Knowledge catalogue
arXiv:2604.21198v1 Announce Type: new Abstract: Object detection models typically perform well on images captured in controlled environments with stable lighting, water clarity, and viewpoint, but the
arXiv:2604.21377v1 Announce Type: new Abstract: Large language models are increasingly being explored as interfaces between humans and robotic systems, yet there remains limited evidence on how such t
arXiv:2604.20996v1 Announce Type: new Abstract: How can language learning systems be developed for languages that lack sufficient training resources? This challenge is increasingly faced by developers
arXiv:2511.23159v2 Announce Type: replace-cross Abstract: Vibe coding, the much-touted use of AI techniques for programming, faces two overwhelming obstacles: the difficulty of specifying goals ('prom
arXiv:2604.21199v1 Announce Type: cross Abstract: Time series question-answering (TSQA), in which we ask natural language questions to infer and reason about properties of time series, is a promising
arXiv:2604.20994v1 Announce Type: cross Abstract: The growth of agentic AI has drawn significant attention to function calling Large Language Models (LLMs), which are designed to extend the capabiliti
arXiv:2604.21352v1 Announce Type: new Abstract: Mental health challenges are increasing worldwide, straining emotional support services and leading to counselor overload. This can result in delayed re
arXiv:2507.03806v3 Announce Type: replace-cross Abstract: This paper presents a learning-based framework for approximating an exact magnetic-field interaction model, supported by both numerical and ex
arXiv:2604.21573v1 Announce Type: new Abstract: Spatial transcriptomics (ST) enables spatially resolved gene profiling but remains expensive and low-throughput, limiting large-cohort studies and routi
arXiv:2604.21308v1 Announce Type: cross Abstract: Enterprise LLM agents can dramatically improve workplace productivity, but their core capability, retrieving and using internal context to act on a us
arXiv:2604.21584v1 Announce Type: new Abstract: Feature discovery from complex unstructured data is fundamentally a reasoning problem: it requires identifying abstractions that are predictive of a tar
arXiv:2509.23744v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) promise enhanced reasoning by integrating diverse inputs such as text, vision, and audio. Yet cross-m
arXiv:2502.03484v2 Announce Type: replace-cross Abstract: Dementia encompasses a group of syndromes that impair cognitive functions such as memory, reasoning, and the ability to perform daily activiti
arXiv:2604.21461v1 Announce Type: new Abstract: Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite
arXiv:2604.21598v1 Announce Type: cross Abstract: Multi-agent frameworks are widely used in autonomous code generation and have applications in complex algorithmic problem-solving. Recent work has add
arXiv:2604.21070v1 Announce Type: new Abstract: Summarizing long, domain-specific documents with large language models (LLMs) remains challenging due to context limitations, information loss, and hall
arXiv:2604.21707v1 Announce Type: new Abstract: Real-world deployments of human--swarm teams depend on balancing operator workload to leverage human strengths without inducing overload. A key challeng
arXiv:2604.21733v1 Announce Type: new Abstract: The increasing integration of artificial intelligence (AI) in higher education has raised important questions regarding students' transparency in report
arXiv:2604.21479v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated strong reasoning capabilities and attracted increasing research attention in the field of autono
arXiv:2601.13711v2 Announce Type: replace Abstract: Authorship verification (AV) is the task of determining whether two texts were written by the same author and has been studied extensively, predomin
arXiv:2604.21133v1 Announce Type: new Abstract: We present GRISP (Guided Recurrent IRI Selection over SPARQL Skeletons), a novel SPARQL-based question-answering method over knowledge graphs based on f
arXiv:2604.21280v1 Announce Type: new Abstract: On-device continual learning (CL) is critical for edge AI systems operating on non-stationary data streams, but most existing methods rely on backpropag
arXiv:2603.27820v2 Announce Type: replace Abstract: Clinical diagnosis is a complex reasoning process in which clinicians gather evidence, form hypotheses, and test them against alternative explanatio
arXiv:2604.21525v1 Announce Type: new Abstract: Span-level skill extraction from job advertisements underpins candidate-job matching and labor-market analytics, yet generative large language models (L
arXiv:2604.21794v1 Announce Type: new Abstract: Multi-agent systems built on large language models have shown strong performance on complex reasoning tasks, yet most work focuses on agent roles and or
arXiv:2505.13527v3 Announce Type: replace-cross Abstract: Despite substantial advancements in aligning large language models (LLMs) with human values, current safety mechanisms remain susceptible to j
arXiv:2604.21489v1 Announce Type: cross Abstract: Multi-modal trajectory generation is essential for safe autonomous driving, yet existing diffusion-based planners suffer from high inference latency d
arXiv:2506.05590v3 Announce Type: replace-cross Abstract: Recent advances have established the identifiability of a directed acyclic graph (DAG) under additive noise models (ANMs), spurring the develo
arXiv:2604.21017v1 Announce Type: cross Abstract: Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precis
arXiv:2604.20981v1 Announce Type: cross Abstract: Pancreatic tumor segmentation in contrast-enhanced computed tomography (CT) is clinically important yet technically challenging: lesions are often sma
