Antonio Galv\'an, Boris Betancourt-Kamenetskaia, Maria Giovanna Dainotti
https://arxiv.org/abs/2506.09402

Magnetic Burial in Millisecond Magnetars and Late GRB Afterglow Signatures
Millisecond magnetars, one of the potential candidates for the central engine of Gamma-ray bursts (GRBs), can experience significant magnetic field enhancement shortly after their formation. In some cases, this evolution is further influenced by the accretion of stellar debris, which modifies the dipole magnetic field strength. During a hypercritical accretion phase that lasts seconds or longer after the progenitor explosion, a thin crust may form, submerging the magnetic field (the so-called m…

[2025-06-12 Thu (UTC), no new articles found for math.LO Logic]
toXiv_bot_toot

Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform
Jay Roberts, Kyle Mylonakis, Sidhartha Roy, Kaan Kale
https://arxiv.org/abs/2506.09452

Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform
The high cost of ownership of AI compute infrastructure and challenges of robust serving of large language models (LLMs) has led to a surge in managed Model-as-a-service deployments. Even when enterprises choose on-premises deployments, the compute infrastructure is typically shared across many teams in order to maximize the return on investment. In both scenarios the deployed models operate only on plaintext data, and so enterprise data owners must allow their data to appear in plaintext on a …

Exploring Nonperturbative Behaviour of Moments and Cumulants in Quantum Theories
Sebastian Schenk
https://arxiv.org/abs/2506.10188 https://

Exploring Nonperturbative Behaviour of Moments and Cumulants in Quantum Theories
The dynamics of quantum fields become nonperturbative when their interactions are probed by a large number of particles. To explore this regime we study correlation functions which involve a large number of fields, focussing on massive scalar theories that feature arbitrary self-interactions, $ϕ^{2p}$. Treating quantum fields as operator-valued distributions, we investigate $n$-point correlation functions at ultra-short distances and compute moments and cumulants of fields, using a semiclassic…

MLLM-Based UI2Code Automation Guided by UI Layout Information
Fan Wu, Cuiyun Gao, Shuqing Li, Xin-Cheng Wen, Qing Liao
https://arxiv.org/abs/2506.10376 htt…

MLLM-Based UI2Code Automation Guided by UI Layout Information
Converting user interfaces into code (UI2Code) is a crucial step in website development, which is time-consuming and labor-intensive. The automation of UI2Code is essential to streamline this task, beneficial for improving the development efficiency. There exist deep learning-based methods for the task; however, they heavily rely on a large amount of labeled training data and struggle with generalizing to real-world, unseen web page designs. The advent of Multimodal Large Language Models (MLLMs…

The Daily Star drops its page three with pictures of scantily dressed women as part of a print and online redesign, with a new strapline "News with a wink" (Charlotte Tobitt/Press Gazette)
https://pressgazette.co.uk/publishers/

Daily Star drops page three pictures of scantily dressed women in redesign
The Daily Star has ditched page three models amid a wider redesign which includes a new masthead, new website/app platforms and new strapline

From Judgment to Interference: Early Stopping LLM Harmful Outputs via Streaming Content Monitoring
Yang Li, Qiang Sheng, Yehan Yang, Xueyao Zhang, Juan Cao
https://arxiv.org/abs/2506.09996

From Judgment to Interference: Early Stopping LLM Harmful Outputs via Streaming Content Monitoring
Though safety alignment has been applied to most large language models (LLMs), LLM service providers generally deploy a subsequent moderation as the external safety guardrail in real-world products. Existing moderators mainly practice a conventional full detection, which determines the harmfulness based on the complete LLM output, causing high service latency. Recent works pay more attention to partial detection where moderators oversee the generation midway and early stop the output if harmful…

Replaced article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new/
[1/1]:
AProVE: Modular Termination Analysis of Memory-Manipulating C Programs

Topological defects as effective dynamical dark energy
Haipeng An, Chengcheng Han, Borui Zhang
https://arxiv.org/abs/2506.10075 https://

Topological defects as effective dynamical dark energy
In this work, we consider the possibility that the dynamical dark energy hinted at by recent DESI data may be mimicked by the effects of additional components in the universe, potentially arising from topological defects. We find that the data does not show a particular preference for the existence of cosmic strings. However, a domain wall contribution at the percent level can improve the fit, yielding a $Δχ^2= -1.72$ compared to the $Λ\rm{CDM}$ model. The improvement indicates that topologi…

Replaced article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new/
[1/1]:
CompilerDream: Learning a Compiler World Model for General Code Optimization

An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation
Rajan Das Gupta, Md Imrul Hasan Showmick, Mushfiqur Rahman Abir, Shanjida Akter, Md. Yeasin Rahat, Md. Jakir Hossen
https://arxiv.org/abs/2506.09161

An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation
Early and accurate detection of brain abnormalities, such as tumors and strokes, is essential for timely intervention and improved patient outcomes. In this study, we present a deep learning-based system capable of identifying both brain tumors and strokes from MRI images, along with their respective stages. We have executed two groundbreaking strategies involving convolutional neural networks, MobileNet V2 and ResNet-50-optimized through transfer learning to classify MRI scans into five diagno…

Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
Maja Franz, Melvin Strobl, Leonid Chaichenets, Eileen Kuehn, Achim Streit, Wolfgang Mauerer
https://arxiv.org/abs/2506.09527

Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
The field of variational quantum algorithms, in particular quantum machine learning (QML), produced numerous theoretical and empirical insights in recent years. As variational quantum circuits (VQCs) can be represented by Fourier series that contain an exponentially large spectrum in the number of input features, hope for quantum advantage remains. Nevertheless, properties of quantum Fourier models (QFMs) are not yet fully understood, in particular how they could potentially outperform classica…

A Deep Generative Model for the Simulation of Discrete Karst Networks
Dany Lauzon, Julien Straubhaar, Philippe Renard
https://arxiv.org/abs/2506.09832 http…

A Deep Generative Model for the Simulation of Discrete Karst Networks
The simulation of discrete karst networks presents a significant challenge due to the complexity of the physicochemical processes occurring within various geological and hydrogeological contexts over extended periods. This complex interplay leads to a wide variety of karst network patterns, each intricately linked to specific hydrogeological conditions. We explore a novel approach that represents karst networks as graphs and applies graph generative models (deep learning techniques) to capture …

Safe Screening Rules for Group SLOPE
Runxue Bao, Quanchao Lu, Yanfu Zhang
https://arxiv.org/abs/2506.09451 https://arxiv.org/pdf/2506…

Safe Screening Rules for Group SLOPE
Variable selection is a challenging problem in high-dimensional sparse learning, especially when group structures exist. Group SLOPE performs well for the adaptive selection of groups of predictors. However, the block non-separable group effects in Group SLOPE make existing methods either invalid or inefficient. Consequently, Group SLOPE tends to incur significant computational costs and memory usage in practical high-dimensional scenarios. To overcome this issue, we introduce a safe screening …

Large Language Models for Toxic Language Detection in Low-Resource Balkan Languages
Amel Muminovic, Amela Kadric Muminovic
https://arxiv.org/abs/2506.09992

