Tootfinder

from my link log —
Why is there a "small house" in IBM's code page 437?
https://blog.glyphdrawing.club/why-is-there-a-small-house-in-ibm-s-code-page-437/
saved 2025-04-12

Why is there a "small house" in IBM's Code page 437?
There's a small house in the moddle of IBM's Code Page 437. Why?

Is Tracking really more challenging in First Person Egocentric Vision?
Matteo Dunnhofer, Zaira Manigrasso, Christian Micheloni
https://arxiv.org/abs/2507.16015

Is Tracking really more challenging in First Person Egocentric Vision?
Visual object tracking and segmentation are becoming fundamental tasks for understanding human activities in egocentric vision. Recent research has benchmarked state-of-the-art methods and concluded that first person egocentric vision presents challenges compared to previously studied domains. However, these claims are based on evaluations conducted across significantly different scenarios. Many of the challenging characteristics attributed to egocentric vision are also present in third person …

Entropy-Driven Pre-Tokenization for Byte-Pair Encoding
Yifan Hu, Frank Liang, Dachuan Zhao, Jonathan Geuter, Varshini Reddy, Craig W. Schmidt, Chris Tanner
https://arxiv.org/abs/2506.15889

Entropy-Driven Pre-Tokenization for Byte-Pair Encoding
Byte-Pair Encoding (BPE) has become a widely adopted subword tokenization method in modern language models due to its simplicity and strong empirical performance across downstream tasks. However, applying BPE to unsegmented languages such as Chinese presents significant challenges, as its frequency-driven merge operation is agnostic to linguistic boundaries. To address this, we propose two entropy-informed pre-tokenization strategies that guide BPE segmentation using unsupervised information-th…

[2025-06-23 Mon (UTC), no new articles found for math.CT Category Theory]
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On the ring of cooperations for real Hermitian K-theory
Jackson Morris
https://arxiv.org/abs/2506.16672 https://arxiv.org/pdf/2506.16…

On the ring of cooperations for real Hermitian K-theory
Let kq denote the very effective cover of the motivic Hermitian K-theory spectrum. We analyze the ring of cooperations $π^\mathbb{R}_{**}(\text{kq} \otimes \text{kq})$ in the stable motivic homotopy category $\text{SH}(\mathbb{R})$, giving a full description in terms of Brown--Gitler comodules. To do this, we decompose the $E_2$-page of the motivic Adams spectral sequence and show that it must collapse. The description of the $E_2$-page is accomplished by a series of algebraic Atiyah--Hirzebru…

A System Level Compiler for Massively-Parallel, Spatial, Dataflow Architectures
Dirk Van Essendelft, Patrick Wingo, Terry Jordan, Ryan Smith, Wissam Saidi
https://arxiv.org/abs/2506.15875

A System Level Compiler for Massively-Parallel, Spatial, Dataflow Architectures
We have developed a novel compiler called the Multiple-Architecture Compiler for Advanced Computing Hardware (MACH) designed specifically for massively-parallel, spatial, dataflow architectures like the Wafer Scale Engine. Additionally, MACH can execute code on traditional unified-memory devices. MACH addresses the complexities in compiling for spatial architectures through a conceptual Virtual Machine, a flexible domain-specific language, and a compiler that can lower high-level languages to m…

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Zachary D King, Maryam Khalid, Han Yu, Kei Shibuya, Khadija Zanna, Marzieh Majd, Ryan L Brown, Yufei Shen, Thomas Vaessen, George Kypriotakis, Christopher P Fagundes, Akane Sano
https://arxiv.org/abs/2506.15834

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Mobile health (mHealth) systems help researchers monitor and care for patients in real-world settings. Studies utilizing mHealth applications use Ecological Momentary Assessment (EMAs), passive sensing, and contextual features to develop emotion recognition models, which rely on EMA responses as ground truth. Due to this, it is crucial to consider EMA compliance when conducting a successful mHealth study. Utilizing machine learning is one approach that can solve this problem by sending EMAs bas…

Fast-VAT: Accelerating Cluster Tendency Visualization using Cython and Numba
MSR Avinash (Presidency University, Bangalore), Ismael Lachheb (EPITA School of Engineering,Computer Science, Paris, France)
https://arxiv.org/abs/2507.15904

Fast-VAT: Accelerating Cluster Tendency Visualization using Cython and Numba
Visual Assessment of Cluster Tendency (VAT) is a widely used unsupervised technique to assess the presence of cluster structure in unlabeled datasets. However, its standard implementation suffers from significant performance limitations due to its O(n^2) time complexity and inefficient memory usage. In this work, we present Fast-VAT, a high-performance reimplementation of the VAT algorithm in Python, augmented with Numba's Just-In-Time (JIT) compilation and Cython's static typing and low-level …

Everybody is crushing their challenges, but I'm ashamed to have lost my 0 post challenge for this year :) Here is the evidence: https://www-gem.codeberg.page/picom/.

Conservative fusion of unbiased partial state estimates: CI is optimal
Jochen Trumpf, Behzad Zamani, Chris Manzie
https://arxiv.org/abs/2507.16216 https://…

Conservative fusion of unbiased partial state estimates: CI is optimal
We show that Covariance Intersection (CI) is optimal amongst all conservative unbiased linear fusion rules also in the general case of information fusion of two unbiased partial state estimates, significantly generalizing the known optimality result for fusion of full state estimates. In fact, we prove the much stronger result that three different optimization problems are equivalent, namely the abstract optimal conservative unbiased linear information fusion problem with respect to a strictly …

[2025-07-22 Tue (UTC), 9 new articles found for cs.LO Logic in Computer Science]
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[2025-06-23 Mon (UTC), 2 new articles found for math.GR Group Theory]
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A note on some reverse inequalities for scalar Birkhoff weak integrable functions
Anca Croitoru, Alina Iosif, Anna Rita Sambucini
https://arxiv.org/abs/2507.16332

A note on some reverse inequalities for scalar Birkhoff weak integrable functions
Some inequalities and reverses of classic Hölder and Minkowski types are obtained for scalar Birkhoff weak integrable functions with respect to a non-additive measure.

