Tootfinder

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs
Kethmi Hirushini Hettige, Jiahao Ji, Cheng Long, Shili Xiang, Gao Cong, Jingyuan Wang
https://arxiv.org/abs/2506.20073

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs
Spatio-temporal data mining plays a pivotal role in informed decision making across diverse domains. However, existing models are often restricted to narrow tasks, lacking the capacity for multi-task inference and complex long-form reasoning that require generation of in-depth, explanatory outputs. These limitations restrict their applicability to real-world, multi-faceted decision scenarios. In this work, we introduce STReason, a novel framework that integrates the reasoning strengths of large…

The role of audio-visual integration in the time course of phonetic encoding in self-supervised speech models
Yi Wang, Oli Danyi Liu, Peter Bell
https://arxiv.org/abs/2506.20361

The role of audio-visual integration in the time course of phonetic encoding in self-supervised speech models
Human speech perception is multimodal. In natural speech, lip movements can precede corresponding voicing by a non-negligible gap of 100-300 ms, especially for specific consonants, affecting the time course of neural phonetic encoding in human listeners. However, it remains unexplored whether self-supervised learning models, which have been used to simulate audio-visual integration in humans, can capture this asynchronicity between audio and visual cues. We compared AV-HuBERT, an audio-visual m…

MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration
Yucheng Zhou, Lingran Song, Jianbing Shen
https://arxiv.org/abs/2506.19835 …

MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration
Recent advancements in medical Large Language Models (LLMs) have showcased their powerful reasoning and diagnostic capabilities. Despite their success, current unified multimodal medical LLMs face limitations in knowledge update costs, comprehensiveness, and flexibility. To address these challenges, we introduce the Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis (MAM). Inspired by our empirical findings highlighting the benefits of role assignment and diagnostic discernment in …

Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search
Nikolaus Salvatore, Qiong Zhang
https://arxiv.org/abs/2506.17424

Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search
Past work has long recognized the important role of context in guiding how humans search their memory. While context-based memory models can explain many memory phenomena, it remains unclear why humans develop such architectures over possible alternatives in the first place. In this work, we demonstrate that foundational architectures in neural machine translation -- specifically, recurrent neural network (RNN)-based sequence-to-sequence models with attention -- exhibit mechanisms that directly…

Improving Compiler Bug Isolation by Leveraging Large Language Models
Yixian Qi, Jiajun Jiang, Fengjie Li, Bowen Chen, Hongyu Zhang, Junjie Chen
https://arxiv.org/abs/2506.17647

Improving Compiler Bug Isolation by Leveraging Large Language Models
Compilers play a foundational role in building reliable software systems, and bugs within them can lead to catastrophic consequences. The compilation process typically involves hundreds of files, making traditional automated bug isolation techniques inapplicable due to scalability or effectiveness issues. Current mainstream compiler bug localization techniques have limitations in test program mutation and resource consumption. Inspired by the recent advances of pre-trained Large Language Models…

Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
Jiayong Qin, Xianyu Zhu, Qiyu Liu, Guangyi Zhang, Zhigang Cai, Jianwei Liao, Sha Hu, Jingshu Peng, Yingxia Shao, Lei Chen
https://arxiv.org/abs/2506.20139

Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
A growing trend in the database and system communities is to augment conventional index structures, such as B+-trees, with machine learning (ML) models. Among these, error-bounded Piecewise Linear Approximation ($ε$-PLA) has emerged as a popular choice due to its simplicity and effectiveness. Despite its central role in many learned indexes, the design and analysis of $ε$-PLA fitting algorithms remain underexplored. In this paper, we revisit $ε$-PLA from both theoretical and empirical perspe…

What You Think Is What You Get: Bridge User Intent and Transfer Function Design through Multimodal Large Language Models
Yiyao Wang, Bo Pan, Ke Wang, Han Liu, Jinyuan Mao, Yuxin Liu, Minfeng Zhu, Bo Zhang, Weifeng Chen, Xiuqi Huang, Wei Chen
https://arxiv.org/abs/2506.18407

What You Think Is What You Get: Bridge User Intent and Transfer Function Design through Multimodal Large Language Models
Direct volume rendering (DVR) is a fundamental technique for visualizing volumetric data, with transfer functions (TFs) playing a crucial role in extracting meaningful structures. However, designing effective TFs remains unintuitive due to the semantic gap between user intent and TF parameter space. Researchers have developed numerous TF optimization methods to bridge this gap. However, existing methods still face two challenges: large exploration space and weak generalizability. To address the…

Importance of User Control in Data-Centric Steering for Healthcare Experts
Aditya Bhattacharya, Simone Stumpf, Katrien Verbert
https://arxiv.org/abs/2506.18770

Importance of User Control in Data-Centric Steering for Healthcare Experts
As Artificial Intelligence (AI) becomes increasingly integrated into high-stakes domains like healthcare, effective collaboration between healthcare experts and AI systems is critical. Data-centric steering, which involves fine-tuning prediction models by improving training data quality, plays a key role in this process. However, little research has explored how varying levels of user control affect healthcare experts during data-centric steering. We address this gap by examining manual and aut…

Existence and Uniqueness of Physically Correct Hydraulic States in Water Distribution Systems -- A theoretical analysis on the solvability of non-linear systems of equations in the context of water distribution systems
Janine Strotherm, Julian Rolfes, Barbara Hammer
https://arxiv.org/abs/2506.17270…

Existence and Uniqueness of Physically Correct Hydraulic States in Water Distribution Systems -- A theoretical analysis on the solvability of non-linear systems of equations in the context of water distribution systems
Planning and extension of water distribution systems (WDSs) plays a key role in the development of smart cities, driven by challenges such as urbanization and climate change. In this context, the correct estimation of physically correct hydraulic states, i.e., pressure heads, water demands and water flows, is of high interest. Hydraulic emulators such as EPANET or more recently, physic-informed surrogate models are used to solve this task. They require a subset of observed states, such as heads…

In pursuit of the low-energy Solar neutron flux
Prithish Halder, Peter A. Dowben
https://arxiv.org/abs/2506.17985 https://arxiv.org/p…

In pursuit of the low-energy Solar neutron flux
Understanding the origin of low-energy solar neutrons flux is crucial for probing solar energetic processes and neutron transport mechanisms in interplanetary space. This study investigates the role of coronal mass ejections (CMEs) in modulating the low-energy solar neutrons. The neutron flux is modeled by incorporating a CME-width scaling factor into existing neutron fluence models. Our analysis, based on CME data from the SECHHI COR2 database during the DANSON experiment (2016-10-27 to 2017-0…

Observational Space Unveils Cosmic Expansion's Role in the Hubble Tension
Kang Jiao, Tong-Jie Zhang, Liang Gao
https://arxiv.org/abs/2506.16669 https:/…

Observational Space Unveils Cosmic Expansion's Role in the Hubble Tension
We introduce a geometric embedding method that directly constrains $H_0$ by exploiting the exact kinematic relation $\dot{z} = H_0(1+z) - H(z)$ in a unified observational framework. This approach reconstructs expansion history without requiring specific cosmological models, incorporating both existing Cosmic Chronometer data and forecasted Sandage-Loeb measurements. Through mathematical embedding of observational constraints, we achieve $\sim 1.6\%$ precision in $H_0$ determination, matching cu…

