Tootfinder

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Yifan Hu, Jie Yang, Tian Zhou, Peiyuan Liu, Yujin Tang, Rong Jin, Liang Sun
https://arxiv.org/abs/2509.14181

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Representation learning techniques like contrastive learning have long been explored in time series forecasting, mirroring their success in computer vision and natural language processing. Yet recent state-of-the-art (SOTA) forecasters seldom adopt these representation approaches because they have shown little performance advantage. We challenge this view and demonstrate that explicit representation alignment can supply critical information that bridges the distributional gap between input hist…

Bridging the Synthetic-Real Gap: Supervised Domain Adaptation for Robust Spacecraft 6-DoF Pose Estimation
Inder Pal Singh, Nidhal Eddine Chenni, Abd El Rahman Shabayek, Arunkumar Rathinam, Djamila Aouada
https://arxiv.org/abs/2509.13792

Bridging the Synthetic-Real Gap: Supervised Domain Adaptation for Robust Spacecraft 6-DoF Pose Estimation
Spacecraft Pose Estimation (SPE) is a fundamental capability for autonomous space operations such as rendezvous, docking, and in-orbit servicing. Hybrid pipelines that combine object detection, keypoint regression, and Perspective-n-Point (PnP) solvers have recently achieved strong results on synthetic datasets, yet their performance deteriorates sharply on real or lab-generated imagery due to the persistent synthetic-to-real domain gap. Existing unsupervised domain adaptation approaches aim to…

Py maidr: Bridging Visual and Non-Visual Data Experiences Through a Unified Python Framework
JooYoung Seo, Saairam Venkatesh, Daksh Pokar, Sanchita Kamath, Krishna Anandan Ganesan
https://arxiv.org/abs/2509.13532

Py maidr: Bridging Visual and Non-Visual Data Experiences Through a Unified Python Framework
Although recent efforts have developed accessible data visualization tools for blind and low-vision (BLV) users, most follow a "design for them" approach that creates an unintentional divide between sighted creators and BLV consumers. This unidirectional paradigm perpetuates a power dynamic where sighted creators produce non-visual content boundaries for BLV consumers to access. This paper proposes a bidirectional approach, "design for us," where both sighted and BLV collaborators can employ th…

Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation
Kiruthika Balakrishnan, Durgadevi Velusamy, Hana E. Hinkle, Zhi Li, Karthikeyan Ramasamy, Hikmat Khan, Srini Ramaswamy, Pir Masoom Shah
https://arxiv.org/abs/2508.11738

Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation
Rural healthcare faces persistent challenges, including inadequate infrastructure, workforce shortages, and socioeconomic disparities that hinder access to essential services. This study investigates the transformative potential of artificial intelligence (AI) in addressing these issues in underserved rural areas. We systematically reviewed 109 studies published between 2019 and 2024 from PubMed, Embase, Web of Science, IEEE Xplore, and Scopus. Articles were screened using PRISMA guidelines and…

Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks
Yu Zhang, Bokui Wan, Yongli Qin
https://arxiv.org/abs/2509.13944 …

Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks
A B testing serves as the gold standard for large scale, data driven decision making in online businesses. To mitigate metric variability and enhance testing sensitivity, control variates and regression adjustment have emerged as prominent variance reduction techniques, leveraging pre experiment data to improve estimator performance. Over the past decade, these methods have spawned numerous derivatives, yet their theoretical connections and comparative properties remain underexplored. In this p…

Bridging Molecular Simulation and Process Modeling for Predictive Multicomponent Adsorption
Sunghyun Yoon, Jui Tu, Li-Chiang Lin, Yongchul G. Chung
https://arxiv.org/abs/2508.12200

Bridging Molecular Simulation and Process Modeling for Predictive Multicomponent Adsorption
Accurate and efficient prediction of multicomponent adsorption equilibria across pressures, temperatures, and compositions remain a central challenge for designing energy-efficient adsorption-based separation processes. Traditional approaches, including model fitting and ideal adsorbed solution theory (IAST), often fail to balance accuracy, computational efficiency, and transferability under process-relevant conditions. Here, we introduce a material-to-process modeling framework that integrates…

Bridging the Gap between Collisional and Collisionless Plasma Shocks: A Simulation Study using OSIRIS
Yossef Nissim Kindi, Asaf Pe'er, Antoine Bret, Lu\'is O. Silva, Kevin M. Schoeffler
https://arxiv.org/abs/2508.11097

Bridging the Gap between Collisional and Collisionless Plasma Shocks: A Simulation Study using OSIRIS
Shock waves in plasmas can be characterized by the mechanisms behind their formation. When binary collisions are frequent, dissipation is collision-driven and the shock width is a few mean free paths. In contrast, collisionless shocks rely on collective plasma processes to establish dissipation on scales well below the mean free path. We bridge these regimes with particle-in-cell simulations using OSIRIS with a Coulomb-collision module, varying parameters that control collisionality. We find a …

Bridging Threat Models and Detections: Formal Verification via CADP
Dumitru-Bogdan Prelipcean (Bitdefender, Ia\c{s}i, Romania, Alexandru Ioan Cuza University, Iasi, Romania, LACL, Universite Paris-Est Creteil, France), C\u{a}t\u{a}lin Dima (LACL, Universit\'e Paris-Est Cr\'et\'eil, France)
https://arxiv.org/abs/2509.13035

Bridging Threat Models and Detections: Formal Verification via CADP
Threat detection systems rely on rule-based logic to identify adversarial behaviors, yet the conformance of these rules to high-level threat models is rarely verified formally. We present a formal verification framework that models both detection logic and attack trees as labeled transition systems (LTSs), enabling automated conformance checking via bisimulation and weak trace inclusion. Detection rules specified in the Generic Threat Detection Language (GTDL, a general-purpose detection langua…

Bridging Perception and Planning: Towards End-to-End Planning for Signal Temporal Logic Tasks
Bowen Ye, Junyue Huang, Yang Liu, Xiaozhen Qiao, Xiang Yin
https://arxiv.org/abs/2509.12813

Bridging Perception and Planning: Towards End-to-End Planning for Signal Temporal Logic Tasks
We investigate the task and motion planning problem for Signal Temporal Logic (STL) specifications in robotics. Existing STL methods rely on pre-defined maps or mobility representations, which are ineffective in unstructured real-world environments. We propose the \emph{Structured-MoE STL Planner} (\textbf{S-MSP}), a differentiable framework that maps synchronized multi-view camera observations and an STL specification directly to a feasible trajectory. S-MSP integrates STL constraints within a…

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/2]:
- Bridging Human and LLM Judgments: Understanding and Narrowing the Gap
Felipe Maia Polo, Xinhe Wang, Mikhail Yurochkin, Gongjun Xu, Moulinath Banerjee, Yuekai Sun

High 5: Best to play for both Cowboys and Bears https://www.dallascowboys.com/news/high-5-best-to-play-for-both-cowboys-and-bears

Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants
Hamied Nabizada, Lasse Beers, Alain Chahine, Felix Gehlhoff, Oliver Niggemann, Alexander Fay
https://arxiv.org/abs/2509.12091

Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants
Engineering models created in Model-Based Systems Engineering (MBSE) environments contain detailed information about system structure and behavior. However, they typically lack symbolic planning semantics such as preconditions, effects, and constraints related to resource availability and timing. This limits their ability to evaluate whether a given system variant can fulfill specific tasks and how efficiently it performs compared to alternatives. To address this gap, this paper presents a mo…

When IIb Ceases To Be: Bridging the Gap Between IIb and Short-plateau Supernovae
Joseph R. Farah, D. Andrew Howell, Daichi Hiramatsu, Curtis McCully, Moira Andrews, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Edo Berger, Peter Blanchard, Sebastian Gomez, Harsh Kumar, K. Azalee Bostroem, Yuan Qi Ni, A. Gagliano, Aravind P. Ravi
https://

When IIb Ceases To Be: Bridging the Gap Between IIb and Short-plateau Supernovae
Hydrogen-rich supernovae (SNe) span a range of hydrogen envelope masses at core collapse, producing diverse light curves from extended plateaus in Type II SNe to double-peaked Type IIb SNe. Recent hydrodynamic modeling predicts a continuous sequence of light-curve morphologies as hydrogen is removed, with short plateau SNe (plateau durations ~50--70 days) emerging as a transitional class. However, the observational boundary between IIb and short-plateau remains poorly defined, and thus far unob…

