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# I. INTRODUCTION Vulnerability management is a cornerstone of effective cyber defense, enabling organizations to prioritize and mitigate risks before attackers can exploit them. However, false positives (FPs) in vulnerability management predominantly stem from limitations in Common Platform Enumeration (CPE) [25] dat...	The dynamic landscape of cybersecurity demands precise and scalable solutions for vulnerability management in heterogeneous systems, where configuration-specific vulnerabilities are often misidentified due to inconsistent data in databases like the National Vulnerability Database (NVD). Inaccurate Common Platform Enume...	[ "cs.CR", "cs.DB", "cs.IR", "D.4.6; H.3.3; H.2.8; I.2.7" ]
# 1 Introduction Software patching is a time-intensive and cognitively demanding task, especially for large and complex codebases. In the real world, effective patching often requires a combination of complementary skills: locating the faulty component, generating plausible fixes, and validating the changes. Recent wo...	Motivated by the success of general-purpose large language models (LLMs) in software patching, recent works started to train specialized patching models. Most works trained one model to handle the end-to-end patching pipeline (including issue localization, patch generation, and patch validation). However, it is hard fo...	[ "cs.AI", "cs.CR", "cs.SE" ]
# 1 Introduction Recent advances in large language models (LLMs) have greatly improved natural language understanding and generation. However, purely pre-trained LLMs often fail to align with human intentions or specific tasks (Ouyang et al., 2022), prompting increasing focus on alignment techniques. Supervised fine-t...	Supervised fine-tuning (SFT) is a critical step in aligning large language models (LLMs) with human instructions and values, yet many aspects of SFT remain poorly understood. We trained a wide range of base models on a variety of datasets including code generation, mathematical reasoning, and general-domain tasks, resu...	[ "cs.CL" ]
# 1. INTRODUCTION Understanding and modeling human driving behavior is fundamental to the development of intelligent transportation systems, ADAS technologies, and autonomous vehicles (Li et al., 2021). Driving actions arise from a complex interplay between internal decision-making processes and external traffic envir...	Accurate and interpretable car-following models are essential for traffic simulation and autonomous vehicle development. However, classical models like the Intelligent Driver Model (IDM) are fundamentally limited by their parsimonious and single-regime structure. They fail to capture the multi-modal nature of human dri...	[ "stat.AP", "cs.LG", "cs.RO" ]
# 1 Introduction Human-written texts vary widely in terms of length, style, communicative intent, lexical/syntactical choices, and numerous other dimensions (Giulianelli et al., 2023; Liu and Zeldes, 2023; Rezapour et al., 2022; Baan et al., 2023). Such variation poses a significant challenge in the evaluation of summ...	Human language production exhibits remarkable richness and variation, reflecting diverse communication styles and intents. However, this variation is often overlooked in summarization evaluation. While having multiple reference summaries is known to improve correlation with human judgments, the impact of using differen...	[ "cs.CL" ]
# 1 Introduction Artificial intelligence-assisted radiology informatics (AIRI) remains challenging to deploy due to the complexity of radiographs (e.g., Xrays with non-distinctive features among overlapping diseases) and the subjective nature of radiologists’ reports [12]. Some examples of AIRI include image segmentat...	In this paper, we construct two research objectives: i) explore the learned embedding space of BiomedCLIP, an open-source large vision language model, to analyse meaningful class separations, and ii) quantify the limitations of BiomedCLIP when applied to a highly imbalanced, out-of-distribution multi-label medical data...	[ "cs.CV" ]
# 1 Introduction In recent years, there has been a significant surge in the capabilities of large language models (LLMs) in generating human-like text and performing a wide range of natural language processing tasks. State-of-the-art models like GPT-4o (Hurst et al., 2024), OpenAI o1/o3 (Contributors et al., 2024), an...	As Large Language Models (LLMs) become integral to software development workflows, their ability to generate structured outputs has become critically important. We introduce StructEval, a comprehensive benchmark for evaluating LLMs' capabilities in producing both non-renderable (JSON, YAML, CSV) and renderable (HTML, R...	[ "cs.SE", "cs.AI", "cs.CL" ]
# I. INTRODUCTION Large Language Models (LLMs) have achieved exceptional capabilities in various Natural Language Processing (NLP) tasks [1]–[3], demonstrating their ability to absorb and retain vast amounts of knowledge. When responding to specific queries, LLMs often provide informative answers, leveraging the exten...	Retrieval-Augmented Generation (RAG) addresses limitations of large language models (LLMs) by leveraging a vector database to provide more accurate and up-to-date information. When a user submits a query, RAG executes a vector search to find relevant documents, which are then used to generate a response. However, ensur...	[ "cs.AI" ]
# 1 Introduction Security vulnerabilities are a major concern for the safety and robustness of software systems. Much of the technological infrastructure in today’s world heavily relies on $\mathrm { C / C } { + + }$ projects, and consequently, these projects are critical targets for security vulnerabilities. Vulnerab...	Large Language Models (LLMs) have shown promise in software engineering tasks, but evaluating their effectiveness in vulnerability detection is challenging due to the lack of high-quality datasets. Most existing datasets are limited to function-level labels, ignoring finer-grained vulnerability patterns and crucial con...	[ "cs.SE" ]
# 1 INTRODUCTION Foundation models have become highly accessible to users thanks to the availability of model hosting platforms such as HuggingFace [59], Ollama [12], and ModelScope [55]. Developers download the pre-trained models hosted on these platforms (e.g., from cloud storage), and then apply them to various tas...	Modern applications commonly leverage large, multi-modal foundation models. These applications often feature complex workflows that demand the storage and usage of similar models in multiple precisions. A straightforward approach is to maintain a separate file for each model precision (e.g., INT8, BF16), which is indee...	[ "cs.DB" ]
# I. INTRODUCTION H sOigLnOifGicRanAtPpHotIeCntdiiaslpilnaythsearaeugwmideenltyedreagnadrdveird uas hreaavliintyg (AR/VR) field due to the rich depth cues they can provide [1], [2]. Computer-generated holograms (CGH) is a method of generating holograms by simulating diffraction models in computer [3] rather than throu...	Holographic displays have significant potential in virtual reality and augmented reality owing to their ability to provide all the depth cues. Deep learning-based methods play an important role in computer-generated holograms (CGH). During the diffraction process, each pixel exerts an influence on the reconstructed ima...	[ "physics.optics", "cs.CV" ]
# 1 Introduction One of the most frequently applied interventions in the intensive care unit (ICU) is invasive mechanical ventilation (MV) [3], and its critical role became even more evident during the COVID-19 pandemic, which saw a surge ICU admissions, prolonged mechanical ventilation needs, and early intubation. H...	Invasive mechanical ventilation (MV) is a life-sustaining therapy for critically ill patients in the intensive care unit (ICU). However, optimizing its settings remains a complex and error-prone process due to patient-specific variability. While Offline Reinforcement Learning (RL) shows promise for MV control, current ...	