DeepRTL2: A Versatile Model for RTL-Related Tasks
Yi Liu, Hongji Zhang, Yunhao Zhou, Zhengyuan Shi, Changran Xu, Qiang Xu
https://arxiv.org/abs/2506.15697 …

DeepRTL2: A Versatile Model for RTL-Related Tasks
The integration of large language models (LLMs) into electronic design automation (EDA) has significantly advanced the field, offering transformative benefits, particularly in register transfer level (RTL) code generation and understanding. While previous studies have demonstrated the efficacy of fine-tuning LLMs for these generation-based tasks, embedding-based tasks, which are equally critical to EDA workflows, have been largely overlooked. These tasks, including natural language code search,…

On the Efficient Discovery of Maximum $k$-Defective Biclique
Donghang Cui, Ronghua Li, Qiangqiang Dai, Hongchao Qin, Guoren Wang
https://arxiv.org/abs/2506.16121

On the Efficient Discovery of Maximum $k$-Defective Biclique
The problem of identifying the maximum edge biclique in bipartite graphs has attracted considerable attention in bipartite graph analysis, with numerous real-world applications such as fraud detection, community detection, and online recommendation systems. However, real-world graphs may contain noise or incomplete information, leading to overly restrictive conditions when employing the biclique model. To mitigate this, we focus on a new relaxed subgraph model, called the $k$-defective biclique…

Probing EFX via PMMS: (Non-)Existence Results in Discrete Fair Division
Jaros{\l}aw Byrka, Franciszek Malinka, Tomasz Ponitka
https://arxiv.org/abs/2507.14957

Probing EFX via PMMS: (Non-)Existence Results in Discrete Fair Division
We study the fair division of indivisible items and provide new insights into the EFX problem, which is widely regarded as the central open question in fair division, and the PMMS problem, a strictly stronger variant of EFX. Our first result constructs a three-agent instance with two monotone valuations and one additive valuation in which no PMMS allocation exists. Since EFX allocations are known to exist under these assumptions, this establishes a formal separation between EFX and PMMS. We p…

Nuclear-spin-related properties of~the dual-frequency Doppler-free resonance
E. A. Tsygankov, K. M. Sabakar, D. S. Chuchelov, M. I. Vaskovskaya, V. V. Vassiliev, S. A. Zibrov, V. L. Velichansky
https://arxiv.org/abs/2507.14659

X-Nav: Learning End-to-End Cross-Embodiment Navigation for Mobile Robots
Haitong Wang, Aaron Hao Tan, Angus Fung, Goldie Nejat
https://arxiv.org/abs/2507.14731

X-Nav: Learning End-to-End Cross-Embodiment Navigation for Mobile Robots
Existing navigation methods are primarily designed for specific robot embodiments, limiting their generalizability across diverse robot platforms. In this paper, we introduce X-Nav, a novel framework for end-to-end cross-embodiment navigation where a single unified policy can be deployed across various embodiments for both wheeled and quadrupedal robots. X-Nav consists of two learning stages: 1) multiple expert policies are trained using deep reinforcement learning with privileged observations …

Multi-Granular Discretization for Interpretable Generalization in Precise Cyberattack Identification
Wen-Cheng Chung, Shu-Ting Huang, Hao-Ting Pai
https://arxiv.org/abs/2507.14223

Multi-Granular Discretization for Interpretable Generalization in Precise Cyberattack Identification
Explainable intrusion detection systems (IDS) are now recognized as essential for mission-critical networks, yet most "XAI" pipelines still bolt an approximate explainer onto an opaque classifier, leaving analysts with partial and sometimes misleading insights. The Interpretable Generalization (IG) mechanism, published in IEEE Transactions on Information Forensics and Security, eliminates that bottleneck by learning coherent patterns - feature combinations unique to benign or malicious traffic …

My "now" page
- what I'm doing and why I might say NO to other projects -
https://muz4now.com/now

What's up now? - Stan Stewart
Stan Stewart - @muz4now

Seeing is Fixing: Cross-Modal Reasoning with Multimodal LLMs for Visual Software Issue Fixing
Kai Huang, Jian Zhang, Xiaofei Xie, Chunyang Chen
https://arxiv.org/abs/2506.16136

Seeing is Fixing: Cross-Modal Reasoning with Multimodal LLMs for Visual Software Issue Fixing
Large language model-(LLM) based automated program repair (APR) techniques have shown promising results in resolving real-world GitHub issue tasks. Existing APR systems are primarily evaluated in unimodal settings (e.g., SWE-bench). However, these autonomous systems struggle to resolve multimodal problem scenarios (e.g., SWE-bench M) due to limitations in interpreting and leveraging visual information. In multimodal scenarios, LLMs need to rely on visual information in the graphical user interf…

MP-GCAN: a highly accurate classifier for $\alpha$-helical membrane proteins and $\beta$-barrel proteins
Kunyang Li, Hongfu Lou, Dinan Peng
https://arxiv.org/abs/2507.14269

MP-GCAN: a highly accurate classifier for $α$-helical membrane proteins and $β$-barrel proteins
Membrane protein classification is a fundamental task in structural bioinformatics, critical to understanding protein functions and accelerating drug discovery. In this study, we propose MP-GCAN, a novel graph-based classification model that leverages both spatial and sequential features of proteins. MP-GCAN combines GCN, GAT, and GIN layers to capture hierarchical structural representations from 3D protein graphs, constructed from high-resolution PDB files with $α$-carbon coordinates and resi…

FW-VTON: Flattening-and-Warping for Person-to-Person Virtual Try-on
Zheng Wang, Xianbing Sun, Shengyi Wu, Jiahui Zhan, Jianlou Si, Chi Zhang, Liqing Zhang, Jianfu Zhang
https://arxiv.org/abs/2507.16010

FW-VTON: Flattening-and-Warping for Person-to-Person Virtual Try-on
Traditional virtual try-on methods primarily focus on the garment-to-person try-on task, which requires flat garment representations. In contrast, this paper introduces a novel approach to the person-to-person try-on task. Unlike the garment-to-person try-on task, the person-to-person task only involves two input images: one depicting the target person and the other showing the garment worn by a different individual. The goal is to generate a realistic combination of the target person with the …

Finance Language Model Evaluation (FLaME)
Glenn Matlin, Mika Okamoto, Huzaifa Pardawala, Yang Yang, Sudheer Chava
https://arxiv.org/abs/2506.15846 https://…

Finance Language Model Evaluation (FLaME)
Language Models (LMs) have demonstrated impressive capabilities with core Natural Language Processing (NLP) tasks. The effectiveness of LMs for highly specialized knowledge-intensive tasks in finance remains difficult to assess due to major gaps in the methodologies of existing evaluation frameworks, which have caused an erroneous belief in a far lower bound of LMs' performance on common Finance NLP (FinNLP) tasks. To demonstrate the potential of LMs for these FinNLP tasks, we present the first…

[2025-06-23 Mon (UTC), 3 new articles found for cs.PL Programming Languages]
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[2025-06-23 Mon (UTC), 23 new articles found for cs.HC Human-Computer Interaction]
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Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
Siyuan Liu, Wenjing Liu, Zhiwei Xu, Xin Wang, Bo Chen, Tao Li
https://arxiv.org/abs/2507.15903

Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
Empowered by large language models (LLMs), intelligent agents have become a popular paradigm for interacting with open environments to facilitate AI deployment. However, hallucinations generated by LLMs-where outputs are inconsistent with facts-pose a significant challenge, undermining the credibility of intelligent agents. Only if hallucinations can be mitigated, the intelligent agents can be used in real-world without any catastrophic risk. Therefore, effective detection and mitigation of hal…

\v{C}ech cohomology of infinite projective spaces, flag manifolds, and related spaces
David Anderson, Matthias Franz
https://arxiv.org/abs/2506.16487 https…

Čech cohomology of infinite projective spaces, flag manifolds, and related spaces
We compute the Čech cohomology ring of a countable product of infinite projective spaces, and that of an infinite flag manifold. The method of our first result in fact computes the cohomology ring (with integer coefficients) of a countably infinite product of paracompact Hausdorff spaces, under some mild assumptions.