Bridging Equilibrium and Kinetics Prediction with a Data-Weighted Neural Network Model of Methane Steam Reforming
Zofia Pizo\'n, Shinji Kimijima, Grzegorz Brus
https://arxiv.org/abs/2506.17224

Bridging Equilibrium and Kinetics Prediction with a Data-Weighted Neural Network Model of Methane Steam Reforming
Hydrogen's role is growing as an energy carrier, increasing the need for efficient production, with methane steam reforming being the most widely used technique. This process is crucial for applications like fuel cells, where hydrogen is converted into electricity, pushing for reactor miniaturization and optimized process control through numerical simulations. Existing models typically address either kinetic or equilibrium regimes, limiting their applicability. Here we show a surrogate model ca…

TAPS: Tool-Augmented Personalisation via Structured Tagging
Ekaterina Taktasheva, Jeff Dalton
https://arxiv.org/abs/2506.20409 https://

TAPS: Tool-Augmented Personalisation via Structured Tagging
Recent advancements in tool-augmented large language models have enabled them to interact with external tools, enhancing their ability to perform complex user tasks. However, existing approaches overlook the role of personalisation in guiding tool use. This work investigates how user preferences can be effectively integrated into goal-oriented dialogue agents. Through extensive analysis, we identify key weaknesses in the ability of LLMs to personalise tool use. To this end, we introduce \name, …

Characterizing Density and Gravitational Potential Fluctuations of the Interstellar Medium
Shaunak Modak, Eve C. Ostriker, Chris Hamilton, Scott Tremaine
https://arxiv.org/abs/2506.17387

Characterizing Density and Gravitational Potential Fluctuations of the Interstellar Medium
Substructure in the interstellar medium (ISM) is crucial for establishing the correlation between star formation and feedback and has the capacity to significantly perturb stellar orbits, thus playing a central role in galaxy dynamics and evolution. Contemporary surveys of gas and dust emission in nearby galaxies resolve structure down to $\sim 10\,$pc scales, demanding theoretical models of ISM substructure with matching fidelity. In this work, we address this need by quantitatively characteri…

Revela: Dense Retriever Learning via Language Modeling
Fengyu Cai, Tong Chen, Xinran Zhao, Sihao Chen, Hongming Zhang, Sherry Tongshuang Wu, Iryna Gurevych, Heinz Koeppl
https://arxiv.org/abs/2506.16552

Revela: Dense Retriever Learning via Language Modeling
Dense retrievers play a vital role in accessing external and specialized knowledge to augment language models (LMs). Training dense retrievers typically requires annotated query-document pairs, which are costly and hard to obtain in specialized domains such as code-motivating growing interest in self-supervised retriever learning. Since LMs are trained to capture token-level dependencies through a self-supervised learning objective (i.e., next-token prediction), we can analogously cast retrieva…

CF-Seg: Counterfactuals meet Segmentation
Raghav Mehta, Fabio De Sousa Ribeiro, Tian Xia, Melanie Roschewitz, Ainkaran Santhirasekaram, Dominic C. Marshall, Ben Glocker
https://arxiv.org/abs/2506.16213

CF-Seg: Counterfactuals meet Segmentation
Segmenting anatomical structures in medical images plays an important role in the quantitative assessment of various diseases. However, accurate segmentation becomes significantly more challenging in the presence of disease. Disease patterns can alter the appearance of surrounding healthy tissues, introduce ambiguous boundaries, or even obscure critical anatomical structures. As such, segmentation models trained on real-world datasets may struggle to provide good anatomical segmentation, leadin…

Topologically protected edge oscillations in nonlinear dynamical units
Sayantan Nag Chowdhury, Hildegard Meyer-Ortmanns
https://arxiv.org/abs/2506.18699 ht…

Topologically protected edge oscillations in nonlinear dynamical units
Many examples from quantum and classical physics are known where topological protection is responsible for the robustness of the dynamics. Less explored is the role of topological protection in the context of classical oscillatory systems. As on-site dynamics, we consider prototypical oscillator models with possible applications in biochemical systems. However, our choice of coupling geometry is inspired by models from condensed matter physics which -- in isolation -- guarantee non-trivial topo…

Doppelg\"anger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Daewon Kang, YeongHwan Shin, Doyeon Kim, Kyu-Hwan Jung, Meong Hi Son
https://arxiv.org/abs/2506.14539

Doppelgänger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Since the advent of large language models, prompt engineering now enables the rapid, low-effort creation of diverse autonomous agents that are already in widespread use. Yet this convenience raises urgent concerns about the safety, robustness, and behavioral consistency of the underlying prompts, along with the pressing challenge of preventing those prompts from being exposed to user's attempts. In this paper, we propose the ''Doppelgänger method'' to demonstrate the risk of an agent being hij…

Price equilibria with positive margins in loyal-strategic markets with discrete prices
Gurkirat Wadhwa, Akansh Verma, Veeraruna Kavitha, Priyank Sinha
https://arxiv.org/abs/2506.17239

Price equilibria with positive margins in loyal-strategic markets with discrete prices
In competitive supply chains (SCs), pricing decisions are crucial, as they directly impact market share and profitability. Traditional SC models often assume continuous pricing for mathematical convenience, overlooking the practical reality of discrete price increments driven by currency constraints. Additionally, customer behavior, influenced by loyalty and strategic considerations, plays a significant role in purchasing decisions. To address these gaps, this study examines a SC model involvin…

Exploring the effect of spatial scales in studying urban mobility pattern
Hoai Nguyen Huynh
https://arxiv.org/abs/2506.16762 https://…

Exploring the effect of spatial scales in studying urban mobility pattern
Urban mobility plays a crucial role in the functioning of cities, influencing economic activity, accessibility, and quality of life. However, the effectiveness of analytical models in understanding urban mobility patterns can be significantly affected by the spatial scales employed in the analysis. This paper explores the impact of spatial scales on the performance of the gravity model in explaining urban mobility patterns using public transport flow data in Singapore. The model is evaluated ac…

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

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

Radiatively Corrected Starobinsky Inflation and Primordial Gravitational Waves in Light of ACT Observations
Waqas Ahmed, Mansoor Ur Rehman
https://arxiv.org/abs/2506.18077

Radiatively Corrected Starobinsky Inflation and Primordial Gravitational Waves in Light of ACT Observations
Nonminimal coupling between the inflaton and the Ricci scalar plays a crucial role in shaping the predictions of single-field inflationary models. While a quartic potential with such coupling represents one of the simplest realizations compatible with cosmological observations, it generically receives important radiative corrections when the inflaton interacts with other fields, particularly those involved in the reheating process. In this work, we focus on radiative corrections arising from bo…

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

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

Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models
Alican Gok, Oguzhan Buyuksolak, Osman Erman Okman, Murat Saraclar
https://arxiv.org/abs/2506.17686

Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models
Keyword Spotting plays a critical role in enabling hands-free interaction for battery-powered edge devices. Few-Shot Keyword Spotting (FS-KWS) addresses the scalability and adaptability challenges of traditional systems by enabling recognition of custom keywords with only a few examples. However, existing FS-KWS systems achieve subpar accuracy at desirable false acceptance rates, particularly in resource-constrained edge environments. To address these issues, we propose a training scheme that l…