[ "cs.LG", "cs.AI" ]
# I. INTRODUCTION The increasing deployment of Artificial Intelligence (AI) systems across critical domains, such as healthcare, finance, public administration, has raised urgent concerns about their ethical, legal, and social implications [1]. In response, the notion of Responsible AI (RAI) has emerged as a multidime...	Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance f...	[ "cs.SE", "cs.AI" ]
# I. INTRODUCTION Interacting with 3D scenes using open-vocabulary perception is a key challenge facing current AI-driven agents [1], [2]. Accurately querying semantic objects and their relationships through complex free-form queries in intricate 3D environments remains an unresolved issue [3]. Recent works [4]–[6] t...	Semantic querying in complex 3D scenes through free-form language presents a significant challenge. Existing 3D scene understanding methods use large-scale training data and CLIP to align text queries with 3D semantic features. However, their reliance on predefined vocabulary priors from training data hinders free-form...	[ "cs.CV" ]
# 1. Introduction Image fusion is a technique that integrates complementary information from multiple sensors or diverse imaging conditions to generate a unified, comprehensive representation of the scene. By leveraging the distinct yet complementary characteristics of different modalities [1, 2]—such as thermal radia...	In the field of image fusion, promising progress has been made by modeling data from different modalities as linear subspaces. However, in practice, the source images are often located in a non-Euclidean space, where the Euclidean methods usually cannot encapsulate the intrinsic topological structure. Typically, th...	[ "cs.CV", "I.4" ]
# 1. Introduction Data systems are increasingly integrating machine learning functionalities to enhance performance and usability, marking a paradigm shift in how data is managed and processed in databases (Ooi et al., 2024; McGregor, 2021; Li et al., 2021). The integration has transformed key database operations such...	Machine learning has demonstrated transformative potential for database operations, such as query optimization and in-database data analytics. However, dynamic database environments, characterized by frequent updates and evolving data distributions, introduce concept drift, which leads to performance degradation for le...	[ "cs.DB", "cs.AI" ]
# I. INTRODUCTION Fact checking refers to the process of comparing a claim with other sources of information to verify its accuracy. It has a wide range of applications, including fake news detection [1] and claim verification in scientific publications [2]. As tables are important carriers of high-density information...	Numerical consistency across tables in disclosure documents is critical for ensuring accuracy, maintaining credibility, and avoiding reputational and economic risks. Automated tabular numerical cross-checking presents two significant challenges: (C1) managing the combinatorial explosion of candidate instances at the do...	[ "cs.CL" ]
# 1 Introduction Deep learning has revolutionized medical imaging by delivering substantial gains in segmentation accuracy, lesion detection, and diagnostic classification across radiology and endoscopy.[1–3] Yet, translating these advances into surgical domains remains challenging due to the complex, dynamic environm...	Deep learning in medical imaging faces obstacles: limited data diversity, ethical issues, high acquisition costs, and the need for precise annotations. Bleeding detection and localization during surgery is especially challenging due to the scarcity of high-quality datasets that reflect real surgical scenarios. We propo...	[ "eess.IV", "cs.AI", "cs.CV" ]
# 1 Introduction Egocentric videos, which capture human daily lives from a first-person perspective, are inherently long - often spanning hours to days or even weeks [75]. Understanding these videos is crucial for supporting practical tasks such as memory recall, multi-step activity tracking, and goal monitoring [5, 2...	We introduce Ego-R1, a novel framework for reasoning over ultra-long (i.e., in days and weeks) egocentric videos, which leverages a structured Chain-of-Tool-Thought (CoTT) process, orchestrated by an Ego-R1 Agent trained via reinforcement learning (RL). Inspired by human problem-solving strategies, CoTT decomposes comp...	[ "cs.CV", "cs.AI" ]
# I. INTRODUCTION Data governance has become increasingly crucial as data is becoming larger and more complex in enterprise data warehouses. For example, in an organization’s data pipeline, data flows from upstream artifacts to downstream services, which may be built by various teams that know little about other teams...	As enterprise data grows in size and complexity, column-level data lineage, which records the creation, transformation, and reference of each column in the warehouse, has been the key to effective data governance that assists tasks like data quality monitoring, storage refactoring, and workflow migration. Unfortunately...	[ "cs.DB" ]
# 1 Introduction In the era of rapidly advancing large language models (LLMs), the widespread dissemination of misinformation, combined with the increasing presence of AI-generated content, has made it significantly harder for individuals to assess the reliability of information. Consequently, claim verification, leve...	Claim verification is a long-standing and challenging task that demands not only high accuracy but also explainability of the verification process. This task becomes an emerging research issue in the era of large language models (LLMs) since real-world claims are often complex, featuring intricate semantic structures o...	[ "cs.CL", "cs.AI", "cs.DB", "cs.IR" ]
# I. INTRODUCTION The publication of the Bitcoin white paper in 2008 and the subsequent launch of the Bitcoin blockchain in 2009 have catalyzed extensive interest and research into blockchain technology. This technology has attracted widespread attention from businesses, researchers, and the software industry due to i...	Research on blockchains addresses multiple issues, with one being writing smart contracts. In our previous research we described methodology and a tool to generate, in automated fashion, smart contracts from BPMN models. The generated smart contracts provide support for multi-step transactions that facilitate repair/up...	[ "cs.SE", "cs.CR", "cs.DC" ]
# I. INTRODUCTION M IbLroLaIdMerETchEaRn-nweal be (nmdwmidWtahvs ) ofrmepqaurend yto atrnadsi iofnfearl sub-6 GHz bands, enabling higher capacity for communication systems. However, their high propagation loss introduces new challenges for system design. While high-gain narrow beams formed by large antenna arrays can ...	This paper introduces a novel neural network framework called M2BeamLLM for beam prediction in millimeter-wave (mmWave) massive multi-input multi-output (mMIMO) communication systems. M2BeamLLM integrates multi-modal sensor data, including images, radar, LiDAR, and GPS, leveraging the powerful reasoning capabilities of...	[ "cs.CL" ]
# I. INTRODUCTION Code clone detection is a fundamental task in software engineering, aimed at identifying duplicated or highly similar code fragments within a software repository [1]. Code clones can arise due to several development practices such as copypasting, reusing code templates, or implementing similar functi...	As one of the most detrimental code smells, code clones significantly increase software maintenance costs and heighten vulnerability risks, making their detection a critical challenge in software engineering. Abstract Syntax Trees (ASTs) dominate deep learning-based code clone detection due to their precise syntactic s...	[ "cs.AI", "cs.SE" ]
# 1 Introduction Generative Music modeling is a subfield of Generative AI, where music modeling through user inputs and concepts such as melody reconstruction, music continuation, text-music, lyric-to-song are explored. None of these research directions have an unified dataset structure and requirement which means it ...	We present Sleeping-DISCO 9M, a large-scale pre-training dataset for music and song. To the best of our knowledge, there are no open-source high-quality dataset representing popular and well-known songs for generative music modeling tasks such as text-music, music-captioning, singing-voice synthesis, melody reconstruct...	