Tootfinder

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

For my next modern culture and media studies work, I'd like to use "THE SIMPLE NEW SOCIO-ECONOMICGEOPOLITICALBIO-HISTORICAL APPROACH" (TSR 1987)
(The eighties were a very weird time, y'all.)
(I found this in WCA #26, ZODIAC ATTACK)

Accelerating Large-Scale Regularized High-Order Tensor Recovery
Wenjin Qin, Hailin Wang, Jingyao Hou, Jianjun Wang
https://arxiv.org/abs/2506.09594 https:/…

Accelerating Large-Scale Regularized High-Order Tensor Recovery
Currently, existing tensor recovery methods fail to recognize the impact of tensor scale variations on their structural characteristics. Furthermore, existing studies face prohibitive computational costs when dealing with large-scale high-order tensor data. To alleviate these issue, assisted by the Krylov subspace iteration, block Lanczos bidiagonalization process, and random projection strategies, this article first devises two fast and accurate randomized algorithms for low-rank tensor approx…

Multi-Dress-State Engineered Rydberg Electrometry: Achieving 100-MHz-level Instantaneous-Bandwidth
Yuhan Yan, Bowen Yang, Xuejie Li, Haojie Zhao, Binghong Yu, Jianliao Deng, L. Q. Chen, Huadong Cheng
https://arxiv.org/abs/2506.10541

[2025-06-12 Thu (UTC), no new articles found for q-fin.CP Computational Finance]
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Experimental observation of subabsorption
D. C. Gold, U. Saglam, S. Carpenter, A. Yadav, M. Beede, T. G. Walker, M. Saffman, D. D. Yavuz
https://arxiv.org/abs/2506.09872

Experimental observation of subabsorption
We predict and experimentally demonstrate a new type of collective (cooperative) coupling effect where a disordered atomic ensemble absorbs light with a rise-time longer (i. e., at a rate slower) than what is dictated by single-atom physics. This effect, which we name subabsorption, can be viewed as the absorptive analog of subradiance. The experiment is performed using a dilute ensemble of ultracold $^{87}$Rb atoms with a low optical depth, and time-resolving the absorption of a weak (tens of …

@… @… on my site I linked the image to a `/uses/` page that had the attribution (if that helps!)

LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge
Sahar Abdelnabi, Aideen Fay, Ahmed Salem, Egor Zverev, Kai-Chieh Liao, Chi-Huang Liu, Chun-Chih Kuo, Jannis Weigend, Danyael Manlangit, Alex Apostolov, Haris Umair, Jo\~ao Donato, Masayuki Kawakita, Athar Mahboob, Tran Huu Bach, Tsun-Han Chiang, Myeongjin Cho, Hajin Choi, Byeonghyeon Kim, Hyeonjin Lee, Benjamin Pannell, Conor McCauley, Mark Russinovich, Andrew Paverd, Giovanni Cherubin

LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge
Indirect Prompt Injection attacks exploit the inherent limitation of Large Language Models (LLMs) to distinguish between instructions and data in their inputs. Despite numerous defense proposals, the systematic evaluation against adaptive adversaries remains limited, even when successful attacks can have wide security and privacy implications, and many real-world LLM-based applications remain vulnerable. We present the results of LLMail-Inject, a public challenge simulating a realistic scenario…

This https://arxiv.org/abs/2308.09696 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Exploring Metric and Strong Metric Dimensions in Inclusion Ideal Graphs of Commutative Rings
The inclusion ideal graph of a commutative unitary ring $R$ is the (undirected) graph $In(R)$ whose vertices all non-trivial ideals of $R$ and two distinct vertices are adjacent if and only if one of them is a proper subset of the other one. In this paper, the metric dimension of $In(R)$ is discussed. Moreover, the structure of the resolving graph of $In(R)$ is characterized and as an application, we compute the strong metric dimension of $In(R)$.

Solving Package Management via Hypergraph Dependency Resolution
Ryan Gibb, Patrick Ferris, David Allsopp, Michael Winston Dales, Mark Elvers, Thomas Gazagnaire, Sadiq Jaffer, Thomas Leonard, Jon Ludlam, Anil Madhavapeddy
https://arxiv.org/abs/2506.10803

Solving Package Management via Hypergraph Dependency Resolution
Package managers are everywhere, with seemingly every language and operating system implementing their own solution. The lack of interoperability between these systems means that multi-lingual projects are unable to express precise dependencies across language ecosystems, and external system and hardware dependencies are typically implicit and unversioned. We define HyperRes, a formal system for describing versioned dependency resolution using a hypergraph that is expressive enough to model man…

[2025-06-12 Thu (UTC), 4 new articles found for cs.CE Computational Engineering, Finance, and Science]
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This https://arxiv.org/abs/2506.05317 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

ProJo4D: Progressive Joint Optimization for Sparse-View Inverse Physics Estimation
Neural rendering has made significant strides in 3D reconstruction and novel view synthesis. With the integration with physics, it opens up new applications. The inverse problem of estimating physics from visual data, however, still remains challenging, limiting its effectiveness for applications like physically accurate digital twin creation in robotics and XR. Existing methods that incorporate physics into neural rendering frameworks typically require dense multi-view videos as input, making …

Recently, the German Society for Planetary Research was founded in #Münster. Congratulations! Web page and press release: https://www.planet-dgp.de 🔭
@…