Large Language Models for Toxic Language Detection in Low-Resource Balkan Languages
Online toxic language causes real harm, especially in regions with limited moderation tools. In this study, we evaluate how large language models handle toxic comments in Serbian, Croatian, and Bosnian, languages with limited labeled data. We built and manually labeled a dataset of 4,500 YouTube and TikTok comments drawn from videos across diverse categories, including music, politics, sports, modeling, influencer content, discussions of sexism, and general topics. Four models (GPT-3.5 Turbo, G…

Noise in Maps of the Sun at Radio Wavelengths II: Solar Use Cases
Timothy Bastian, Bin Chen, Surajit Mondal, Pascal Saint-Hilaire
https://arxiv.org/abs/2506.09843

Noise in Maps of the Sun at Radio Wavelengths II: Solar Use Cases
Noise in images of strong celestial sources at radio wavelengths using Fourier synthesis arrays can be dominated by the source itself, so-called self-noise. We outlined the theory of self-noise for strong sources in a companion paper. Here we consider the case of noise in maps of radio emission from the Sun which, as we show, is always dominated by self noise. We consider several classes of science use cases for current and planned arrays designed to observe the Sun in order to understand limit…

Choreographic Quick Changes: First-Class Location (Set) Polymorphism
Ashley Samuelson, Andrew K. Hirsch, Ethan Cecchetti
https://arxiv.org/abs/2506.10913 h…

Choreographic Quick Changes: First-Class Location (Set) Polymorphism
Choreographic programming is a promising new paradigm for programming concurrent systems where a developer writes a single centralized program that compiles to individual programs for each node. Existing choreographic languages, however, lack critical features integral to modern systems, like the ability of one node to dynamically compute who should perform a computation and send that decision to others. This work addresses this gap with $λ_{QC}$, the first typed choreographic language with \e…

Spatial and Pulse Efficiency Constraints in Atom Interferometric Gravitational Wave Detectors
Patrik Schach, Enno Giese
https://arxiv.org/abs/2506.09511 ht…

Spatial and Pulse Efficiency Constraints in Atom Interferometric Gravitational Wave Detectors
Currently planned and constructed terrestrial detectors for gravitational waves and dark matter based on differential light-pulse atom interferometry are designed around three primary strategies to enhance their sensitivity: (i) Resonant-mode enhancement using multiple diamonds, (ii) large-momentum-transfer techniques to increase arm separation within the interferometer, and (iii) very-long baseline schemes that increase the distance between the two interferometers. Both resonant-mode enhanceme…

eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures
Venkatesh Pattabiraman, Zizhou Huang, Daniele Panozzo, Denis Zorin, Lerrel Pinto, Raunaq Bhirangi
https://arxiv.org/abs/2506.09994

eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures
If human experience is any guide, operating effectively in unstructured environments -- like homes and offices -- requires robots to sense the forces during physical interaction. Yet, the lack of a versatile, accessible, and easily customizable tactile sensor has led to fragmented, sensor-specific solutions in robotic manipulation -- and in many cases, to force-unaware, sensorless approaches. With eFlesh, we bridge this gap by introducing a magnetic tactile sensor that is low-cost, easy to fabr…

Searching for sub-TeV IceCube neutrinos correlated to sub-threshold GW events
Tista Mukherjee (for the IceCube collaboration)
https://arxiv.org/abs/2506.09694

Searching for sub-TeV IceCube neutrinos correlated to sub-threshold GW events
The IceCube Neutrino Observatory is active in multi-messenger follow-ups of Gravitational Wave (GW) events. Since the release of the Gravitational Wave Transient Catalogue (GWTC)-2.1 by the LIGO-Virgo-KAGRA (LVK) collaboration, sub-threshold GW candidates have been made publicly available. However, they were not followed up in real-time to search for neutrino counterparts. For a deeper understanding of these sub-threshold candidates, archival searches are essential. Finding evidence for a neutr…

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds
Vahan Arsenyan, Elen Vardanyan, Arnak Dalalyan
https://arxiv.org/abs/2506.09681

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds
Generative modeling aims to produce new random examples from an unknown target distribution, given access to a finite collection of examples. Among the leading approaches, denoising diffusion probabilistic models (DDPMs) construct such examples by mapping a Brownian motion via a diffusion process driven by an estimated score function. In this work, we first provide empirical evidence that DDPMs are robust to constant-variance noise in the score evaluations. We then establish finite-sample guara…

variability.dev: Towards an Online Toolbox for Feature Modeling
Tobias He{\ss}, Lukas Ostheimer, Tobias Betz, Simon Karrer, Tim Jannik Schmidt, Pierre Coquet, Sean Semmler, Thomas Th\"um
https://arxiv.org/abs/2506.09845

variability.dev: Towards an Online Toolbox for Feature Modeling
The emergence of feature models as the default to model the variability in configurable systems fosters a rich diversity in applications, application domains, and perspectives. Independent of their domain, modelers require to open, view, edit, transform, save, and configure models as well as to collaborate with others. However, at the time of writing, the top five results when googling ``Online Editor Feature Model'' point to editors that either have minimal functionality, are unmaintained or d…

Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
Haoxiang Ye, Tao Sun, Qing Ling
https://arxiv.org/abs/2506.09438

Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
Decentralized learning, which facilitates joint model training across geographically scattered agents, has gained significant attention in the field of signal and information processing in recent years. While the optimization errors of decentralized learning algorithms have been extensively studied, their generalization errors remain relatively under-explored. As the generalization errors reflect the scalability of trained models on unseen data and are crucial in determining the performance of …

Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs
Hiroshi Matsuda, Chunpeng Ma, Masayuki Asahara
https://arxiv.org/abs/2506.09983

Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs
Recent advances in large language models (LLMs) have enabled impressive performance in various tasks. However, standard prompting often struggles to produce structurally valid and accurate outputs, especially in dependency parsing. We propose a novel step-by-step instruction strategy, where universal part-of-speech tagging precedes the prediction of syntactic heads and dependency labels, and a simplified CoNLL-U like output format, our method achieves state-of-the-art accuracy on Universal Depe…

Noise in Maps of the Sun at Radio Wavelengths I: Theoretical Considerations
Timothy Bastian, Bin Chen, Surajit Mondal, Pascal Saint-Hilaire
https://arxiv.org/abs/2506.09838

Noise in Maps of the Sun at Radio Wavelengths I: Theoretical Considerations
The Sun is a powerful source of radio emissions, so much so that, unlike most celestial sources, this emission can dominate the system noise of radio telescopes. We outline the theory of noise in maps formed by Fourier synthesis techniques at radio wavelengths, with a focus on self-noise: that is, noise due to the source itself. As a means of developing intuition we consider noise for the case of a single dish, a two-element interferometer, and an n-element array for simple limiting cases. We t…

Hazel Deriver: A Live Editor for Constructing Rule-Based Derivations
Zhiyao Zhong, Cyrus Omar
https://arxiv.org/abs/2506.10781 https://

Hazel Deriver: A Live Editor for Constructing Rule-Based Derivations
Students in programming languages and formal logic courses often struggle with constructing rule-based derivation trees due to the complexity of applying inference rules, the lack of immediate feedback, and the manual effort required for handwritten proofs. We present Hazel Deriver, a live, web-based editor designed to scaffold derivation construction through multiple layers of support. Built on the Hazel live programming environment, it provides a structured, interactive experience that encour…