[ "cs.SD", "cs.LG", "eess.AS" ]
# 1 INTRODUCTION Hindle et al. [29] show that software is repetitive and predictable like natural language, and hence can be modeled using statistical techniques like LLMs. Subsequently, LLMs have been used effectively for a wide variety of software engineering (SWE) tasks , including code generation [13], language tr...	The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks. These advancements have extended into the domain of code, facilitating complex tasks such as code generation, translation, summarization, and repair. However, their utility for real-world deployment ...	[ "cs.SE", "cs.AI" ]
# I. INTRODUCTION By 2050, global agricultural production must double to feed 10 billion people—a Herculean task exacerbated by climate-induced disruptions to pest populations. Rising temperatures accelerate insect reproduction cycles, amplifying crop destruction: the UN Food and Agriculture Organization estimates ann...	To empower precision agriculture through distributed machine learning (DML), split learning (SL) has emerged as a promising paradigm, partitioning deep neural networks (DNNs) between edge devices and servers to reduce computational burdens and preserve data privacy. However, conventional SL frameworks' one-split-fits-a...	[ "cs.LG", "cs.DC", "cs.ET" ]
# 1 Introduction AI Agents have recently proven themselves as a competitive way of scaling test-time compute, especially in SE (Chowdhury et al., 2024). A crucial yet underexplored component of AI agents is their memory, which allows them to dynamically adapt their behavior based on prior experiences. Early approaches...	We introduce CTIM-Rover, an AI agent for Software Engineering (SE) built on top of AutoCodeRover (Zhang et al., 2024) that extends agentic reasoning frameworks with an episodic memory, more specifically, a general and repository-level Cross-Task-Instance Memory (CTIM). While existing open-source SE agents mostly rely o...	[ "cs.SE", "cs.AI" ]
introduction of "shortcuts"—instructional transitions learned from historically successful trajectories—which allows to bypass redundant reasoning agents and expedite the collective problem-solving process. Experiments for software development tasks demonstrate significant advantages over existing methods. Specifically...	Recent advancements in Large Language Models (LLMs) and autonomous agents have demonstrated remarkable capabilities across various domains. However, standalone agents frequently encounter limitations when handling complex tasks that demand extensive interactions and substantial computational resources. Although Multi-A...	[ "cs.CL", "cs.AI", "cs.MA", "cs.SE" ]
# Introduction Chest X-ray (CXR) imaging remains a cornerstone of thoracic diagnostics, enabling rapid detection of critical conditions such as pneumonia, pneumothorax, and cardiomegaly. Despite its ubiquity, clinical interpretation of CXRs still largely relies on manual reading by radiologists, which is subject to in...	Chest X ray (CXR) imaging remains a critical diagnostic tool for thoracic conditions, but current automated systems face limitations in pathology coverage, diagnostic accuracy, and integration of visual and textual reasoning. To address these gaps, we propose RadFabric, a multi agent, multimodal reasoning framework tha...	[ "cs.CV", "cs.CL" ]
# I. Introduction R hRaEsCbEeNenTLpYr, poesresdo, asluiczhe f fdedrearteatd laeranrinig P(FL) with meta-learning. It tailors learning processes to provide personalized models for individual clients while benefiting from the global perception offered by FL, thereby capturing both generalization and personalization in...	Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). While FL is unaffected by personalized model training, in Ditto, PL depends on the outcome of the FL. However, the clie...	[ "cs.LG", "cs.DC" ]
# 1 Introduction Social media platforms like Twitter (now X) provide a space for people to express their opinions and stay informed about trending topics. However, like other social media platforms, Twitter is vulnerable to manipulation by malicious actors. These actors often engage in coordinated attacks that artific...	Coordinated campaigns frequently exploit social media platforms by artificially amplifying topics, making inauthentic trends appear organic, and misleading users into engagement. Distinguishing these coordinated efforts from genuine public discourse remains a significant challenge due to the sophisticated nature of suc...	[ "cs.SI", "cs.LG" ]
# 1 Introduction Clustering is a fundamental task in both machine learning and data mining [24, 35, 25]. Edge-colored clustering (ECC), in particular, is a useful model when interactions between the items to be clustered are represented as categorical data [8, 4]. To provide intuition, let us consider the following si...	Clustering is a fundamental task in both machine learning and data mining. Among various methods, edge-colored clustering (ECC) has emerged as a useful approach for handling categorical data. Given a hypergraph with (hyper)edges labeled by colors, ECC aims to assign vertex colors to minimize the number of edges where t...	[ "cs.LG", "cs.DB", "cs.DS" ]
# 1 Introduction Language models (LMs) perform well on coding benchmarks like HumanEval [4] or LiveCodeBench [13] but struggle with real-world software engineering (SWE) tasks [15]. Unlike standardized coding problems, real issues—such as GitHub issues [15]—are often under-specified and require reasoning across multip...	Language models (LMs) perform well on standardized coding benchmarks but struggle with real-world software engineering tasks such as resolving GitHub issues in SWE-Bench, especially when model parameters are less than 100B. While smaller models are preferable in practice due to their lower computational cost, improving...	[ "cs.CL", "cs.AI", "cs.SE" ]
# Evolutionary chemical learning in dimerization networks Alexei V. Tkachenko1,\, Bortolo Matteo Mognetti2, and Sergei Maslov3,4,5,\ 1Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY 11973, USA; 2 Interdisciplinary Center for Nonlinear Phenomena and Complex Systems, Université Libre de...	We present a novel framework for chemical learning based on Competitive Dimerization Networks (CDNs) - systems in which multiple molecular species, e.g. proteins or DNA/RNA oligomers, reversibly bind to form dimers. We show that these networks can be trained in vitro through directed evolution, enabling the implementat...	[ "cond-mat.stat-mech", "cond-mat.dis-nn", "cs.LG", "nlin.AO", "physics.data-an", "q-bio.MN" ]
# I. INTRODUCTION I NTERACTING with visual data using natural language is a rapidly advancing field [1]–[7] with real-world applications such as robotics and AR/VR. Within this domain, 3D visual grounding, commonly referred to as text-guided 3D visual grounding (T-3DVG), focuses on locating specific objects based on p...	Existing 3D visual grounding methods rely on precise text prompts to locate objects within 3D scenes. Speech, as a natural and intuitive modality, offers a promising alternative. Real-world speech inputs, however, often suffer from transcription errors due to accents, background noise, and varying speech rates, limitin...	[ "cs.CV" ]
# 1 INTRODUCTION L parogerelsasnignubaogtehmgeonderlsa (dLoLmMasin)ahpaplviecamtiaodnes (re.mg.a,rokpaebnledomain question answering [332], cross-modal video summarization [175], general-purpose code generation [191]) and specific domain applications (e.g., biomedical literature analysis [394], legal document review [...	The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationships. On the one hand, DATA4LLM, spanning large-scale data processing, storage, and serving, feeds LLMs with high quality, diversity, and time...	[ "cs.DB", "cs.AI", "cs.CL", "cs.IR", "cs.LG" ]
1 Introduction 3 2 Related Work 4 # 3 Method 5 # 3.1 Supervised Fine-Tuning 5 3.1.1 Prompt collection and filtering 5 3.1.2 Scaling of SFT Data . 5 3.2 Reinforcement Learning 6 3.2.1 Overview 6 3.2.2 Data curation 6 3.2.3 Training process 7 # 4 Evaluation 8 # 4.1 Benchmark 8 4.2 Baselines 8 4.3 Main Results 9 4...	In this work, we investigate the synergy between supervised fine-tuning (SFT) and reinforcement learning (RL) in developing strong reasoning models. We begin by curating the SFT training data through two scaling strategies: increasing the number of collected prompts and the number of generated responses per prompt. Bot...	[ "cs.CL", "cs.AI", "cs.LG" ]