arXiv:2604.21104v1 Announce Type: new Abstract: New geospatial foundation models introduce a new model architecture and pretraining dataset, often sampled using different notions of data diversity. Pe
arXiv:2604.21611v1 Announce Type: cross Abstract: Inference-time scaling for LLM reasoning has focused on three axes: chain depth, sample breadth, and learned step-scorers (PRMs). We introduce a fourt
arXiv:2603.27112v2 Announce Type: replace Abstract: As Automatic Train Operation (ATO) advances toward GoA4 and beyond, it increasingly depends on efficient, reliable cab-view visual perception and de
arXiv:2604.20930v1 Announce Type: cross Abstract: Internal Safety Collapse (ISC) is a failure mode in which frontier LLMs, when executing legitimate professional tasks whose correct completion structu
arXiv:2604.20937v1 Announce Type: new Abstract: Video Large Language Models (Video LLMs) incur high inference latency due to a large number of visual tokens provided to LLMs. To address this, training
arXiv:2604.16395v2 Announce Type: replace-cross Abstract: Context retrieval systems for LLM inference face a critical challenge: high retrieval latency creates a fundamental tension between waiting fo
arXiv:2603.21697v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) extend text-only LLMs with visual reasoning, but also introduce new safety failure modes under visual
arXiv:2604.21689v1 Announce Type: cross Abstract: Creative face stylization aims to render portraits in diverse visual idioms such as cartoons, sketches, and paintings while retaining recognizable ide
arXiv:2604.21172v1 Announce Type: cross Abstract: This paper develops a refined version of TAPO-description logic for the analysis of information behavior. The framework is treated not as a single hom
arXiv:2604.20891v1 Announce Type: cross Abstract: Memristive crossbars store numerical weights needing aggregation and decoding; a single junction means nothing alone. This paper presents a fundamenta
arXiv:2604.21537v1 Announce Type: new Abstract: Identifying critical nodes in complex networks is a fundamental task in graph mining. Yet, methods addressing an all-or-nothing coverage mechanics in a
arXiv:2604.20917v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable capabilities across diverse coding tasks. However, their adoption requires a true understanding of
arXiv:2604.21361v1 Announce Type: new Abstract: Distributed AI inference pipelines rely heavily on timestamp-based observability to understand system behavior. This work demonstrates that even small c
arXiv:2604.21860v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into sensitive workflows, raising the stakes for adversarial robustness and safety. This pape
arXiv:2604.21549v1 Announce Type: new Abstract: Estimating the prevalence of a category in a population using imperfect measurement devices (diagnostic tests, classifiers, or large language models) is
arXiv:2604.21904v1 Announce Type: new Abstract: In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent
arXiv:2604.21914v1 Announce Type: new Abstract: Recently, end-to-end robotic manipulation models have gained significant attention for their generalizability and scalability. However, they often suffe
arXiv:2604.21082v1 Announce Type: new Abstract: Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data. This work evaluat
arXiv:2604.21911v1 Announce Type: cross Abstract: Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i.e., outputs
arXiv:2604.21400v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, yet existing methods remain predominantly research prototypes ill-suited for productio
arXiv:2507.07800v3 Announce Type: replace-cross Abstract: Segmenting cytoskeletal filaments in microscopy images is essential for studying their roles in cellular processes. However, this task is high
arXiv:2603.23089v2 Announce Type: replace Abstract: Multi-view capture systems have been an important tool in research for recording human motion under controlling conditions. Most existing systems ar
arXiv:2604.20204v1 Announce Type: new Abstract: Cross-sectional stock ranking is a fundamental task in quantitative investment, relying on both temporal modeling of individual stocks and the capture o
arXiv:2604.20279v1 Announce Type: cross Abstract: Mobile GUI agents can automate smartphone tasks by interacting directly with app interfaces, but how they should communicate with users during executi
arXiv:2511.19367v2 Announce Type: replace-cross Abstract: Accurate tumor staging in lung cancer is crucial for prognosis and treatment planning and is governed by explicit anatomical criteria under fi
arXiv:2604.19925v1 Announce Type: cross Abstract: AI agents powered by large language models are increasingly acting on behalf of humans in social and economic environments. Prior research has focused
arXiv:2509.25844v3 Announce Type: replace Abstract: When people query Vision-Language Models (VLMs) but cannot see the accompanying visual context (e.g. for blind and low-vision users), augmenting VLM
arXiv:2510.11423v3 Announce Type: replace-cross Abstract: Community Notes, the crowd-sourced misinformation governance system on X (formerly Twitter), allows users to flag misleading posts, attach con
arXiv:2604.20348v1 Announce Type: cross Abstract: Language Models (LLMs) have emerged as powerful reasoning engines for embodied control. In particular, In-Context Learning (ICL) enables off-the-shelf
arXiv:2601.03396v2 Announce Type: replace Abstract: Procedural content generation has enabled vast virtual worlds through levels, maps, and quests, but large-scale character generation remains underex