Seeding an Uncertain Technology
Eric Gao
https://arxiv.org/abs/2506.10340 https://arxiv.org/pdf/2506.10340

Seeding an Uncertain Technology
I study how a startup with uncertainty over product quality and no knowledge of the underlying diffusion network optimally chooses initial seeds. To ensure widespread adoption when the product is good while minimizing negative perceptions when it is bad, the optimal number of initial seeds should grow logarithmically with network size. When there are agents of different types that govern their connectivity, it is asymptotically optimal to seed agents of a single type: the type that minimizes th…

Light PIDM in Warped Extra Dimensions
Mathew T Arun, Prachiti P. Athalye, Basabendu Barman
https://arxiv.org/abs/2506.09135 https://a…

Light PIDM in Warped Extra Dimensions
Traditional Planckian Interacting Dark Matter (PIDM), which interacts exclusively through gravity, typically requires heavy DM candidates (with mass $10^3-10^{15}$ GeV) and very high reheating temperature ($T_{\rm rh}\gtrsim 10^{15}$ GeV). In this article, we explore a novel realization of PIDM in warped five-dimensions, consisting of an "Ultra Violet"$-$"Dark"$-$"Infra Red" (UV-DB-IR) brane setup, where the DM can be a Dark brane composite light state with mass 1 MeV $-$ 1 TeV. The DM sector i…

This https://arxiv.org/abs/2505.23836 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Large Language Models Often Know When They Are Being Evaluated
If AI models can detect when they are being evaluated, the effectiveness of evaluations might be compromised. For example, models could have systematically different behavior during evaluations, leading to less reliable benchmarks for deployment and governance decisions. We investigate whether frontier language models can accurately classify transcripts based on whether they originate from evaluations or real-world deployment, a capability we call evaluation awareness. To achieve this, we const…

Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks
Achim Hekler, Lukas Kuhn, Florian Buettner
https://arxiv.org/abs/2506.09593

Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks
Reliable uncertainty calibration is essential for safely deploying deep neural networks in high-stakes applications. Deep neural networks are known to exhibit systematic overconfidence, especially under distribution shifts. Although foundation models such as ConvNeXt, EVA and BEiT have demonstrated significant improvements in predictive performance, their calibration properties remain underexplored. This paper presents a comprehensive investigation into the calibration behavior of foundation mo…

Rydberg atom arrays as quantum simulators for molecular dynamics
Simon Euchner, Igor Lesanovsky
https://arxiv.org/abs/2506.09865 https://

Rydberg atom arrays as quantum simulators for molecular dynamics
Rydberg atoms held in optical tweezer arrays combine vibrational and electronic degrees of freedom which can be coupled and manipulated at a microscopic level. This opens opportunities for the quantum simulation of artificial molecular systems and offers in particular a platform for probing complex vibronic dynamics in controlled settings with increasing complexity. Tailored interatomic interactions and electron-phonon couplings yield handles for designing electronic state manifolds, for studyi…

Oracle-Based Multistep Strategy for Solving Polynomial Systems Over Finite Fields and Algebraic Cryptanalysis of the Aradi Cipher
La Scala Roberto, Sharwan Kumar Tiwari
https://arxiv.org/abs/2506.09950

Oracle-Based Multistep Strategy for Solving Polynomial Systems Over Finite Fields and Algebraic Cryptanalysis of the Aradi Cipher
The multistep solving strategy consists in a divide-and-conquer approach: when a multivariate polynomial system is computationally infeasible to solve directly, one variable is assigned over the elements of the base finite field, and the procedure is recursively applied to the resulting simplified systems. In a previous work by the same authors (among others), this approach proved effective in the algebraic cryptanalysis of the Trivium cipher. In this paper, we present a new implementation of t…

High-resolution spectroscopy of barium monofluoride: Odd isotopologues, hyperfine structure and isotope shifts
Felix Kogel, Yuly Chamorro, Mangesh Bhattarai, Marian Rockenh\"auser, Tatsam Garg, David DeMille, Anastasia Borschevsky, Tim Langen
https://arxiv.org/abs/2506.10940

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Surya Jasper, Minh Luu, Evan Pan, Aakash Tyagi, Michael Quinn, Jiang Hu, David Kebo Houngninou
https://arxiv.org/abs/2506.10501

BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis
Hardware complexity continues to strain verification resources, motivating the adoption of machine learning (ML) methods to improve debug efficiency. However, ML-assisted debugging critically depends on diverse and scalable bug datasets, which existing manual or automated bug insertion methods fail to reliably produce. We introduce BugGen, a first of its kind, fully autonomous, multi-agent pipeline leveraging Large Language Models (LLMs) to systematically generate, insert, and validate realisti…

This https://arxiv.org/abs/2408.04802 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Homotopy types of Hom complexes of graph homomorphisms whose codomains are cycles
For simple graphs $G$ and $H$, the Hom complex $\mathrm{Hom}(G,H)$ is a polyhedral complex whose vertices are the graph homomorphisms $G\to H$ and whose edges connect the pairs of homomorphisms which differ in a single vertex of $G$. Hom complexes play an important role in an algebro-topological approach to the graph coloring problem. It is known that $\mathrm{Hom}(G,H)$ is homotopy equivalent to a disjoint union of points and circles when both $G$ and $H$ are cycles. We generalize this known r…

