Knowledge catalogue
arXiv:2604.27151v1 Announce Type: new Abstract: Computer-use agents provide a promising path toward general software automation because they can interact directly with arbitrary graphical user interfa
arXiv:2604.28179v1 Announce Type: new Abstract: Bronchoscopic navigation relies on registering endoscopic video to a preoperative CT scan, but respiratory motion deforms the airway by 5-20 mm, creatin
arXiv:2604.28175v1 Announce Type: new Abstract: Machine learning (ML) inference serving systems host deep neural network (DNN) models and schedule incoming inference requests across deployed GPUs. How
arXiv:2604.27293v1 Announce Type: new Abstract: In classroom teaching, student behavior can reflect their learning state and classroom participation, which is of great significance for teaching qualit
arXiv:2602.16516v2 Announce Type: replace Abstract: This paper introduces ParlaCAP, a large-scale dataset for analyzing parliamentary agenda setting across Europe, and proposes a cost-effective method
arXiv:2604.27468v1 Announce Type: new Abstract: Maintaining information in context is essential in successful real-time language comprehension, but maintenance is cognitively costly and can slow proce
arXiv:2604.28181v1 Announce Type: new Abstract: Realistic long-horizon productivity work is strongly conditioned on user-specific computer environments, where much of the work context is stored and or
arXiv:2604.28045v1 Announce Type: new Abstract: Scalable compression is essential for bandwidth-adaptive transmission, yet most learned codecs are optimized for a fixed rate-distortion point, making r
arXiv:2604.27833v1 Announce Type: new Abstract: Prototype-based Personalized Federated Learning (ProtoPFL) enables efficient multi-domain adaptation by communicating compact class prototypes, but dire
arXiv:2604.27927v1 Announce Type: new Abstract: We introduce a framework called LAPITHS (Language model Analysis through Paradigm grounded Interpretations of Theses about Human likenesS) and use it to
arXiv:2604.27232v1 Announce Type: new Abstract: Models of sign language have historically lagged behind those for spoken language (text and speech). Recent work has greatly improved their performance
arXiv:2601.08418v2 Announce Type: replace-cross Abstract: Tax code prediction is a crucial yet underexplored task in automating invoicing and compliance management for large-scale e-commerce platforms
arXiv:2501.12632v2 Announce Type: replace Abstract: Weakly supervised object localization (WSOL) models are trained using only image-level class labels. They can predict both the object class and spat
arXiv:2509.05753v2 Announce Type: replace-cross Abstract: The rise of synthetic media has blurred the boundary between reality and fabrication under the evolving power of artificial intelligence, fuel
arXiv:2604.27789v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as core dependencies in software systems. However, the hosted LLM services evolve continuously thro
arXiv:2506.02671v3 Announce Type: replace Abstract: Deep neural networks often suffer performance degradation upon deployment due to distribution shifts. Continual Test-Time Adaptation (CTTA) aims to
arXiv:2604.27953v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) become increasingly integrated into decision-making systems, it is essential to understand how visual inputs influence
arXiv:2601.20969v3 Announce Type: replace Abstract: Epistemic planning extends (multi-agent) automated planning by making agents' knowledge and beliefs first-class aspects of the planning formalism. O
arXiv:2604.27168v1 Announce Type: new Abstract: We present the Field of Safe Motion (FSM), a quantitative safety model for determining whether a driver maintains a collision-free escape route, or 'out
arXiv:2604.26965v1 Announce Type: cross Abstract: The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, f
arXiv:2604.26954v1 Announce Type: cross Abstract: Strategic model selection and reasoning settings are more effective than ensembling for optimizing automated scoring with large language models (LLMs)
arXiv:2604.27274v1 Announce Type: new Abstract: As AI transitions toward multi-agent systems (MAS) to solve complex workflows, research paradigms operate on the axiomatic assumption that agent collabo
arXiv:2604.27282v1 Announce Type: cross Abstract: Pretrial risk assessment tools are used on over one million U.S. defendants each year, yet their use for predicting rare violent re-offense faces a ba
arXiv:2412.05135v2 Announce Type: replace-cross Abstract: We propose a novel method for measuring the discrepancy between a set of samples and a desired posterior distribution for Bayesian inference.
