Knowledge catalogue
arXiv:2606.26099v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisat
arXiv:2606.26541v1 Announce Type: new Abstract: Data from affected populations are crucial for informing humanitarian response, but their value depends on timely and consistent interpretation of nuanc
arXiv:2606.27264v1 Announce Type: new Abstract: Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form text jud
arXiv:2606.27079v1 Announce Type: new Abstract: In embodied intelligence, safety is a prerequisite for reliable robot deployment in the physical world. Current vision-language-action (VLA) models cont
arXiv:2606.26654v1 Announce Type: new Abstract: Personalized language-model assistants are often evaluated through a memory lens: can a model recall preferences users have explicitly stated in dialogu
arXiv:2606.26984v1 Announce Type: new Abstract: Unified multimodal models capable of both understanding and generation have achieved remarkable strides. However, despite their unified designs, existin
arXiv:2606.25962v1 Announce Type: new Abstract: Modern stereo-capable smartphones enable immersive XR content capture. However, hardware heterogeneity across camera modules often causes severe asymmet
arXiv:2606.25128v1 Announce Type: cross Abstract: Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs
arXiv:2606.25915v1 Announce Type: new Abstract: Color fundus photography (CFP) is the most common ophthalmic imaging modality for large-scale screening. However, it is highly susceptible to degradatio
Yohei Nakajima questions the legitimacy of token-saving solutions in the AI space, noting anecdotal reports from peers that some of these tools may actually increase token consumption rather than redu
arXiv:2606.25298v1 Announce Type: new Abstract: Children are naturally energetic, and during their spontaneous activities, they often encounter potentially dangerous situations, especially when lackin
arXiv:2606.25057v1 Announce Type: new Abstract: The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large languag
arXiv:2606.24965v1 Announce Type: cross Abstract: Reasoning about relational structures remains a significant challenge for neural models, particularly when they must systematically apply learned know
arXiv:2606.25990v1 Announce Type: new Abstract: As multimodal conversational systems increasingly engage in spoken interaction, their ability to navigate paralinguistic social cues has become a critic
arXiv:2606.25877v1 Announce Type: new Abstract: Vision-based tactile sensors (VBTSs) enable robots to infer contact geometry and force-related cues by imaging deformation through an internal camera, y
arXiv:2604.00725v2 Announce Type: replace Abstract: End-to-end OCR for historical newspapers remains challenging, as models must handle long text sequences, degraded print quality, and complex layouts
arXiv:2606.24176v1 Announce Type: new Abstract: Reliable structural health monitoring (SHM) of offshore wind turbine (OWT) support structures requires fast state estimation from sparse measurements. R
arXiv:2606.24548v1 Announce Type: new Abstract: Text-to-image (T2I) generation models have achieved remarkable progress in producing visually realistic images from natural language prompts. Yet it rem
arXiv:2606.24394v1 Announce Type: cross Abstract: Electroencephalography (EEG) is the dominant non-invasive modality for brain-computer interfaces (BCIs), yet reliable decoding of motor imagery is ham
arXiv:2606.24162v1 Announce Type: new Abstract: Foundation models have been increasingly applied to behavioral science domains such as psychology, sociology, and economics. While these models show pro
arXiv:2606.24714v1 Announce Type: new Abstract: Chinese news text contains dense written forms such as scores, hyphenated model names, ranges, unit symbols, percentages, English abbreviations, and mix
arXiv:2503.14862v3 Announce Type: replace Abstract: Open-vocabulary detectors are proposed to locate and recognize objects in novel classes. However, variations in vision-aware language vocabulary dat
arXiv:2606.24829v1 Announce Type: new Abstract: Camera-prompted text-to-video (T2V) models are increasingly used to synthesize virtual camera captures, such as orbiting objects or moving through stati
arXiv:2606.24173v1 Announce Type: cross Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardwa
arXiv:2505.10931v4 Announce Type: replace Abstract: Single-source remote sensing object detection using optical or SAR images struggles in complex environments. Optical images offer rich textural deta
arXiv:2606.21109v1 Announce Type: new Abstract: Virtual-cell and perturbation models are increasingly used to predict cellular responses for biomedical discovery, but chemical and genetic perturbation
arXiv:2606.21116v1 Announce Type: new Abstract: Proofreading--correcting segmentation errors in 3D brain reconstructions--is the rate-limiting step in synapse-resolution connectomics. We release Conne
arXiv:2606.23453v1 Announce Type: new Abstract: Location encoders transform geographic coordinates into high dimensional embeddings for downstream machine learning, but it is unclear how well these re
arXiv:2606.23686v1 Announce Type: new Abstract: Despite the impressive manipulation capabilities of Vision-Language-Action (VLA) models, their operational safety under strict constraints remains large