It Takes a Village: Bridging the Gaps between Current and Formal Specifications for Protocols
David Basin, Nate Foster, Kenneth L. McMillan, Kedar S. Namjoshi, Cristina Nita-Rotaru, Jonathan M. Smith, Pamela Zave, Lenore D. Zuck
https://arxiv.org/abs/2509.13208

It Takes a Village: Bridging the Gaps between Current and Formal Specifications for Protocols
Formal specifications have numerous benefits for both designers and users of network protocols. They provide clear, unambiguous representations, which are useful as documentation and for testing. They can help reveal disagreements about what a protocol "is" and identify areas where further work is needed to resolve ambiguities or internal inconsistencies. They also provide a foundation for formal reasoning, making it possible to establish important security and correctness guarantees on all inp…

Statistics makes a difference: Machine learning adsorption dynamics of functionalized cyclooctine on Si(001) at DFT accuracy
Hendrik Weiske, Rhyan Barrett, Ralf Tonner-Zech, Patrick Melix, Julia Westermayr
https://arxiv.org/abs/2509.14828

Statistics makes a difference: Machine learning adsorption dynamics of functionalized cyclooctine on Si(001) at DFT accuracy
The interpretation of experiments on reactive semiconductor surfaces requires statistically significant sampling of molecular dynamics, but conventional ab initio methods are limited due to prohibitive computational costs. Machine-learning interatomic potentials provide a promising solution, bridging the gap between the chemical accuracy of short ab initio molecular dynamics (AIMD) and the extensive sampling required to simulate experiment. Using ethinyl-functionalized cyclooctyne adsorption on…

Bridging Solidity Evolution Gaps: An LLM-Enhanced Approach for Smart Contract Compilation Error Resolution
Likai Ye, Mengliang Li, Dehai Zhao, Jiamou Sun, Xiaoxue Ren
https://arxiv.org/abs/2508.10517

Bridging Solidity Evolution Gaps: An LLM-Enhanced Approach for Smart Contract Compilation Error Resolution
Solidity, the dominant smart contract language for Ethereum, has rapidly evolved with frequent version updates to enhance security, functionality, and developer experience. However, these continual changes introduce significant challenges, particularly in compilation errors, code migration, and maintenance. Therefore, we conduct an empirical study to investigate the challenges in the Solidity version evolution and reveal that 81.68% of examined contracts encounter errors when compiled across di…

Diffusion-Link: Diffusion Probabilistic Model for Bridging the Audio-Text Modality Gap
KiHyun Nam, Jongmin Choi, Hyeongkeun Lee, Jungwoo Heo, Joon Son Chung
https://arxiv.org/abs/2510.11330

Diffusion-Link: Diffusion Probabilistic Model for Bridging the Audio-Text Modality Gap
Contrastive audio-language pretraining yields powerful joint representations, yet a persistent audio-text modality gap limits the benefits of coupling multimodal encoders with large language models (LLMs). We present Diffusion-Link, a diffusion-based modality-bridging module that generatively maps audio embeddings into the text-embedding distribution. The module is trained at the output embedding from the frozen multimodal encoder and implemented as a lightweight network with three residual MLP…

The Cybersecurity of a Humanoid Robot
V\'ictor Mayoral-Vilches
https://arxiv.org/abs/2509.14096 https://arxiv.org/pdf/2509.14096

The Cybersecurity of a Humanoid Robot
The rapid advancement of humanoid robotics presents unprecedented cybersecurity challenges that existing theoretical frameworks fail to adequately address. This report presents a comprehensive security assessment of a production humanoid robot platform, bridging the gap between abstract security models and operational vulnerabilities. Through systematic static analysis, runtime observation, and cryptographic examination, we uncovered a complex security landscape characterized by both sophistica…

Bridging Modality Gaps in e-Commerce Products via Vision-Language Alignment
Yipeng Zhang, Hongju Yu, Aritra Mandal, Canran Xu, Qunzhi Zhou, Zhe Wu
https://arxiv.org/abs/2508.10116

Bridging Modality Gaps in e-Commerce Products via Vision-Language Alignment
Item information, such as titles and attributes, is essential for effective user engagement in e-commerce. However, manual or semi-manual entry of structured item specifics often produces inconsistent quality, errors, and slow turnaround, especially for Customer-to-Customer sellers. Generating accurate descriptions directly from item images offers a promising alternative. Existing retrieval-based solutions address some of these issues but often miss fine-grained visual details and struggle with…

Bridging mid and near infrared by combining optomechanics and self mixing
Tecla Gabbrielli, Chenghong Zhang, Francesco Cappelli, Iacopo Galli, Andrea Ottomaniello, J\'er\^ome Faist, Alessandro Tredicucci, Alessandro Pitanti, Paolo De Natale, Simone Borri, Paolo Vezio
https://arxiv.org/abs/2509.11729

Bridging mid and near infrared by combining optomechanics and self mixing
This work describes a self-mixing-assisted optomechanical platform for transferring information between near- and mid-infrared radiation. In particular, the self-mixing signal of a mid-infrared quantum cascade laser is used to detect the oscillation of a membrane induced by radiation pressure exerted by a near-infrared excitation beam, which is amplitude-modulated at the membrane resonance frequency. This technique is, in principle, wavelength-independent and, therefore, can link different spec…

First PROPL 2025 talk: Bridging disciplinary gaps in climate research.
Presented by Christian Urlea.
#icfpsplash25

Bridging Structure and Activity in Nanocatalysts via Machine Learning and Global Structure Representations
Sofia Zinzani, Francesca Baletto, Kevin Rossi
https://arxiv.org/abs/2509.10985

Bridging Structure and Activity in Nanocatalysts via Machine Learning and Global Structure Representations
Establishing a mapping between nanocatalysts structure and their catalytic properties is essential for efficient design. To this end, we demonstrate the accuracy of a general machine learning framework on a representative and challenging application: predicting the mass activity of Pt nanoparticles for the electrochemical oxygen reduction reaction, estimated via a microkinetic model. Accurate models are obtained when leveraging either a nanocatalyst's structure representation accessible at the …

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Bridging Theory and Practice in Quantum Game Theory: Optimized Implementation of the Battle of the Sexes with Error Mitigation on NISQ Hardware
Germ\'an D\'iaz Agreda, Carlos Andres Duran Paredes, Mateo Buenaventura Samboni, Jhon Alejandro Andrade, Sebasti\'an Andr\'es Cajas Ordo\~nez
https://arxiv.org/abs/2508.09050…

Bridging Theory and Practice in Quantum Game Theory: Optimized Implementation of the Battle of the Sexes with Error Mitigation on NISQ Hardware
Implementing quantum game theory on real hardware is challenging due to noise, decoherence, and limited qubit connectivity, yet such demonstrations are essential to validate theoretical predictions. We present one of the first full experimental realizations of the Battle of the Sexes game under the Eisert-Wilkens-Lewenstein (EWL) framework on IBM Quantum's ibm sherbrooke superconducting processor. Four quantum strategies (I, H, $R(Ï€/4)$, $R(Ï€)$) were evaluated across 31 entanglement values $Î…

Orthrus: Dual-Loop Automated Framework for System-Technology Co-Optimization
Yi Ren, Baokang Peng, Chenhao Xue, Kairong Guo, Yukun Wang, Guoyao Cheng, Yibo Lin, Lining Zhang, Guangyu Sun
https://arxiv.org/abs/2509.13029

Orthrus: Dual-Loop Automated Framework for System-Technology Co-Optimization
With the diminishing return from Moore's Law, system-technology co-optimization (STCO) has emerged as a promising approach to sustain the scaling trends in the VLSI industry. By bridging the gap between system requirements and technology innovations, STCO enables customized optimizations for application-driven system architectures. However, existing research lacks sufficient discussion on efficient STCO methodologies, particularly in addressing the information gap across design hierarchies and …

Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices
Ruiwei Xiao, Qing Xiao, Xinying Hou, Hanqi Jane Li, Phenyo Phemelo Moletsane, Hong Shen, John Stamper
https://arxiv.org/abs/2509.10780

Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices
Generative AI (GenAI) is rapidly entering K-12 classrooms, offering teachers new ways for teaching practices. Yet GenAI models are often trained on culturally uneven datasets, embedding a "default culture" that often misaligns with local classrooms. To understand how teachers navigate this gap, we defined the new concept Cultural Distance (the gap between GenAI's default cultural repertoire and the situated demands of teaching practice) and conducted in-depth interviews with 30 K-12 teachers, 1…

Viscosity CBFs: Bridging the Control Barrier Function and Hamilton-Jacobi Reachability Frameworks in Safe Control Theory
Dylan Hirsch, Jaime Fern\'andez Fisac, Sylvia Herbert
https://arxiv.org/abs/2510.09929

Viscosity CBFs: Bridging the Control Barrier Function and Hamilton-Jacobi Reachability Frameworks in Safe Control Theory
Control barrier functions (CBFs) and Hamilton-Jacobi reachability (HJR) are central frameworks in safe control. Traditionally, these frameworks have been viewed as distinct, with the former focusing on optimally safe controller design and the latter providing sufficient conditions for safety. A previous work introduced the notion of a control barrier value function (CB-VF), which is defined similarly to the other value functions studied in HJR but has certain CBF-like properties. In this work, …

Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings for Improved Diffusion Generation
Sung-Lin Tsai, Bo-Lun Huang, Yu Ting Shen, Cheng Yu Yeo, Chiang Tseng, Bo-Kai Ruan, Wen-Sheng Lien, Hong-Han Shuai
https://arxiv.org/abs/2509.10058

Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings for Improved Diffusion Generation
Accurate color alignment in text-to-image (T2I) generation is critical for applications such as fashion, product visualization, and interior design, yet current diffusion models struggle with nuanced and compound color terms (e.g., Tiffany blue, lime green, hot pink), often producing images that are misaligned with human intent. Existing approaches rely on cross-attention manipulation, reference images, or fine-tuning but fail to systematically resolve ambiguous color descriptions. To precisely…

Replaced article(s) found for astro-ph.IM. https://arxiv.org/list/astro-ph.IM/new
[1/1]:
- Artificial Precision Timing Array: bridging the decihertz gravitational-wave sensitivity gap with...
Alves, Sullivan, Ji, Veske, Bartos, Will, M\'arka, M\'arka

Accelerating Protein Molecular Dynamics Simulation with DeepJump
Allan dos Santos Costa, Manvitha Ponnapati, Dana Rubin, Tess Smidt, Joseph Jacobson
https://arxiv.org/abs/2509.13294

Accelerating Protein Molecular Dynamics Simulation with DeepJump
Unraveling the dynamical motions of biomolecules is essential for bridging their structure and function, yet it remains a major computational challenge. Molecular dynamics (MD) simulation provides a detailed depiction of biomolecular motion, but its high-resolution temporal evolution comes at significant computational cost, limiting its applicability to timescales of biological relevance. Deep learning approaches have emerged as promising solutions to overcome these computational limitations by…

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Bridging Clarity and Accuracy: A Simple Spectral Longwave Radiation Scheme for Idealized Climate Modeling
Andrew I. L. Williams
https://arxiv.org/abs/2508.09353 https://

Bridging Clarity and Accuracy: A Simple Spectral Longwave Radiation Scheme for Idealized Climate Modeling
Parameterizing radiative transfer in means navigating trade-offs between physical accuracy and conceptual clarity. However, currently available schemes sit at the extremes of this spectrum: correlated-k schemes are fast and accurate but rely on lookup tables which obscure the underlying physics and make such schemes difficult to modify, while gray radiation schemes are conceptually straightforward but introduce significant biases in atmospheric circulation. Here we introduce a Simple Spectral M…

MCMC: Bridging Rendering, Optimization and Generative AI
Gurprit Singh, Wenzel Jakob
https://arxiv.org/abs/2510.09078 https://arxiv.org/pdf/2510.09078

MCMC: Bridging Rendering, Optimization and Generative AI
Generative artificial intelligence (AI) has made unprecedented advances in vision language models over the past two years. During the generative process, new samples (images) are generated from an unknown high-dimensional distribution. Markov Chain Monte Carlo (MCMC) methods are particularly effective in drawing samples from such complex, high-dimensional distributions. This makes MCMC methods an integral component for models like EBMs, ensuring accurate sample generation. Gradient-based opti…

Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models
Jugal Gajjar, Kaustik Ranaware, Kamalasankari Subramaniakuppusamy
https://arxiv.org/abs/2510.10321

Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models
Software vulnerabilities remain a persistent risk, yet static and dynamic analyses often overlook structural dependencies that shape insecure behaviors. Viewing programs as heterogeneous graphs, we capture control- and data-flow relations as complex interaction networks. Our hybrid framework combines these graph representations with light-weight (<4B) local LLMs, uniting topological features with semantic reasoning while avoiding the cost and privacy concerns of large cloud models. Evaluated on…

Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization
Kevin Bradley Dsouza, Graham Alexander Watt, Yuri Leonenko, Juan Moreno-Cruz
https://arxiv.org/abs/2508.06386

Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization
Agricultural landscapes face the dual challenge of sustaining food production while reversing biodiversity loss. Agri-environmental policies often fall short of delivering ecological functions such as landscape connectivity, in part due to a persistent disconnect between farm-level economic decisions and landscape-scale spatial planning. We introduce a novel hierarchical optimization framework that bridges this gap. First, an Ecological Intensification (EI) model determines the economically opt…

Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field
Tianze Zheng, Xingyuan Xu, Zhi Wang, Xu Han, Zhenliang Mu, Ziqing Zhang, Sheng Gong, Kuang Yu, Wen Yan
https://arxiv.org/abs/2508.08575

Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field
Molecular dynamics (MD) simulations are essential tools for unraveling atomistic insights into the structure and dynamics of condensed-phase systems. However, the universal and accurate prediction of macroscopic properties from ab initio calculations remains a significant challenge, often hindered by the trade-off between computational cost and simulation accuracy. Here, we present ByteFF-Pol, a graph neural network (GNN)-parameterized polarizable force field, trained exclusively on high-level …

BridgeScope: A Universal Toolkit for Bridging Large Language Models and Databases
Lianggui Weng, Dandan Liu, Rong Zhu, Bolin Ding, Jingren Zhou
https://arxiv.org/abs/2508.04031 …

BridgeScope: A Universal Toolkit for Bridging Large Language Models and Databases
As large language models (LLMs) demonstrate increasingly powerful reasoning and orchestration capabilities, LLM-based agents are rapidly proliferating for complex data-related tasks. Despite this progress, the current design of how LLMs interact with databases exhibits critical limitations in usability, security, privilege management, and data transmission efficiency. To resolve these challenges, we introduce BridgeScope, a universal toolkit bridging LLMs and databases through three key innovat…

More bridging ligands activate direct exchange: the case of anisotropic Kitaev effective magnetic interactions
Pritam Bhattacharyya, Nikolay A. Bogdanov, Liviu Hozoi
https://arxiv.org/abs/2510.04149

More bridging ligands activate direct exchange: the case of anisotropic Kitaev effective magnetic interactions
A magnet is a collection of magnetic moments. How those interact is determined by what lies in between. In transition-metal and rare-earth magnetic compounds, the configuration of the ligands around each magnetic center and the connectivity of the ligand cages are therefore pivotal -- for example, the mutual interaction of magnetic species connected through one single ligand is qualitatively different from the case of two bridging anions. Two bridging ligands are encountered in Kitaev magnets. …

Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
Sajad Khatiri, Francisco Eli Vina Barrientos, Maximilian Wulf, Paolo Tonella, Sebastiano Panichella
https://arxiv.org/abs/2510.09396

Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements. This paper presents the industrial adoption of Surrealist, a simulation-based test generation framework originally for UAVs, now applied to the ANYmal quadrupedal robot for industrial inspection. Our method uses a search-based algorithm to automatically generate challenging obstacle avoidance scenarios, uncovering fa…