# 1. Introduction Earth observation satellites capture our planet through different lenses, each offering distinct spectral, spatial, and temporal configurations. Many satellites are being launched with diverse spectral, spatial and temporal resolutions. While the first Earth observation satellite has been launched in...	The growing number of Earth observation satellites has led to increasingly diverse remote sensing data, with varying spatial, spectral, and temporal configurations. Most existing models rely on fixed input formats and modality-specific encoders, which require retraining when new configurations are introduced, limiting ...	[ "cs.CV" ]
introduction,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017. [26] A. K. Tanwani, N. Mor, J. Kubiatowicz, J. E. Gonzalez, and K. Goldberg, “A fog robotics approach to deep robot learning: Application to object recognition and grasp planning in surface decluttering,” in Proc. IEEE Int. Con...	Recent results suggest that very large datasets of teleoperated robot demonstrations can be used to train transformer-based models that have the potential to generalize to new scenes, robots, and tasks. However, curating, distributing, and loading large datasets of robot trajectories, which typically consist of video, ...	[ "cs.RO", "cs.AI", "cs.DB", "cs.LG" ]
# 1 INTRODUCTION In the current era of data-intensive applications, the demand for high-performance, cost-effective storage solutions is paramount. Key-value store, with their promise of scalability, flexibility, and rapid data access, have emerged as a pivotal component in this landscape. Renowned key-value stores su...	In the current era of data-intensive applications, the demand for high-performance, cost-effective storage solutions is paramount. This paper introduces a Space-Performance Cost Model for key-value store, designed to guide cost-effective storage configuration decisions. The model quantifies the trade-offs between perfo...	[ "cs.DB", "cs.DC" ]
# 1 Introduction CodeBase Generated Perf Test “optimize SWE Agent llama_cpp for Is the model patch llama_cpp/ llm = Llama("qwen2-7b.gguf") this workload” correct and examples/ prompts $\mathbf { \sigma } = \mathbf { \sigma }$ load("sharegpt.json") match or exceed setup.py reef euxrpnerlilme(nptr(o)m:p ts) Model Patch ...	Developing high-performance software is a complex task that requires specialized expertise. We introduce GSO, a benchmark for evaluating language models' capabilities in developing high-performance software. We develop an automated pipeline that generates and executes performance tests to analyze repository commit hist...	[ "cs.SE", "cs.AI", "cs.CL", "cs.LG" ]
# 1 Introduction Modeling human motion remains a significant challenge across neuroscience, medicine, computer graphics, and robotics due to data scarcity, high dimensionality, motor redundancy, and the requirement for real-time computation [22]. While neural networks are commonly employed, their effectiveness depends...	We present the Gaussian process dynamical mixture model (GPDMM) and show its utility in single-example learning of human motion data. The Gaussian process dynamical model (GPDM) is a form of the Gaussian process latent variable model (GPLVM), but optimized with a hidden Markov model dynamical prior. The GPDMM combines ...	[ "cs.LG" ]
# 1 INTRODUCTION # 1.1 Background and Motivation Data stream monitoring has become instrumental in deriving valuable insights for a myriad of applications, such as anomaly detection [2–5], network measurement [6, 7], predictive maintenance [8–10], and customer behavior analysis [11, 12], among others [13–15]. These t...	Data stream monitoring is a crucial task which has a wide range of applications. The majority of existing research in this area can be broadly classified into two types, monitoring value sum and monitoring value cardinality. In this paper, we define a third type, monitoring value variation, which can help us detect flo...	[ "cs.DB" ]
# I. INTRODUCTION Mining app reviews from mobile app repositories has gained significant attention in requirements engineering (RE) research over the past decade [1]. Relevant descriptors from mobile app reviews include numerical rating [2], review type [3] (e.g., bug report, feature request, praise), topic [4] (e.g.,...	Opinion mining plays a vital role in analysing user feedback and extracting insights from textual data. While most research focuses on sentiment polarity (e.g., positive, negative, neutral), fine-grained emotion classification in app reviews remains underexplored. This paper addresses this gap by identifying and addres...	[ "cs.IR", "cs.SE" ]
# 1 Introduction Aquaculture plays a crucial role in satisfying the growing global demand for fish and providing a sustainable food source (Boyd et al., 2022). According to the Food and Agriculture Organization of the United Nations, global fisheries and aquaculture production has reached 223.2 million tons, with aqua...	In recirculating aquaculture systems, accurate and effective assessment of fish feeding intensity is crucial for reducing feed costs and calculating optimal feeding times. However, current studies have limitations in modality selection, feature extraction and fusion, and co-inference for decision making, which restrict...	[ "cs.CV", "cs.AI", "cs.ET" ]
# 1 Introduction As Large Language Models (LLMs) continues to scale, LLMs exhibit emergent In-Context Learning (ICL) capabilities (Brown et al., 2020), enabling them to perform target tasks by conditioning on a few exemplars without any additional parameter updates. Furthermore, the use of Chain-of-Thought (CoT) exemp...	In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs), and recent studies introduce Chain-of-Thought (CoT) to exemplars of ICL to enhance the reasoning capability, especially in mathematics tasks. However, given the continuous advancement of model capabilities, it remains unclear wh...	[ "cs.CL", "cs.AI", "cs.LG" ]
# 1 Introduction In recent years GPUs have emerged as mainstream processing units, more than just accelerators [73,67,66,29]. Modern GPUs provide support for more finegrained shared memory access patterns, allowing programmers to optimize performance beyond the traditional lock-step execution model typically associate...	GPU computing is embracing weak memory concurrency for performance improvement. However, compared to CPUs, modern GPUs provide more fine-grained concurrency features such as scopes, have additional properties like divergence, and thereby follow different weak memory consistency models. These features and properties mak...	[ "cs.LO", "cs.PL", "cs.SE" ]
# I. INTRODUCTION Microservices have emerged as a promising architecture for implementing large-scale applications. When using microservices, applications are designed as a set of loosely coupled components that may be easily developed and maintained by independent teams [31], [21]. The adoption of this architectural ...	The advent of microservices has led multiple companies to migrate their monolithic systems to this new architecture. When decomposing a monolith, a functionality previously implemented as a transaction may need to be implemented as a set of independent sub-transactions, possibly executed by multiple microservices. The ...	[ "cs.SE" ]
# 1 Introduction The rapid development of video generation models has driven the continuous growth of the demand for high-fidelity and high-resolution content in fields such as film production, immersive media, and interactive entertainment [20]. However, the performance of text-to-video (T2V) models is severely limit...	The quality of the video dataset (image quality, resolution, and fine-grained caption) greatly influences the performance of the video generation model. The growing demand for video applications sets higher requirements for high-quality video generation models. For example, the generation of movie-level Ultra-High Defi...	