This https://arxiv.org/abs/2506.05096 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Astraea: A GPU-Oriented Token-wise Acceleration Framework for Video Diffusion Transformers
Video diffusion transformers (vDiTs) have made impressive progress in text-to-video generation, but their high computational demands present major challenges for practical deployment. While existing acceleration methods reduce workload at various granularities, they often rely on heuristics, limiting their applicability. We introduce ASTRAEA, an automatic framework that searches for near-optimal configurations for vDiT-based video generation. At its core, ASTRAEA proposes a lightweight token …

Cursed Equilibria and Knightian Uncertainty in a Trading Game
Jurek Preker
https://arxiv.org/abs/2506.10663 https://arxiv.org/pdf/250…

Cursed Equilibria and Knightian Uncertainty in a Trading Game
We introduce a novel equilibrium concept that incorporates Knightian uncertainty into the cursed equilibrium (Eyster and Rabin, 2005). This concept is then applied to a two-player game in which agents can engage in trade or refuse to do so. While the Bayesian Nash equilibrium predicts that trade never happens, players do trade in a cursed equilibrium. The inclusion of uncertainty enhances this effect for cursed and uncertainty averse players. This contrasts general findings that uncertainty red…

This https://arxiv.org/abs/2505.22240 has been replaced.
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BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain
Biomedical reasoning often requires traversing interconnected relationships across entities such as drugs, diseases, and proteins. Despite the increasing prominence of large language models (LLMs), existing benchmarks lack the ability to evaluate multi-hop reasoning in the biomedical domain, particularly for queries involving one-to-many and many-to-many relationships. This gap leaves the critical challenges of biomedical multi-hop reasoning underexplored. To address this, we introduce BioHopR,…

MOORL: A Framework for Integrating Offline-Online Reinforcement Learning
Gaurav Chaudhary, Wassim Uddin Mondal, Laxmidhar Behera
https://arxiv.org/abs/2506.09574

MOORL: A Framework for Integrating Offline-Online Reinforcement Learning
Sample efficiency and exploration remain critical challenges in Deep Reinforcement Learning (DRL), particularly in complex domains. Offline RL, which enables agents to learn optimal policies from static, pre-collected datasets, has emerged as a promising alternative. However, offline RL is constrained by issues such as out-of-distribution (OOD) actions that limit policy performance and generalization. To overcome these limitations, we propose Meta Offline-Online Reinforcement Learning (MOORL), …

Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice
Junqing Zhang, Francesco Ardizzon, Mattia Piana, Guanxiong Shen, Stefano Tomasin
https://arxiv.org/abs/2506.09807

Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice
The identification of the devices from which a message is received is part of security mechanisms to ensure authentication in wireless communications. Conventional authentication approaches are cryptography-based, which, however, are usually computationally expensive and not adequate in the Internet of Things (IoT), where devices tend to be low-cost and with limited resources. This paper provides a comprehensive survey of physical layer-based device fingerprinting, which is an emerging device a…

Nuclear Electric Quadrupole Moment-Induced Parity Doubling in Molecules for Symmetry-Violation Searches
Xing Fan, Lan Cheng
https://arxiv.org/abs/2506.10904

Bug Classification in Quantum Software: A Rule-Based Framework and Its Evaluation
Mir Mohammad Yousuf, Shabir Ahmad Sofi
https://arxiv.org/abs/2506.10397 h…

Bug Classification in Quantum Software: A Rule-Based Framework and Its Evaluation
Accurate classification of software bugs is essential for improving software quality. This paper presents a rule-based automated framework for classifying issues in quantum software repositories by bug type, category, severity, and impacted quality attributes, with additional focus on quantum-specific bug types. The framework applies keyword and heuristic-based techniques tailored to quantum computing. To assess its reliability, we manually classified a stratified sample of 4,984 issues from a …

This https://arxiv.org/abs/2506.05008 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Structure-Aware Radar-Camera Depth Estimation
Monocular depth estimation aims to determine the depth of each pixel from an RGB image captured by a monocular camera. The development of deep learning has significantly advanced this field by facilitating the learning of depth features from some well-annotated datasets \cite{Geiger_Lenz_Stiller_Urtasun_2013,silberman2012indoor}. Eigen \textit{et al.} \cite{eigen2014depth} first introduce a multi-scale fusion network for depth regression. Following this, subsequent improvements have come from r…

STOAT: Spatial-Temporal Probabilistic Causal Inference Network
Yang Yang, Du Yin, Hao Xue, Flora Salim
https://arxiv.org/abs/2506.09544 https://

STOAT: Spatial-Temporal Probabilistic Causal Inference Network
Spatial-temporal causal time series (STC-TS) involve region-specific temporal observations driven by causally relevant covariates and interconnected across geographic or network-based spaces. Existing methods often model spatial and temporal dynamics independently and overlook causality-driven probabilistic forecasting, limiting their predictive power. To address this, we propose STOAT (Spatial-Temporal Probabilistic Causal Inference Network), a novel framework for probabilistic forecasting in …

Black hole/quantum machine learning correspondence
Jae-Weon Lee, Zae Young Kim
https://arxiv.org/abs/2506.09678 https://arxiv.org/pdf…