ZapGPT: Free-form Language Prompting for Simulated Cellular Control
Nam H. Le, Patrick Erickson, Yanbo Zhang, Michael Levin, Josh Bongard
https://arxiv.org/abs/2509.10660 https:…

ZapGPT: Free-form Language Prompting for Simulated Cellular Control
Human language is one of the most expressive tools for conveying intent, yet most artificial or biological systems lack mechanisms to interpret or respond meaningfully to it. Bridging this gap could enable more natural forms of control over complex, decentralized systems. In AI and artificial life, recent work explores how language can specify high-level goals, but most systems still depend on engineered rewards, task-specific supervision, or rigid command sets, limiting generalization to novel…

Agora: Bridging the GPU Cloud Resource-Price Disconnect
Ian McDougall, Noah Scott, Joon Huh, Kirthevasan Kandasamy, Karthikeyan Sankaralingam
https://arxiv.org/abs/2510.05111 ht…

Agora: Bridging the GPU Cloud Resource-Price Disconnect
The historic trend of Moore's Law, which predicted exponential growth in computational performance per dollar, has diverged for modern Graphics Processing Units (GPUs). While Floating Point Operations per Second (FLOPs) capabilities have continued to scale economically, memory bandwidth has not, creating a significant price-performance disconnect. This paper argues that the prevailing time-based pricing models for cloud GPUs are economically inefficient for bandwidth-bound workloads. These mode…

Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems
Minghang Zhu, Zhengliang Shi, Zhiwei Xu, Shiguang Wu, Lingjie Wang, Pengjie Ren, Zhaochun Ren, Zhumin Chen
https://arxiv.org/abs/2509.09629

Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems
The advancement of large language models (LLMs) has enabled the construction of multi-agent systems to solve complex tasks by dividing responsibilities among specialized agents, such as a planning agent for subgoal generation and a grounding agent for executing tool-use actions. Most existing methods typically fine-tune these agents independently, leading to capability gaps among them with poor coordination. To address this, we propose MOAT, a Multi-Agent Joint Alignment Tuning framework that i…

Bridging the gap between ultrafast optics and resonant photonics via omni-resonance
Abbas Shiri, Kenneth L. Schepler, Ayman F. Abouraddy
https://arxiv.org/abs/2510.12002 https:/…

Bridging the gap between ultrafast optics and resonant photonics via omni-resonance
High-finesse planar Fabry-P{é}rot (FP) cavities spectrally filter the incident field at discrete resonances, and thus cannot be utilized to resonantly enhance the field of ultrashort pulses. Introducing judicious angular dispersion into a pulse can give rise to `omni-resonance', whereby the entire bandwidth of a spatiotemporally structured ultrafast pulse couples to a single longitudinal cavity resonance, even when the pulse bandwidth far exceeds the resonant linewidth. Here we show that omni-…

MusicScaffold: Bridging Machine Efficiency and Human Growth in Adolescent Creative Education through Generative AI
Zhejing Hu, Yan Liu, Zhi Zhang, Gong Chen, Bruce X. B. Yu, Junxian Li, Jiannong Cao
https://arxiv.org/abs/2509.10327

MusicScaffold: Bridging Machine Efficiency and Human Growth in Adolescent Creative Education through Generative AI
Adolescence is marked by strong creative impulses but limited strategies for structured expression, often leading to frustration or disengagement. While generative AI lowers technical barriers and delivers efficient outputs, its role in fostering adolescents' expressive growth has been overlooked. We propose MusicScaffold, the first adolescent-centered framework that repositions AI as a guide, coach, and partner, making expressive strategies transparent and learnable, and supporting autonomy. I…

xRWA: A Cross-Chain Framework for Interoperability of Real-World Assets
Yihao Guo, Haoming Zhu, Minghui Xu, Xiuzhen Cheng, Bin Xiao
https://arxiv.org/abs/2509.12957 https://

xRWA: A Cross-Chain Framework for Interoperability of Real-World Assets
Real-World Assets (RWAs) have recently attracted increasing attention as a means of bridging traditional financial instruments with decentralized infrastructures. By representing assets such as bonds, commodities, and real estate on blockchains, RWAs can enhance liquidity, broaden accessibility, and extend the scope of decentralized finance. Industry forecasts further suggest rapid growth of tokenized RWAs in the coming years, underscoring their potential role in the evolution of digital financ…

Bridging Caribbean Literature and Digital Humanities https://call-for-papers.sas.upenn.edu/cfp/2025/10/01/bridging-caribbean-literature-and-digital-humanities

Crossover between kite growth and vibrational bridging in pillar-assisted controlled formation of carbon nanotube networks
Yuanjia Liu, Taiki Inoue, Yoshihiro Kobayashi
https://arxiv.org/abs/2509.08301

Crossover between kite growth and vibrational bridging in pillar-assisted controlled formation of carbon nanotube networks
Pillar-assisted growth is a technique in which short carbon nanotubes (CNTs) form suspended networks by growing across closely spaced microfabricated pillars. During growth, the CNT tips exhibit vibrations that allow them to bridge the neighboring pillars. To improve the complexity and controllability of the CNT networks, we introduce a kite-growth mechanism in which CNTs are elongated and aligned by gas flow during growth, enabling a longer bridging distance compared to vibrational bridging. B…

Bridging Classical and Quantum Computing for Next-Generation Language Models
Yi Pan, Hanqi Jiang, Junhao Chen, Yiwei Li, Huaqin Zhao, Lin Zhao, Yohannes Abate, Yingfeng Wang, Tianming Liu
https://arxiv.org/abs/2508.07026

Bridging Classical and Quantum Computing for Next-Generation Language Models
Integrating Large Language Models (LLMs) with quantum computing is a critical challenge, hindered by the severe constraints of Noisy Intermediate-Scale Quantum (NISQ) devices, including barren plateaus and limited coherence. Current approaches often fail due to static quantum-classical partitioning. We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge this gap through dynamic, quantum-classical co-design. AQCF's core principle is real-time adaptation: it analyzes…

A class of graphs: Bridging structures in commutative algebra
Fahimeh Khosh-Ahang Ghasr
https://arxiv.org/abs/2510.06729 https://arxiv.org/pdf/2510.06729…

A class of graphs: Bridging structures in commutative algebra
We characterize classes of simplicial complexes $Δ$ for which the generators of the determinantal facet ideal $J_Δ$ form a reduced Gröbner basis. This result provides a new proof for a known fact about determinantal ideals in a special case and extends several existing results concerning binomial edge ideals and determinantal facet ideals. We also introduce and examine generalizations of interval graphs and proper interval graphs, aiming to establish equivalent characterizations for these …

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Tsuyoshi Okita
https://arxiv.org/abs/2510.08295

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Conventional time-series generation often ignores domain-specific physical constraints, limiting statistical and physical consistency. We propose a hierarchical framework that embeds the inherent hierarchy of physical laws-conservation, dynamics, boundary, and empirical relations-directly into deep generative models, introducing a new paradigm of physics-informed inductive bias. Our method combines Fourier Neural Operators (FNOs) for learning physical operators with Conditional Flow Matching (C…

WiNPA: Wireless Neural Processing Architecture
Sai Xu, Yanan Du
https://arxiv.org/abs/2510.11150 https://arxiv.org/pdf/2510.11150

WiNPA: Wireless Neural Processing Architecture
This article presents a wireless neural processing architecture (WiNPA), providing a novel perspective for accelerating edge inference of deep neural network (DNN) workloads via joint optimization of wireless and computing resources. WiNPA enables fine-grained integration of wireless communication and edge computing, bridging the research gap between wireless and edge intelligence and significantly improving DNN inference performance. To fully realize its potential, we explore a set of fundamen…

A Warm-basis Method for Bridging Learning and Iteration: a Case Study in Fluorescence Molecular Tomography
Ruchi Guo, Jiahua Jiang, Bangti Jin, Wuwei Ren, Jianru Zhang
https://arxiv.org/abs/2510.05926 …