Data-Centric Safety and Ethical Measures for Data and AI Governance
Srija Chakraborty
https://arxiv.org/abs/2506.10217 https://arxiv.…

Data-Centric Safety and Ethical Measures for Data and AI Governance
Datasets play a key role in imparting advanced capabilities to artificial intelligence (AI) foundation models that can be adapted to various downstream tasks. These downstream applications can introduce both beneficial and harmful capabilities -- resulting in dual use AI foundation models, with various technical and regulatory approaches to monitor and manage these risks. However, despite the crucial role of datasets, responsible dataset design and ensuring data-centric safety and ethical pract…

SEP-GCN: Leveraging Similar Edge Pairs with Temporal and Spatial Contexts for Location-Based Recommender Systems
Tan Loc Nguyen, Tin T. Tran
https://arxiv.org/abs/2506.16003

SEP-GCN: Leveraging Similar Edge Pairs with Temporal and Spatial Contexts for Location-Based Recommender Systems
Recommender systems play a crucial role in enabling personalized content delivery amidst the challenges of information overload and human mobility. Although conventional methods often rely on interaction matrices or graph-based retrieval, recent approaches have sought to exploit contextual signals such as time and location. However, most existing models focus on node-level representation or isolated edge attributes, underutilizing the relational structure between interactions. We propose SEP-GC…

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Daniya Najiha Abdul Kareem, Abdul Hannan, Mubashir Noman, Jean Lahoud, Mustansar Fiaz, Hisham Cholakkal
https://arxiv.org/abs/2506.12208

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolutional neural networks (CNNs) and transformer-based models have been extensively studied to enhance receptive fields and improve medical image segmentation task. However, they often struggle to capture complex cellular and tissue structures in challenging scenarios such as background clutter and object overlap. Moreove…

Coriolis force acting on near-surface horizontal flows during simulations of flux emergence produces a tilt angle consistent with Joy's law on the Sun
William Roland-Batty, Hannah Schunker, Robert H. Cameron, Damien Przybylski, Laurent Gizon, David I. Pontin
https://arxiv.org/abs/2506.15935

Coriolis force acting on near-surface horizontal flows during simulations of flux emergence produces a tilt angle consistent with Joy's law on the Sun
Joy's law describes the tilt of bipolar active regions on the Sun away from an east-west orientation, where the flux of the polarity concentrated at the prograde side tends to be closer to the equator than the polarity on the retrograde side. Joy's law is attributed to the Coriolis force because of the observed increase in tilt angle at higher latitudes. This tilt plays a crucial role in some solar dynamo models. Our goal is to model the effects of the Coriolis force on a flux tube as it rises …

Estimating Signal-to-Noise Ratios for Multivariate High-dimensional Linear Models
Xiaohan Hu, Zhentao Li, Xiaodong Li
https://arxiv.org/abs/2506.10370 http…

Estimating Signal-to-Noise Ratios for Multivariate High-dimensional Linear Models
Signal-to-noise ratios (SNR) play a crucial role in various statistical models, with important applications in tasks such as estimating heritability in genomics. The method-of-moments estimator is a widely used approach for estimating SNR, primarily explored in single-response settings. In this study, we extend the method-of-moments SNR estimation framework to encompass both fixed effects and random effects linear models with multivariate responses. In particular, we establish and compare the a…

Matter symmetries in supersymmetric standard models from non-invertible selection rules
Tatsuo Kobayashi, Hironobu Mita, Hajime Otsuka, Riku Sakuma
https://arxiv.org/abs/2506.10241

Matter symmetries in supersymmetric standard models from non-invertible selection rules
We discuss phenomenological implications of non-invertible selection rules in the framework of the supersymmetric standard model. We find that a remnant $\mathbb{Z}_2$ symmetry of fusion algebras which holds at all-loop order plays the role of $R$-parity, forbidding baryon and lepton violating operators. In addition, a combination of Standard Model gauge symmetry and the non-invertible selection rules lead to baryon triality and proton hexality that can protect the proton from decay. In general…

Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects
Zihan Hong, Yushi Wu, Zhiting Zhao, Shanshan Feng, Jianghong Ma, Jiao Liu, Tianjun Wei
https://arxiv.org/abs/2506.16893

Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects
With the recent progress in generative artificial intelligence (Generative AI), particularly in the development of large language models, recommendation systems are evolving to become more versatile. Unlike traditional techniques, generative AI not only learns patterns and representations from complex data but also enables content generation, data synthesis, and personalized experiences. This generative capability plays a crucial role in the field of recommendation systems, helping to address t…

System Prompt Extraction Attacks and Defenses in Large Language Models
Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu
https://arxiv.org/abs/2505.23817 https…

System Prompt Extraction Attacks and Defenses in Large Language Models
The system prompt in Large Language Models (LLMs) plays a pivotal role in guiding model behavior and response generation. Often containing private configuration details, user roles, and operational instructions, the system prompt has become an emerging attack target. Recent studies have shown that LLM system prompts are highly susceptible to extraction attacks through meticulously designed queries, raising significant privacy and security concerns. Despite the growing threat, there is a lack of…

Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
Shahab Azarfar, Joseph B. Choi, Phong CH. Nguyen, Yen T. Nguyen, Pradeep Seshadri, H. S. Udaykumar, Stephen Baek
https://arxiv.org/abs/2506.12996

Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
Coupling of physics across length and time scales plays an important role in the response of microstructured materials to external loads. In a multi-scale framework, unresolved (subgrid) meso-scale dynamics is upscaled to the homogenized (macro-scale) representation of the heterogeneous material through closure models. Deep learning models trained using meso-scale simulation data are now a popular route to assimilate such closure laws. However, meso-scale simulations are computationally taxing,…

Evolutionary models for the Very Massive Stars in the R136 cluster of 30 Doradus in the Large Magellanic Cloud
Z. Keszthelyi, S. A. Brands, A. de Koter, N. Langer, J. Puls
https://arxiv.org/abs/2506.15230

Evolutionary models for the Very Massive Stars in the R136 cluster of 30 Doradus in the Large Magellanic Cloud
The cluster R136 in the LMC contains a population of stars in excess of 100 M$_\odot$, including R136a1, the most massive star known. Very Massive Stars (VMSs) play an influential role in feedback processes and may potentially produce exotic supernova types and black holes of tens of solar masses. The evolutionary history and final fate of the three most luminous stars, R136a1, R136a2, and R136a3, has been a puzzling issue. We aim to resolve this by rotating single-star MESA models. We produce …

Revealing Dark Matter's Role in Neutron Stars Anisotropy: A Bayesian Approach Using Multi-messenger Observations
Xue-Zhi Liu, Premachand Mahapatra, Chun Huang, Ayush Hazarika, Chiranjeeb Singha, Prasanta Kumar Das
https://arxiv.org/abs/2506.08376

Revealing Dark Matter's Role in Neutron Stars Anisotropy: A Bayesian Approach Using Multi-messenger Observations
Dark matter (DM) continues to evade direct detection, but neutron stars (NSs) serve as natural laboratories where even a modest DM component can alter their structure. While many studies have examined DM effects on NSs, they often rely on specific choices of equations of state (EOS) models, assume isotropy, and lack a Bayesian statistical framework, limiting their predictive power. In this work, we present a Bayesian framework that couples pressure-anisotropic nuclear EOS to a self-interacting …