arXiv:2604.27673v1 Announce Type: new Abstract: We introduce Target-Event-Agent Networks (TEA Nets) as a computational framework to extract subjects (``Agents'), verbs (``Events'), and objects (``Targ
arXiv:2604.27292v1 Announce Type: new Abstract: Every system that performs effects has two boundaries: what it can do (expressiveness) and what governance covers (governance). In nearly all deployed A
arXiv:2604.27209v1 Announce Type: cross Abstract: Large language models can now generate substantial code and draft research text, but research-software projects require more than either artifact alon
arXiv:2604.27096v1 Announce Type: new Abstract: The purpose of our paper is to develop a unified multi-agent architecture that automates end-to-end machine learning (ML) pipeline generation from datas
arXiv:2601.02845v2 Announce Type: replace-cross Abstract: Long-horizon conversational agents have to manage ever-growing interaction histories that quickly exceed the finite context windows of large l
arXiv:2604.27359v1 Announce Type: new Abstract: Intent-based networking promises to revolutionize telecommunications network management by enabling operators to specify high-level goals rather than lo
arXiv:2604.28053v1 Announce Type: cross Abstract: Responsible AI research typically focuses on examining the use and impacts of deployed AI systems. Yet, there is currently limited visibility into the
arXiv:2604.27296v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code editing, yet the prevalent full-code generation paradigm suffers from severe efficiency bo
arXiv:2604.28076v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced Table Question Answering, where most queries can be answered by extracting information or simple aggregatio
arXiv:2601.20239v4 Announce Type: replace Abstract: Fine-grained and contact-rich manipulation remain challenging for robots, largely due to the underutilization of tactile feedback. To address this,
arXiv:2604.27321v1 Announce Type: cross Abstract: Security Operations Centers (SOCs) face mounting operational challenges. These challenges come from increasing threat volumes, heterogeneous SIEM plat
arXiv:2604.27217v1 Announce Type: new Abstract: Cognitive decline is highly heterogeneous across individuals, which complicates prognosis, trial design, and treatment planning. We present the Personal
arXiv:2604.27478v1 Announce Type: new Abstract: Terrestrial network limitations drive the integration of non-terrestrial networks (NTNs), notably mega-constellations comprising thousands of low Earth
arXiv:2604.27256v1 Announce Type: cross Abstract: We present extsc{dm-PhiSNet}, a physically constrained extsc{PhiSNet}-based equivariant model that predicts one-electron reduced density matrices (1-R
arXiv:2604.27499v1 Announce Type: new Abstract: Off-road nighttime autonomous driving suffers from unreliable visible-light perception, making infrared modality crucial for accurate freespace detectio
arXiv:2604.27247v1 Announce Type: new Abstract: Hedges and other linear woody features provide valuable ecosystem services, particularly within intensively managed agricultural landscapes. They are ke
arXiv:2604.28087v1 Announce Type: cross Abstract: Rule-based systems remain central in safety-critical domains but often struggle with scalability, brittleness, and goal misspecification. These limita
arXiv:2602.21361v3 Announce Type: replace-cross Abstract: Ptychographic imaging at synchrotron and XFEL sources requires dense overlapping scans, limiting throughput and increasing dose. Extending coh
arXiv:2604.27586v1 Announce Type: new Abstract: Reasoning over heterogeneous artifacts (PDFs, spreadsheets, slide decks, etc.) increasingly occurs within structured agent workflows that iteratively ex
arXiv:2509.25845v3 Announce Type: replace-cross Abstract: Recent advancements in diffusion and flow-matching models have demonstrated remarkable capabilities in high-fidelity image synthesis. A promin
arXiv:2604.27928v1 Announce Type: cross Abstract: Tunnel inspection requires outputs that can support defect localization, measurement, severity grading, and engineering documentation. Existing traini
arXiv:2504.18902v2 Announce Type: replace-cross Abstract: In the forthcoming era of 6G networks, characterized by unprecedented data rates, ultra-low latency, and ubiquitous connectivity, effective ma
arXiv:2511.09416v2 Announce Type: replace Abstract: Transformer Semantic Genetic Programming (TSGP) is a semantic search approach that uses a pre-trained transformer model as a variation operator to g
arXiv:2503.22676v5 Announce Type: replace Abstract: We present TranSplat, a method for instant, accurate object relighting within the Gaussian Splatting (GS) framework. Rather than relying on costly i
arXiv:2604.27975v1 Announce Type: cross Abstract: Traditional Shot Boundary Detection (SBD) inherently struggles with complex transitions by formulating the task around isolated cut points, frequently
arXiv:2604.27958v1 Announce Type: new Abstract: Due to the scarcity of large-scale in-the-wild triplet data and the improper use of masks, the performance of video virtual try-on models remains limite
arXiv:2604.27132v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification, yet centralized approaches suffer four
arXiv:2604.27861v1 Announce Type: cross Abstract: Decompositional jailbreaks pose a critical threat to large language models (LLMs) by allowing adversaries to fragment a malicious objective into a seq
arXiv:2604.27356v1 Announce Type: cross Abstract: Heterogeneous graphs are widely used to model multi-relational systems, but missing node attributes remain a major bottleneck for downstream learning.
arXiv:2604.28095v1 Announce Type: new Abstract: Accurate lesion segmentation is crucial for clinical diagnosis and treatment planning. However, lesions often resemble surrounding tissues and exhibit i
arXiv:2507.13486v2 Announce Type: replace Abstract: Uncertainty quantification of the photogrammetry process is essential for providing per-point accuracy credentials of the point clouds. Unlike airbo
arXiv:2604.27414v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporti
arXiv:2510.17196v3 Announce Type: replace-cross Abstract: Effectively processing long contexts is a critical challenge for language models. While standard Transformers are limited by quadratic complex
arXiv:2604.27491v1 Announce Type: new Abstract: Modeling 4D human-object interaction (HOI) is a compelling challenge in computer vision and an essential technology powering virtual and mixed-reality a
arXiv:2604.28021v1 Announce Type: cross Abstract: Cooking is a cultural expression of human creativity that transcends geography and time through the orchestration of ingredients and techniques, much
arXiv:2604.27134v1 Announce Type: new Abstract: Generative AI is reshaping higher education programming through vibe coding, where students collaborate with AI via natural language rather than writing