Black hole/quantum machine learning correspondence
We explore a potential connection between the black hole information paradox and the double descent phenomenon in quantum machine learning. Information retrieval from Hawking radiation can be viewed through the lens of quantum linear regression over black hole microstates, with the Page time corresponding to the interpolation threshold, beyond which test error decreases despite overparameterization. Using the Marchenko-Pastur law, we derive the variance in test error for the quantum linear regr…

On the Virtues of Information Security in the UK Climate Movement
Mikaela Brough, Rikke Bjerg Jensen, Martin R. Albrecht
https://arxiv.org/abs/2506.09719 h…

On the Virtues of Information Security in the UK Climate Movement
We report on an ethnographic study with members of the climate movement in the United Kingdom (UK). We conducted participant observation and interviews at protests and in various activist settings. Reporting on the findings as they relate to information security, we show that members of the UK climate movement wrestled with (i) a fundamental tension between openness and secrecy; (ii) tensions between autonomy and collective interdependence in information-security decision-making; (iii) conflict…

Revisiting the electron affinity of selenium
Rui Zhang, Wenru Jie, Jiayi Chen, Qihan Liu, Chuangang Ning
https://arxiv.org/abs/2506.10300 https://

Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models
Shuai Wang, Zhenhua Liu, Jiaheng Wei, Xuanwu Yin, Dong Li, Emad Barsoum
https://arxiv.org/abs/2506.09532

Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models
We present Athena-PRM, a multimodal process reward model (PRM) designed to evaluate the reward score for each step in solving complex reasoning problems. Developing high-performance PRMs typically demands significant time and financial investment, primarily due to the necessity for step-level annotations of reasoning steps. Conventional automated labeling methods, such as Monte Carlo estimation, often produce noisy labels and incur substantial computational costs. To efficiently generate high-q…

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 …

PlayerOne: Egocentric World Simulator
Yuanpeng Tu, Hao Luo, Xi Chen, Xiang Bai, Fan Wang, Hengshuang Zhao
https://arxiv.org/abs/2506.09995 https://

PlayerOne: Egocentric World Simulator
We introduce PlayerOne, the first egocentric realistic world simulator, facilitating immersive and unrestricted exploration within vividly dynamic environments. Given an egocentric scene image from the user, PlayerOne can accurately construct the corresponding world and generate egocentric videos that are strictly aligned with the real scene human motion of the user captured by an exocentric camera. PlayerOne is trained in a coarse-to-fine pipeline that first performs pretraining on large-scale…

Neural Functions for Learning Periodic Signal
Woojin Cho, Minju Jo, Kookjin Lee, Noseong Park
https://arxiv.org/abs/2506.09526 https://

Neural Functions for Learning Periodic Signal
As function approximators, deep neural networks have served as an effective tool to represent various signal types. Recent approaches utilize multi-layer perceptrons (MLPs) to learn a nonlinear mapping from a coordinate to its corresponding signal, facilitating the learning of continuous neural representations from discrete data points. Despite notable successes in learning diverse signal types, coordinate-based MLPs often face issues of overfitting and limited generalizability beyond the train…

Empirical Quantification of Spurious Correlations in Malware Detection
Bianca Perasso, Ludovico Lozza, Andrea Ponte, Luca Demetrio, Luca Oneto, Fabio Roli
https://arxiv.org/abs/2506.09662

Empirical Quantification of Spurious Correlations in Malware Detection
End-to-end deep learning exhibits unmatched performance for detecting malware, but such an achievement is reached by exploiting spurious correlations -- features with high relevance at inference time, but known to be useless through domain knowledge. While previous work highlighted that deep networks mainly focus on metadata, none investigated the phenomenon further, without quantifying their impact on the decision. In this work, we deepen our understanding of how spurious correlation affects d…

Laser Stabilised Ionising Transitions
Erika Cortese, Simone De Liberato
https://arxiv.org/abs/2506.10183 https://arxiv.org/pdf/2506.1…

Minimizing False Positives in Static Bug Detection via LLM-Enhanced Path Feasibility Analysis
Xueying Du, Kai Yu, Chong Wang, Yi Zou, Wentai Deng, Zuoyu Ou, Xin Peng, Lingming Zhang, Yiling Lou
https://arxiv.org/abs/2506.10322

Minimizing False Positives in Static Bug Detection via LLM-Enhanced Path Feasibility Analysis
Static bug analyzers play a crucial role in ensuring software quality. However, existing analyzers for bug detection in large codebases often suffer from high false positive rates. This is primarily due to the limited capabilities of analyzers in path feasibility validation with multiple conditional branches and complex data dependencies. While current LLM-based approaches attempt to address this issue, their effectiveness remains limited due to insufficient constraint cascade analysis and scal…

Text-Aware Image Restoration with Diffusion Models
Jaewon Min, Jin Hyeon Kim, Paul Hyunbin Cho, Jaeeun Lee, Jihye Park, Minkyu Park, Sangpil Kim, Hyunhee Park, Seungryong Kim
https://arxiv.org/abs/2506.09993

Text-Aware Image Restoration with Diffusion Models
Image restoration aims to recover degraded images. However, existing diffusion-based restoration methods, despite great success in natural image restoration, often struggle to faithfully reconstruct textual regions in degraded images. Those methods frequently generate plausible but incorrect text-like patterns, a phenomenon we refer to as text-image hallucination. In this paper, we introduce Text-Aware Image Restoration (TAIR), a novel restoration task that requires the simultaneous recovery of…