Quantum mixture-density network for multimodal probabilistic prediction
Jaemin Seo
https://arxiv.org/abs/2506.09497 https://arxiv.org…

Quantum mixture-density network for multimodal probabilistic prediction
Multimodal probability distributions are common in both quantum and classical systems, yet modeling them remains challenging when the number of modes is large or unknown. Classical methods such as mixture-density networks (MDNs) scale poorly, requiring parameter counts that grow quadratically with the number of modes. We introduce a Quantum Mixture-Density Network (Q-MDN) that employs parameterized quantum circuits to efficiently model multimodal distributions. By representing an exponential nu…

Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation
Wenbo Zhang, Tianrun Hu, Yanyuan Qiao, Hanbo Zhang, Yuchu Qin, Yang Li, Jiajun Liu, Tao Kong, Lingqiao Liu, Xiao Ma
https://arxiv.org/abs/2506.09990

Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation
We present Chain-of-Action (CoA), a novel visuo-motor policy paradigm built upon Trajectory Autoregressive Modeling. Unlike conventional approaches that predict next step action(s) forward, CoA generates an entire trajectory by explicit backward reasoning with task-specific goals through an action-level Chain-of-Thought (CoT) process. This process is unified within a single autoregressive structure: (1) the first token corresponds to a stable keyframe action that encodes the task-specific goals…

Average Power-Density Spectrum of short and long Fermi-GBM Gamma-Ray Bursts
Else Magnus, Jannes Loonen, Rose S. Stanley, Paul Coppin, Krijn D. de Vries, Nick van Eijndhoven
https://arxiv.org/abs/2506.09610

Average Power-Density Spectrum of short and long Fermi-GBM Gamma-Ray Bursts
Gamma-ray bursts (GRBs) are the most powerful electromagnetic outbursts in the Universe and emit a vast amount of their energy in the form of gamma rays. Their duration is extremely short on cosmic timescales, but they show a wealth of time variability in their light curves. Properties of this variability may carry information about the processes the gamma rays emerge from, which are still poorly understood. This research investigates the redshift-corrected gamma-ray light curves of 159 long GR…

Scaling Laws for Uncertainty in Deep Learning
Mattia Rosso, Simone Rossi, Giulio Franzese, Markus Heinonen, Maurizio Filippone
https://arxiv.org/abs/2506.09648

Scaling Laws for Uncertainty in Deep Learning
Deep learning has recently revealed the existence of scaling laws, demonstrating that model performance follows predictable trends based on dataset and model sizes. Inspired by these findings and fascinating phenomena emerging in the over-parameterized regime, we examine a parallel direction: do similar scaling laws govern predictive uncertainties in deep learning? In identifiable parametric models, such scaling laws can be derived in a straightforward manner by treating model parameters in a B…

AutoGEEval : A Multi-Level and Multi-Geospatial-Modality Automated Evaluation Framework for Large Language Models in Geospatial Code Generation on Google Earth Engine
Shuyang Hou, Zhangxiao Shen, Huayi Wu, Haoyue Jiao, Ziqi Liu, Lutong Xie, Chang Liu, Jianyuan Liang, Yaxian Qing, Xiaopu Zhang, Dehua Peng, Zhipeng Gui, Xuefeng Guan
https://

AutoGEEval++: A Multi-Level and Multi-Geospatial-Modality Automated Evaluation Framework for Large Language Models in Geospatial Code Generation on Google Earth Engine
Geospatial code generation is becoming a key frontier in integrating artificial intelligence with geo-scientific analysis, yet standardised automated evaluation tools for this task remain absent. This study presents AutoGEEval++, an enhanced framework building on AutoGEEval, and the first automated assessment system for large language models (LLMs) generating geospatial code on Google Earth Engine (GEE). It supports diverse data modalities and varying task complexities. Built on the GEE Python …

Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training
Shurui Gui, Shuiwang Ji
https://arxiv.org/abs/2506.09433 https://

Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training
While large language models (LLMs) have demonstrated remarkable capabilities in language modeling, recent studies reveal that they often fail on out-of-distribution (OOD) samples due to spurious correlations acquired during pre-training. Here, we aim to mitigate such spurious correlations through causality-aware post-training (CAPT). By decomposing a biased prediction into two unbiased steps, known as \textit{event estimation} and \textit{event intervention}, we reduce LLMs' pre-training biases…

When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text
Hillary Dawkins, Kathleen C. Fraser, Svetlana Kiritchenko
https://arxiv.org/abs/2506.09975

When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text
Detecting AI-generated text is a difficult problem to begin with; detecting AI-generated text on social media is made even more difficult due to the short text length and informal, idiosyncratic language of the internet. It is nonetheless important to tackle this problem, as social media represents a significant attack vector in online influence campaigns, which may be bolstered through the use of mass-produced AI-generated posts supporting (or opposing) particular policies, decisions, or event…

ABYSS III: Observing accretion activity in young stars through empirical veiling measurements
Serat Saad, Marina Kounkel, Keivan G. Stassun, A. Roman-Lopes, Carlos G. Rom\'an-Z\'u\~niga, Jinyoung Serena Kim, Jonathan C. Tan, R. Lopez-Valdivia
https://arxiv.org/abs/2506.09826

ABYSS III: Observing accretion activity in young stars through empirical veiling measurements
Stellar accretion plays an important role in the early stages of stellar evolution, particularly in Classical T Tauri Stars (CTTSs). Accretion of a CTTS can be related to different physical parameters such as effective temperature (T$_{\text{eff}}$), age, abundance of hydrogen, etc. We can infer how accretion works by examining it across different wavelength regions. Accretion can be traced using veiling, a parameter that measures how excess emission from accretion affects the photospheric spec…

A Language-Agnostic Logical Relation for Message-Passing Protocols
Tesla Zhang, Sonya Simkin, Rui Li, Yue Yao, Stephanie Balzer
https://arxiv.org/abs/2506.10026

A Language-Agnostic Logical Relation for Message-Passing Protocols
Today's computing landscape has been gradually shifting to applications targeting distributed and *heterogeneous* systems, such as cloud computing and Internet of Things (IoT) applications. These applications are predominantly *concurrent*, employ *message-passing*, and interface with *foreign objects*, ranging from externally implemented code to actual physical devices such as sensors. Verifying that the resulting systems adhere to the intended protocol of interaction is challenging -- the usu…

Generation of frequency entanglement by rotating Doppler effect
Bolong Yi, Ling Chen, Baocheng Zhang
https://arxiv.org/abs/2506.09488 https://

Generation of frequency entanglement by rotating Doppler effect
We propose a method to generate the frequency entanglement, allowing a continuous generation of entangled two-photon states in a hybrid degree of freedom by post-manipulation. Our method is based on type-II spontaneous parametric down-conversion in a nonlinear crystal and the rotation Doppler effect by rotating the q-plates, without preset discrete frequency entanglement. This allows the arbitrary modification of frequency entangled photons in a wide frequency range at room temperature, offerin…

Locomotion on Constrained Footholds via Layered Architectures and Model Predictive Control
Zachary Olkin, Aaron D. Ames
https://arxiv.org/abs/2506.09979 ht…