[ "cs.CV" ]
# 1. Introduction Large Language Models (LLMs) have demonstrated exceptional performance in general-purpose applications and have achieved notable success in vertical domains, such as finance, healthcare, law, and scientific research, by providing precise domain knowledge and specialized text generation (Li et al., 20...	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...	[ "cs.AI" ]
# 1 INTRODUCTION Knowing the effect of interventions is key to understanding the effect of a treatment in medicine or the effect of a maintenance operation in IT monitoring systems for example. When one cannot perform interventions in practice, for example when these interventions may endanger people’s life or when th...	The identifiability problem for interventions aims at assessing whether the total causal effect can be written with a do-free formula, and thus be estimated from observational data only. We study this problem, considering multiple interventions, in the context of time series when only an abstraction of the true causal ...	[ "math.ST", "cs.AI", "stat.TH" ]
# 1 Introduction "Testing with AI will take software test automation to the next level." The integration of artificial intelligence (AI) within the fields of software engineering and software testing has become a prominent area of research. Various novel testing methods and tools are being discussed and presented, ye...	In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test automation and model-bas...	[ "cs.SE", "cs.AI", "D.2.5" ]
# 1 Introduction Deep learning has emerged as a powerful technique to learn complex patterns from data [29], leading to advancements in many areas of science, such as protein modeling [22], genetics [43], and climate science [48]. However, the black-box nature of neural networks makes it challenging to translate their...	Neural networks are a powerful tool for learning patterns from data. However, they do not respect known scientific laws, nor can they reveal novel scientific insights due to their black-box nature. In contrast, scientific reasoning distills biological or physical principles from observations and controlled experiments,...	[ "cs.LG", "cs.AI" ]
# 1 Introduction Recent advances in large language models (LLMs) have made remarkable progress in complex reasoning and problem solving across domains such as mathematics [6, 15, 26] and programming [4, 3, 21]. Yet despite these impressive capabilities, conventional LLM reasoning approaches remain fundamentally limite...	Despite impressive progress on complex reasoning, current large language models (LLMs) typically operate in isolation - treating each problem as an independent attempt, without accumulating or integrating experiential knowledge. In contrast, expert problem solvers - such as Olympiad or programming contest teams - lever...	[ "cs.CL", "cs.AI" ]
# 1. Introduction As machine learning systems become increasingly pervasive in sensitive domains, such as medical diagnostics and userfacing recommendation engines, ensuring compliance with privacy regulations is paramount. The “right to be forgotten,” codified in regulations such as the EU’s General Data Several arc...	As machine learning systems increasingly rely on data subject to privacy regulation, selectively unlearning specific information from trained models has become essential. In image classification, this involves removing the influence of particular training samples, semantic classes, or visual styles without full retrain...	[ "cs.LG", "cs.CV" ]
# 1 INTRODUCTION V IDEO Scene Parsing (VSP) is a fundamental problem in pixel in a video sequence. It includes key tasks such as Video Semantic Segmentation (VSS), Video Instance Segmentation (VIS), and Video Panoptic Segmentation (VPS). By bridging the gap between static image analysis [1] and dynamic scene understan...	Video Scene Parsing (VSP) has emerged as a cornerstone in computer vision, facilitating the simultaneous segmentation, recognition, and tracking of diverse visual entities in dynamic scenes. In this survey, we present a holistic review of recent advances in VSP, covering a wide array of vision tasks, including Video Se...	[ "cs.CV" ]
# 1 Introduction architectural choices can otherwise influence the interpretation of experimental data. The Thermodynamic Kolmogorov-Arnold Model (T-KAM) provides a solution to the problems posed by arbitrarily deep, uninformed neural networks by focusing on finite sums of continuous functions at the core of generati...	We adapt the Kolmogorov-Arnold Representation Theorem to generative modeling by reinterpreting its inner functions as a Markov Kernel between probability spaces via inverse transform sampling. We present a generative model that is interpretable, easy to design, and efficient. Our approach couples a Kolmogorov-Arnold Ne...	[ "cs.LG" ]
# 1 Introduction Every second, streams of data have been generated enormously from both human (e.g. online social networks, content creators) and machines (e.g. data logs of systems, GPS coordinates of devices). With these mountains of data stream, there are countless patterns and insight that might be inferred and ut...	Finding the most similar subsequences between two multidimensional time series has many applications: e.g. capturing dependency in stock market or discovering coordinated movement of baboons. Considering one pattern occurring in one time series, we might be wondering whether the same pattern occurs in another time seri...	[ "cs.LG", "cs.AI", "cs.DB", "stat.ME" ]
# I. INTRODUCTION Accurate separation of moving and static objects is crucial for efficient path planning and safe navigation in dynamic traffic environments [1]. Moving Object Segmentation (MOS), particularly for pedestrians, cyclists, and vehicles, reduces system errors caused by dynamic objects and improves environ...	Motion Object Segmentation (MOS) is crucial for autonomous driving, as it enhances localization, path planning, map construction, scene flow estimation, and future state prediction. While existing methods achieve strong performance, balancing accuracy and real-time inference remains a challenge. To address this, we pro...	[ "cs.CV", "cs.AI", "cs.RO" ]
# 1 Introduction In this work, we develop a task-oriented SDS that can regulate emotional TTS based on contextual cues (emotional SDS) to enable more empathetic news conversations. Task-oriented SDSs must balance task- and social-goals to create engaging interactions (Clavel et al., 2022), with emotional speech regula...	We develop a task-oriented spoken dialogue system (SDS) that regulates emotional speech based on contextual cues to enable more empathetic news conversations. Despite advancements in emotional text-to-speech (TTS) techniques, task-oriented emotional SDSs remain underexplored due to the compartmentalized nature of SDS a...	[ "cs.CL" ]
# 1 Introduction Diffusion-based generative models [44, 17, 47] have shown unprecedented capability in modeling high-dimensional distribution and have become the dominant choice in various domains. The attractive potential has incentivized advances in multiple dimensions, such as prediction targets [26, 42, 33], diffu...	Diffusion models have achieved remarkable success in generative modeling. Despite more stable training, the loss of diffusion models is not indicative of absolute data-fitting quality, since its optimal value is typically not zero but unknown, leading to confusion between large optimal loss and insufficient model capac...	[ "cs.LG", "cs.AI", "cs.CV", "stat.ML" ]
# 1 Introduction Automating coding using Large Language Models (LLMs) and LLM-based agents has become a very active area of research. Popular benchmarks like LiveCodeBench [16] and SWE-bench [17] respectively test coding abilities on standalone competitive coding problems and GitHub issues over library or application ...	Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code remains underexplored. Due to the size and complexities of systems code, making changes to a systems codebase is a daunting task, even for humans. It requires researching about many ...	