Efficient Preference-Based Reinforcement Learning: Randomized Exploration Meets Experimental Design
Andreas Schlaginhaufen, Reda Ouhamma, Maryam Kamgarpour
https://arxiv.org/abs/2506.09508

Efficient Preference-Based Reinforcement Learning: Randomized Exploration Meets Experimental Design
We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in this setting is to design algorithms that select informative preference queries to identify the underlying reward while ensuring theoretical guarantees. We propose a meta-algorithm based on randomized exploration, which avoids the computational challenges associated with optimistic approaches and remains tractable. We e…

The Everyday Security of Living with Conflict
Jessica McClearn, Reem Talhouk, Rikke Bjerg Jensen
https://arxiv.org/abs/2506.09580 https://

The Everyday Security of Living with Conflict
When `cyber' is used as a prefix, attention is typically drawn to the technological and spectacular aspects of war and conflict -- and, by extension, security. We offer a different approach to engaging with and understanding security in such contexts, by foregrounding the everyday -- mundane -- experiences of security within communities living with and fleeing from war. We do so through three vignettes from our field research in Colombia, Lebanon and Sweden, respectively, and by highlighting th…

[2025-06-13 Fri (UTC), 5 new articles found for physics.atom-ph Atomic Physics]
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A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processes
Thomas J. Ringstrom, Paul R. Schrater
https://arxiv.org/abs/2506.09499

A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processes
We introduce Option Kernel Bellman Equations (OKBEs) for a new reward-free Markov Decision Process. Rather than a value function, OKBEs directly construct and optimize a predictive map called a state-time option kernel (STOK) to maximize the probability of completing a goal while avoiding constraint violations. STOKs are compositional, modular, and interpretable initiation-to-termination transition kernels for policies in the Options Framework of Reinforcement Learning. This means: 1) STOKs can…

Hearing Hands: Generating Sounds from Physical Interactions in 3D Scenes
Yiming Dou, Wonseok Oh, Yuqing Luo, Antonio Loquercio, Andrew Owens
https://arxiv.org/abs/2506.09989

Hearing Hands: Generating Sounds from Physical Interactions in 3D Scenes
We study the problem of making 3D scene reconstructions interactive by asking the following question: can we predict the sounds of human hands physically interacting with a scene? First, we record a video of a human manipulating objects within a 3D scene using their hands. We then use these action-sound pairs to train a rectified flow model to map 3D hand trajectories to their corresponding audio. At test time, a user can query the model for other actions, parameterized as sequences of hand pos…

The Rabin cryptosystem over number fields
Alessandro Cobbe, Andreas Nickel, Akay Schuster
https://arxiv.org/abs/2506.09569 https://ar…

The Rabin cryptosystem over number fields
We extend Rabin's cryptosystem to general number fields. We show that decryption of a random plaintext is as hard as the integer factorisation problem, provided the modulus in our scheme has been chosen carefully. We investigate the performance of our new cryptosystem in comparison with the classical Rabin scheme and a more recent version over the Gaussian integers.

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Dingyi Rong, Haotian Lu, Wenzhuo Zheng, Fan Zhang, Shuangjia Zheng, Ning Liu
https://arxiv.org/abs/2506.09496

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Designing protein sequences with optimal energetic stability is a key challenge in protein inverse folding, as current deep learning methods are primarily trained by maximizing sequence recovery rates, often neglecting the energy of the generated sequences. This work aims to overcome this limitation by developing a model that directly generates low-energy, stable protein sequences. We propose EnerBridge-DPO, a novel inverse folding framework focused on generating low-energy, high-stability prot…

EditInspector: A Benchmark for Evaluation of Text-Guided Image Edits
Ron Yosef, Moran Yanuka, Yonatan Bitton, Dani Lischinski
https://arxiv.org/abs/2506.09988

EditInspector: A Benchmark for Evaluation of Text-Guided Image Edits
Text-guided image editing, fueled by recent advancements in generative AI, is becoming increasingly widespread. This trend highlights the need for a comprehensive framework to verify text-guided edits and assess their quality. To address this need, we introduce EditInspector, a novel benchmark for evaluation of text-guided image edits, based on human annotations collected using an extensive template for edit verification. We leverage EditInspector to evaluate the performance of state-of-the-art…

TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning
Songze Li, Mingxuan Zhang, Oubo Ma, Kang Wei, Shouling Ji
https://arxiv.org/abs/2506.09562

TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning
Deep reinforcement learning (DRL) has achieved remarkable success in a wide range of sequential decision-making domains, including robotics, healthcare, smart grids, and finance. Recent research demonstrates that attackers can efficiently exploit system vulnerabilities during the training phase to execute backdoor attacks, producing malicious actions when specific trigger patterns are present in the state observations. However, most existing backdoor attacks rely primarily on simplistic and heu…

On a few pitfalls in KL divergence gradient estimation for RL
Yunhao Tang, R\'emi Munos
https://arxiv.org/abs/2506.09477 https://…

On a few pitfalls in KL divergence gradient estimation for RL
We point out a few pitfalls in implementing gradient estimation for KL divergence in RL training for LLM, as seen in a number of open source projects and papers. The first major pitfall is to differentiate through the KL estimate as loss functions to minimize KL divergence. We show that such implementations are generally incorrect and do not produce the desired KL gradient. Secondly, we show that some implementations do not account for the sequential nature of the estimation problem and produce…