A Warm-basis Method for Bridging Learning and Iteration: a Case Study in Fluorescence Molecular Tomography
Fluorescence Molecular Tomography (FMT) is a widely used non-invasive optical imaging technology in biomedical research. It usually faces significant accuracy challenges in depth reconstruction, and conventional iterative methods struggle with poor $z$-resolution even with advanced regularization. Supervised learning approaches can improve recovery accuracy but rely on large, high-quality paired training dataset that is often impractical to acquire in practice. This naturally raises the questio…

Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia
Yifan Wang, Jingyuan Sun, Jichen Zheng, Yunhao Zhang, Chunyu Ye, Jixing Li, Chengqing Zong, Shaonan Wang
https://arxiv.org/abs/2508.04749

Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia
The striking alignment between large language models (LLMs) and human brain activity positions them as powerful models of healthy cognition. This parallel raises a fundamental question: if LLMs can model the intact brain, can we lesion them to simulate the linguistic deficits of the injured brain? In this work, we introduce a methodology to model aphasia - a complex language disorder caused by neural injury - by selectively disabling components in a modular Mixture-of-Experts (MoE) language mod…

Scale-Invariant Regret Matching and Online Learning with Optimal Convergence: Bridging Theory and Practice in Zero-Sum Games
Brian Hu Zhang, Ioannis Anagnostides, Tuomas Sandholm
https://arxiv.org/abs/2510.04407

Scale-Invariant Regret Matching and Online Learning with Optimal Convergence: Bridging Theory and Practice in Zero-Sum Games
A considerable chasm has been looming for decades between theory and practice in zero-sum game solving through first-order methods. Although a convergence rate of $T^{-1}$ has long been established since Nemirovski's mirror-prox algorithm and Nesterov's excessive gap technique in the early 2000s, the most effective paradigm in practice is *counterfactual regret minimization*, which is based on *regret matching* and its modern variants. In particular, the state of the art across most benchmarks …

Bridging Clinical Narratives and ACR Appropriateness Guidelines: A Multi-Agent RAG System for Medical Imaging Decisions
Satrio Pambudi, Filippo Menolascina
https://arxiv.org/abs/2510.04969

Bridging Clinical Narratives and ACR Appropriateness Guidelines: A Multi-Agent RAG System for Medical Imaging Decisions
The selection of appropriate medical imaging procedures is a critical and complex clinical decision, guided by extensive evidence-based standards such as the ACR Appropriateness Criteria (ACR-AC). However, the underutilization of these guidelines, stemming from the difficulty of mapping unstructured patient narratives to structured criteria, contributes to suboptimal patient outcomes and increased healthcare costs. To bridge this gap, we introduce a multi-agent cognitive architecture that autom…

If you run Debian 13 systemd-networkd bridging you are going to have a very bad day if you do a stable update of systemd
(It will segfault leaving you with no network)
Update: as pointed out by Colin you also have to be using VLANs.
https://bugs.debian.org/cgi-bin/bugrep

Bridging ASR and LLMs for Dysarthric Speech Recognition: Benchmarking Self-Supervised and Generative Approaches
Ahmed Aboeitta, Ahmed Sharshar, Youssef Nafea, Shady Shehata
https://arxiv.org/abs/2508.08027

Bridging ASR and LLMs for Dysarthric Speech Recognition: Benchmarking Self-Supervised and Generative Approaches
Speech Recognition (ASR) due to phoneme distortions and high variability. While self-supervised ASR models like Wav2Vec, HuBERT, and Whisper have shown promise, their effectiveness in dysarthric speech remains unclear. This study systematically benchmarks these models with different decoding strategies, including CTC, seq2seq, and LLM-enhanced decoding (BART,GPT-2, Vicuna). Our contributions include (1) benchmarking ASR architectures for dysarthric speech, (2) introducing LLM-based decoding to …

Bridging Centralized and Distributed Frameworks in Unknown Input Observer Design
Ruixuan Zhao, Guitao Yang, Peng Li, Boli Chen
https://arxiv.org/abs/2509.08914 https://

Bridging Centralized and Distributed Frameworks in Unknown Input Observer Design
State estimation for linear time-invariant systems with unknown inputs is a fundamental problem in various research domains. In this article, we establish conditions for the design of unknown input observers (UIOs) from a geometric approach perspective. Specifically, we derive a necessary and sufficient geometric condition for the existence of a centralized UIO. Compared to existing results, our condition offers a more general design framework, allowing designers the flexibility to estimate par…

Neutrino and Electromagnetic Signals from Tidal Disruption Events: Bridging the Theory with Observations
Chengchao Yuan, Walter Winter, Cecilia Lunardini, B. Theodore Zhang, Kohta Murase, Bing Zhang
https://arxiv.org/abs/2508.07940

Neutrino and Electromagnetic Signals from Tidal Disruption Events: Bridging the Theory with Observations
This proceeding presents recent results from a joint analysis of time-dependent neutrino and electromagnetic emissions from tidal disruption events (TDEs), using both isotropic wind models and relativistic jets. We discuss constraints from Fermi Large Area Telescope (LAT) $γ$-ray upper limits on the size of the radiation zone and the maximum energies of accelerated cosmic rays, as well as the resulting neutrino productions from TDEs and candidates, including AT 2019dsg, AT 2019fdr, AT 2019aalc…

Bridging the Gap Between Theoretical and Practical Reinforcement Learning in Undergraduate Education
Muhammad Ahmed Atif, Mohammad Shahid Shaikh
https://arxiv.org/abs/2509.05689

Bridging the Gap Between Theoretical and Practical Reinforcement Learning in Undergraduate Education
This innovative practice category paper presents an innovative framework for teaching Reinforcement Learning (RL) at the undergraduate level. Recognizing the challenges posed by the complex theoretical foundations of the subject and the need for hands-on algorithmic practice, the proposed approach integrates traditional lectures with interactive lab-based learning. Drawing inspiration from effective pedagogical practices in computer science and engineering, the framework engages students throug…

Bridging Imperative Process Models and Process Data Queries-Translation and Relaxation
Abdur Rehman Anwar Qureshi, Adrian Rebmann, Timotheus Kampik, Matthias Weidlich, Mathias Weske
https://arxiv.org/abs/2510.06414

Bridging Imperative Process Models and Process Data Queries-Translation and Relaxation
Business process management is increasingly practiced using data-driven approaches. Still, classical imperative process models, which are typically formalized using Petri nets, are not straightforwardly applicable to the relational databases that contain much of the available structured process execution data. This creates a gap between the traditional world of process modeling and recent developments around data-driven process analysis, ultimately leading to the under-utilization of often read…

Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Technical Solutions
Parsa Omidi, Xingshuai Huang, Axel Laborieux, Bahareh Nikpour, Tianyu Shi, Armaghan Eshaghi
https://arxiv.org/abs/2508.10824

Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Technical Solutions
Memory is fundamental to intelligence, enabling learning, reasoning, and adaptability across biological and artificial systems. While Transformer architectures excel at sequence modeling, they face critical limitations in long-range context retention, continual learning, and knowledge integration. This review presents a unified framework bridging neuroscience principles, including dynamic multi-timescale memory, selective attention, and consolidation, with engineering advances in Memory-Augment…

Unified Semiclassical Theory of Nonlinear Hall Effect:Bridging Ballistic and Diffusive Transport Regime
Xinyu Liu, Haozhi Liao, Guangyun Qi, Hao Geng, Li Sheng, Dingyu Xing
https://arxiv.org/abs/2508.07445

Unified Semiclassical Theory of Nonlinear Hall Effect:Bridging Ballistic and Diffusive Transport Regime
The nonlinear Hall effect has attracted considerable attention and undergone extensive investigation in recent years. However, theoretical studies addressing size-dependent effects remain largely unexplored. In this work, we establish a unified semiclassical framework based on the Boltzmann transport equation, incorporating generalized boundary conditions to bridge the ballistic and diffusive transport regimes. Our analysis reveals that the nonlinear Hall effect arises from the combined action …

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Bridging Memory Gaps: Scaling Federated Learning for Heterogeneous Clients
Yebo Wu, Jingguang Li, Chunlin Tian, Kahou Tam, Li Li, Chengzhong Xu

Bridging the Gap Between Binary and Source Based Package Management in Spack
John Gouwar, Gregory Becker, Tamara Dahlgren, Nathan Hanford, Arjun Guha, Todd Gamblin
https://arxiv.org/abs/2509.07728