Parallel Polyhedral Projection Method for the Convex Feasibility Problem
Pablo Barros, Roger Behling, Vincent Guigues
https://arxiv.org/abs/2506.15895 http…

Parallel Polyhedral Projection Method for the Convex Feasibility Problem
In this paper, we introduce and study the Parallel Polyhedral Projection Method (3PM) and the Approximate Parallel Polyhedral Projection Method (A3PM) for finding a point in the intersection of finitely many closed convex sets. Each iteration has two phases: parallel projections onto the target sets (exact in 3PM, approximate in A3PM), followed by an exact or approximate projection onto a polyhedron defined by supporting half-spaces. These strategies appear novel, as existing methods largely fo…

A meta-modal logic for bisimulations
Alfredo Burrieza, Fernando Soler-Toscano, Antonio Yuste-Ginel
https://arxiv.org/abs/2507.15117 https://

A meta-modal logic for bisimulations
We propose a modal study of the notion of bisimulation. Our contribution is twofold. First, we extend the basic modal language with a new modality [b], whose intended meaning is universal quantification over all states that are bisimilar to the current one. We show that bisimulations are definable in this object language. Second, we provide a sound and complete axiomatisation of the class of all pairs of Kripke models linked by bisimulations.

Local fibrations and morphisms of relative toposes
L\'eo Bartoli, Olivia Caramello
https://arxiv.org/abs/2507.14379 https://arxiv…

Local fibrations and morphisms of relative toposes
We introduce the notion of local fibration, a generalization of the notion of fibration which takes into account the presence of Grothendieck topologies on the two categories, and show that the classical results about fibrations lift to this more general setting. As an application of this notion, we obtain a characterization of the functors between relative sites that induce a morphism between the corresponding relative toposes: these are exactly the morphisms of sites which are morphisms of lo…

A Lightweight Face Quality Assessment Framework to Improve Face Verification Performance in Real-Time Screening Applications
Ahmed Aman Ibrahim, Hamad Mansour Alawar, Abdulnasser Abbas Zehi, Ahmed Mohammad Alkendi, Bilal Shafi Ashfaq Ahmed Mirza, Shan Ullah, Ismail Lujain Jaleel, Hassan Ugail
https://arxiv.org/abs/2507.15961

A Lightweight Face Quality Assessment Framework to Improve Face Verification Performance in Real-Time Screening Applications
Face image quality plays a critical role in determining the accuracy and reliability of face verification systems, particularly in real-time screening applications such as surveillance, identity verification, and access control. Low-quality face images, often caused by factors such as motion blur, poor lighting conditions, occlusions, and extreme pose variations, significantly degrade the performance of face recognition models, leading to higher false rejection and false acceptance rates. In th…

[2025-06-23 Mon (UTC), 6 new articles found for cs.AR Hardware Architecture]
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Leveraging Extrinsic Dexterity for Occluded Grasping on Grasp Constraining Walls
Keita Kobashi, Masayoshi Tomizuka
https://arxiv.org/abs/2507.14721 https:/…

Leveraging Extrinsic Dexterity for Occluded Grasping on Grasp Constraining Walls
This study addresses the problem of occluded grasping, where primary grasp configurations of an object are not available due to occlusion with environment. Simple parallel grippers often struggle with such tasks due to limited dexterity and actuation constraints. Prior works have explored object pose reorientation such as pivoting by utilizing extrinsic contacts between an object and an environment feature like a wall, to make the object graspable. However, such works often assume the presence …

MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
Zijian Zhou, Ao Qu, Zhaoxuan Wu, Sunghwan Kim, Alok Prakash, Daniela Rus, Jinhua Zhao, Bryan Kian Hsiang Low, Paul Pu Liang
https://arxiv.org/abs/2506.15841

MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
Modern language agents must operate over long-horizon, multi-turn interactions, where they retrieve external information, adapt to observations, and answer interdependent queries. Yet, most LLM systems rely on full-context prompting, appending all past turns regardless of their relevance. This leads to unbounded memory growth, increased computational costs, and degraded reasoning performance on out-of-distribution input lengths. We introduce MEM1, an end-to-end reinforcement learning framework …

GPU-Accelerated Interpretable Generalization for Rapid Cyberattack Detection and Forensics
Shu-Ting Huang, Wen-Cheng Chung, Hao-Ting Pai
https://arxiv.org/abs/2507.14222

GPU-Accelerated Interpretable Generalization for Rapid Cyberattack Detection and Forensics
The Interpretable Generalization (IG) mechanism recently published in IEEE Transactions on Information Forensics and Security delivers state-of-the-art, evidence-based intrusion detection by discovering coherent normal and attack patterns through exhaustive intersect-and-subset operations-yet its cubic-time complexity and large intermediate bitsets render full-scale datasets impractical on CPUs. We present IG-GPU, a PyTorch re-architecture that offloads all pairwise intersections and subset eva…

Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques
Yupeng Jiang, Shuaiyi Sun, Xi Zheng
https://arxiv.org/abs/2506.16101

Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques
Regression testing plays a critical role in maintaining software reliability, particularly for ROS-based autonomous systems (ROSAS), which frequently undergo continuous integration and iterative development. However, conventional regression testing techniques face significant challenges when applied to autonomous systems due to their dynamic and non-deterministic behaviors, complex multi-modal sensor data, asynchronous distributed architectures, and stringent safety and real-time constraints. A…

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique
Mohammad Asifur Rahim, Muhammad Nazmul Arefin, Md. Mizanur Rahman, Md Ali Hossain, Ahmed Moustafa
https://arxiv.org/abs/2507.15915

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique
Context: Mpox is a zoonotic disease caused by the Mpox virus, which shares similarities with other skin conditions, making accurate early diagnosis challenging. Artificial intelligence (AI), especially Deep Learning (DL), has a strong tool for medical image analysis; however, pre-trained models like CNNs and XAI techniques for mpox detection is underexplored. Objective: This study aims to evaluate the effectiveness of pre-trained CNN models (VGG16, VGG19, InceptionV3, MobileNetV2) for the early…