Locomotion on Constrained Footholds via Layered Architectures and Model Predictive Control
Computing stabilizing and optimal control actions for legged locomotion in real time is difficult due to the nonlinear, hybrid, and high dimensional nature of these robots. The hybrid nature of the system introduces a combination of discrete and continuous variables which causes issues for numerical optimal control. To address these challenges, we propose a layered architecture that separates the choice of discrete variables and a smooth Model Predictive Controller (MPC). The layered formulatio…

The intermediate-mass black hole 2XMM J123103.2 110648: a varying disc accretion rate during possible X-ray quasi-periodic eruptions?
Z. Cao, P. G. Jonker, S. Wen, N. C. Stone, A. I. Zabludoff
https://arxiv.org/abs/2506.09603

The intermediate-mass black hole 2XMM J123103.2+110648: a varying disc accretion rate during possible X-ray quasi-periodic eruptions?
We fit the evolving X-ray spectra of the variable and fading source 2XMM J123103.2+110648 (J1231), which is an intermediate-mass black hole (IMBH) candidate. Recent X-ray timing studies have proposed that J1231's quasi-periodic oscillation (QPO) observed at the peak of its X-ray lightcurve is a variant of the quasi-periodic eruptions (QPEs) observed in other sources. Here, we fit X-ray spectra from XMM-Newton, Swift, and Chandra using a slim disc model for the black hole's accretion disc, obtai…

Evasion Attacks Against Bayesian Predictive Models
Pablo G. Arce, Roi Naveiro, David R\'ios Insua
https://arxiv.org/abs/2506.09640 https://

Evasion Attacks Against Bayesian Predictive Models
There is an increasing interest in analyzing the behavior of machine learning systems against adversarial attacks. However, most of the research in adversarial machine learning has focused on studying weaknesses against evasion or poisoning attacks to predictive models in classical setups, with the susceptibility of Bayesian predictive models to attacks remaining underexplored. This paper introduces a general methodology for designing optimal evasion attacks against such models. We investigate …

Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Junliang Xing, Yifan Zhang, Jian Cheng
https://arxiv.org/abs/2506.09404

Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Combinatorial optimization problems are notoriously challenging due to their discrete structure and exponentially large solution space. Recent advances in deep reinforcement learning (DRL) have enabled the learning heuristics directly from data. However, DRL methods often suffer from limited exploration and susceptibility to local optima. On the other hand, evolutionary algorithms such as Genetic Algorithms (GAs) exhibit strong global exploration capabilities but are typically sample inefficien…

Resa: Transparent Reasoning Models via SAEs
Shangshang Wang, Julian Asilis, \"Omer Faruk Akg\"ul, Enes Burak Bilgin, Ollie Liu, Deqing Fu, Willie Neiswanger
https://arxiv.org/abs/2506.09967

Resa: Transparent Reasoning Models via SAEs
How cost-effectively can we elicit strong reasoning in language models by leveraging their underlying representations? We answer this question with Resa, a family of 1.5B reasoning models trained via a novel and efficient sparse autoencoder tuning (SAE-Tuning) procedure. This method first trains an SAE to capture reasoning abilities from a source model, and then uses the trained SAE to guide a standard supervised fine-tuning process to elicit such abilities in a target model, all using verified…

A core-sensitive mixed $f$/$g$ mode of the Sun predicted by wave topology and hydrodynamical simulation
Arthur Le Saux, Armand Leclerc, Guillaume Laibe, Pierre Delplace, Antoine Venaille
https://arxiv.org/abs/2506.09572

A core-sensitive mixed $f$/$g$ mode of the Sun predicted by wave topology and hydrodynamical simulation
Helioseismology has revolutionized our understanding of the Sun by analyzing its global oscillation modes. However, the solar core remains elusive, limiting a full understanding of its evolution. In this work, we study a previously unnoticed global oscillation mode of the Sun using a fully compressible, hydrodynamical simulation of the solar interior, and assess that it is a mixed $f$/$g$ mode with a period of about one hour. This is the first global stellar hydrodynamics simulation that succes…

From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop
Jordi de la Torre
https://arxiv.org/abs/2506.10021 https://

From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop
We propose a novel architecture for integrating large language models (LLMs) with a persistent, interactive Lisp environment. This setup enables LLMs to define, invoke, and evolve their own tools through programmatic interaction with a live REPL. By embedding Lisp expressions within generation and intercepting them via a middleware layer, the system allows for stateful external memory, reflective programming, and dynamic tool creation. We present a design framework and architectural principles …

Covert Entanglement Generation over Bosonic Channels
Evan J. D. Anderson, Michael S. Bullock, Ohad Kimelfeld, Christopher K. Eyre, Filip Rozp\k{e}dek, Uzi Pereg, Boulat A. Bash
https://arxiv.org/abs/2506.09474

Covert Entanglement Generation over Bosonic Channels
We explore covert entanglement generation over the lossy thermal-noise bosonic channel, which is a quantum-mechanical model of many practical settings, including optical, microwave, and radio-frequency (RF) channels. Covert communication ensures that an adversary is unable to detect the presence of transmissions, which are concealed in channel noise. We show that a $\textit{square root law}$ (SRL) for covert entanglement generation similar to that for classical: $L_{\rm EG}\sqrt{n}$ entangled b…

Fluoroscopic Shape and Pose Tracking of Catheters with Custom Radiopaque Markers
Jared Lawson, Rohan Chitale, Nabil Simaan
https://arxiv.org/abs/2506.09934

Fluoroscopic Shape and Pose Tracking of Catheters with Custom Radiopaque Markers
Safe navigation of steerable and robotic catheters in the cerebral vasculature requires awareness of the catheters shape and pose. Currently, a significant perception burden is placed on interventionalists to mentally reconstruct and predict catheter motions from biplane fluoroscopy images. Efforts to track these catheters are limited to planar segmentation or bulky sensing instrumentation, which are incompatible with microcatheters used in neurointervention. In this work, a catheter is equippe…

Evidence for Dual Periodic Signatures in PKS 0805-07 from Multi-technique Time Series Analysis
Sikandar Akbar, Zahir Shah, Ranjeev Misra, Sajad Boked, Naseer Iqbal
https://arxiv.org/abs/2506.09602

Evidence for Dual Periodic Signatures in PKS 0805-07 from Multi-technique Time Series Analysis
We report the identification of two statistically significant quasi-periodic oscillations in the weekly binned $γ$-ray light curve of the flat-spectrum radio quasar PKS 0805$-$07, observed by Fermi-LAT over the period MJD 59047.5-59740.5. By applying a suite of complementary time-series analysis techniques, we identify periodic signatures at approximately 255 and 112 days. These techniques include the Lomb-Scargle periodogram (LSP), Weighted Wavelet Z-transform (WWZ), REDFIT, Date-Compensated …

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames
Haiyang Yu, Yuchao Lin, Xuan Zhang, Xiaofeng Qian, Shuiwang Ji
https://arxiv.org/abs/2506.09398

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames
We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials science. Motivated by the inherent relationship between the off-diagonal blocks of the Hamiltonian matrix and the SO(2) local frame, we propose a novel and efficient network, called QHNetV2, that achieves global SO(3) equivariance without the costly SO(3) Clebsch-Gordan tensor products. This is achieved by introducing a set o…