[ "cs.SE", "cs.AI" ]
# 1 Introduction Data linkage is the process of identifying records that refer to the same entities within or across databases [5, 20]. The entities to be linked are most commonly people, such as patients in hospital databases or beneficiaries in social security databases. In the commercial sector, data linkage is emp...	The process of linking databases that contain sensitive information about individuals across organisations is an increasingly common requirement in the health and social science research domains, as well as with governments and businesses. To protect personal data, protocols have been developed to limit the leakage of ...	[ "cs.CR", "cs.DB" ]
# 1. Introduction Approximately 70 million deaf individuals worldwide use sign language as their first language (WHO, 2021), yet they continue to face communication barriers in education, healthcare, and public services. Most mainstream sign language assistance systems rely on a multi-stage pipeline of speech $ \mathr...	This paper presents a human-centered, real-time, user-adaptive speech-to-sign language animation system that integrates Transformer-based motion generation with a transparent, user-editable JSON intermediate layer. The framework overcomes key limitations in prior sign language technologies by enabling direct user inspe...	[ "cs.HC", "cs.AI" ]
# 1 Introduction In recent times, end-to-end (E2E) ASR models have started taking the main stage in industrial usecases (Povey et al., 2016). Recurrent neural networks (RNNs) are crucial as they can model the temporal dependencies in audio sequences effectively (Chiu et al., 2018; Rao et al., 2017; Sainath et al., 202...	There has been increasing interest in unifying streaming and non-streaming automatic speech recognition (ASR) models to reduce development, training, and deployment costs. We present a unified framework that trains a single end-to-end ASR model for both streaming and non-streaming applications, leveraging future contex...	[ "cs.SD", "cs.AI", "eess.AS" ]
# 1 Introduction # 1.1 Background and Motivation In the era of big data, processing massive data streams has become a crucial challenge across various domains. Mining valuable information in data streams has broad applications such as network traffic monitoring[1], social network analysis[2], financial transaction mo...	Modern data stream applications demand memory-efficient solutions for accurately tracking frequent items, such as heavy hitters and heavy changers, under strict resource constraints. Traditional sketches face inherent accuracy-memory trade-offs: they either lose precision to reduce memory usage or inflate memory costs ...	[ "cs.DB" ]
# 1 Introduction Database engines take advantage of physical design such as index structures, zone maps [32] and partitioning to prune irrelevant data as early as possible during query evaluation. In order to prune data, database systems need to determine statically (at query compile time) what data is needed to answe...	Provenance-based data skipping compactly over-approximates the provenance of a query using so-called provenance sketches and utilizes such sketches to speed-up the execution of subsequent queries by skipping irrelevant data. However, a sketch captured at some time in the past may become stale if the data has been updat...	[ "cs.DB" ]
# 1 Introduction As large language models (LLMs) demonstrate increasingly sophisticated reasoning capabilities, the question of whether they possess a form of Theory of Mind (ToM) [Premack and Woodruff, 1978] has emerged as a central topic. ToM, also known as mentalizing, is the ability to infer the mental and emotion...	Theory of Mind (ToM) in Large Language Models (LLMs) refers to their capacity for reasoning about mental states, yet failures in this capacity often manifest as systematic implicit bias. Evaluating this bias is challenging, as conventional direct-query methods are susceptible to social desirability effects and fail to ...	[ "cs.CL" ]
# 1 Introduction Automated end-to-end (E2E) web tests created with tools such as Selenium are renowned for being fragile as the the web application under test evolves [14, 20]. Researchers have singled out web element locators as the main cause of fragility [10, 34]. Locators are commands used by test automation tools...	Fragile web tests, primarily caused by locator breakages, are a persistent challenge in web development. Hence, researchers have proposed techniques for web-element re-identification in which algorithms utilize a range of element properties to relocate elements on updated versions of websites based on similarity scorin...	[ "cs.SE" ]
# 1 Introduction Humans have an innate desire to create and inhabit personalized worlds, whether it’s children building sandcastles or artists designing landscapes. This creative drive extends to digital spaces, especially in VR/XR applications, where users expect to be immersed in custom environments with panoramic v...	Automatic creation of 3D scenes for immersive VR presence has been a significant research focus for decades. However, existing methods often rely on either high-poly mesh modeling with post-hoc simplification or massive 3D Gaussians, resulting in a complex pipeline or limited visual realism. In this paper, we demonstra...	[ "cs.GR", "cs.CV" ]
# 1. Introduction Nowadays, modern avionics systems are based on Integrated Modular Avionics (IMA), which simplifies software development and certification. The ARINC 653 standard [1], which specifies the Real-Time Operating System (RTOS) interface [2], has been widely accepted. One of the prominent concepts adopted b...	Multi-Processor Systems-on-Chip (MPSoC) can deliver high performance needed in many industrial domains, including aerospace. However, their high power consumption, combined with avionics safety standards, brings new thermal management challenges. This paper investigates techniques for offline thermal-aware allocation o...	[ "cs.SE" ]
# 1 Introduction With the advent of foundation models, it has become of great interest to exploit and transfer their capabilities to other models, e.g. via knowledge or dataset distillation. The goal of knowledge distillation is to train a smaller student on a small amount of data derived from a teacher [26]; dataset ...	Dataset distillation aims to compress training data into fewer examples via a teacher, from which a student can learn effectively. While its success is often attributed to structure in the data, modern neural networks also memorize specific facts, but if and how such memorized information is can transferred in distilla...	[ "cs.LG" ]
introduction resulted in a greater number and diversity of needs, suggesting that a two-phase approach is beneficial. Based on our findings, we recommend a hybrid approach combining surveys and interviews to balance efficiency and coverage. Future research should explore how automation can support elicitation and how t...	As software systems grow increasingly complex, explainability has become a crucial non-functional requirement for transparency, user trust, and regulatory compliance. Eliciting explainability requirements is challenging, as different methods capture varying levels of detail and structure. This study examines the effici...	[ "cs.SE" ]