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz, Clinton Fookes, Olivier Salvado
https://arxiv.org/abs/2507.15900

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates
Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data. Once trained, decoding a random latent vector from the prior usually does not produce meaningful data, at least when the latent space has more than a dozen dimensions. In this paper, we investigate this issue by drawing insight from high dimensional statistics: in these regimes, the latent vectors of a standard VAE are by construction distributed uniformly on a hyperspher…

Ahlfors-regularity for minimizers of a multiphase optimal design problem
Luca Esposito, Lorenzo Lamberti, Giovanni Pisante
https://arxiv.org/abs/2506.15861

Ahlfors-regularity for minimizers of a multiphase optimal design problem
We establish an Alhfors-regularity result for minimizers of a multiphase optimal design problem. It is a variant of the classical variational problem which involves a finite number of chambers $\mathcal{E}(i)$ of prescribed volume that partition a given domain $Ω\subset\mathbb{R}^n$. The cost functional associated with a configuration $\left(\{\mathcal{E}(i)\}_i,u\right)$ is made up of the perimeter of the partition interfaces and a Dirichlet energy term, which is discontinuous across the inte…

Rethinking LLM Training through Information Geometry and Quantum Metrics
Riccardo Di Sipio
https://arxiv.org/abs/2506.15830 https://a…

Rethinking LLM Training through Information Geometry and Quantum Metrics
Optimization in large language models (LLMs) unfolds over high-dimensional parameter spaces with non-Euclidean structure. Information geometry frames this landscape using the Fisher information metric, enabling more principled learning via natural gradient descent. Though often impractical, this geometric lens clarifies phenomena such as sharp minima, generalization, and observed scaling laws. We argue that curvature-aware approaches deepen our understanding of LLM training. Finally, we specula…

PSPACE-completeness of Grammar logics of bounded density
Olivier Gasquet
https://arxiv.org/abs/2507.14956 https://arxiv.org/pdf/2507.…

PSPACE-completeness of Grammar logics of bounded density
We introduce the family of multi-modal logics of bounded density and with a tableau-like approach using finite \emph{windows} which were introduced in \cite{BalGasq25}, we prove that their satisfiability problem is PSPACE-complete. As a side effect, the monomodal logic of density is shown to be in para-PSPACE.

[2025-07-22 Tue (UTC), 3 new articles found for math.CT Category Theory]
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Formal Analysis of Networked PLC Controllers Interacting with Physical Environments
Jaeseo Lee, Kyungmin Bae
https://arxiv.org/abs/2507.15596 https://

Formal Analysis of Networked PLC Controllers Interacting with Physical Environments
Programmable Logic Controllers (PLCs) are widely used in industrial automation to control physical systems. As PLC applications become increasingly complex, ensuring their correctness is crucial. Existing formal verification techniques focus on individual PLC programs in isolation, often neglecting interactions with physical environments and network communication between controllers. This limitation poses significant challenges in analyzing real-world industrial systems, where continuous dynami…

Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation
Nicholas Mohammad, Nicola Bezzo
https://arxiv.org/abs/2507.14700

Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation
Safe navigation in unknown and cluttered environments remains a challenging problem in robotics. Model Predictive Contour Control (MPCC) has shown promise for performant obstacle avoidance by enabling precise and agile trajectory tracking, however, existing methods lack formal safety assurances. To address this issue, we propose a general Control Lyapunov Function (CLF) and Control Barrier Function (CBF) enabled MPCC framework that enforces safety constraints derived from a free-space corridor …

Magneto-Ionic Hardware Security Primitives: Embedding Data Protection at the Material Level
Irena Spasojevic, Federica Celegato, Alessandro Magni, Paola Tiberto, Jordi Sort
https://arxiv.org/abs/2507.14213

Magneto-Ionic Hardware Security Primitives: Embedding Data Protection at the Material Level
The Big Data revolution has heightened the demand for robust, energy-efficient security hardware capable of withstanding increasingly sophisticated cyber threats. Conventional encryption schemes, reliant on complex algorithms, are resource-intensive and remain vulnerable. To fortify sensitive information, society needs innovative anti-hacking and anti-counterfeiting technologies that exploit new materials and designs. Here, we present a magneto-ionic strategy for hardware-level security based o…

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Shamse Tasnim Cynthia, Nuri Almarimi, Banani Roy
https://arxiv.org/abs/2506.15884

How Do Community Smells Influence Self-Admitted Technical Debt in Machine Learning Projects?
Community smells reflect poor organizational practices that often lead to socio-technical issues and the accumulation of Self-Admitted Technical Debt (SATD). While prior studies have explored these problems in general software systems, their interplay in machine learning (ML)-based projects remains largely underexamined. In this study, we investigated the prevalence of community smells and their relationship with SATD in open-source ML projects, analyzing data at the release level. First, we ex…

Veracity: An Open-Source AI Fact-Checking System
Taylor Lynn Curtis, Maximilian Puelma Touzel, William Garneau, Manon Gruaz, Mike Pinder, Li Wei Wang, Sukanya Krishna, Luda Cohen, Jean-Fran\c{c}ois Godbout, Reihaneh Rabbany, Kellin Pelrine
https://arxiv.org/abs/2506.15794

Veracity: An Open-Source AI Fact-Checking System
The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key feat…

ReDi: Rectified Discrete Flow
Jaehoon Yoo, Wonjung Kim, Seunghoon Hong
https://arxiv.org/abs/2507.15897 https://arxiv.org/pdf/2507.15…

ReDi: Rectified Discrete Flow
Discrete Flow-based Models (DFMs) are powerful generative models for high-quality discrete data but typically suffer from slow sampling speeds due to their reliance on iterative decoding processes. This reliance on a multi-step process originates from the factorization approximation of DFMs, which is necessary for handling high-dimensional data. In this paper, we rigorously characterize the approximation error from factorization using Conditional Total Correlation (TC), which depends on the cou…

[2025-06-23 Mon (UTC), 13 new articles found for math.OC Optimization and Control]
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PSPACE-completeness of bimodal transitive weak-density logic
Philippe Balbiani, Olivier Gasquet
https://arxiv.org/abs/2507.14949 https://

PSPACE-completeness of bimodal transitive weak-density logic
Windows have been introduce in \cite{BalGasq25} as a tool for designing polynomial algorithms to check satisfiability of a bimodal logic of weak-density. In this paper, after revisiting the ``folklore'' case of bimodal $\K4$ already treated in \cite{Halpern} but which is worth a fresh review, we show that windows allow to polynomially solve the satisfiability problem when adding transitivity to weak-density, by mixing algorithms for bimodal K together with windows-approach. The conclusion is th…