Bridging the Gap Between Binary and Source Based Package Management in Spack
Binary package managers install software quickly but they limit configurability due to rigid ABI requirements that ensure compatibility between binaries. Source package managers provide flexibility in building software, but compilation can be slow. For example, installing an HPC code with a new MPI implementation may result in a full rebuild. Spack, a widely deployed, HPC-focused package manager, can use source and pre-compiled binaries, but lacks a binary compatibility model, so it cannot mix …

Bridging the Gap in Phishing Detection: A Comprehensive Phishing Dataset Collector
Aditya Kulkarni, Shahil Manishbhai Patel, Shivam Pradip Tirmare, Vivek Balachandran, Tamal Das
https://arxiv.org/abs/2509.09592

Bridging the Gap in Phishing Detection: A Comprehensive Phishing Dataset Collector
To combat phishing attacks -- aimed at luring web users to divulge their sensitive information -- various phishing detection approaches have been proposed. As attackers focus on devising new tactics to bypass existing detection solutions, researchers have adapted by integrating machine learning and deep learning into phishing detection. Phishing dataset collection is vital to developing effective phishing detection approaches, which highly depend on the diversity of the gathered datasets. The l…

Translating Milli/Microrobots with A Value-Centered Readiness Framework
Hakan Ceylan, Edoardo Sinibaldi, Sanjay Misra, Pankaj J. Pasricha, Dietmar W. Hutmacher
https://arxiv.org/abs/2510.12090

Translating Milli/Microrobots with A Value-Centered Readiness Framework
Untethered mobile milli/microrobots hold transformative potential for interventional medicine by enabling more precise and entirely non-invasive diagnosis and therapy. Realizing this promise requires bridging the gap between groundbreaking laboratory demonstrations and successful clinical integration. Despite remarkable technical progress over the past two decades, most millirobots and microrobots remain confined to laboratory proof-of-concept demonstrations, with limited real-world feasibility…

Bridging Search and Recommendation through Latent Cross Reasoning
Teng Shi, Weicong Qin, Weijie Yu, Xiao Zhang, Ming He, Jianping Fan, Jun Xu
https://arxiv.org/abs/2508.04152 ht…

Bridging Search and Recommendation through Latent Cross Reasoning
Search and recommendation (S&R) are fundamental components of modern online platforms, yet effectively leveraging search behaviors to improve recommendation remains a challenging problem. User search histories often contain noisy or irrelevant signals that can even degrade recommendation performance, while existing approaches typically encode S&R histories either jointly or separately without explicitly identifying which search behaviors are truly useful. Inspired by the human decision-making p…

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification
Chen Xu, Giao Vu, Ba Trung Cao, Zhen Liu, Fabian Diewald, Yong Yuan, G\"unther Meschke
https://arxiv.org/abs/2508.04538

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification
Reliable assessment of concrete degradation is critical for ensuring structural safety and longevity of engineering structures. This study proposes a self-supervised domain adaptation framework for robust concrete damage classification using coda wave signals. To support this framework, an advanced virtual testing platform is developed, combining multiscale modeling of concrete degradation with ultrasonic wave propagation simulations. This setup enables the generation of large-scale labeled syn…

A Phase-Field Approach to Fracture and Fatigue Analysis: Bridging Theory and Simulation
M. Castill\'on, I. Romero, J. Segurado
https://arxiv.org/abs/2509.08939 https://

A Phase-Field Approach to Fracture and Fatigue Analysis: Bridging Theory and Simulation
This article presents a novel, robust and efficient framework for fatigue crack-propagation that combines the principles of Linear Elastic Fracture Mechanics (LEFM) with phase-field fracture (PFF). Contrary to cycle-by-cycle PFF approaches, this work relies on a single simulation and uses standard crack propagation models such as Paris' law for the material response, simplifying its parametrization. The core of the methodology is the numerical evaluation of the derivative of a specimen's comp…

Bridging the Prediction Error Method and Subspace Identification: A Weighted Null Space Fitting Method
Jiabao He, S. Joe Qin, H\r{a}kan Hjalmarsson
https://arxiv.org/abs/2510.02529

Bridging the Prediction Error Method and Subspace Identification: A Weighted Null Space Fitting Method
Subspace identification methods (SIMs) have proven to be very useful and numerically robust for building state-space models. While most SIMs are consistent, few if any can achieve the efficiency of the maximum likelihood estimate (MLE). Conversely, the prediction error method (PEM) with a quadratic criteria is equivalent to MLE, but it comes with non-convex optimization problems and requires good initialization points. This contribution proposes a weighted null space fitting (WNSF) approach for…

Network Models of Neurodegeneration: Bridging Neuronal Dynamics and Disease Progression
Christoffer G. Alexandersen, Georgia S. Brennan, Julia K. Brynildsen, Michael X. Henderson, Yasser Iturria-Medina, Dani S. Bassett
https://arxiv.org/abs/2509.05151

Network Models of Neurodegeneration: Bridging Neuronal Dynamics and Disease Progression
Neurodegenerative diseases are characterized by the accumulation of misfolded proteins and widespread disruptions in brain function. Computational modeling has advanced our understanding of these processes, but efforts have traditionally focused on either neuronal dynamics or the underlying biological mechanisms of disease. One class of models uses neural mass and whole-brain frameworks to simulate changes in oscillations, connectivity, and network stability. A second class focuses on biologica…

ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generation
Daniel Lee, Nikhil Sharma, Donghoon Shin, DaEun Choi, Harsh Sharma, Jeonghwan Kim, Heng Ji
https://arxiv.org/abs/2508.06065

ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generation
Generative AI has made image creation more accessible, yet aligning outputs with nuanced creative intent remains challenging, particularly for non-experts. Existing tools often require users to externalize ideas through prompts or references, limiting fluid exploration. We introduce ThematicPlane, a system that enables users to navigate and manipulate high-level semantic concepts (e.g., mood, style, or narrative tone) within an interactive thematic design plane. This interface bridges the gap b…

Sketch Animation: State-of-the-art Report
Gaurav Rai, Ojaswa Sharma
https://arxiv.org/abs/2510.10218 https://arxiv.org/pdf/2510.10218

Sketch Animation: State-of-the-art Report
Sketch animation has emerged as a transformative technology, bridging art and science to create dynamic visual narratives across various fields such as entertainment, education, healthcare, and virtual reality. This survey explores recent trends and innovations in sketch animation, with a focus on methods that have advanced the state of the art. The paper categorizes and evaluates key methodologies, including keyframe interpolation, physics-based animation, data-driven, motion capture, and deep…

AnomalyLMM: Bridging Generative Knowledge and Discriminative Retrieval for Text-Based Person Anomaly Search
Hao Ju, Hu Zhang, Zhedong Zheng
https://arxiv.org/abs/2509.04376 http…

AnomalyLMM: Bridging Generative Knowledge and Discriminative Retrieval for Text-Based Person Anomaly Search
With growing public safety demands, text-based person anomaly search has emerged as a critical task, aiming to retrieve individuals with abnormal behaviors via natural language descriptions. Unlike conventional person search, this task presents two unique challenges: (1) fine-grained cross-modal alignment between textual anomalies and visual behaviors, and (2) anomaly recognition under sparse real-world samples. While Large Multi-modal Models (LMMs) excel in multi-modal understanding, their pot…

Bridged Clustering for Representation Learning: Semi-Supervised Sparse Bridging
Patrick Peixuan Ye, Chen Shani, Ellen Vitercik
https://arxiv.org/abs/2510.07182 https://

Bridged Clustering for Representation Learning: Semi-Supervised Sparse Bridging
We introduce Bridged Clustering, a semi-supervised framework to learn predictors from any unpaired input $X$ and output $Y$ dataset. Our method first clusters $X$ and $Y$ independently, then learns a sparse, interpretable bridge between clusters using only a few paired examples. At inference, a new input $x$ is assigned to its nearest input cluster, and the centroid of the linked output cluster is returned as the prediction $\hat{y}$. Unlike traditional SSL, Bridged Clustering explicitly levera…