# 1 Introduction Commonsense reasoning is an important problem in natural language understanding. It helps models make inferences that match human knowledge about the world, such as cause and effect, physical actions, social behavior, and hidden assumptions (Sap et al., 2019; Aggarwal et al., 2021). Unlike models that...	This review explores recent advances in commonsense reasoning and intent detection, two key challenges in natural language understanding. We analyze 28 papers from ACL, EMNLP, and CHI (2020-2025), organizing them by methodology and application. Commonsense reasoning is reviewed across zero-shot learning, cultural adapt...	[ "cs.CL", "cs.HC" ]
# 1 INTRODUCTION The usability of open source software (OSS) is a qualitative characteristic evaluating its ease of learning and usage, significantly impacting their adoption, development efficiency, and user satisfaction [1, 5, 6, 8]. A highly usable OSS platform helps efficient task completion, enhances developer pr...	Usability evaluation is critical to the impact and adoption of open source software (OSS), yet traditional methods relying on human evaluators suffer from high costs and limited scalability. To address these limitations, we introduce OSS-UAgent, an automated, configurable, and interactive agent-based usability evaluati...	[ "cs.SE", "cs.AI" ]
# 1 Introduction The rapid development of Large Language Models (LLMs) has led to an expansion of their applications and effectiveness across various domains [42, 37, 39, 64]. One important area where LLMs have shown impressive results is code translation, including tasks such as code generation from natural languages...	Large language models (LLMs) have the potential to revolutionize how we design and implement compilers and code translation tools. However, existing LLMs struggle to handle long and complex programs. We introduce LEGO-Compiler, a novel neural compilation system that leverages LLMs to translate high-level languages into...	[ "cs.PL", "cs.AI", "cs.SE" ]
# 1 INTRODUCTION Recent reinforcement learning methods for language models (LMs), particularly those using verifiable rewards (RLVR; Lambert et al., 2024), typically rely on signals that reflect output accuracy to guide training. These approaches have proven effective in enhancing reasoning by reinforcing correct outp...	Balancing exploration and exploitation is a central goal in reinforcement learning (RL). Despite recent advances in enhancing language model (LM) reasoning, most methods lean toward exploitation, and increasingly encounter performance plateaus. In this work, we revisit entropy -- a signal of exploration in RL -- and ex...	[ "cs.CL" ]
# 1 INTRODUCTION Directed fuzzing is an approach that aims to reach a target site of a program under test, e.g., a target line of code, by iteratively generating inputs named seeds. Due to its directed nature, it plays a vital role in various software testing and debugging tasks, e.g., patch testing [11, 22], crash re...	Directed fuzzing is a critical technique in cybersecurity, targeting specific sections of a program. This approach is essential in various security-related domains such as crash reproduction, patch testing, and vulnerability detection. Despite its importance, current directed fuzzing methods exhibit a trade-off between...	[ "cs.CR", "cs.SE" ]
# 1 Introduction The increase in data accessibility and complexity of organisational information systems has given rise to a persistent problem of information silos [43] wherein knowledge workers have to navigate across different systems and forms of information artefacts to perform their tasks. The diversity of infor...	A range of integrated modeling approaches have been developed to enable a holistic representation of business process logic together with all relevant business rules. These approaches address inherent problems with separate documentation of business process models and business rules. In this study, we explore how exper...	[ "cs.IR", "cs.HC", "cs.SE" ]
# 1 Introduction Transformer-based Large Language Models (LLMs) have revolutionized natural language processing, excelling at tasks ranging from text generation and translation to question answering and summarization. Despite these advances, a fundamental understanding of how these models store and recall information,...	This paper studies how the model architecture and data configurations influence the empirical memorization capacity of generative transformers. The models are trained using synthetic text datasets derived from the Systematized Nomenclature of Medicine (SNOMED) knowledge graph: triplets, representing static connections,...	[ "cs.CL" ]
# Introduction Recent advances in large language models (LLMs) have led to significant breakthroughs in Text-to-SQL — the task of translating natural language questions into SQL queries [1, 2, 3, 4]. Such advancements have the potential to democratize advanced usage of relational databases by empowering non-expert use...	Recent advances in leveraging the agentic paradigm of large language models (LLMs) utilization have significantly enhanced Text-to-SQL capabilities, enabling users without specialized database expertise to query data intuitively. However, deploying these agentic LLM-based Text-to-SQL systems in production poses substan...	[ "cs.DB" ]
# 1 INTRODUCTION Nteractive image segmentation requires users to indicate the target by providing simple indications such as boxes [1], [2], scribbles [3], [4], [5], [6], and clicks [7], [8], [9], [10]. Compared with traditional annotation tools like the lasso or brush, interactive models could largely reduce the cost...	Interactive segmentation enables users to extract binary masks of target objects through simple interactions such as clicks, scribbles, and boxes. However, existing methods often support only limited interaction forms and struggle to capture fine details. In this paper, we revisit the classical coarse-to-fine design of...	[ "cs.CV" ]
# 1 Introduction Incorporating real-world knowledge is essential for enhancing natural language processing capabilities (Xie et al., 2023; Peng et al., 2023; Pan et al., 2024). Directly extracting specific knowledge or facts from various unstructured texts is time-consuming and laborious, hence knowledge graph (KG) is...	Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks. Previous methods focused on either graph structure learning or semantic reasoning, failing to integrate dual reasoning perspectives to handle different prediction scenarios...	[ "cs.CL" ]
# 1 Introduction The dream of automating software engineering (SE) has long captivated both the SE and artificial intelligence (AI) communities [1, 2, 3]. Recent advancements in Large Language Models (LLMs) have shown promising results, particularly in code generation at the function level, with models achieving resol...	Recent advances in Large Language Models (LLMs) have shown promise in function-level code generation, yet repository-level software engineering tasks remain challenging. Current solutions predominantly rely on proprietary LLM agents, which introduce unpredictability and limit accessibility, raising concerns about data ...	[ "cs.SE", "cs.LG" ]
# 1 Introduction Autoregressive LLMs demonstrate impressive performance across a wide range of tasks, including logical reasoning [Pan et al., 2023], theorem proving [Yang et al., 2023], and code generation [et. al., 2021]. However, because they generate one token at a time, they can be slow when producing long respon...	Diffusion LLMs have emerged as a promising alternative to conventional autoregressive LLMs, offering significant potential for improved runtime efficiency. However, existing diffusion models lack the ability to provably enforce user-specified formal constraints, such as regular expressions, which makes them unreliable ...	[ "cs.LG", "cs.PL", "cs.SE" ]
# 1 INTRODUCTION Cosmos DB [2] is a large-scale distributed database service that supports multiple consistency levels [3] across geographic “regions”. Horizontal scalability is achieved by organizing data into “partitions” [4] containing data from non-overlapping hashbased key ranges. Partitions are organized into “p...	Azure Cosmos DB is a cloud-native distributed database, operating at a massive scale, powering Microsoft Cloud. Think 10s of millions of database partitions (replica-sets), 100+ PBs of data under management, 20M+ vCores. Failovers are an integral part of distributed databases to provide data availability during outages...	[ "cs.DB", "H.2.4; H.2.7" ]
# 1 Introduction Inference-time scaling has emerged as a critical technique for enhancing the reasoning capabilities of LLMs [2, 18, 20, 21]. Methods such as Chain-of-Thought (CoT) [20] explicitly guide models through intermediate reasoning steps, while recent models like DeepSeek-R1 [8] and QWQ [17] incorporate reaso...	Recent reasoning large language models (LLMs), such as OpenAI o1 and DeepSeek-R1, exhibit strong performance on complex tasks through test-time inference scaling. However, prior studies have shown that these models often incur significant computational costs due to excessive reasoning, such as frequent switching betwee...	