LLM-Powered CPI Prediction Inference with Online Text Time Series
Yingying Fan, Jinchi Lv, Ao Sun, Yurou Wang
https://arxiv.org/abs/2506.09516 https://

LLM-Powered CPI Prediction Inference with Online Text Time Series
Forecasting the Consumer Price Index (CPI) is an important yet challenging task in economics, where most existing approaches rely on low-frequency, survey-based data. With the recent advances of large language models (LLMs), there is growing potential to leverage high-frequency online text data for improved CPI prediction, an area still largely unexplored. This paper proposes LLM-CPI, an LLM-based approach for CPI prediction inference incorporating online text time series. We collect a large se…

Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking
Wuwei Zhang, Fangcong Yin, Howard Yen, Danqi Chen, Xi Ye
https://arxiv.org/abs/2506.09944

Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking
Recent work has identified retrieval heads (Wu et al., 2025b), a subset of attention heads responsible for retrieving salient information in long-context language models (LMs), as measured by their copy-paste behavior in Needle-in-a-Haystack tasks. In this paper, we introduce QRHEAD (Query-Focused Retrieval Head), an improved set of attention heads that enhance retrieval from long context. We identify QRHEAD by aggregating attention scores with respect to the input query, using a handful of exa…

Apparent motion of penumbral grains in a sunspot simulation
Michal Sobotka, Markus Schmassmann
https://arxiv.org/abs/2506.09504 https://

Apparent motion of penumbral grains in a sunspot simulation
Context. The bright heads of penumbral filaments, penumbral grains (PGs), are manifestations of hot plasma flows rising to the surface. They are observed to move horizontally toward the sunspot umbra or away from it. Recent analyses of observations indicate that the direction of this motion is related to the inclination of the surrounding magnetic field. Aims. The penumbra of a sunspot simulated by the radiative magnetohydrodynamic code MURaM is analysed to get typical physical conditions in PG…

[2025-06-13 Fri (UTC), 4 new articles found for cs.PL Programming Languages]
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Optimal quantum metrology under energy constraints
Longyun Chen, Yuxiang Yang
https://arxiv.org/abs/2506.09436 https://arxiv.org/pdf/…

Optimal quantum metrology under energy constraints
Quantum metrology promises enhanced precision in parameter estimation by exploiting quantum effects. The traditional framework of metrology commonly assumes unlimited access to resources, overlooking resource constraints in realistic scenarios. As such, the optimal strategies therein are often infeasible in practice. Here, we investigate quantum metrology where the total energy consumption of the probe state preparation, intermediate control operations, and the final measurement is subject to a…

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation
Bowen Zheng, Tianming Yang
https://arxiv.org/abs/2506.09376 https://

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation
Diffusion distillation is a widely used technique to reduce the sampling cost of diffusion models, yet it often requires extensive training, and the student performance tends to be degraded. Recent studies show that incorporating a GAN objective may alleviate these issues, yet the underlying mechanism remains unclear. In this work, we first identify a key limitation of distillation: mismatched step sizes and parameter numbers between the teacher and the student model lead them to converge to di…

X-ray linear polarization prediction in Black-Hole binaries and Active Galactic Nuclei and its measurements by IXPE
Lev Titarchuk, Paolo Soffitta, Elena Seifina, Fabio Muleri, Romana Mikusincova
https://arxiv.org/abs/2506.09571

X-ray linear polarization prediction in Black-Hole binaries and Active Galactic Nuclei and its measurements by IXPE
We present a theoretical framework for the formation of X-ray linear polarization in black hole (BH) source. The X-ray linear polarization originates from up-scatterings of initially soft photons within a hot, optically thick Compton cloud (CC) characterized by a flat geometry. The IXPE observations of several BHs X-ray binaries and of one Seyfert-1 galaxy confirm our theoretical prediction regarding values of the linear polarization P. The goal of this paper is to demonstrate that the main phy…

Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation
Chendong Xin, Mingrui Yu, Yongpeng Jiang, Zhefeng Zhang, Xiang Li
https://arxiv.org/abs/2506.09384

Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation
Kinematic retargeting from human hands to robot hands is essential for transferring dexterity from humans to robots in manipulation teleoperation and imitation learning. However, due to mechanical differences between human and robot hands, completely reproducing human motions on robot hands is impossible. Existing works on retargeting incorporate various optimization objectives, focusing on different aspects of hand configuration. However, the lack of experimental comparative studies leaves the…

Attention-Bayesian Hybrid Approach to Modular Multiple Particle Tracking
Piyush Mishra (I2M, FRESNEL, TCLS, AMU), Philippe Roudot (FRESNEL, TCLS, CNRS)
https://arxiv.org/abs/2506.09441

Attention-Bayesian Hybrid Approach to Modular Multiple Particle Tracking
Tracking multiple particles in noisy and cluttered scenes remains challenging due to a combinatorial explosion of trajectory hypotheses, which scales super-exponentially with the number of particles and frames. The transformer architecture has shown a significant improvement in robustness against this high combinatorial load. However, its performance still falls short of the conventional Bayesian filtering approaches in scenarios presenting a reduced set of trajectory hypothesis. This suggests …

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Hao Peng, Yunjia Qi, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
https://arxiv.org/abs/2506.09942 …

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing large language models (LLMs), with verification engineering playing a central role. However, best practices for RL in instruction following remain underexplored. In this work, we explore the verification challenge in RL for instruction following and propose VerIF, a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model (e.g., QwQ-32B). …

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization
Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Yuhui Chen, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao Lu, Yanqing Ma, Shiyin Lu, Qifeng Chen
https://arxiv.org/abs/2506.09373

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization
The advent of autonomous agents is transforming interactions with Graphical User Interfaces (GUIs) by employing natural language as a powerful intermediary. Despite the predominance of Supervised Fine-Tuning (SFT) methods in current GUI agents for achieving spatial localization, these methods face substantial challenges due to their limited capacity to accurately perceive positional data. Existing strategies, such as reinforcement learning, often fail to assess positional accuracy effectively, …

Local surrogates for quantum machine learning
Sreeraj Rajindran Nair, Christopher Ferrie
https://arxiv.org/abs/2506.09425 https://arx…

Local surrogates for quantum machine learning
Surrogates have been proposed as classical simulations of the pretrained quantum learning models, which are capable of mimicking the input-output relation inherent in the quantum model. Quantum hardware within this framework is used for training and for generating the classical surrogates. Inference is relegated to the classical surrogate, hence alleviating the extra quantum computational cost once training is done. Taking inspiration from interpretable models, we introduce a local surrogation …

Delving into the depths of NGC 3783 with XRISM. I. Kinematic and ionization structure of the highly ionized outflows
Missagh Mehdipour, Jelle S. Kaastra, Megan E. Eckart, Liyi Gu, Ralf Ballhausen, Ehud Behar, Camille M. Diez, Keigo Fukumura, Matteo Guainazzi, Kouichi Hagino, Timothy R. Kallman, Erin Kara, Chen Li, Jon M. Miller, Misaki Mizumoto, Hirofumi Noda, Shoji Ogawa, Christos Panagiotou, Atsushi Tanimoto, Keqin Zhao