NDCG-Consistent Softmax Approximation with Accelerated Convergence
Yuanhao Pu, Defu Lian, Xiaolong Chen, Xu Huang, Jin Chen, Enhong Chen
https://arxiv.org/abs/2506.09454

NDCG-Consistent Softmax Approximation with Accelerated Convergence
Ranking tasks constitute fundamental components of extreme similarity learning frameworks, where extremely large corpora of objects are modeled through relative similarity relationships adhering to predefined ordinal structures. Among various ranking surrogates, Softmax (SM) Loss has been widely adopted due to its natural capability to handle listwise ranking via global negative comparisons, along with its flexibility across diverse application scenarios. However, despite its effectiveness, SM …

Identity and Access Management for the Computing Continuum
Chalima Dimitra Nassar Kyriakidou, Athanasia Maria Papathanasiou, Vasilios A. Siris, Nikos Fotiou, George C. Polyzos, Eduardo C\'anovas Mart\'inez, Antonio Skarmeta
https://arxiv.org/abs/2506.09559

Identity and Access Management for the Computing Continuum
The computing continuum introduces new challenges for access control due to its dynamic, distributed, and heterogeneous nature. In this paper, we propose a Zero-Trust (ZT) access control solution that leverages decentralized identification and authentication mechanisms based on Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Additionally, we employ Relationship-Based Access Control (ReBAC) to define policies that capture the evolving trust relationships inherent in the contin…

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Benno Krojer, Mojtaba Komeili, Candace Ross, Quentin Garrido, Koustuv Sinha, Nicolas Ballas, Mahmoud Assran
https://arxiv.org/abs/2506.09987

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Existing benchmarks for assessing the spatio-temporal understanding and reasoning abilities of video language models are susceptible to score inflation due to the presence of shortcut solutions based on superficial visual or textual cues. This paper mitigates the challenges in accurately assessing model performance by introducing the Minimal Video Pairs (MVP) benchmark, a simple shortcut-aware video QA benchmark for assessing the physical understanding of video language models. The benchmark is…

Beyond Personalization: Federated Recommendation with Calibration via Low-rank Decomposition
Jundong Chen, Honglei Zhang, Haoxuan Li, Chunxu Zhang, Zhiwei Li, Yidong Li
https://arxiv.org/abs/2506.09525

Beyond Personalization: Federated Recommendation with Calibration via Low-rank Decomposition
Federated recommendation (FR) is a promising paradigm to protect user privacy in recommender systems. Distinct from general federated scenarios, FR inherently needs to preserve client-specific parameters, i.e., user embeddings, for privacy and personalization. However, we empirically find that globally aggregated item embeddings can induce skew in user embeddings, resulting in suboptimal performance. To this end, we theoretically analyze the user embedding skew issue and propose Personalized Fe…

InterActHuman: Multi-Concept Human Animation with Layout-Aligned Audio Conditions
Zhenzhi Wang, Jiaqi Yang, Jianwen Jiang, Chao Liang, Gaojie Lin, Zerong Zheng, Ceyuan Yang, Dahua Lin
https://arxiv.org/abs/2506.09984

InterActHuman: Multi-Concept Human Animation with Layout-Aligned Audio Conditions
End-to-end human animation with rich multi-modal conditions, e.g., text, image and audio has achieved remarkable advancements in recent years. However, most existing methods could only animate a single subject and inject conditions in a global manner, ignoring scenarios that multiple concepts could appears in the same video with rich human-human interactions and human-object interactions. Such global assumption prevents precise and per-identity control of multiple concepts including humans and …

The Secure Overview and Analysis OF 3GPP MAC CE
Jin Cao, Yuanyuan Yang, Ruhui Ma, Sheng Li, Hui Li
https://arxiv.org/abs/2506.09502 https://

The Secure Overview and Analysis OF 3GPP MAC CE
To more effectively control and allocate network resources, MAC CE has been introduced into the network protocol, which is a type of control signaling located in the MAC layer. Since MAC CE lacks encryption and integrity protection mechanisms provided by PDCP, the control signaling carried by MAC CE is vulnerable to interception or tampering by attackers during resource scheduling and allocation. Currently, the 3GPP has analyzed the security risks of Layer 1/Layer 2 Triggered Mobility (LTM), wh…

AnimateAnyMesh: A Feed-Forward 4D Foundation Model for Text-Driven Universal Mesh Animation
Zijie Wu, Chaohui Yu, Fan Wang, Xiang Bai
https://arxiv.org/abs/2506.09982

AnimateAnyMesh: A Feed-Forward 4D Foundation Model for Text-Driven Universal Mesh Animation
Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due to the complexity of modeling spatio-temporal distributions and the scarcity of 4D training data. In this paper, we present AnimateAnyMesh, the first feed-forward framework that enables efficient text-driven animation of arbitrary 3D meshes. Our approach leverages a novel DyMeshVAE architecture that effectively compresses and reconstructs dynamic mes…

Efficient Modular Multiplier over GF (2^m) for ECPM
Ruby Kumari, Gaurav Purohit, Abhijit Karmakar
https://arxiv.org/abs/2506.09464 https://