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Bridging AI Innovation and Healthcare Needs: Lessons Learned from Incorporating Modern NLP at The...
Lovedeep Gondara, Gregory Arbour, Raymond Ng, Jonathan Simkin, Shebnum Devji

Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots
Filip Bjelonic, Fabian Tischhauser, Marco Hutter
https://arxiv.org/abs/2509.06342 https://

Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots
Legged robots must achieve both robust locomotion and energy efficiency to be practical in real-world environments. Yet controllers trained in simulation often fail to transfer reliably, and most existing approaches neglect actuator-specific energy losses or depend on complex, hand-tuned reward formulations. We propose a framework that integrates sim-to-real reinforcement learning with a physics-grounded energy model for permanent magnet synchronous motors. The framework requires a minimal para…

Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
Zhengjia Wang, Qiang Sheng, Danding Wang, Beizhe Hu, Juan Cao
https://arxiv.org/abs/2509.01660

Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
Fake news detection is an important and challenging task for defending online information integrity. Existing state-of-the-art approaches typically extract news semantic clues, such as writing patterns that include emotional words, stylistic features, etc. However, detectors tuned solely to such semantic clues can easily fall into surface detection patterns, which can shift rapidly in dynamic environments, leading to limited performance in the evolving news landscape. To address this issue, thi…

Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective
Bui Duc Manh, Soumyaratna Debnath, Zetong Zhang, Shriram Damodaran, Arvind Kumar, Yueyi Zhang, Lu Mi, Erik Cambria, Lin Wang
https://arxiv.org/abs/2509.09154

Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective
Recent advances in agentic AI have led to systems capable of autonomous task execution and language-based reasoning, yet their spatial reasoning abilities remain limited and underexplored, largely constrained to symbolic and sequential processing. In contrast, human spatial intelligence, rooted in integrated multisensory perception, spatial memory, and cognitive maps, enables flexible, context-aware decision-making in unstructured environments. Therefore, bridging this gap is critical for advan…

Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing
Kurt Butler, Guanchao Feng, Petar Djuric
https://arxiv.org/abs/2510.06165

Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing
Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation is straightforward when features act independently, but becomes less direct when the predictive model involves interactions such as multiplicative relationships or joint feature contributions. In this work, we propose a general theory of higher-order feature attribution, which we develop on the foundation of Integrate…

Bridging Gaps Between Student and Expert Evaluations of AI-Generated Programming Hints
Tung Phung, Mengyan Wu, Heeryung Choi, Gustavo Soares, Sumit Gulwani, Adish Singla, Christopher Brooks
https://arxiv.org/abs/2509.03269

Bridging Gaps Between Student and Expert Evaluations of AI-Generated Programming Hints
Generative AI has the potential to enhance education by providing personalized feedback to students at scale. Recent work has proposed techniques to improve AI-generated programming hints and has evaluated their performance based on expert-designed rubrics or student ratings. However, it remains unclear how the rubrics used to design these techniques align with students' perceived helpfulness of hints. In this paper, we systematically study the mismatches in perceived hint quality from students…

An Enactivist Approach to Human-Computer Interaction: Bridging the Gap Between Human Agency and Affordances
Angjelin Hila
https://arxiv.org/abs/2509.07871 https://

An Enactivist Approach to Human-Computer Interaction: Bridging the Gap Between Human Agency and Affordances
Emerging paradigms in XR, AI, and BCI contexts necessitate novel theoretical frameworks for understanding human autonomy and agency in HCI. Drawing from enactivist theories of cognition, we conceptualize human agents as self-organizing, operationally closed systems that actively enact their cognitive domains through dynamic interaction with their environments. To develop measurable variables aligned with this framework, we introduce "feelings of agency" (FoA) as an alternative to the establishe…

Bridging integrated information theory and the free-energy principle in living neuronal networks
Teruki Mayama, Sota Shimizu, Yuki Takano, Dai Akita, Hirokazu Takahashi
https://arxiv.org/abs/2510.04084

Bridging integrated information theory and the free-energy principle in living neuronal networks
The relationship between Integrated Information Theory (IIT) and the Free-Energy Principle (FEP) remains unresolved, particularly with respect to how integrated information, proposed as the intrinsic substrate of consciousness, behaves within variational Bayesian inference. We investigated this issue using dissociated neuronal cultures, previously shown to perform perceptual inference consistent with the FEP. Repeated stimulation from hidden sources induced robust source selectivity: variationa…

F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
Qi Lv, Weijie Kong, Hao Li, Jia Zeng, Zherui Qiu, Delin Qu, Haoming Song, Qizhi Chen, Xiang Deng, Jiangmiao Pang
https://arxiv.org/abs/2509.06951

F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
Executing language-conditioned tasks in dynamic visual environments remains a central challenge in embodied AI. Existing Vision-Language-Action (VLA) models predominantly adopt reactive state-to-action mappings, often leading to short-sighted behaviors and poor robustness in dynamic scenes. In this paper, we introduce F1, a pretrained VLA framework which integrates the visual foresight generation into decision-making pipeline. F1 adopts a Mixture-of-Transformer architecture with dedicated modul…

Low-Temperature Skyrmions and Spiral Reorientation Processes in Chiral Magnets with Cubic Anisotropy: Guidelines for Bridging Theory and Experiment
A. O. Leonov, G. G\"odecke, J. Grefe, S. S\"ullow, D. Menzel
https://arxiv.org/abs/2510.07640

Low-Temperature Skyrmions and Spiral Reorientation Processes in Chiral Magnets with Cubic Anisotropy: Guidelines for Bridging Theory and Experiment
We revisit the phenomenological Dzyaloshinskii framework, a central theoretical approach for describing magnetization processes in bulk chiral magnets, and demonstrate how magnetocrystalline cubic anisotropy reshapes the phase diagrams of states and provides the key mechanism stabilizing low-temperature skyrmion phases. We show that, for magnetic field directions along the easy anisotropy axes, the phase diagrams feature stable skyrmion pockets for both signs of the anisotropy constant. We furt…

MultiStream-LLM: Bridging Modalities for Robust Sign Language Translation
Marshall Thomas, Edward Fish, Richard Bowden
https://arxiv.org/abs/2509.00030 https://

MultiStream-LLM: Bridging Modalities for Robust Sign Language Translation
Despite progress in gloss-free Sign Language Translation (SLT), monolithic end-to-end models consistently fail on two critical components of natural signing: the precise recognition of high-speed fingerspelling and the integration of asynchronous non-manual cues from the face. Recent progress in Automated Sign Language Translation with Large Language Models has side stepped this challenge, forcing a single network to learn these simultaneously resulting in poor performance when tasked with tran…

Embodied Natural Language Interaction (NLI): Speech Input Patterns in Immersive Analytics
Hyemi Song, Matthew Johnson, Kirsten Whitley, Eric Krokos, Amitabh Varshney
https://arxiv.org/abs/2510.12156

Embodied Natural Language Interaction (NLI): Speech Input Patterns in Immersive Analytics
Embodiment shapes how users verbally express intent when interacting with data through speech interfaces in immersive analytics. Despite growing interest in Natural Language Interaction (NLI) for visual analytics in immersive environments, users' speech patterns and their use of embodiment cues in speech remain underexplored. Understanding their interplay is crucial to bridging the gap between users' intent and an immersive analytic system. To address this, we report the results from 15 partici…

Bridging the Synthesizability Gap in Perovskites by Combining Computations, Literature Data, and PU Learning
Rushik Desai, Junyeong Ahn, Alejandro Strachan, Arun Mannodi-Kanakkithodi
https://arxiv.org/abs/2510.06166

Bridging the Synthesizability Gap in Perovskites by Combining Computations, Literature Data, and PU Learning
Among emerging energy materials, halide and chalcogenide perovskites have garnered significant attention over the last decade owing to the abundance of their constituent species, low manufacturing costs, and their highly tunable composition-structure-property space. Navigating the vast perovskite compositional landscape is possible using density functional theory (DFT) computations, but they are not easily extended to predictions of the synthesizability of new materials and their properties. As…

A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
Jiale Guo, Suizhi Huang, Mei Li, Dong Huang, Xingsheng Chen, Regina Zhang, Zhijiang Guo, Han Yu, Siu-Ming Yiu, Christian Jensen, Pietro Lio, Kwok-Yan Lam
https://arxiv.org/abs/2510.09721