Delving into the depths of NGC 3783 with XRISM. I. Kinematic and ionization structure of the highly ionized outflows
We present our study of the XRISM observation of the Seyfert-1 galaxy NGC 3783. For the first time, XRISM's Resolve microcalorimeter enables a detailed characterization of the highly ionized outflows in this active galactic nucleus. Our analysis constrains their outflow and turbulent velocities, along with their ionization parameter $ξ$ and column density $N_{\rm H}$. The high-resolution Resolve spectrum reveals a distinct series of Fe absorption lines between 6.4 and 7.8 keV, ranging from Fe …

Know What You Don't Know: Uncertainty Calibration of Process Reward Models
Young-Jin Park, Kristjan Greenewald, Kaveh Alim, Hao Wang, Navid Azizan
https://arxiv.org/abs/2506.09338

Know What You Don't Know: Uncertainty Calibration of Process Reward Models
Process reward models (PRMs) play a central role in guiding inference-time scaling algorithms for large language models (LLMs). However, we observe that even state-of-the-art PRMs can be poorly calibrated and often overestimate success probabilities. To address this, we present a calibration approach, performed via quantile regression, that adjusts PRM outputs to better align with true success probabilities. Leveraging these calibrated success estimates and their associated confidence bounds, w…

Anomaly Detection and Generation with Diffusion Models: A Survey
Yang Liu, Jing Liu, Chengfang Li, Rui Xi, Wenchao Li, Liang Cao, Jin Wang, Laurence T. Yang, Junsong Yuan, Wei Zhou
https://arxiv.org/abs/2506.09368

Anomaly Detection and Generation with Diffusion Models: A Survey
Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by identifying unexpected patterns that deviate from established norms in real-world data. Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest due to their ability to learn complex data distributions and generate high-fidelity samples, offering a robust framework for unsupervised AD. In this surve…

Conclusive Local State Marking: More Nonlocality With No Entanglement
Samrat Sen
https://arxiv.org/abs/2506.09415 https://arxiv.org/p…

Conclusive Local State Marking: More Nonlocality With No Entanglement
Nonlocality exhibited by ensembles of composite quantum states, wherein local operations and classical communication (LOCC) yield suboptimal discrimination probabilities compared to global strategies, is one of the striking nonclassical features of quantum theory. A variant of this phenomenon arises in conclusive local state discrimination, where the correct state must be identified with zero error, albeit allowing for inconclusive outcomes. More recently, the notion of local state marking has …

Electromagnetic signatures from pulsar remnants of binary neutron star mergers: prospects for unique identification using multi-wavelength signatures
Mainak Mukhopadhyay, Shigeo S. Kimura
https://arxiv.org/abs/2506.09157

Electromagnetic signatures from pulsar remnants of binary neutron star mergers: prospects for unique identification using multi-wavelength signatures
Binary neutron star (BNS) mergers can result in the formation of long-lived magnetar remnants which can enhance neutrino and electromagnetic (EM) emissions. In this work, we study the resulting multi-wavelength EM emissions and the prospects of their detectability in the current and upcoming EM telescopes. We model the pulsar-wind neubla system where the long-lived pulsar with dipolar magnetic fields of $10^{13} - 10^{15}$ G (magnetar) spins down and is surrounded by an outward expanding nebula…

[2025-06-12 Thu (UTC), 7 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

Adversarial Surrogate Risk Bounds for Binary Classification
Natalie S. Frank
https://arxiv.org/abs/2506.09348 https://arxiv.org/pdf/2…

Adversarial Surrogate Risk Bounds for Binary Classification
A central concern in classification is the vulnerability of machine learning models to adversarial attacks. Adversarial training is one of the most popular techniques for training robust classifiers, which involves minimizing an adversarial surrogate risk. Recent work characterized when a minimizing sequence of an adversarial surrogate risk is also a minimizing sequence of the adversarial classification risk for binary classification -- a property known as adversarial consistency. However, thes…

Efficient Implementation of a Quantum Algorithm with a Trapped Ion Qudit
Xiaoyang Shi, Jasmine Sinanan-Singh, Timothy J. Burke, John Chiaverini, Isaac L. Chuang
https://arxiv.org/abs/2506.09371

Efficient Implementation of a Quantum Algorithm with a Trapped Ion Qudit
Demonstration of quantum advantage remains challenging due to the increased overhead of controlling large quantum systems. While significant effort has been devoted to qubit-based devices, qudits ($d$-level systems) offer potential advantages in both hardware efficiency and algorithmic performance. In this paper, we demonstrate multi-tone control of a single trapped ion qudit of up to eight levels, as well as the first implementation of Grover's search algorithm on a qudit with dimension five a…

What Exactly Does Guidance Do in Masked Discrete Diffusion Models
He Ye, Rojas Kevin, Tao Molei
https://arxiv.org/abs/2506.10971 https://

What Exactly Does Guidance Do in Masked Discrete Diffusion Models
We study masked discrete diffusion models with classifier-free guidance (CFG). Assuming no score error nor discretization error, we derive an explicit solution to the guided reverse dynamics, so that how guidance influences the sampling behavior can be precisely characterized. When the full data distribution is a mixture over classes and the goal is to sample from a specific class, guidance amplifies class-specific regions while suppresses regions shared with other classes. This effect depends …

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Xuemei Cao, Hanlin Gu, Xin Yang, Bingjun Wei, Haoyang Liang, Xiangkun Wang, Tianrui Li
https://arxiv.org/abs/2506.09347

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, we propose a novel perspective: CL not only needs to prevent forgetting, but also requires intentional forgetting.This arises from existing CL methods ignoring biases in real-world data, leading the model to learn spurious correlations that transfer and amplify across tasks. From feature extraction and prediction resul…

Speeding up adiabatic holonomic quantum gates via $\pi$-pulse modulation
Jiang Zhang, Tonghao Xing, Guilu Long
https://arxiv.org/abs/2506.09326 https://

Speeding up adiabatic holonomic quantum gates via $π$-pulse modulation
Holonomic quantum computation (HQC) offers an inherently robust approach to quantum gate implementation by exploiting quantum holonomies. While adiabatic HQC benefits from robustness against certain control errors, its long runtime limits practical utility due to increased exposure to environmental noise. Nonadiabatic HQC addresses this issue by enabling faster gate operations but compromises robustness. In this work, we propose a scheme for fast holonomic quantum gates based on the $π$-pulse …

Natural Language Guided Ligand-Binding Protein Design
Zhenqiao Song, Ramith Hettiarachchi, Chuan Li, Jianwen Xie, Lei Li
https://arxiv.org/abs/2506.09332 h…

Natural Language Guided Ligand-Binding Protein Design
Can AI protein models follow human language instructions and design proteins with desired functions (e.g. binding to a ligand)? Designing proteins that bind to a given ligand is crucial in a wide range of applications in biology and chemistry. Most prior AI models are trained on protein-ligand complex data, which is scarce due to the high cost and time requirements of laboratory experiments. In contrast, there is a substantial body of human-curated text descriptions about protein-ligand interac…

Probably Approximately Correct Labels
Emmanuel J. Cand\`es, Andrew Ilyas, Tijana Zrnic
https://arxiv.org/abs/2506.10908 https://arxiv…

Probably Approximately Correct Labels
Obtaining high-quality labeled datasets is often costly, requiring either extensive human annotation or expensive experiments. We propose a method that supplements such "expert" labels with AI predictions from pre-trained models to construct labeled datasets more cost-effectively. Our approach results in probably approximately correct labels: with high probability, the overall labeling error is small. This solution enables rigorous yet efficient dataset curation using modern AI models. We demon…