Joint User Association and Beamforming Design for ISAC Networks with Large Language Models
Haoyun Li, Ming Xiao, Kezhi Wang, Robert Schober, Dong In Kim, Yong Liang Guan
https://arxiv.org/abs/2506.05637

Joint User Association and Beamforming Design for ISAC Networks with Large Language Models
Integrated sensing and communication (ISAC) has been envisioned to play a more important role in future wireless networks. However, the design of ISAC networks is challenging, especially when there are multiple communication and sensing (C\&S) nodes and multiple sensing targets. We investigate a multi-base station (BS) ISAC network in which multiple BSs equipped with multiple antennas simultaneously provide C\&S services for multiple ground communication users (CUs) and targets. To enhance the …

Collaborative Editable Model
Kaiwen Tang, Aitong Wu, Yao Lu, Guangda Sun
https://arxiv.org/abs/2506.14146 https://arxiv.org/pdf/2506.…

Collaborative Editable Model
Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues c…

Adversarial Attack Classification and Robustness Testing for Large Language Models for Code
Yang Liu, Armstrong Foundjem, Foutse Khomh, Heng Li
https://arxiv.org/abs/2506.07942

Adversarial Attack Classification and Robustness Testing for Large Language Models for Code
Large Language Models (LLMs) have become vital tools in software development tasks such as code generation, completion, and analysis. As their integration into workflows deepens, ensuring robustness against vulnerabilities especially those triggered by diverse or adversarial inputs becomes increasingly important. Such vulnerabilities may lead to incorrect or insecure code generation when models encounter perturbed task descriptions, code, or comments. Prior research often overlooks the role of …

Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction
Shuai Li, Azarakhsh Keipour, Sicong Zhao, Srinath Rajagopalan, Charles Swan, Kostas E. Bekris
https://arxiv.org/abs/2506.09765

Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction
Warehouse automation plays a pivotal role in enhancing operational efficiency, minimizing costs, and improving resilience to workforce variability. While prior research has demonstrated the potential of machine learning (ML) models to increase picking success rates in large-scale robotic fleets by prioritizing high-probability picks and packages, these efforts primarily focused on predicting success probabilities for picks sampled using heuristic methods. Limited attention has been given, howev…

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Harsh Goel, Mohammad Omama, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Sandeep Chinchali
https://arxiv.org/abs/2505.24265

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has achieved significant progress in large-scale traffic control, autonomous vehicles, and robotics. Drawing inspiration from biological systems where roles naturally emerge to enable coordination, role-based MARL methods have been proposed to enhance cooperation learning for complex tasks. However, existing methods exclusively derive roles from an agent's past experience during training, neglecting their influence on its future trajectories. This paper…

Learning thermodynamic master equations for open quantum systems
Peter Sentz, Stanley Nicholson, Yujin Cho, Sohail Reddy, Brendan Keith, Stefanie G\"unther
https://arxiv.org/abs/2506.01882

Learning thermodynamic master equations for open quantum systems
The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We …

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
T. T. J. E. Arets, G. Perugia, M. Houben, W. A. IJsselsteijn
https://arxiv.org/abs/2506.10927

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
Reduced social connectedness increasingly poses a threat to mental health, life expectancy, and general well-being. Generative AI (GAI) technologies, such as large language models (LLMs) and image generation tools, are increasingly integrated into applications aimed at enhancing human social experiences. Despite their growing presence, little is known about how these technologies influence social interactions. This scoping review investigates how GAI-based applications are currently designed to…

Holographic D-brane constructions with dynamical gauge fields
Yongjun Ahn, Matteo Baggioli, Hyun-Sik Jeong, Masataka Matsumoto
https://arxiv.org/abs/2506.09461

Holographic D-brane constructions with dynamical gauge fields
Holographic D-brane constructions, governed by the Dirac-Born-Infeld (DBI) action, play a central role in the AdS/CFT correspondence, particularly in applications to quantum chromodynamics and condensed matter systems. In this work, we demonstrate how to equip these bottom-up holographic models with dynamical boundary gauge fields, thereby introducing electromagnetic interactions into their dual field theory descriptions. As a direct application of this formalism, we compute the dispersion rela…

This https://arxiv.org/abs/2410.01991 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Whittaker-Shintani functions for Fourier-Jacobi models on unitary groups
We state and prove a formula for the Whittaker-Shintani functions associated to Fourier-Jacobi models for p-adic unitary groups and general linear groups. These generalized spherical functions play a fundamental role in the proof of the Gan-Gross-Prasad conjecture for Fourier-Jacobi models. As an application we prove the unramified Ichino-Ikeda conjecture.

When Does Meaning Backfire? Investigating the Role of AMRs in NLI
Junghyun Min, Xiulin Yang, Shira Wein
https://arxiv.org/abs/2506.14613 https://

When Does Meaning Backfire? Investigating the Role of AMRs in NLI
Natural Language Inference (NLI) relies heavily on adequately parsing the semantic content of the premise and hypothesis. In this work, we investigate whether adding semantic information in the form of an Abstract Meaning Representation (AMR) helps pretrained language models better generalize in NLI. Our experiments integrating AMR into NLI in both fine-tuning and prompting settings show that the presence of AMR in fine-tuning hinders model generalization while prompting with AMR leads to sligh…

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant, David J. Love, Christopher G. Brinton
https://arxiv.org/abs/2506.00374

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generativ…

AI doesn’t learn like we do. GoPro'd toddlers see mostly knees—but they can ID a dog after a few glimpses, while LLMs need billions of examples. Neuroscience suggests AI intelligence is gathered very differently than ours https://www.linkedin.com/p…

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Yifan Hao, Xingyuan Pan, Hanning Zhang, Chenlu Ye, Rui Pan, Tong Zhang
https://arxiv.org/abs/2506.01901

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Supervised fine-tuning (SFT) on domain-specific data is the dominant approach for adapting foundation models to specialized tasks. However, it has been observed that SFT models tend to forget knowledge acquired during pretraining. In vision models, ensembling a pretrained model with its fine-tuned counterpart has been shown to mitigate this issue. In this work, we demonstrate that the same holds for language models, and, more strikingly, we observe an overadaptation phenomenon: the ensemble mod…

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Breaking Resource Barriers in Speech Emotion Recognition via Data Distillation
Speech emotion recognition (SER) plays a crucial role in human-computer interaction. The emergence of edge devices in the Internet of Things (IoT) presents challenges in constructing intricate deep learning models due to constraints in memory and computational resources. Moreover, emotional speech data often contains private information, raising concerns about privacy leakage during the deployment of SER models. To address these challenges, we propose a data distillation framework to facilitate…

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TRENDY: Gene Regulatory Network Inference Enhanced by Transformer
Gene regulatory networks (GRNs) play a crucial role in the control of cellular functions. Numerous methods have been developed to infer GRNs from gene expression data, including mechanism-based approaches, information-based approaches, and more recent deep learning techniques, the last of which often overlook the underlying gene expression mechanisms. In this work, we introduce TRENDY, a novel GRN inference method that integrates transformer models to enhance the mechanism-based WENDY approach.…

Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
Haoqing Li, Ricardo Borsoi, Tales Imbiriba, Pau Closas
https://arxiv.org/abs/2506.13733

Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
Multiresolution image fusion is a key problem for real-time satellite imaging and plays a central role in detecting and monitoring natural phenomena such as floods. It aims to solve the trade-off between temporal and spatial resolution in remote sensing instruments. Although several algorithms have been proposed for this problem, the presence of outliers such as clouds downgrades their performance. Moreover, strategies that integrate robustness, recursive operation and learned models are missin…

This https://arxiv.org/abs/2505.21177 has been replaced.
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SOLIDGEO: Measuring Multimodal Spatial Math Reasoning in Solid Geometry
Geometry is a fundamental branch of mathematics and plays a crucial role in evaluating the reasoning capabilities of multimodal large language models (MLLMs). However, existing multimodal mathematics benchmarks mainly focus on plane geometry and largely ignore solid geometry, which requires spatial reasoning and is more challenging than plane geometry. To address this critical gap, we introduce SolidGeo, the first large-scale benchmark specifically designed to evaluate the performance of MLLMs …

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ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
With the widespread adoption of Large Language Models (LLMs), serving LLM inference requests has become an increasingly important task, attracting active research advancements. Practical workloads play an essential role in this process: they are critical for motivating and benchmarking serving techniques and systems. However, the existing understanding of real-world LLM serving workloads is limited due to the lack of a comprehensive workload characterization. Prior analyses remain insufficient …

Improving spliced alignment by modeling splice sites with deep learning
Siying Yang, Neng Huang, Heng Li
https://arxiv.org/abs/2506.12986 https://

Improving spliced alignment by modeling splice sites with deep learning
Motivation: Spliced alignment refers to the alignment of messenger RNA (mRNA) or protein sequences to eukaryotic genomes. It plays a critical role in gene annotation and the study of gene functions. Accurate spliced alignment demands sophisticated modeling of splice sites, but current aligners use simple models, which may affect their accuracy given dissimilar sequences. Results: We implemented minisplice to learn splice signals with a one-dimensional convolutional neural network (1D-CNN) and…

When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
Ruining Sun, Hongsheng Hu, Wei Luo, Zhaoxi Zhang, Yanjun Zhang, Haizhuan Yuan, Leo Yu Zhang
https://arxiv.org/abs/2506.05743

When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
With the rapid advancement of deep learning technology, pre-trained encoder models have demonstrated exceptional feature extraction capabilities, playing a pivotal role in the research and application of deep learning. However, their widespread use has raised significant concerns about the risk of training data privacy leakage. This paper systematically investigates the privacy threats posed by membership inference attacks (MIAs) targeting encoder models, focusing on contrastive learning framew…

Torsion in Persistent Homology and Neural Networks
Maria Walch
https://arxiv.org/abs/2506.03049 https://arxiv.org/pdf/2506.03049

Torsion in Persistent Homology and Neural Networks
We explore the role of torsion in hybrid deep learning models that incorporate topological data analysis, focusing on autoencoders. While most TDA tools use field coefficients, this conceals torsional features present in integer homology. We show that torsion can be lost during encoding, altered in the latent space, and in many cases, not reconstructed by standard decoders. Using both synthetic and high-dimensional data, we evaluate torsion sensitivity to perturbations and assess its recoverabi…

hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation
Charles Hong, Brendan Roberts, Huijae An, Alex Um, Advay Ratan, Yakun Sophia Shao
https://arxiv.org/abs/2506.04544

hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation
Large language models (LLMs) are playing an increasingly large role in domains such as code generation, including hardware code generation, where Verilog is the key language. However, the amount of publicly available Verilog code pales in comparison to the amount of code available for software languages like Python. In this work, we present hdl2v ("HDL-to-Verilog"), a dataset which seeks to increase the amount of available human-written Verilog data by translating or compiling three other hardw…

Prompt Engineer: Analyzing Skill Requirements in the AI Job Market
An Vu, Jonas Oppenlaender
https://arxiv.org/abs/2506.00058 https://

Prompt Engineer: Analyzing Skill Requirements in the AI Job Market
The rise of large language models (LLMs) has created a new job role: the Prompt Engineer. Despite growing interest in this position, we still do not fully understand what skills this new job role requires or how common these jobs are. We analyzed 20,662 job postings on LinkedIn, including 72 prompt engineer positions, to learn more about this emerging role. We found that prompt engineering is still rare (less than 0.5% of sampled job postings) but has a unique skill profile. Prompt engineers ne…

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Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems?
Large Language Models (LLMs) have shown impressive capabilities in language understanding and reasoning across diverse domains. Recently, there has been increasing interests in utilizing LLMs not merely as assistants in optimization tasks, but as active optimizers, particularly for network-structured combinatorial problems. However, before LLMs can be reliably deployed in this role, a fundamental question must be addressed: Can LLMs iteratively manipulate solutions that consistently adhere to p…

Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets
Md. Yeasin Rahat, Rajan Das Gupta, Nur Raisa Rahman, Sudipto Roy Pritom, Samiur Rahman Shakir, Md Imrul Hasan Showmick, Md. Jakir Hossen
https://arxiv.org/abs/2506.09851

Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets
The prediction of foreign exchange rates, such as the US Dollar (USD) to Bangladeshi Taka (BDT), plays a pivotal role in global financial markets, influencing trade, investments, and economic stability. This study leverages historical USD/BDT exchange rate data from 2018 to 2023, sourced from Yahoo Finance, to develop advanced machine learning models for accurate forecasting. A Long Short-Term Memory (LSTM) neural network is employed, achieving an exceptional accuracy of 99.449%, a Root Mean Sq…

Graph Neural Networks in Modern AI-aided Drug Discovery
Odin Zhang, Haitao Lin, Xujun Zhang, Xiaorui Wang, Zhenxing Wu, Qing Ye, Weibo Zhao, Jike Wang, Kejun Ying, Yu Kang, Chang-yu Hsieh, Tingjun Hou
https://arxiv.org/abs/2506.06915

Graph Neural Networks in Modern AI-aided Drug Discovery
Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on molecular graphs, GNNs offer an intuitive and expressive framework for learning the complex topological and geometric features of drug-like molecules, cementing their role in modern molecular modeling. This review provides a comprehensive overview of the methodological foundations and representative applications of GNN…

Towards Operation Proof Obligation Generation for VDM
Nick Battle, Peter Gorm Larsen
https://arxiv.org/abs/2506.12858 https://arxiv.o…

Towards Operation Proof Obligation Generation for VDM
All formalisms have the ability to ensure that their models are internally consistent. Potential inconsistencies are generally highlighted by assertions called proof obligations, and the generation of these obligations is an important role of the tools that support the method. This capability has been available for VDM tools for many years. However, support for obligation generation for explicit operation bodies has always been limited. This work describes the current state of work to address t…

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CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning
Medical audio signals, such as heart and lung sounds, play a crucial role in clinical diagnosis. However, analyzing these signals remains challenging: traditional methods rely on handcrafted features or supervised deep learning models that demand extensive labeled datasets, limiting their scalability and applicability. To address these issues, we propose CaReAQA, an audio-language model that integrates a foundation audio model with the reasoning capabilities of large language models, enabling c…

Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning
Yihong Tang, Kehai Chen, Muyun Yang, Zhengyu Niu, Jing Li, Tiejun Zhao, Min Zhang
https://arxiv.org/abs/2506.01748

Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning
The advancement of Large Language Models (LLMs) has spurred significant interest in Role-Playing Agents (RPAs) for applications such as emotional companionship and virtual interaction. However, recent RPAs are often built on explicit dialogue data, lacking deep, human-like internal thought processes, resulting in superficial knowledge and style expression. While Large Reasoning Models (LRMs) can be employed to simulate character thought, their direct application is hindered by attention diversi…

Joint ASR and Speaker Role Tagging with Serialized Output Training
Anfeng Xu, Tiantian Feng, Shrikanth Narayanan
https://arxiv.org/abs/2506.10349 https://

Joint ASR and Speaker Role Tagging with Serialized Output Training
Automatic Speech Recognition systems have made significant progress with large-scale pre-trained models. However, most current systems focus solely on transcribing the speech without identifying speaker roles, a function that is critical for conversational AI. In this work, we investigate the use of serialized output training (SOT) for joint ASR and speaker role tagging. By augmenting Whisper with role-specific tokens and fine-tuning it with SOT, we enable the model to generate role-aware trans…

Hybrid Monetary Ecosystems: Integrating Stablecoins and Fiat in the Future of Currency Systems
Hongzhe Wen, Songbai Li
https://arxiv.org/abs/2505.10997 htt…

Hybrid Monetary Ecosystems: Integrating Stablecoins and Fiat in the Future of Currency Systems
With market capitalization exceeding USD 200 billion as of early 2025, stablecoins have evolved from a crypto-focused innovation into a vital component of the global monetary structure. This paper identifies the characteristics of stablecoins from an analytical perspective and investigates the role of stablecoins in forming hybrid monetary ecosystems where public (fiat, CBDC) and private (USDC, USDT, DAI) monies coexist. Through econometric analysis with multiple models, we find that stablecoin…

Advancing Loss Functions in Recommender Systems: A Comparative Study with a R\'enyi Divergence-Based Solution
Shengjia Zhang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang
https://arxiv.org/abs/2506.15120

Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective. Their theoretical connections and differences warrant in-depth exploration. This work conducts comprehensive analyses of these losses, yielding significant insights: 1) Common strengths -- both can be viewed as augmentations of traditional losses with Distributional Robust Optimization (DRO), enhancing robustness to…

3D Skin Segmentation Methods in Medical Imaging: A Comparison
Martina Paccini, Giuseppe Patan\`e
https://arxiv.org/abs/2506.11852 https://

3D Skin Segmentation Methods in Medical Imaging: A Comparison
Automatic segmentation of anatomical structures is critical in medical image analysis, aiding diagnostics and treatment planning. Skin segmentation plays a key role in registering and visualising multimodal imaging data. 3D skin segmentation enables applications in personalised medicine, surgical planning, and remote monitoring, offering realistic patient models for treatment simulation, procedural visualisation, and continuous condition tracking. This paper analyses and compares algorithmic an…

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OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs
Role-based access control (RBAC) and hierarchical structures are foundational to how information flows and decisions are made within virtually all organizations. As the potential of Large Language Models (LLMs) to serve as unified knowledge repositories and intelligent assistants in enterprise settings becomes increasingly apparent, a critical, yet under explored, challenge emerges: \textit{can these models reliably understand and operate within the complex, often nuanced, constraints imposed b…

Logic Mining from Process Logs: Towards Automated Specification and Verification
Radoslaw Klimek, Julia Witek
https://arxiv.org/abs/2506.08628 https://

Logic Mining from Process Logs: Towards Automated Specification and Verification
Logical specifications play a key role in the formal analysis of behavioural models. Automating the derivation of such specifications is particularly valuable in complex systems, where manual construction is time-consuming and error-prone. This article presents an approach for generating logical specifications from process models discovered via workflow mining, combining pattern-based translation with automated reasoning techniques. In contrast to earlier work, we evaluate the method on both ge…

Evaluating Gemini in an arena for learning
LearnLM Team, Abhinit Modi, Aditya Srikanth Veerubhotla, Aliya Rysbek, Andrea Huber, Ankit Anand, Avishkar Bhoopchand, Brett Wiltshire, Daniel Gillick, Daniel Kasenberg, Eleni Sgouritsa, Gal Elidan, Hengrui Liu, Holger Winnemoeller, Irina Jurenka, James Cohan, Jennifer She, Julia Wilkowski, Kaiz Alarakyia, Kevin R. McKee, Komal Singh, Lisa Wang, Markus Kunesch, Miruna P\^islar, Niv Efron, Parsa Mahmoudieh, Pierre-Alexandre Kamienny, Sara Wiltb…

Evaluating Gemini in an arena for learning
Artificial intelligence (AI) is poised to transform education, but the research community lacks a robust, general benchmark to evaluate AI models for learning. To assess state-of-the-art support for educational use cases, we ran an "arena for learning" where educators and pedagogy experts conduct blind, head-to-head, multi-turn comparisons of leading AI models. In particular, $N = 189$ educators drew from their experience to role-play realistic learning use cases, interacting with two models se…

Passing the Turing Test in Political Discourse: Fine-Tuning LLMs to Mimic Polarized Social Media Comments
. Pazzaglia, V. Vendetti, L. D. Comencini, F. Deriu, V. Modugno
https://arxiv.org/abs/2506.14645

Passing the Turing Test in Political Discourse: Fine-Tuning LLMs to Mimic Polarized Social Media Comments
The increasing sophistication of large language models (LLMs) has sparked growing concerns regarding their potential role in exacerbating ideological polarization through the automated generation of persuasive and biased content. This study explores the extent to which fine-tuned LLMs can replicate and amplify polarizing discourse within online environments. Using a curated dataset of politically charged discussions extracted from Reddit, we fine-tune an open-source LLM to produce context-aware…

Adapting General-Purpose Embedding Models to Private Datasets Using Keyword-based Retrieval
Yubai Wei, Jiale Han, Yi Yang
https://arxiv.org/abs/2506.00363 …

Adapting General-Purpose Embedding Models to Private Datasets Using Keyword-based Retrieval
Text embedding models play a cornerstone role in AI applications, such as retrieval-augmented generation (RAG). While general-purpose text embedding models demonstrate strong performance on generic retrieval benchmarks, their effectiveness diminishes when applied to private datasets (e.g., company-specific proprietary data), which often contain specialized terminology and lingo. In this work, we introduce BMEmbed, a novel method for adapting general-purpose text embedding models to private data…

NestedFP: High-Performance, Memory-Efficient Dual-Precision Floating Point Support for LLMs
Haeun Lee, Omin Kwon, Yeonhong Park, Jae W. Lee
https://arxiv.org/abs/2506.02024

NestedFP: High-Performance, Memory-Efficient Dual-Precision Floating Point Support for LLMs
Large Language Models (LLMs) are playing a crucial role in latency-critical, high-throughput services like virtual assistants and code generation. While techniques such as continuous batching and paged attention address service-level objectives (SLOs), and quantization methods accelerate inference, the dynamic and efficient adaptation of precision at runtime remains a significant, largely underexplored challenge. The emergence of hardware support for FP8 arithmetic, offering up to 2x the throug…

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VIDEX: A Disaggregated and Extensible Virtual Index for the Cloud and AI Era
Virtual index, also known as hypothetical indexes, play a crucial role in database query optimization. However, with the rapid advancement of cloud computing and AI-driven models for database optimization, traditional virtual index approaches face significant challenges. Cloud-native environments often prohibit direct conducting query optimization process on production databases due to stability requirements and data privacy concerns. Moreover, while AI models show promising progress, their int…