Efficient Modular Multiplier over GF (2^m) for ECPM
Elliptic curve cryptography (ECC) has emerged as the dominant public-key protocol, with NIST standardizing parameters for binary field GF(2^m) ECC systems. This work presents a hardware implementation of a Hybrid Multiplication technique for modular multiplication over binary field GF(2m), targeting NIST B-163, 233, 283, and 571 parameters. The design optimizes the combination of conventional multiplication (CM) and Karatsuba multiplication (KM) to enhance elliptic curve point multiplication (E…

This https://arxiv.org/abs/2505.11370 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Understanding Nonlinear Implicit Bias via Region Counts in Input Space
One explanation for the strong generalization ability of neural networks is implicit bias. Yet, the definition and mechanism of implicit bias in non-linear contexts remains little understood. In this work, we propose to characterize implicit bias by the count of connected regions in the input space with the same predicted label. Compared with parameter-dependent metrics (e.g., norm or normalized margin), region count can be better adapted to nonlinear, overparameterized models, because it is de…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new/
[4/4]:
DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion...

LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
Songze Li, Chuokun Xu, Jiaying Wang, Xueluan Gong, Chen Chen, Jirui Zhang, Jun Wang, Kwok-Yan Lam, Shouling Ji
https://arxiv.org/abs/2506.09443

LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
Large Language Models (LLMs) have demonstrated remarkable intelligence across various tasks, which has inspired the development and widespread adoption of LLM-as-a-Judge systems for automated model testing, such as red teaming and benchmarking. However, these systems are susceptible to adversarial attacks that can manipulate evaluation outcomes, raising concerns about their robustness and, consequently, their trustworthiness. Existing evaluation methods adopted by LLM-based judges are often pie…

This https://arxiv.org/abs/2504.12849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning
Federated Learning (FL) allows collaborative training among multiple devices without data sharing, thus enabling privacy-sensitive applications on mobile or Internet of Things (IoT) devices, such as mobile health and asset tracking. However, designing an FL system with good model utility that works with low computation/communication overhead on heterogeneous, resource-constrained mobile/IoT devices is challenging. To address this problem, this paper proposes FedX, a novel adaptive model decompo…

Securing Open RAN: A Survey of Cryptographic Challenges and Emerging Solutions for 5G
Ryan Barker, Fatemeh Afghah
https://arxiv.org/abs/2506.09418 https://…

Securing Open RAN: A Survey of Cryptographic Challenges and Emerging Solutions for 5G
The advent of Open Radio Access Networks (O-RAN) introduces modularity and flexibility into 5G deployments but also surfaces novel security challenges across disaggregated interfaces. This literature review synthesizes recent research across thirteen academic and industry sources, examining vulnerabilities such as cipher bidding-down attacks, partial encryption exposure on control/user planes, and performance trade-offs in securing O-RAN interfaces like E2 and O1. The paper surveys key cryptogr…

Epass: Efficient and Privacy-Preserving Asynchronous Payment on Blockchain
Weijie Wang, Jinwen Liang, Chuan Zhang, Ximeng Liu, Liehuang Zhu, Song Guo
https://arxiv.org/abs/2506.09387

Epass: Efficient and Privacy-Preserving Asynchronous Payment on Blockchain
Buy Now Pay Later (BNPL) is a rapidly proliferating e-commerce model, offering consumers to get the product immediately and defer payments. Meanwhile, emerging blockchain technologies endow BNPL platforms with digital currency transactions, allowing BNPL platforms to integrate with digital wallets. However, the transparency of transactions causes critical privacy concerns because malicious participants may derive consumers' financial statuses from on-chain asynchronous payments. Furthermore, th…

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 …

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Ronal Singh, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris
https://arxiv.org/abs/2506.09365

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Modern Security Operations Centres (SOCs) integrate diverse tools, such as SIEM, IDS, and XDR systems, offering rich contextual data, including alert enrichments, flow features, and similar case histories. Yet, analysts must still manually determine which of these contextual cues are most relevant when validating specific alerts. We introduce ContextBuddy, an AI assistant that learns from analysts' prior investigations to help them identify the most relevant context for new alerts. Rather than …

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 …

DAVSP: Safety Alignment for Large Vision-Language Models via Deep Aligned Visual Safety Prompt
Yitong Zhang, Jia Li, Liyi Cai, Ge Li
https://arxiv.org/abs/2506.09353

DAVSP: Safety Alignment for Large Vision-Language Models via Deep Aligned Visual Safety Prompt
Large Vision-Language Models (LVLMs) have achieved impressive progress across various applications but remain vulnerable to malicious queries that exploit the visual modality. Existing alignment approaches typically fail to resist malicious queries while preserving utility on benign ones effectively. To address these challenges, we propose Deep Aligned Visual Safety Prompt (DAVSP), which is built upon two key innovations. First, we introduce the Visual Safety Prompt, which appends a trainable p…

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 …

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Erik Buchholz, Natasha Fernandes, David D. Nguyen, Alsharif Abuadbba, Surya Nepal, Salil S. Kanhere
https://arxiv.org/abs/2506.09312

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy (DP) offers formal protection, but achieving a favourable utility-privacy trade-off remains challenging. Recent works explore deep learning-based generative models to produce synthetic trajectories. However, current models lack formal privacy guarantees and rely on conditional information derived from real data during generation. This work investigates the utility cost of e…

