Knowledge catalogue
arXiv:2606.28332v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for medical and health-related questions, yet their safety in high-risk medical scenarios remains p
arXiv:2602.20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground the
arXiv:2606.27895v1 Announce Type: cross Abstract: Differentiable partial differential equation (PDE) solvers underpin solver-in-the-loop ML training, gradient-based optimal control, and inverse proble
arXiv:2606.27826v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly deployed as embodied planners in egocentric environments, where task success requires not only
arXiv:2606.27876v1 Announce Type: cross Abstract: Spatial intelligence is essential for low-altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation. However, existing UAV benc
arXiv:2505.10764v4 Announce Type: replace Abstract: Innovations in digital intelligence are transforming robotic surgery with more informed decision-making. Real-time awareness of surgical instrument
arXiv:2606.27554v1 Announce Type: new Abstract: Camera-based perception systems for autonomous driving are typically developed and evaluated using fixed sensor rigs, while real-world vehicle fleets ex
arXiv:2606.27128v1 Announce Type: new Abstract: Wildfire monitoring from UAVs requires reliable reasoning over complex aerial scenes, where smoke, scale variation, and occlusions often limit RGB-only
arXiv:2606.26265v1 Announce Type: new Abstract: Robot autonomous navigation that accounts for surrounding human activities is crucial for ensuring both safety and natural human-robot interaction in re
arXiv:2606.27247v1 Announce Type: new Abstract: In NLP, mental health conditions are often modeled as isolated phenomena, without interpersonal context. We use Reddit posts about long-distance relatio
arXiv:2606.26443v1 Announce Type: cross Abstract: A robot working alongside people must reason about what they have done, in what order, and with what intent. Video carries the spatial layouts, object
arXiv:2606.24944v1 Announce Type: cross Abstract: Automated classification of acute lymphoblastic leukemia (ALL) from peripheral blood smear images has often reported near-perfect performance on the C
arXiv:2510.11047v2 Announce Type: replace Abstract: Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep learning (DL) model development. We present Larynge
arXiv:2606.25819v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments. Although recent tool-use benc
arXiv:2606.25445v1 Announce Type: new Abstract: While Change Captioning systems have garnered substantial attention to respond to our evolving world, their true performance on diverse real-world chang
arXiv:2606.25391v1 Announce Type: cross Abstract: Recent Large Audio Language Models (LALMs) have achieved remarkable progress in audio perceptual tasks across individual acoustic layers, including sp
arXiv:2606.25491v1 Announce Type: new Abstract: Automated homework assessment depends not only on recognizing student answers, but also on accurately locating where each answer and each intermediate r
arXiv:2606.19157v2 Announce Type: replace-cross Abstract: AudioLLMs enable speech recognition conditioned on textual prompts such as domain descriptions or entity lists. However, it remains unclear wh
arXiv:2606.24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamental
arXiv:2604.05738v2 Announce Type: replace Abstract: Medical Vision-Language Models (Med-VLMs) have achieved expert-level proficiency in interpreting diagnostic imaging. However, current models are pre
arXiv:2606.18936v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature a
arXiv:2606.25529v1 Announce Type: cross Abstract: Speech-to-speech translation (S2ST) should preserve not only lexical meaning, but also expressive attributes: emotion, scenario style (e.g., news repo
arXiv:2606.25760v1 Announce Type: cross Abstract: Computer-use agents turn vision-language model (VLM) predictions into executable GUI clicks, so reliable uncertainty estimates are essential for rejec
arXiv:2606.25374v1 Announce Type: new Abstract: Recent learned fault-tolerant-control (FTC) work reports high success on spacecraft actuator faults, but often in simulation, on narrow fault sets, and
arXiv:2606.24115v1 Announce Type: cross Abstract: Vision-language models (VLMs) are prone to hallucination, which remains a major barrier to their safe deployment in clinical practice. To date, most h
arXiv:2606.24526v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grou
arXiv:2603.13343v3 Announce Type: replace-cross Abstract: Predictive maintenance for connected vehicles offers the potential to reduce unexpected breakdowns and improve fleet reliability, but most exi
arXiv:2606.20146v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied to computer-aided design (CAD) to generate design artifacts from textual instructions. In engi
arXiv:2606.24797v1 Announce Type: cross Abstract: Recent advances in Video Large Language Models (Video-LLMs) have yielded promising performance on video question answering (VideoQA). Nevertheless, ex
arXiv:2606.24648v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have been widely used as judge models for the automatic evaluation of generated speech. However, prior approaches