Bayesian Separation Logic
Shing Hin Ho, Nicolas Wu, Azalea Raad
https://arxiv.org/abs/2507.15530 https://arxiv.org/pdf/2507.15530

Bayesian Separation Logic
Bayesian probabilistic programming languages (BPPLs) let users denote statistical models as code while the interpreter infers the posterior distribution. The semantics of BPPLs are usually mathematically complex and unable to reason about desirable properties such as expected values and independence of random variables. To reason about these properties in a non-Bayesian setting, probabilistic separation logics such as PSL and Lilac interpret separating conjunction as probabilistic independence …

Uncertainty-aware Probabilistic 3D Human Motion Forecasting via Invertible Networks
Yue Ma, Kanglei Zhou, Fuyang Yu, Frederick W. B. Li, Xiaohui Liang
https://arxiv.org/abs/2507.14694

Uncertainty-aware Probabilistic 3D Human Motion Forecasting via Invertible Networks
3D human motion forecasting aims to enable autonomous applications. Estimating uncertainty for each prediction (i.e., confidence based on probability density or quantile) is essential for safety-critical contexts like human-robot collaboration to minimize risks. However, existing diverse motion forecasting approaches struggle with uncertainty quantification due to implicit probabilistic representations hindering uncertainty modeling. We propose ProbHMI, which introduces invertible networks to p…

[2025-06-23 Mon (UTC), 78 new articles found for cs.CL Computation and Language]
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Secure Goal-Oriented Communication: Defending against Eavesdropping Timing Attacks
Federico Mason, Federico Chiariotti, Pietro Talli, Andrea Zanella
https://arxiv.org/abs/2507.14212

Secure Goal-Oriented Communication: Defending against Eavesdropping Timing Attacks
Goal-oriented Communication (GoC) is a new paradigm that plans data transmission to occur only when it is instrumental for the receiver to achieve a certain goal. This leads to the advantage of reducing the frequency of transmissions significantly while maintaining adherence to the receiver's objectives. However, GoC scheduling also opens a timing-based side channel that an eavesdropper can exploit to obtain information about the state of the system. This type of attack sidesteps even informati…

[2025-06-23 Mon (UTC), 20 new articles found for cs.SE Software Engineering]
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Prompt Smart, Pay Less: Cost-Aware APO for Real-World Applications
Jayesh Choudhari, Piyush Kumar Singh, Douglas McIlwraith, Snehal Nair
https://arxiv.org/abs/2507.15884

Prompt Smart, Pay Less: Cost-Aware APO for Real-World Applications
Prompt design is a critical factor in the effectiveness of Large Language Models (LLMs), yet remains largely heuristic, manual, and difficult to scale. This paper presents the first comprehensive evaluation of Automatic Prompt Optimization (APO) methods for real-world, high-stakes multiclass classification in a commercial setting, addressing a critical gap in the existing literature where most of the APO frameworks have been validated only on benchmark classification tasks of limited complexity…

Local Dense Logit Relations for Enhanced Knowledge Distillation
Liuchi Xu, Kang Liu, Jinshuai Liu, Lu Wang, Lisheng Xu, Jun Cheng
https://arxiv.org/abs/2507.15911

Local Dense Logit Relations for Enhanced Knowledge Distillation
State-of-the-art logit distillation methods exhibit versatility, simplicity, and efficiency. Despite the advances, existing studies have yet to delve thoroughly into fine-grained relationships within logit knowledge. In this paper, we propose Local Dense Relational Logit Distillation (LDRLD), a novel method that captures inter-class relationships through recursively decoupling and recombining logit information, thereby providing more detailed and clearer insights for student learning. To furthe…

A Proof System with Causal Labels (Part II): checking Counterfactual Fairness
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2507.14655 https:…

A Proof System with Causal Labels (Part II): checking Counterfactual Fairness
In this article we propose an extension to the typed natural deduction calculus TNDPQ to model verification of counterfactual fairness in probabilistic classifiers. This is obtained formulating specific structural conditions for causal labels and checking that evaluation is robust under their variation.

A Few Fit Most: Improving Performance Portability of SGEMM on GPUs using Multi-Versioning
Robert Hochgraf (Rochester Institute of Technology), Sreepathi Pai (University of Rochester)
https://arxiv.org/abs/2507.15277

A Few Fit Most: Improving Performance Portability of SGEMM on GPUs using Multi-Versioning
Hand-optimizing linear algebra kernels for different GPU devices and applications is complex and labor-intensive. Instead, many developers use automatic performance tuning (autotuning) to achieve high performance on a variety of devices. However, autotuning "overfits", and must be redone if any part of the environment changes, such as if the device or input characteristics change. In most non-trivial cases, a single compute kernel cannot maintain near-optimal performance across all environmen…

Retention analysis of edited knowledge after fine-tuning
Fufang Wen, Shichang Zhang
https://arxiv.org/abs/2507.14198 https://arxiv.or…

Retention analysis of edited knowledge after fine-tuning
Large language models (LLMs) store vast amounts of knowledge, which often requires updates to correct factual errors, incorporate newly acquired information, or adapt model behavior. Model editing methods have emerged as efficient solutions for such updates, offering localized and precise knowledge modification at significantly lower computational cost than continual training. In parallel, LLMs are frequently fine-tuned for a wide range of downstream tasks. However, the effect of fine-tuning on…

Koopman Operator Based Linear Model Predictive Control for 2D Quadruped Trotting, Bounding, and Gait Transition
Chun-Ming Yang, Pranav A. Bhounsule
https://arxiv.org/abs/2507.14605

Koopman Operator Based Linear Model Predictive Control for 2D Quadruped Trotting, Bounding, and Gait Transition
Online optimal control of quadrupedal robots would enable them to plan their movement in novel scenarios. Linear Model Predictive Control (LMPC) has emerged as a practical approach for real-time control. In LMPC, an optimization problem with a quadratic cost and linear constraints is formulated over a finite horizon and solved on the fly. However, LMPC relies on linearizing the equations of motion (EOM), which may lead to poor solution quality. In this paper, we use Koopman operator theory and …

FARFETCH'D: A Side-Channel Analysis Framework for Privacy Applications on Confidential Virtual Machines
Ruiyi Zhang, Albert Cheu, Adria Gascon, Daniel Moghimi, Phillipp Schoppmann, Michael Schwarz, Octavian Suciu
https://arxiv.org/abs/2506.15924