An Exact Moment-Based Approach for Chemical Reaction-Diffusion Networks: From Mass Action to Hill Functions
Manuel Eduardo Hern\'andez-Garc\'ia, Eduardo Moreno-Barbosa, Jorge Vel\'azquez-Castro
https://arxiv.org/abs/2505.09053

An Exact Moment-Based Approach for Chemical Reaction-Diffusion Networks: From Mass Action to Hill Functions
Biochemical systems are inherently stochastic, particularly those with small-molecule populations. The spatial distribution of molecules plays a critical role and requires the inclusion of spatial coordinates in their analysis. Stochastic models such as the chemical master equation are commonly used to study these systems. However, analytical solutions are limited to specific cases, and stochastic simulations require significant computational resources. To mitigate these challenges, approximati…

This https://arxiv.org/abs/2501.15081 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems?
Large Language Models (LLMs) have shown impressive capabilities in language understanding and reasoning across diverse domains. Recently, there has been increasing interests in utilizing LLMs not merely as assistants in optimization tasks, but as active optimizers, particularly for network-structured combinatorial problems. However, before LLMs can be reliably deployed in this role, a fundamental question must be addressed: Can LLMs iteratively manipulate solutions that consistently adhere to p…

This https://arxiv.org/abs/2503.09117 has been replaced.
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GRU: Mitigating the Trade-off between Unlearning and Retention for Large Language Models
Large language model (LLM) unlearning has demonstrated its essential role in removing privacy and copyright-related responses, crucial for their legal and safe applications. However, the pursuit of complete unlearning often comes with substantial costs due to its compromises in their general functionality, leading to a notorious trade-off between unlearning and retention. In examining the update process for unlearning dynamically, we find gradients hold essential information for revealing this …

The role of the Lorentz force in sunspot equilibrium
J. M. Borrero, A. Pastor Yabar, M. Schmassmann, M. Rempel, M. van Noort, M. Collados
https://arxiv.org/abs/2505.23986

The role of the Lorentz force in sunspot equilibrium
Sunspots survive on the solar surface for time-scales ranging from days to months. This requires them to be in an equilibrium involving magnetic fields and hydrodynamic forces. Unfortunately, theoretical models of sunspot equilibrium are very simplified as they assume that spots are static and possess a self-similar and axially symmetric magnetic field. These assumptions neglect the role of small scale variations of the magnetic field along the azimuthal direction produced by umbral dots, light…

Hatevolution: What Static Benchmarks Don't Tell Us
Chiara Di Bonaventura, Barbara McGillivray, Yulan He, Albert Mero\~no-Pe\~nuela
https://arxiv.org/abs/2506.12148

Hatevolution: What Static Benchmarks Don't Tell Us
Language changes over time, including in the hate speech domain, which evolves quickly following social dynamics and cultural shifts. While NLP research has investigated the impact of language evolution on model training and has proposed several solutions for it, its impact on model benchmarking remains under-explored. Yet, hate speech benchmarks play a crucial role to ensure model safety. In this paper, we empirically evaluate the robustness of 20 language models across two evolving hate speec…

Beyond the LUMIR challenge: The pathway to foundational registration models
Junyu Chen, Shuwen Wei, Joel Honkamaa, Pekka Marttinen, Hang Zhang, Min Liu, Yichao Zhou, Zuopeng Tan, Zhuoyuan Wang, Yi Wang, Hongchao Zhou, Shunbo Hu, Yi Zhang, Qian Tao, Lukas F\"orner, Thomas Wendler, Bailiang Jian, Benedikt Wiestler, Tim Hable, Jin Kim, Dan Ruan, Frederic Madesta, Thilo Sentker, Wiebke Heyer, Lianrui Zuo, Yuwei Dai, Jing Wu, Jerry L. Prince, Harrison Bai, Yong Du, Yihao Liu, Alessa He…

Beyond the LUMIR challenge: The pathway to foundational registration models
Medical image challenges have played a transformative role in advancing the field, catalyzing algorithmic innovation and establishing new performance standards across diverse clinical applications. Image registration, a foundational task in neuroimaging pipelines, has similarly benefited from the Learn2Reg initiative. Building on this foundation, we introduce the Large-scale Unsupervised Brain MRI Image Registration (LUMIR) challenge, a next-generation benchmark designed to assess and advance u…

Audio-Aware Large Language Models as Judges for Speaking Styles
Cheng-Han Chiang, Xiaofei Wang, Chung-Ching Lin, Kevin Lin, Linjie Li, Radu Kopetz, Yao Qian, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang
https://arxiv.org/abs/2506.05984

Audio-Aware Large Language Models as Judges for Speaking Styles
Audio-aware large language models (ALLMs) can understand the textual and non-textual information in the audio input. In this paper, we explore using ALLMs as an automatic judge to assess the speaking styles of speeches. We use ALLM judges to evaluate the speeches generated by SLMs on two tasks: voice style instruction following and role-playing. The speaking style we consider includes emotion, volume, speaking pace, word emphasis, pitch control, and non-verbal elements. We use four spoken langu…

This https://arxiv.org/abs/2503.16456 has been replaced.
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Position: Beyond Assistance -- Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care
This position paper argues for a fundamental shift in how Large Language Models (LLMs) are integrated into the mental health care domain. We advocate for their role as co-creators rather than mere assistive tools. While LLMs have the potential to enhance accessibility, personalization, and crisis intervention, their adoption remains limited due to concerns about bias, evaluation, over-reliance, dehumanization, and regulatory uncertainties. To address these challenges, we propose two structured …

This https://arxiv.org/abs/2409.14644 has been replaced.
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An Effective Approach to Embedding Source Code by Combining Large Language and Sentence Embedding Models
The advent of large language models (LLMs) has significantly advanced artificial intelligence (AI) in software engineering (SE), with source code embeddings playing a crucial role in tasks such as source code clone detection and source code clustering. However, existing methods for source code embedding, including those based on LLMs, often rely on costly supervised training or fine-tuning for domain adaptation. This paper proposes a novel approach to embedding source code by combining large la…

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
Junli Shao, Jing Dong, Dingzhou Wang, Kowei Shih, Dannier Li, Chengrui Zhou
https://arxiv.org/abs/2506.11421

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model architectures, the key challenge for real-time recommendation systems is how to reduce inference latency and increase system throughput without sacrificing recommendation quality. This paper addresses the high computational cost and resource bottlenecks of deep learning models in real-time settings by proposing a combined s…

Trade-offs in Data Memorization via Strong Data Processing Inequalities
Vitaly Feldman, Guy Kornowski, Xin Lyu
https://arxiv.org/abs/2506.01855 https://

Trade-offs in Data Memorization via Strong Data Processing Inequalities
Recent research demonstrated that training large language models involves memorization of a significant fraction of training data. Such memorization can lead to privacy violations when training on sensitive user data and thus motivates the study of data memorization's role in learning. In this work, we develop a general approach for proving lower bounds on excess data memorization, that relies on a new connection between strong data processing inequalities and data memorization. We then demonst…