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…

Adversarial Text Generation with Dynamic Contextual Perturbation
Hetvi Waghela, Jaydip Sen, Sneha Rakshit, Subhasis Dasgupta
https://arxiv.org/abs/2506.09148

Adversarial Text Generation with Dynamic Contextual Perturbation
Adversarial attacks on Natural Language Processing (NLP) models expose vulnerabilities by introducing subtle perturbations to input text, often leading to misclassification while maintaining human readability. Existing methods typically focus on word-level or local text segment alterations, overlooking the broader context, which results in detectable or semantically inconsistent perturbations. We propose a novel adversarial text attack scheme named Dynamic Contextual Perturbation (DCP). DCP dyn…

[2025-06-12 Thu (UTC), 19 new articles found for cs.CR Cryptography and Security]
toXiv_bot_toot

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…

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…

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…

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, …

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…

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…

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…

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…

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…

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…

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…

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…

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…

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…

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…

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…

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…

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…

mLaSDI: Multi-stage latent space dynamics identification
William Anderson, Kevin Chung, Youngsoo Choi
https://arxiv.org/abs/2506.09207 https://

mLaSDI: Multi-stage latent space dynamics identification
Determining accurate numerical solutions of partial differential equations (PDEs) is an important task in many scientific disciplines. However, solvers can be computationally expensive, leading to the development of reduced-order models (ROMs). Recently, Latent Space Dynamics Identification (LaSDI) was proposed as a data-driven, non-intrusive ROM framework. LaSDI compresses the training data using an autoencoder and learns a system of user-chosen ordinary differential equations (ODEs), which go…

Policy-Based Trajectory Clustering in Offline Reinforcement Learning
Hao Hu, Xinqi Wang, Simon Shaolei Du
https://arxiv.org/abs/2506.09202 https://

Policy-Based Trajectory Clustering in Offline Reinforcement Learning
We introduce a novel task of clustering trajectories from offline reinforcement learning (RL) datasets, where each cluster center represents the policy that generated its trajectories. By leveraging the connection between the KL-divergence of offline trajectory distributions and a mixture of policy-induced distributions, we formulate a natural clustering objective. To solve this, we propose Policy-Guided K-means (PG-Kmeans) and Centroid-Attracted Autoencoder (CAAE). PG-Kmeans iteratively trains…

FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems
Val Andrei Fajardo, David B. Emerson, Amandeep Singh, Veronica Chatrath, Marcelo Lotif, Ravi Theja, Alex Cheung, Izuki Matsubi
https://arxiv.org/abs/2506.09200

FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems
Retrieval-augmented generation (RAG) systems have been shown to be effective in addressing many of the drawbacks of relying solely on the parametric memory of large language models. Recent work has demonstrated that RAG systems can be improved via fine-tuning of their retriever and generator models. In this work, we introduce FedRAG, a framework for fine-tuning RAG systems across centralized and federated architectures. FedRAG supports state-of-the-art fine-tuning methods, offering a simple and…

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
Hariharan Ramesh, Jyotikrishna Dass
https://arxiv.org/abs/2506.09199

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
Integrating Low-Rank Adaptation (LoRA) into federated learning offers a promising solution for parameter-efficient fine-tuning of Large Language Models (LLMs) without sharing local data. However, several methods designed for federated LoRA present significant challenges in balancing communication efficiency, model accuracy, and computational cost, particularly among heterogeneous clients. These methods either rely on simplistic averaging of local adapters, which introduces aggregation noise, re…

LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting
Yilin Zhuang, Karthik Duraisamy
https://arxiv.org/abs/2506.09193 https://…

LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting
Accurate probabilistic weather forecasting demands both high accuracy and efficient uncertainty quantification, challenges that overburden both ensemble numerical weather prediction (NWP) and recent machine-learning methods. We introduce LaDCast, the first global latent-diffusion framework for medium-range ensemble forecasting, which generates hourly ensemble forecasts entirely in a learned latent space. An autoencoder compresses high-dimensional ERA5 reanalysis fields into a compact representa…

Multi-Task Reward Learning from Human Ratings
Mingkang Wu, Devin White, Evelyn Rose, Vernon Lawhern, Nicholas R Waytowich, Yongcan Cao
https://arxiv.org/abs/2506.09183

Multi-Task Reward Learning from Human Ratings
Reinforcement learning from human feeback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when making decisions, current RLHF approaches often simplify this process by modeling human reasoning through isolated tasks such as classification or regression. In this paper, we propose a novel reinforcement learning (RL) method that mimics human decision-making by jointly considering multiple tasks. Specifically, we lever…

Multivariate Long-term Time Series Forecasting with Fourier Neural Filter
Chenheng Xu, Dan Wu, Yixin Zhu, Ying Nian Wu
https://arxiv.org/abs/2506.09174 htt…

Multivariate Long-term Time Series Forecasting with Fourier Neural Filter
Multivariate long-term time series forecasting has been suffering from the challenge of capturing both temporal dependencies within variables and spatial correlations across variables simultaneously. Current approaches predominantly repurpose backbones from natural language processing or computer vision (e.g., Transformers), which fail to adequately address the unique properties of time series (e.g., periodicity). The research community lacks a dedicated backbone with temporal-specific inductiv…