arXiv:2606.21119v1 Announce Type: new Abstract: Mammography is an essential tool for breast cancer detection, with millions of examinations conducted annually. However, publicly available high-quality
arXiv:2512.21988v3 Announce Type: replace-cross Abstract: Background: Smartphone-based dermatology requires inter-device colorimetric reliability that holds across calibration regimes, yet quantitativ
arXiv:2606.21861v1 Announce Type: new Abstract: Automated classroom engagement recognition holds substantial promise for scalable learning analytics, yet the suitability of modern Vision-Language Mode
arXiv:2606.12226v1 Announce Type: new Abstract: While deep learning has significantly advanced image reconstruction of Electrical Capacitance Tomography (ECT), most data-driven methods map directly be
arXiv:2606.11204v1 Announce Type: new Abstract: Accurate extraction of structured information from Safety Data Sheets (SDS) remains challenging in industrial safety due to heterogeneous document forma
arXiv:2606.11257v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generati
arXiv:2606.11562v1 Announce Type: cross Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repu
arXiv:2602.19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely
arXiv:2606.11826v1 Announce Type: new Abstract: Designing anthropomorphic dexterous robotic hands remains challenging as the design space straddles morphology, actuation, and sensing properties, and p
arXiv:2606.12300v1 Announce Type: cross Abstract: Temporal grounding--returning the interval [t_s, t_e] for a natural-language query over a video--is the language interface to long-form video, yet has
arXiv:2606.11150v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly acquiring capabilities relevant to biological research, from literature synthesis to interpretation of experime
arXiv:2505.23851v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly applied to symbolic mathematics, yet existing evaluations often conflate pattern memorization wi
arXiv:2606.10364v1 Announce Type: new Abstract: Reconstructing printable 3D models from Mars rover imagery is challenging because Martian terrain is low-texture, irregular, and partially observed. We
arXiv:2606.10479v1 Announce Type: new Abstract: Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structu
arXiv:2606.09908v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challen
arXiv:2606.09890v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents capable of executing multi-step action trajectories toward a given objecti
arXiv:2606.10967v1 Announce Type: new Abstract: Visual in-context learning has been proposed as a pathway towards dynamic models that can generate predictions based on a provided context and thereby c
arXiv:2606.10287v1 Announce Type: cross Abstract: Evaluating Knowledge Graph Completion (KGC) models remains challenging because standard assessment relies on isolated rank-based metrics such as MRR,
arXiv:2606.09882v1 Announce Type: new Abstract: The paradigm of digital twin cities is shifting from coarse visual mapping toward more precise and actionable digitization of urban assets. However, exi
arXiv:2603.04125v2 Announce Type: replace Abstract: Few-Shot Action Recognition (FS-AR) has shown promising results but is often limited by a closed-set assumption that fails in real-world open-set sc
arXiv:2606.08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.
arXiv:2606.09080v1 Announce Type: new Abstract: Pruning has emerged as a dominant paradigm for accelerating large language model (LLM) inference, spanning a broad spectrum of methods that remove compu
arXiv:2606.08736v1 Announce Type: new Abstract: We study a capability the dominant paradigm in synthetic tabular data does not provide: exact satisfaction of a declared analytical outcome with no sour
arXiv:2511.18421v2 Announce Type: replace-cross Abstract: Existing Test-time Adaptation (TTA) studies rely heavily on static and homogeneous corruption protocols, such as ImageNet-C and CIFAR-10-C/100
arXiv:2606.08194v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) integrate audio perception and language understanding within a unified framework, enabling a wide range of real-wo
arXiv:2606.08123v1 Announce Type: cross Abstract: Vision-based driver monitoring systems are increasingly deployed in safety-critical intelligent transportation settings, yet they are almost always co
arXiv:2601.04498v2 Announce Type: replace-cross Abstract: Infographics are composite visual artifacts that combine data visualizations with textual and illustrative elements to communicate information
arXiv:2510.12171v2 Announce Type: replace Abstract: Large Language Models have shown strong scientific reasoning ability, but their performance on materials science problems remains less studied. To f
arXiv:2511.18676v2 Announce Type: replace-cross Abstract: Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., 'Is this normal or abnormal
arXiv:2606.07659v1 Announce Type: new Abstract: Automated surface defect detection is critical for ensuring rigorous quality control in high-speed manufacturing environments. While deep learning model
arXiv:2606.07591v1 Announce Type: cross Abstract: AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We presen