arXiv:2606.23892v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used as perception-reasoning backbones for embodied intelligence in safety-critical physical systems, whe
arXiv:2606.24361v1 Announce Type: new Abstract: Sign language models are typically trained on datasets captured under constrained conditions, with limited viewpoint, background, and signer-identity di
arXiv:2602.14257v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents have made remarkable progress on complex reasoning, evaluating them in real-world environments remains
arXiv:2606.22188v1 Announce Type: new Abstract: Large multi-modal language models are increasingly deployed in high-stakes domains, making well-calibrated uncertainty essential. Traditional Bayesian m
arXiv:2606.22497v1 Announce Type: new Abstract: Microscopic imaging provides essential visual evidence for studying plant biology and pathology at the cellular and subcellular levels. However, existin
arXiv:2606.21020v1 Announce Type: new Abstract: The evaluation of vision-language models (VLMs) for chest X-ray (CXR) analysis has largely been limited to disease-presence classification without visua
arXiv:2606.22476v1 Announce Type: new Abstract: Humans can effortlessly reason about scenes across different viewpoints, yet it remains unclear whether Vision-Language Models (VLMs) possess similar cr
arXiv:2606.21194v1 Announce Type: new Abstract: Medical Vision-Language Models (Med-VLMs) achieve strong expert-level performance, yet their ability to generate patient-accessible descriptions remains
arXiv:2603.14145v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance in visual and audio understanding when evaluated in isolation. However,
arXiv:2606.22890v1 Announce Type: new Abstract: Optical microscopy enables rapid, label-free imaging of live bacteria and is the standard instrument for species identification across clinical, environ
arXiv:2606.22339v1 Announce Type: new Abstract: Recent advances in vision-language models and generative editing systems have made it increasingly easy to produce persuasive multimodal misinformation
arXiv:2606.11435v1 Announce Type: new Abstract: The growth of agent skills has transformed how agentic systems are built, evaluated, and deployed. As skill libraries continue to scale, rigorous evalua
arXiv:2606.11208v1 Announce Type: cross Abstract: Biomedical findings often seem to conflict across studies, but many of these differences are context-dependent rather than true contradictions. Variat
arXiv:2602.22638v2 Announce Type: replace Abstract: Route-planning agents powered by large language models (LLMs) have emerged as a promising paradigm for supporting everyday human mobility through na
arXiv:2606.11260v1 Announce Type: cross Abstract: Humans process rich auditory environments through tightly integrated cognitive capabilities such as audio perception, audio reasoning, and memory. Des
arXiv:2606.10156v1 Announce Type: cross Abstract: As recommender systems transition toward agentic, multi-turn conversational interfaces, evaluation paradigms have struggled to keep pace. Current benc
arXiv:2606.10620v1 Announce Type: cross Abstract: Image generation models now produce high-quality static images, yet their ability to represent how a visual world changes over time remains poorly und
arXiv:2606.11063v1 Announce Type: new Abstract: AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted model. This part
arXiv:2507.22017v4 Announce Type: replace-cross Abstract: Pancreatic cancer is projected to be the second-deadliest cancer by 2030, making early detection critical. Intraductal papillary mucinous neop
arXiv:2606.11001v1 Announce Type: new Abstract: Zinc-based alloys are indispensable emerging absorbable metallic biomaterials, and their macroscopic performance is governed by microstructural characte
arXiv:2606.10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can b
arXiv:2606.11105v1 Announce Type: cross Abstract: Hallucinations, where language models (LMs) generate factually ungrounded responses, pose serious risks, as users tend to blindly rely on them. This i
arXiv:2606.09570v2 Announce Type: replace Abstract: As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. W
arXiv:2512.11995v2 Announce Type: replace-cross Abstract: While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in
arXiv:2606.07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance. Despite the proliferation o
arXiv:2511.03877v2 Announce Type: replace Abstract: Social and collaborative platforms emit multivariate time-series traces in which early interactions -- such as views, likes, or downloads -- are fol
arXiv:2606.09401v1 Announce Type: new Abstract: Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees. How
arXiv:2606.07805v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) from passive assistants to autonomous, execution-capable agents has introduced critical operational
arXiv:2602.19330v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are increasingly explored for physical design analysis in Electronic Design Automation, particularly for modeling Clock
arXiv:2606.08036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in academic research workflows, but scholarly tasks require high factual precision and therefore ex