[ "cs.CR", "cs.LG" ]
# I. INTRODUCTION Code review (CR) is a cornerstone of modern software engineering, ensuring code quality, defect detection, and team knowledge sharing. Nevertheless, as software systems scale and development cycles accelerate, traditional manual code review practices struggle with inefficiencies, reviewer fatigue, an...	Code reviews are a critical yet time-consuming aspect of modern software development, increasingly challenged by growing system complexity and the demand for faster delivery. This paper presents a study conducted at WirelessCar Sweden AB, combining an exploratory field study of current code review practices with a fiel...	[ "cs.SE" ]
# I. INTRODUCTION Point Clouds (PC), as one of the most representative forms of data in immersive media, are experiencing increasing demand in various fields, such as autonomous driving [1] and medical imaging [2]. A point cloud consists of a collection of discrete points, each described by its coordinates in 3D space...	Recently, the advancements in Virtual/Augmented Reality (VR/AR) have driven the demand for Dynamic Point Clouds (DPC). Unlike static point clouds, DPCs are capable of capturing temporal changes within objects or scenes, offering a more accurate simulation of the real world. While significant progress has been made in t...	[ "cs.CV", "cs.DB" ]
# I. INTRODUCTION IVEN the vast amounts of satellite data coming from multiple constellations, such as Copernicus Sentinel-2, the focus has recently been shifted to Self-Supervised Learning (SSL). Masked Autoencoders [1] and Contrastive Learning [2] aim at reducing the need for large labeled datasets and have led to a...	Today, Earth Observation (EO) satellites generate massive volumes of data, with the Copernicus Sentinel-2 constellation alone producing approximately 1.6TB per day. To fully exploit this information, it is essential to pretrain EO Foundation Models (FMs) on large unlabeled datasets, enabling efficient fine-tuning for s...	[ "cs.CV" ]
# 1 Introduction 360 cameras, a.k.a, panoramic cameras, provide an ultra-large field of view (FoV) of $3 6 0 \times 1 8 0$ for the surrounding environment. Therefore, 360 video enables comprehensive situational awareness that surpasses the FoV limitations of perspective 2D cameras. This makes 360 camera-based scene un...	360 video captures the complete surrounding scenes with the ultra-large field of view of 360X180. This makes 360 scene understanding tasks, eg, segmentation and tracking, crucial for appications, such as autonomous driving, robotics. With the recent emergence of foundation models, the community is, however, impeded by ...	[ "cs.CV" ]
# 1 Introduction Time series foundation models (TSFMs) have emerged as a transformative direction within the time series forecasting (TSF) community [2, 42, 8]. By pretraining on extensive time series datasets, these models possess universal knowledge, enabling them to achieve impressive zero-shot performance on vario...	Time series foundation models (TSFMs) demonstrate impressive zero-shot performance for time series forecasting. However, an important yet underexplored challenge is how to effectively finetune TSFMs on specific downstream tasks. While naive finetuning can yield performance gains, we argue that it falls short of fully l...	[ "cs.LG" ]
# 1 Introduction Autonomous driving systems typically adopt a modular paradigm, decomposing the driving task into different sub-modules, such as perception [1–3], prediction [4–6], and planning [7–9]. While this design enables structured development, it may cause error accumulation and a lack of joint optimization acr...	Recent advancements in Vision-Language-Action (VLA) models have shown promise for end-to-end autonomous driving by leveraging world knowledge and reasoning capabilities. However, current VLA models often struggle with physically infeasible action outputs, complex model structures, or unnecessarily long reasoning. In th...	[ "cs.CV" ]
# 1 Introduction The development of human-like chatbots has been a long-standing aspiration in the history of AI chatbot research. Over the years, researchers have introduced various aspects that constitute humanlikeness, such as persona (Zhang et al., 2018; Ahn et al., 2023), long-term memory (Xu et al., 2022a,b), co...	While research on dialogue response generation has primarily focused on generating coherent responses conditioning on textual context, the critical question of when to respond grounded on the temporal context remains underexplored. To bridge this gap, we propose a novel task called timely dialogue response generation a...	[ "cs.CL" ]
# 1. Introduction Over the last decade, AI, supported by deep learning, has become increasingly more prevalent in our lives. However, as we rely more on deep learning technologies to make critical decisions, concerns regarding their safety, reliability and explainability naturally emerge. Indeed, deep learning models,...	Over the last decade, as we rely more on deep learning technologies to make critical decisions, concerns regarding their safety, reliability and interpretability have emerged. We introduce a novel Neural Argumentative Learning (NAL) architecture that integrates Assumption-Based Argumentation (ABA) with deep learning fo...	[ "cs.LG", "cs.AI" ]
# 1. Introduction Causal inference often involves estimating the average treatment effect (ATE), which represents the causal impact of an exposure on an outcome. Under controlled study setups of randomized controlled trials (RCTs), valid inference methods for ATE estimation are well established (Deaton & Cartwright, 2...	There is growing interest in extending average treatment effect (ATE) estimation to incorporate non-tabular data, such as images and text, which may act as sources of confounding. Neglecting these effects risks biased results and flawed scientific conclusions. However, incorporating non-tabular data necessitates sophis...	[ "stat.ML", "cs.AI", "cs.LG", "stat.CO", "stat.ME" ]
# 1 Introduction Large multimodal models (LMMs) such as GPT-4o [1] exhibit omni-capabilities across text, vision, and speech modalities, unlocking broad potential across applications. Compared to vision-oriented LMMs [2, 3], omni-modal LMMs can support speech interaction based on visual information. Furthermore, advan...	The emergence of GPT-4o-like large multimodal models (LMMs) has raised the exploration of integrating text, vision, and speech modalities to support more flexible multimodal interaction. Existing LMMs typically concatenate representation of modalities along the sequence dimension and feed them into a large language mod...	[ "cs.AI", "cs.CL", "cs.CV", "cs.SD", "eess.AS" ]
# I. INTRODUCTION cination, focuses on reconstructing high-resolution (HR) facial images from low-resolution (LR) inputs. FSR plays a critical role in real-world scenarios where LR faces may appear in surveillance videos, historical archives, or mobile imaging. Enhancing facial image resolution is essential for improv...	Face super-resolution (FSR) under limited computational costs remains an open problem. Existing approaches typically treat all facial pixels equally, resulting in suboptimal allocation of computational resources and degraded FSR performance. CNN is relatively sensitive to high-frequency facial features, such as compone...	[ "cs.CV" ]
# 1 Introduction The Goal-oriented Proactive Dialogue System (GPDS) focuses on achieving specific objectives by actively guiding and anticipating user needs (Liu et al., 2020; Wang et al., 2024a, 2023b). Unlike traditional dialogue systems that passively respond to user requests (Touvron et al., 2023; Achiam et al., 2...	Goal-oriented proactive dialogue systems are designed to guide user conversations seamlessly towards specific objectives by planning a goal-oriented path. However, previous research has focused predominantly on optimizing these paths while neglecting the inconsistencies that may arise between generated responses and di...	[ "cs.CL" ]

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