Toward Scalable Quantum Compilation for Modular Architecture: Qubit Mapping and Reuse via Deep Reinforcement Learning
Sokea Sang, Leanghok Hour, Youngsun Han
https://arxiv.org/abs/2506.09323

Toward Scalable Quantum Compilation for Modular Architecture: Qubit Mapping and Reuse via Deep Reinforcement Learning
Modular quantum architectures have emerged as a promising approach for scaling quantum computing systems by connecting multiple Quantum Processing Units (QPUs). However, this approach introduces significant challenges due to costly inter-core operations between chips and quantum state transfers, which contribute to noise and quantum decoherence. This paper presents QARMA, a novel Qubit mapping using Attention-based deep Reinforcement learning (DRL) for Modular quantum Architectures, along with …

On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention
Yeonju Ro, Zhenyu Zhang, Souvik Kundu, Zhangyang Wang, Aditya Akella
https://arxiv.org/abs/2506.09316

On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention
Large language models (LLMs) excel at capturing global token dependencies via self-attention but face prohibitive compute and memory costs on lengthy inputs. While sub-quadratic methods (e.g., linear attention) can reduce these costs, they often degrade accuracy due to overemphasizing recent tokens. In this work, we first propose \textit{dual-state linear attention} (\textbf{\dsla}), a novel design that maintains two specialized hidden states-one for preserving historical context and one for tr…

Demystifying Spectral Feature Learning for Instrumental Variable Regression
Dimitri Meunier, Antoine Moulin, Jakub Wornbard, Vladimir R. Kostic, Arthur Gretton
https://arxiv.org/abs/2506.10899

Demystifying Spectral Feature Learning for Instrumental Variable Regression
We address the problem of causal effect estimation in the presence of hidden confounders, using nonparametric instrumental variable (IV) regression. A leading strategy employs spectral features - that is, learned features spanning the top eigensubspaces of the operator linking treatments to instruments. We derive a generalization error bound for a two-stage least squares estimator based on spectral features, and gain insights into the method's performance and failure modes. We show that perform…

Accelerating quantum adiabatic evolution with $\pi$-pulse sequences
Tonghao Xing, Jiang Zhang, Guilu Long
https://arxiv.org/abs/2506.09320 https://

Accelerating quantum adiabatic evolution with $π$-pulse sequences
In quantum information processing, the development of fast and robust control schemes remains a central challenge. Although quantum adiabatic evolution is inherently robust against control errors, it typically demands long evolution times. In this work, we propose to achieve rapid adiabatic evolution, in which nonadiabatic transitions induced by fast changes in the system Hamiltonian are mitigated by flipping the nonadiabatic transition matrix using $π$ pulses. This enables a faster realizatio…

Causal Graph Recovery in Neuroimaging through Answer Set Programming
Mohammadsajad Abavisani, Kseniya Solovyeva, David Danks, Vince Calhoun, Sergey Plis
https://arxiv.org/abs/2506.09286

Causal Graph Recovery in Neuroimaging through Answer Set Programming
Learning graphical causal structures from time series data presents significant challenges, especially when the measurement frequency does not match the causal timescale of the system. This often leads to a set of equally possible underlying causal graphs due to information loss from sub-sampling (i.e., not observing all possible states of the system throughout time). Our research addresses this challenge by incorporating the effects of sub-sampling in the derivation of causal graphs, resulting…

Practical Improvements of A/B Testing with Off-Policy Estimation
Sakhi Otmane, Gilotte Alexandre, Rohde David
https://arxiv.org/abs/2506.10677 https://

Practical Improvements of A/B Testing with Off-Policy Estimation
We address the problem of A/B testing, a widely used protocol for evaluating the potential improvement achieved by a new decision system compared to a baseline. This protocol segments the population into two subgroups, each exposed to a version of the system and estimates the improvement as the difference between the measured effects. In this work, we demonstrate that the commonly used difference-in-means estimator, while unbiased, can be improved. We introduce a family of unbiased off-policy e…

A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV
Ziyi Chen, Yiyang Liu, Mattia Prosperi, Krishna Vaddiparti, Robert L Cook, Jiang Bian, Yi Guo, Yonghui Wu
https://arxiv.org/abs/2506.09279

A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV
Objective: To characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV (PLWHs) seeking care, using natural language processing methods applied to a large collection of electronic health record (EHR) clinical notes from a large integrated health system in the southeast United States. Methods: We identified 9,140 cohort of PLWHs from the UF Health IDR and performed topic modeling analysis using Latent Dirichlet Allocation (LDA) to uncover stigma dimen…

Canonical Partition Function on a Quantum Computer through Trotter Interpolation
Taozhi Guo, Gumaro Rendon, Rutuja Kshirsagar
https://arxiv.org/abs/2506.09318

Canonical Partition Function on a Quantum Computer through Trotter Interpolation
In this work, we present a Gibbs state observable estimation algorithm based on Trotter interpolation, which reaches a state-of-the-art quantum computational cost of $ \tilde{O}(β\log{1/ε})$. Our approach saves $\log(Γ)$ ancilla qubits compared with the qubitization-based methods for Hamiltonian with $Γ$ stages. To provide a robust assessment of our approach, we benchmark our results against state-of-the-art methodology using the SYK model as a testbed. Our method provides an efficient alte…

Logarithmic Smoothing for Adaptive PAC-Bayesian Off-Policy Learning
Maxime Haddouche, Otmane Sakhi
https://arxiv.org/abs/2506.10664 https://

Logarithmic Smoothing for Adaptive PAC-Bayesian Off-Policy Learning
Off-policy learning serves as the primary framework for learning optimal policies from logged interactions collected under a static behavior policy. In this work, we investigate the more practical and flexible setting of adaptive off-policy learning, where policies are iteratively refined and re-deployed to collect higher-quality data. Building on the success of PAC-Bayesian learning with Logarithmic Smoothing (LS) in static settings, we extend this framework to the adaptive scenario using tool…

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…

Effective criteria for entanglement witnesses in small dimensions
{\L}ukasz Grzelka, {\L}ukasz Skowronek, Karol \.Zyczkowski
https://arxiv.org/abs/2506.09298

Effective criteria for entanglement witnesses in small dimensions
We present an effective set of necessary and sufficient criteria for block-positivity of matrices of order $4$ over $\mathbb{C}$. The approach is based on Sturm sequences and quartic polynomial positivity conditions presented in recent literature. The procedure allows us to test whether a given $4\times 4$ complex matrix corresponds to an entanglement witness, and it is exact when the matrix coefficients belong to the rationals, extended by $\mathrm{i}$. The method can be generalized to $\mathc…

Box-Constrained Softmax Function and Its Application for Post-Hoc Calibration
Kyohei Atarashi, Satoshi Oyama, Hiromi Arai, Hisashi Kashima
https://arxiv.org/abs/2506.10572