FARFETCH'D: A Side-Channel Analysis Framework for Privacy Applications on Confidential Virtual Machines
Confidential virtual machines (CVMs) based on trusted execution environments (TEEs) enable new privacy-preserving solutions. Yet, they leave side-channel leakage outside their threat model, shifting the responsibility of mitigating such attacks to developers. However, mitigations are either not generic or too slow for practical use, and developers currently lack a systematic, efficient way to measure and compare leakage across real-world deployments. In this paper, we present FARFETCH'D, an ope…

An open dataset of neural networks for hypernetwork research
David Kurtenbach, Lior Shamir
https://arxiv.org/abs/2507.15869 https://a…

An open dataset of neural networks for hypernetwork research
Despite the transformative potential of AI, the concept of neural networks that can produce other neural networks by generating model weights (hypernetworks) has been largely understudied. One of the possible reasons is the lack of available research resources that can be used for the purpose of hypernetwork research. Here we describe a dataset of neural networks, designed for the purpose of hypernetworks research. The dataset includes $10^4$ LeNet-5 neural networks trained for binary image cla…

PAT : a cautionary tale about generative visual augmentation for Object Re-identification
Leonardo Santiago Benitez Pereira, Arathy Jeevan
https://arxiv.org/abs/2507.15888

PAT++: a cautionary tale about generative visual augmentation for Object Re-identification
Generative data augmentation has demonstrated gains in several vision tasks, but its impact on object re-identification - where preserving fine-grained visual details is essential - remains largely unexplored. In this work, we assess the effectiveness of identity-preserving image generation for object re-identification. Our novel pipeline, named PAT++, incorporates Diffusion Self-Distillation into the well-established Part-Aware Transformer. Using the Urban Elements ReID Challenge dataset, we c…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/2]:
- Foundational Competencies and Responsibilities of a Research Software Engineer: Current State and...
Florian Goth, et al.

A Proof System with Causal Labels (Part I): checking Individual Fairness and Intersectionality
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2507.14650

A Proof System with Causal Labels (Part I): checking Individual Fairness and Intersectionality
In this article we propose an extension to the typed natural deduction calculus TNDPQ to model verification of individual fairness and intersectionality in probabilistic classifiers. Their interpretation is obtained by formulating specific conditions for the application of the structural rule of Weakening. Such restrictions are given by causal labels used to check for conditional independence between protected and target variables.

Invariant Generation for Floating-Point Programs via Constraint Solving
Xuran Cai, Liqian Chen, Hongfei Fu
https://arxiv.org/abs/2507.15017 https://…

Invariant Generation for Floating-Point Programs via Constraint Solving
In numeric-intensive computations, it is well known that the execution of floating-point programs is imprecise as floating point arithmetics (e.g., addition, subtraction, multiplication, division, etc.) incurs rounding errors. Albeit the rounding error is small for every single floating-point operation, the aggregation of such error in multiple operations may be dramatic and cause catastrophic program failures. Therefore, to ensure the correctness of floating-point programs, the effect of float…

RDMA: Cost Effective Agent-Driven Rare Disease Discovery within Electronic Health Record Systems
John Wu, Adam Cross, Jimeng Sun
https://arxiv.org/abs/2507.15867

RDMA: Cost Effective Agent-Driven Rare Disease Discovery within Electronic Health Record Systems
Rare diseases affect 1 in 10 Americans, yet standard ICD coding systems fail to capture these conditions in electronic health records (EHR), leaving crucial information buried in clinical notes. Current approaches struggle with medical abbreviations, miss implicit disease mentions, raise privacy concerns with cloud processing, and lack clinical reasoning abilities. We present Rare Disease Mining Agents (RDMA), a framework that mirrors how medical experts identify rare disease patterns in EHR. R…

BT-TL-DMPs: A Novel Robot TAMP Framework Combining Behavior Tree, Temporal Logic and Dynamical Movement Primitives
Zezhi Liu, Shizhen Wu, Hanqian Luo, Deyun Qin, Yongchun Fang
https://arxiv.org/abs/2507.14582

BT-TL-DMPs: A Novel Robot TAMP Framework Combining Behavior Tree, Temporal Logic and Dynamical Movement Primitives
In the field of Learning from Demonstration (LfD), enabling robots to generalize learned manipulation skills to novel scenarios for long-horizon tasks remains challenging. Specifically, it is still difficult for robots to adapt the learned skills to new environments with different task and motion requirements, especially in long-horizon, multi-stage scenarios with intricate constraints. This paper proposes a novel hierarchical framework, called BT-TL-DMPs, that integrates Behavior Tree (BT), Te…

Document Haystack: A Long Context Multimodal Image/Document Understanding Vision LLM Benchmark
Goeric Huybrechts, Srikanth Ronanki, Sai Muralidhar Jayanthi, Jack Fitzgerald, Srinivasan Veeravanallur
https://arxiv.org/abs/2507.15882

Document Haystack: A Long Context Multimodal Image/Document Understanding Vision LLM Benchmark
The proliferation of multimodal Large Language Models has significantly advanced the ability to analyze and understand complex data inputs from different modalities. However, the processing of long documents remains under-explored, largely due to a lack of suitable benchmarks. To address this, we introduce Document Haystack, a comprehensive benchmark designed to evaluate the performance of Vision Language Models (VLMs) on long, visually complex documents. Document Haystack features documents ra…

Sudoku: Decomposing DRAM Address Mapping into Component Functions
Minbok Wi, Seungmin Baek, Seonyong Park, Mattan Erez, Jung Ho Ahn
https://arxiv.org/abs/2506.15918

Sudoku: Decomposing DRAM Address Mapping into Component Functions
Decomposing DRAM address mappings into component-level functions is critical for understanding memory behavior and enabling precise RowHammer attacks, yet existing reverse-engineering methods fall short. We introduce novel timing-based techniques leveraging DRAM refresh intervals and consecutive access latencies to infer component-specific functions. Based on this, we present Sudoku, the first software-based tool to automatically decompose full DRAM address mappings into channel, rank, bank gro…

Enhancing Repository-Level Code Generation with Call Chain-Aware Multi-View Context
Yang Liu, Li Zhang, Fang Liu, Zhuohang Wang, Donglin Wei, Zhishuo Yang, Kechi Zhang, Jia Li, Lin Shi
https://arxiv.org/abs/2507.14791

Enhancing Repository-Level Code Generation with Call Chain-Aware Multi-View Context
Repository-level code generation aims to generate code within the context of a specified repository. Existing approaches typically employ retrieval-augmented generation (RAG) techniques to provide LLMs with relevant contextual information extracted from the repository. However, these approaches often struggle with effectively identifying truly relevant contexts that capture the rich semantics of the repository, and their contextual perspectives remains narrow. Moreover, most approaches fail to …

[2025-06-23 Mon (UTC), 4 new articles found for cs.LO Logic in Computer Science]
toXiv_bot_toot