Probing Non-Thermal Processes in Stellar Flares on AU Mic
Adalyn Gibson, Adam F. Kowalski, Adina D. Feinstein
https://arxiv.org/abs/2506.10201 https://

Probing Non-Thermal Processes in Stellar Flares on AU Mic
The role of non-thermal proton energy transportation during solar and stellar flares is largely unknown; a better understanding of this physical process will allow us to rectify longstanding deficiencies in flare models. One way to detect the presence of non-thermal protons during flares is through the Orrall-Zirker (OZ) effect whereby an enhanced red wing appears in hydrogen emission lines (e.g., Lyman-$α$ at 1215.67 Angstrom). We analyze archival Hubble Space Telescope/Cosmic Origins Spectro…

This https://arxiv.org/abs/2505.10981 has been replaced.
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Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory
Recently, scaling test-time compute on Large Language Models (LLM) has garnered wide attention. However, there has been limited investigation of how various reasoning prompting strategies perform as scaling. In this paper, we focus on a standard and realistic scaling setting: majority voting. We systematically conduct experiments on 6 LLMs $\times$ 8 prompting strategies $\times$ 6 benchmarks. Experiment results consistently show that as the sampling time and computational overhead increase, co…

This https://arxiv.org/abs/2505.07270 has been replaced.
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Automated Repair of Ambiguous Natural Language Requirements
The widespread adoption of large language models (LLMs) in software engineering has amplified the role of natural language (NL). The inherent ambiguity of NL threatens software quality, because ambiguous requirements may lead to faulty program generation. The complexity of ambiguity detection and resolution motivates us to introduce automated repair of ambiguous NL requirements, which we approach by reducing code generation uncertainty and aligning NL with input-output examples. Repairing ambig…

Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
Xiangning Yu, Zhuohan Wang, Linyi Yang, Haoxuan Li, Anjie Liu, Xiao Xue, Jun Wang, Mengyue Yang
https://arxiv.org/abs/2506.09853

Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamental challenges: (1) Sufficiency, which ensures that the generated intermediate inference steps comprehensively cover and substantiate the final conclusion; and (2) Necessity, which identifies the inference steps that are truly indispensable for the soundness of the resulting answer. We propose a causal framework that …

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Towards More Effective Fault Detection in LLM-Based Unit Test Generation
Unit tests play a vital role in uncovering potential faults in software. While tools like EvoSuite focus on maximizing code coverage, recent advances in large language models (LLMs) have shifted attention toward LLM-based test generation. However, code coverage metrics -- such as line and branch coverage -- remain overly emphasized in reported research, despite being weak indicators of a test suite's fault-detection capability. In contrast, \textit{mutation score} offers a more reliable and str…

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

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

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HyGenar: An LLM-Driven Hybrid Genetic Algorithm for Few-Shot Grammar Generation
Grammar plays a critical role in natural language processing and text/code generation by enabling the definition of syntax, the creation of parsers, and guiding structured outputs. Although large language models (LLMs) demonstrate impressive capabilities across domains, their ability to infer and generate grammars has not yet been thoroughly explored. In this paper, we aim to study and improve the ability of LLMs for few-shot grammar generation, where grammars are inferred from sets of a small …

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Stefan Dascalescu, Adrian Marius Dumitran, Mihai Alexandru Vasiluta
https://arxiv.org/abs/2506.05990

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Competitive programming contests play a crucial role in cultivating computational thinking and algorithmic skills among learners. However, generating comprehensive test cases to effectively assess programming solutions remains resource-intensive and challenging for educators. This paper introduces an innovative NLP-driven method leveraging generative AI (large language models) to automate the creation of high-quality test cases for competitive programming assessments. We extensively evaluated o…

Towards More Effective Fault Detection in LLM-Based Unit Test Generation
Guancheng Wang, Qinghua Xu, Lionel C. Briand, Kui Liu
https://arxiv.org/abs/2506.02954

Towards More Effective Fault Detection in LLM-Based Unit Test Generation
Unit tests play a vital role in uncovering potential faults in software. While tools like EvoSuite focus on maximizing code coverage, recent advances in large language models (LLMs) have shifted attention toward LLM-based test generation. However, code coverage metrics -- such as line and branch coverage -- remain overly emphasized in reported research, despite being weak indicators of a test suite's fault-detection capability. In contrast, \textit{mutation score} offers a more reliable and str…

Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books
Chen Zhang, Jiuheng Lin, Xiao Liu, Zekai Zhang, Yansong Feng
https://arxiv.org/abs/2506.01796

Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books
While large language models (LLMs) have shown promise in translating extremely low-resource languages using resources like dictionaries, the effectiveness of grammar books remains debated. This paper investigates the role of grammar books in translating extremely low-resource languages by decomposing it into two key steps: grammar rule retrieval and application. To facilitate the study, we introduce ZhuangRules, a modularized dataset of grammar rules and their corresponding test sentences. Our …

Reuse or Generate? Accelerating Code Editing via Edit-Oriented Speculative Decoding
Peiding Wang, Li Zhang, Fang Liu, Yinghao Zhu, Wang Xu, Lin Shi, Xiaoli Lian, Minxiao Li, Bo Shen, An Fu
https://arxiv.org/abs/2506.02780

Reuse or Generate? Accelerating Code Editing via Edit-Oriented Speculative Decoding
Large Language Models (LLMs) have demonstrated remarkable capabilities in code editing, substantially enhancing software development productivity. However, the inherent complexity of code editing tasks forces existing approaches to rely on LLMs' autoregressive end-to-end generation, where decoding speed plays a critical role in efficiency. While inference acceleration techniques like speculative decoding are applied to improve the decoding efficiency, these methods fail to account for the uniqu…

Toward Understanding Bugs in Vector Database Management Systems
Yinglin Xie, Xinyi Hou, Yanjie Zhao, Shenao Wang, Kai Chen, Haoyu Wang
https://arxiv.org/abs/2506.02617

Toward Understanding Bugs in Vector Database Management Systems
Vector database management systems (VDBMSs) play a crucial role in facilitating semantic similarity searches over high-dimensional embeddings from diverse data sources. While VDBMSs are widely used in applications such as recommendation, retrieval-augmented generation (RAG), and multimodal search, their reliability remains underexplored. Traditional database reliability models cannot be directly applied to VDBMSs because of fundamental differences in data representation, query mechanisms, and s…

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The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs
Static analysis plays a crucial role in software vulnerability detection, yet faces a persistent precision-scalability tradeoff. In large codebases like the Linux kernel, traditional static analysis tools often generate excessive false positives due to simplified vulnerability modeling and overapproximation of path and data constraints. While large language models (LLMs) demonstrate promising code understanding capabilities, their direct application to program analysis remains unreliable due to…

Temac: Multi-Agent Collaboration for Automated Web GUI Testing
Chenxu Liu, Zhiyu Gu, Guoquan Wu, Ying Zhang, Jun Wei, Tao Xie
https://arxiv.org/abs/2506.00520

Temac: Multi-Agent Collaboration for Automated Web GUI Testing
Quality assurance of web applications is critical, as web applications play an essential role in people's daily lives. To reduce labor costs, automated web GUI testing (AWGT) is widely adopted, exploring web applications via GUI actions such as clicks and text inputs. However, these approaches face limitations in generating continuous and meaningful action sequences capable of covering complex functionalities. Recent work incorporates large language models (LLMs) for GUI testing. However, these…