Box-Constrained Softmax Function and Its Application for Post-Hoc Calibration
Controlling the output probabilities of softmax-based models is a common problem in modern machine learning. Although the $\mathrm{Softmax}$ function provides soft control via its temperature parameter, it lacks the ability to enforce hard constraints, such as box constraints, on output probabilities, which can be critical in certain applications requiring reliable and trustworthy models. In this work, we propose the box-constrained softmax ($\mathrm{BCSoftmax}$) function, a novel generalizatio…

G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Samuel Holt, Max Ruiz Luyten, Antonin Berthon, Mihaela van der Schaar
https://arxiv.org/abs/2506.09272

G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Constructing robust simulators is essential for asking "what if?" questions and guiding policy in critical domains like healthcare and logistics. However, existing methods often struggle, either failing to generalize beyond historical data or, when using Large Language Models (LLMs), suffering from inaccuracies and poor empirical alignment. We introduce G-Sim, a hybrid framework that automates simulator construction by synergizing LLM-driven structural design with rigorous empirical calibration…

Classical algorithm inspired by the feedback-based algorithm for quantum optimization and local counterdiabatic driving
Takuya Hatomura
https://arxiv.org/abs/2506.09214

Classical algorithm inspired by the feedback-based algorithm for quantum optimization and local counterdiabatic driving
We propose a quantum-inspired classical algorithm for combinatorial optimization problems, named the counterdiabaticity-assisted classical algorithm for optimization (CACAO). In this algorithm, a solution of a given combinatorial optimization problem is heuristically searched with classical spin dynamics based on quantum Lyapunov control of local counterdiabatic driving. We compare the performance of CACAO with that of quantum time-evolution algorithms, i.e., quantum annealing, the feedback-bas…

Uncertainty Prioritized Experience Replay
Rodrigo Carrasco-Davis, Sebastian Lee, Claudia Clopath, Will Dabney
https://arxiv.org/abs/2506.09270 https://

Uncertainty Prioritized Experience Replay
Prioritized experience replay, which improves sample efficiency by selecting relevant transitions to update parameter estimates, is a crucial component of contemporary value-based deep reinforcement learning models. Typically, transitions are prioritized based on their temporal difference error. However, this approach is prone to favoring noisy transitions, even when the value estimation closely approximates the target mean. This phenomenon resembles the noisy TV problem postulated in the explo…

Measuring Semantic Information Production in Generative Diffusion Models
Florian Handke, F\'elix Koulischer, Gabriel Raya, Luca Ambrogioni
https://arxiv.org/abs/2506.10433

Measuring Semantic Information Production in Generative Diffusion Models
It is well known that semantic and structural features of the generated images emerge at different times during the reverse dynamics of diffusion, a phenomenon that has been connected to physical phase transitions in magnets and other materials. In this paper, we introduce a general information-theoretic approach to measure when these class-semantic "decisions" are made during the generative process. By using an online formula for the optimal Bayesian classifier, we estimate the conditional ent…

CFMI: Flow Matching for Missing Data Imputation
Vaidotas Simkus, Michael U. Gutmann
https://arxiv.org/abs/2506.09258 https://arxiv.or…

CFMI: Flow Matching for Missing Data Imputation
We introduce conditional flow matching for imputation (CFMI), a new general-purpose method to impute missing data. The method combines continuous normalising flows, flow-matching, and shared conditional modelling to deal with intractabilities of traditional multiple imputation. Our comparison with nine classical and state-of-the-art imputation methods on 24 small to moderate-dimensional tabular data sets shows that CFMI matches or outperforms both traditional and modern techniques across a wide…

Distributionally-Constrained Adversaries in Online Learning
Mo\"ise Blanchard, Samory Kpotufe
https://arxiv.org/abs/2506.10293 https://

Distributionally-Constrained Adversaries in Online Learning
There has been much recent interest in understanding the continuum from adversarial to stochastic settings in online learning, with various frameworks including smoothed settings proposed to bridge this gap. We consider the more general and flexible framework of distributionally constrained adversaries in which instances are drawn from distributions chosen by an adversary within some constrained distribution class [RST11]. Compared to smoothed analysis, we consider general distributional classe…

Agent-based Condition Monitoring Assistance with Multimodal Industrial Database Retrieval Augmented Generation
Karl L\"owenmark, Daniel Str\"ombergsson, Chang Liu, Marcus Liwicki, Fredrik Sandin
https://arxiv.org/abs/2506.09247

Agent-based Condition Monitoring Assistance with Multimodal Industrial Database Retrieval Augmented Generation
Condition monitoring (CM) plays a crucial role in ensuring reliability and efficiency in the process industry. Although computerised maintenance systems effectively detect and classify faults, tasks like fault severity estimation, and maintenance decisions still largely depend on human expert analysis. The analysis and decision making automatically performed by current systems typically exhibit considerable uncertainty and high false alarm rates, leading to increased workload and reduced effici…

Momentum Multi-Marginal Schr\"odinger Bridge Matching
Panagiotis Theodoropoulos, Augustinos D. Saravanos, Evangelos A. Theodorou, Guan-Horng Liu
https://arxiv.org/abs/2506.10168

Momentum Multi-Marginal Schrödinger Bridge Matching
Understanding complex systems by inferring trajectories from sparse sample snapshots is a fundamental challenge in a wide range of domains, e.g., single-cell biology, meteorology, and economics. Despite advancements in Bridge and Flow matching frameworks, current methodologies rely on pairwise interpolation between adjacent snapshots. This hinders their ability to capture long-range temporal dependencies and potentially affects the coherence of the inferred trajectories. To address these issues…

SoK: Machine Unlearning for Large Language Models
Jie Ren, Yue Xing, Yingqian Cui, Charu C. Aggarwal, Hui Liu
https://arxiv.org/abs/2506.09227 https://

SoK: Machine Unlearning for Large Language Models
Large language model (LLM) unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining the model from scratch. A variety of techniques have been proposed, including Gradient Ascent, model editing, and re-steering hidden representations. While existing surveys often organize these methods by their technical characteristics, such classifications tend to overlook a more fundamental dimension: the underlying…

Fundamental Limits of Learning High-dimensional Simplices in Noisy Regimes
Seyed Amir Hossein Saberi, Amir Najafi, Abolfazl Motahari, Babak H. khalaj
https://arxiv.org/abs/2506.10101

Fundamental Limits of Learning High-dimensional Simplices in Noisy Regimes
In this paper, we establish sample complexity bounds for learning high-dimensional simplices in $\mathbb{R}^K$ from noisy data. Specifically, we consider $n$ i.i.d. samples uniformly drawn from an unknown simplex in $\mathbb{R}^K$, each corrupted by additive Gaussian noise of unknown variance. We prove an algorithm exists that, with high probability, outputs a simplex within $\ell_2$ or total variation (TV) distance at most $\varepsilon$ from the true simplex, provided $n \ge (K^2/\varepsilon^2…

Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs
Greyson Brothers
https://arxiv.org/abs/2506.09215 https://arxiv.…

Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs
We investigate the design of pooling methods used to summarize the outputs of transformer embedding models, primarily motivated by reinforcement learning and vision applications. This work considers problems where a subset of the input vectors contains requisite information for a downstream task (signal) while the rest are distractors (noise). By framing pooling as vector quantization with the goal of minimizing signal loss, we demonstrate that the standard methods used to aggregate transformer…

[2025-06-13 Fri (UTC), 10 new articles found for stat.ML Machine Learning]
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