Hear Your Code Fail, Voice-Assisted Debugging for Python
Sayed Mahbub Hasan Amiri, Md. Mainul Islam, Mohammad Shakhawat Hossen, Sayed Majhab Hasan Amiri, Mohammad Shawkat Ali Mamun, Sk. Humaun Kabir, Naznin Akter
https://arxiv.org/abs/2507.15007

Hear Your Code Fail, Voice-Assisted Debugging for Python
This research introduces an innovative voice-assisted debugging plugin for Python that transforms silent runtime errors into actionable audible diagnostics. By implementing a global exception hook architecture with pyttsx3 text-to-speech conversion and Tkinter-based GUI visualization, the solution delivers multimodal error feedback through parallel auditory and visual channels. Empirical evaluation demonstrates 37% reduced cognitive load (p<0.01, n=50) compared to traditional stack-trace debugg…

Quantifying Holistic Review: A Multi-Modal Approach to College Admissions Prediction
Jun-Wei Zeng, Jerry Shen
https://arxiv.org/abs/2507.15862 https://

Quantifying Holistic Review: A Multi-Modal Approach to College Admissions Prediction
This paper introduces the Comprehensive Applicant Profile Score (CAPS), a novel multi-modal framework designed to quantitatively model and interpret holistic college admissions evaluations. CAPS decomposes applicant profiles into three interpretable components: academic performance (Standardized Academic Score, SAS), essay quality (Essay Quality Index, EQI), and extracurricular engagement (Extracurricular Impact Score, EIS). Leveraging transformer-based semantic embeddings, LLM scoring, and XGB…

Salience Adjustment for Context-Based Emotion Recognition
Bin Han, Jonathan Gratch
https://arxiv.org/abs/2507.15878 https://arxiv.org…

Salience Adjustment for Context-Based Emotion Recognition
Emotion recognition in dynamic social contexts requires an understanding of the complex interaction between facial expressions and situational cues. This paper presents a salience-adjusted framework for context-aware emotion recognition with Bayesian Cue Integration (BCI) and Visual-Language Models (VLMs) to dynamically weight facial and contextual information based on the expressivity of facial cues. We evaluate this approach using human annotations and automatic emotion recognition systems in…

A 21-DOF Humanoid Dexterous Hand with Hybrid SMA-Motor Actuation: CYJ Hand-0
Jin Chai, Xiang Yao, Mengfan Hou, Yanghong Li, Erbao Dong
https://arxiv.org/abs/2507.14538

A 21-DOF Humanoid Dexterous Hand with Hybrid SMA-Motor Actuation: CYJ Hand-0
CYJ Hand-0 is a 21-DOF humanoid dexterous hand featuring a hybrid tendon-driven actuation system that combines shape memory alloys (SMAs) and DC motors. The hand employs high-strength fishing line as artificial tendons and uses a fully 3D-printed AlSi10Mg metal frame designed to replicate the skeletal and tendon-muscle structure of the human hand. A linear motor-driven module controls finger flexion, while an SMA-based module enables finger extension and lateral abduction. These modules are int…

A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners
Anna Raymaker, Akshaya Kumar, Miuyin Yong Wong, Ryan Pickren, Animesh Chhotaray, Frank Li, Saman Zonouz, Raheem Beyah
https://arxiv.org/abs/2506.15842

A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners
Maritime systems, including ships and ports, are critical components of global infrastructure, essential for transporting over 80% of the world's goods and supporting internet connectivity. However, these systems face growing cybersecurity threats, as shown by recent attacks disrupting Maersk, one of the world's largest shipping companies, causing widespread impacts on international trade. The unique challenges of the maritime environment--such as diverse operational conditions, extensive physi…

Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
Khalid Hasan, Jamil Saquer
https://arxiv.org/abs/2507.14231

Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
Bipolar disorder is a chronic mental illness frequently underdiagnosed due to subtle early symptoms and social stigma. This paper explores the advanced natural language processing (NLP) models for recognizing signs of bipolar disorder based on user-generated social media text. We conduct a comprehensive evaluation of transformer-based models (BERT, RoBERTa, ALBERT, ELECTRA, DistilBERT) and Long Short Term Memory (LSTM) models based on contextualized (BERT) and static (GloVe, Word2Vec) word embe…

Quantum Programming in Polylogarithmic Time
Florent Ferrari (ENS de Lyon), Emmanuel Hainry (MOCQUA, LORIA), Romain P\'echoux (LORIA, MOCQUA), M\'ario Silva (LORIA, MOCQUA)
https://arxiv.org/abs/2507.15415

Quantum Programming in Polylogarithmic Time
Polylogarithmic time delineates a relevant notion of feasibility on several classical computational models such as Boolean circuits or parallel random access machines. As far as the quantum paradigm is concerned, this notion yields the complexity class FBQPOLYLOG of functions approximable in polylogarithmic time with a quantum random-access Turing machine. We introduce a quantum programming language with first-order recursive procedures, which provides the first programming-language-based chara…

Low Overhead Allocation Sampling in a Garbage Collected Virtual Machine
Christoph Jung, C. F. Bolz-Tereick
https://arxiv.org/abs/2506.16883 https://…

Low Overhead Allocation Sampling in a Garbage Collected Virtual Machine
Compared to the more commonly used time-based profiling, allocation profiling provides an alternate view of the execution of allocation heavy dynamically typed languages. However, profiling every single allocation in a program is very inefficient. We present a sampling allocation profiler that is deeply integrated into the garbage collector of PyPy, a Python virtual machine. This integration ensures tunable low overhead for the allocation profiler, which we measure and quantify. Enabling alloca…

[2025-07-23 Wed (UTC), 80 new articles found for cs.CV Computer Vision and Pattern Recognition]
toXiv_bot_toot

Koopman Operator Based Time-Delay Embeddings and State History Augmented LQR for Periodic Hybrid Systems: Bouncing Pendulum and Bipedal Walking
Chun-Ming Yang, Pranav A. Bhounsule
https://arxiv.org/abs/2507.14455

Koopman Operator Based Time-Delay Embeddings and State History Augmented LQR for Periodic Hybrid Systems: Bouncing Pendulum and Bipedal Walking
Time-delay embedding is a technique that uses snapshots of state history over time to build a linear state space model of a nonlinear smooth system. We demonstrate that periodic non-smooth or hybrid system can also be modeled as a linear state space system using this approach as long as its behavior is consistent in modes and timings. We extended time-delay embeddings to generate a linear model of two periodic hybrid systems: the bouncing pendulum and the simplest walker with control inputs. Th…

ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis
Chenyang Peng, Haijun Wang, Yin Wu, Hao Wu, Ming Fan, Yitao Zhao, Ting Liu
https://arxiv.org/abs/2506.15790

ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis
With the advance application of blockchain technology in various fields, ensuring the security and stability of smart contracts has emerged as a critical challenge. Current security analysis methodologies in vulnerability detection can be categorized into static analysis and dynamic analysis methods.However, these existing traditional vulnerability detection methods predominantly rely on analyzing original contract code, not all smart contracts provide accessible code.We present ETrace, a novel…

Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale
Rui Zhao, Vladyslav Melnychuk, Jun Zhao, Jesse Wright, Nigel Shadbolt
https://arxiv.org/abs/2507.14214

Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale
In modern times, people have numerous online accounts, but they rarely read the Terms of Service or Privacy Policy of those sites despite claiming otherwise. This paper introduces PoliAnalyzer, a neuro-symbolic system that assists users with personalized privacy policy analysis. PoliAnalyzer uses Natural Language Processing (NLP) to extract formal representations of data usage practices from policy texts. In favor of deterministic, logical inference is applied to compare user preferences with t…

WAMI: Compilation to WebAssembly through MLIR without Losing Abstraction
Byeongjee Kang, Harsh Desai, Limin Jia, Brandon Lucia
https://arxiv.org/abs/2506.16048

WAMI: Compilation to WebAssembly through MLIR without Losing Abstraction
WebAssembly (Wasm) is a portable bytecode format that serves as a compilation target for high-level languages, enabling their secure and efficient execution across diverse platforms, including web browsers and embedded systems. To improve support for high-level languages without incurring significant code size or performance overheads, Wasm continuously evolves by integrating high-level features such as Garbage Collection and Stack Switching. However, existing compilation approaches either lack…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[7/7]:
- Controllable Weather Synthesis and Removal with Video Diffusion Models
Chih-Hao Lin, Zian Wang, Ruofan Liang, Yuxuan Zhang, Sanja Fidler, Shenlong Wang, Zan Gojcic

Personalized Socially Assistive Robots With End-to-End Speech-Language Models For Well-Being Support
Mengxue Fu, Zhonghao Shi, Minyu Huang, Siqi Liu, Mina Kian, Yirui Song, Maja J. Matari\'c
https://arxiv.org/abs/2507.14412

Personalized Socially Assistive Robots With End-to-End Speech-Language Models For Well-Being Support
Socially assistive robots (SARs) have shown great potential for supplementing well-being support. However, prior studies have found that existing dialogue pipelines for SARs remain limited in real-time latency, back-channeling, and personalized speech dialogue. Toward addressing these limitations, we propose using integrated end-to-end speech-language models (SLMs) with SARs. This work 1) evaluated the usability of an SLM-enabled SAR dialogue system through a small user study, and 2) identified…

Open-Source LLMs Collaboration Beats Closed-Source LLMs: A Scalable Multi-Agent System
Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, Peng Ye
https://arxiv.org/abs/2507.14200

Open-Source LLMs Collaboration Beats Closed-Source LLMs: A Scalable Multi-Agent System
This paper aims to demonstrate the potential and strengths of open-source collectives. It leads to a promising question: Can we harness multiple open-source LLMs to match or even beat the closed-source LLMs? To answer this, we propose SMACS, a scalable multi-agent collaboration system (MACS) framework with high performance. Specifically, for continuous integration of new LLMs and generalization to diverse questions, we first propose a Retrieval-based Prior Selection (RPS), which assigns a proxy…

[2025-06-23 Mon (UTC), 28 new articles found for cs.CR Cryptography and Security]
toXiv_bot_toot

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[6/7]:
- CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path ...
Abhinav Sagar, Sai Teja Gilukara

A Recursive Lie-Group Formulation for the Second-Order Time Derivatives of the Inverse Dynamics of parallel Kinematic Manipulators
Andreas Mueller, Shivesh Kumar, Thomas Kordik
https://arxiv.org/abs/2507.14274

A Recursive Lie-Group Formulation for the Second-Order Time Derivatives of the Inverse Dynamics of parallel Kinematic Manipulators
Series elastic actuators (SEA) were introduced for serial robotic arms. Their model-based trajectory tracking control requires the second time derivatives of the inverse dynamics solution, for which algorithms were proposed. Trajectory control of parallel kinematics manipulators (PKM) equipped with SEAs has not yet been pursued. Key element for this is the computationally efficient evaluation of the second time derivative of the inverse dynamics solution. This has not been presented in the lite…

Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models
Rithesh Murthy, Ming Zhu, Liangwei Yang, Jielin Qiu, Juntao Tan, Shelby Heinecke, Huan Wang, Caiming Xiong, Silvio Savarese
https://arxiv.org/abs/2507.14241

Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models
Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix, an automatic prompt optimization framework that transforms natural language task descriptions into high-quality prompts without requiring manual tuning or domain expertise. Promptomatix supports both a lightweight meta-prompt-based optimizer and a DSPy-powered compiler, with modular design enabling future extension …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/7]:
- Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distrib...
Behraj Khan, Tahir Qasim Syed, Nouman M. Durrani, Bilal Naseem, Shabir Ahmad, Rizwan Qureshi
…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/7]:
- DualReal: Adaptive Joint Training for Lossless Identity-Motion Fusion in Video Customization
Wenchuan Wang, Mengqi Huang, Yijing Tu, Zhendong Mao

The Emergence of Deep Reinforcement Learning for Path Planning
Thanh Thi Nguyen, Saeid Nahavandi, Imran Razzak, Dung Nguyen, Nhat Truong Pham, Quoc Viet Hung Nguyen
https://arxiv.org/abs/2507.15469

The Emergence of Deep Reinforcement Learning for Path Planning
The increasing demand for autonomous systems in complex and dynamic environments has driven significant research into intelligent path planning methodologies. For decades, graph-based search algorithms, linear programming techniques, and evolutionary computation methods have served as foundational approaches in this domain. Recently, deep reinforcement learning (DRL) has emerged as a powerful method for enabling autonomous agents to learn optimal navigation strategies through interaction with t…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/7]:
- Zero-AVSR: Zero-Shot Audio-Visual Speech Recognition with LLMs by Learning Language-Agnostic Spee...
Jeong Hun Yeo, Minsu Kim, Chae Won Kim, Stavros Petridis, Yong Man Ro

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/7]:
- DOGR: Towards Versatile Visual Document Grounding and Referring
Yinan Zhou, Yuxin Chen, Haokun Lin, Shuyu Yang, Zhongang Qi, Chen Ma, Li Zhu, Ying Shan

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/7]:
- Defective Convolutional Networks
Tiange Luo, Tianle Cai, Mengxiao Zhang, Siyu Chen, Di He, Liwei Wang