A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
The integration of LLMs into software engineering has catalyzed a paradigm shift from traditional rule-based systems to sophisticated agentic systems capable of autonomous problem-solving. Despite this transformation, the field lacks a comprehensive understanding of how benchmarks and solutions interconnect, hindering systematic progress and evaluation. This survey presents the first holistic analysis of LLM-empowered software engineering, bridging the critical gap between evaluation and soluti…

Bridging Synthetic and Real-World Domains: A Human-in-the-Loop Weakly-Supervised Framework for Industrial Toxic Emission Segmentation
Yida Tao, Yen-Chia Hsu
https://arxiv.org/abs/2507.22002

Bridging Synthetic and Real-World Domains: A Human-in-the-Loop Weakly-Supervised Framework for Industrial Toxic Emission Segmentation
Industrial smoke segmentation is critical for air-quality monitoring and environmental protection but is often hampered by the high cost and scarcity of pixel-level annotations in real-world settings. We introduce CEDANet, a human-in-the-loop, class-aware domain adaptation framework that uniquely integrates weak, citizen-provided video-level labels with adversarial feature alignment. Specifically, we refine pseudo-labels generated by a source-trained segmentation model using citizen votes, and …

Bridging Minds and Machines: Toward an Integration of AI and Cognitive Science
Rui Mao, Qian Liu, Xiao Li, Erik Cambria, Amir Hussain
https://arxiv.org/abs/2508.20674 https://…

Bridging Minds and Machines: Toward an Integration of AI and Cognitive Science
Cognitive Science has profoundly shaped disciplines such as Artificial Intelligence (AI), Philosophy, Psychology, Neuroscience, Linguistics, and Culture. Many breakthroughs in AI trace their roots to cognitive theories, while AI itself has become an indispensable tool for advancing cognitive research. This reciprocal relationship motivates a comprehensive review of the intersections between AI and Cognitive Science. By synthesizing key contributions from both perspectives, we observe that AI pr…

Rethinking Layer-wise Gaussian Noise Injection: Bridging Implicit Objectives and Privacy Budget Allocation
Qifeng Tan (Xi'an Jiaotong University), Shusen Yang (Xi'an Jiaotong University), Xuebin Ren (Xi'an Jiaotong University), Yikai Zhang (Xi'an Jiaotong University)
https://arxiv.org/abs/2509.04232

Rethinking Layer-wise Gaussian Noise Injection: Bridging Implicit Objectives and Privacy Budget Allocation
Layer-wise Gaussian mechanisms (LGM) enhance flexibility in differentially private deep learning by injecting noise into partitioned gradient vectors. However, existing methods often rely on heuristic noise allocation strategies, lacking a rigorous understanding of their theoretical grounding in connecting noise allocation to formal privacy-utility tradeoffs. In this paper, we present a unified analytical framework that systematically connects layer-wise noise injection strategies with their im…

Bridging Language Gaps in Open-Source Documentation with Large-Language-Model Translation
Elijah Kayode Adejumo, Brittany Johnson, Mariam Guizani
https://arxiv.org/abs/2508.02497

Bridging Language Gaps in Open-Source Documentation with Large-Language-Model Translation
While open source communities attract diverse contributors globally, few repositories provide essential documentation in languages other than English. Large language models (LLMs) have demonstrated remarkable capabilities in software engineering tasks and translations across domains. However, little is known about LLM capabilities in translating open-source technical documentation, which mixes natural language, code, URLs, and markdown formatting. To understand the need and potential for LLMs i…

Video-STAR: Reinforcing Open-Vocabulary Action Recognition with Tools
Zhenlong Yuan, Xiangyan Qu, Chengxuan Qian, Rui Chen, Jing Tang, Lei Sun, Xiangxiang Chu, Dapeng Zhang, Yiwei Wang, Yujun Cai, Shuo Li
https://arxiv.org/abs/2510.08480

Video-STAR: Reinforcing Open-Vocabulary Action Recognition with Tools
Multimodal large language models (MLLMs) have demonstrated remarkable potential in bridging visual and textual reasoning, yet their reliance on text-centric priors often limits their ability to disentangle semantically similar actions in open-vocabulary scenarios. To address this, we propose Video-STAR, a framework that harmonizes contextual sub-motion decomposition with tool-augmented reinforcement learning for open-vocabulary action recognition (OVAR). Unlike prior methods that treat actions …

Bridging the Gap: Enhancing Gaze-Performance Link in Children with ASD through Dual-Level Visual Guidance in MR-DMT
Weiying Liu, Yanran Yuan, Zhiqiang Sheng, Dandan Lian, Sheng Li, Yufan Zhang, Yulong Bian, Juan Liu
https://arxiv.org/abs/2510.03724

Bridging the Gap: Enhancing Gaze-Performance Link in Children with ASD through Dual-Level Visual Guidance in MR-DMT
Autism Spectrum Disorder (ASD) is marked by action imitation deficits stemming from visuomotor integration impairments, posing challenges to imitation-based learning, such as dance movement therapy in mixed reality (MR-DMT). Previous gaze-guiding interventions in ASD have mainly focused on optimizing gaze in isolation, neglecting the crucial "gaze-performance link". This study investigates enhancing this link in MR-DMT for children with ASD. Initially, we experimentally confirmed the weak link:…

PolySim: Bridging the Sim-to-Real Gap for Humanoid Control via Multi-Simulator Dynamics Randomization
Zixing Lei, Zibo Zhou, Sheng Yin, Yueru Chen, Qingyao Xu, Weixin Li, Yunhong Wang, Bowei Tang, Wei Jing, Siheng Chen
https://arxiv.org/abs/2510.01708

PolySim: Bridging the Sim-to-Real Gap for Humanoid Control via Multi-Simulator Dynamics Randomization
Humanoid whole-body control (WBC) policies trained in simulation often suffer from the sim-to-real gap, which fundamentally arises from simulator inductive bias, the inherent assumptions and limitations of any single simulator. These biases lead to nontrivial discrepancies both across simulators and between simulation and the real world. To mitigate the effect of simulator inductive bias, the key idea is to train policies jointly across multiple simulators, encouraging the learned controller to…

Bridging Kolmogorov Complexity and Deep Learning: Asymptotically Optimal Description Length Objectives for Transformers
Peter Shaw, James Cohan, Jacob Eisenstein, Kristina Toutanova
https://arxiv.org/abs/2509.22445

Bridging Kolmogorov Complexity and Deep Learning: Asymptotically Optimal Description Length Objectives for Transformers
The Minimum Description Length (MDL) principle offers a formal framework for applying Occam's razor in machine learning. However, its application to neural networks such as Transformers is challenging due to the lack of a principled, universal measure for model complexity. This paper introduces the theoretical notion of asymptotically optimal description length objectives, grounded in the theory of Kolmogorov complexity. We establish that a minimizer of such an objective achieves optimal compre…

JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation
Guillem Capellera, Luis Ferraz, Antonio Rubio, Alexandre Alahi, Antonio Agudo
https://arxiv.org/abs/2509.22522

JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation
Generative models often treat continuous data and discrete events as separate processes, creating a gap in modeling complex systems where they interact synchronously. To bridge this gap, we introduce JointDiff, a novel diffusion framework designed to unify these two processes by simultaneously generating continuous spatio-temporal data and synchronous discrete events. We demonstrate its efficacy in the sports domain by simultaneously modeling multi-agent trajectories and key possession events. …

Quantifying Visualization Vibes: Measuring Socio-Indexicality at Scale
Amy Rae Fox, Michelle Morgenstern, Graham M. Jones, Arvind Satyanarayan
https://arxiv.org/abs/2508.06786 h…

Quantifying Visualization Vibes: Measuring Socio-Indexicality at Scale
What impressions might readers form with visualizations that go beyond the data they encode? In this paper, we build on recent work that demonstrates the socio-indexical function of visualization, showing that visualizations communicate more than the data they explicitly encode. Bridging this with prior work examining public discourse about visualizations, we contribute an analytic framework for describing inferences about an artifact's social provenance. Via a series of attribution-elicitation…