Knowledge catalogue
arXiv:2602.09642v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have significantly improved table understanding tasks such as Table Question Answering (TableQ
arXiv:2604.19129v1 Announce Type: new Abstract: Existing facial reenactment methods struggle with a trade-off between expressiveness and fine-grained controllability. Holistic facial reenactment model
arXiv:2601.18891v3 Announce Type: replace Abstract: Caribou across the Arctic has declined in recent decades, motivating scalable and accurate monitoring approaches to guide evidence-based conservatio
arXiv:2604.16550v1 Announce Type: new Abstract: Despite the high accuracy of 'black box' deep learning models, drug discovery still relies on protein-ligand interaction principles and heuristics. To i
arXiv:2510.07143v3 Announce Type: replace Abstract: Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectivene
arXiv:2511.02356v2 Announce Type: replace-cross Abstract: Despite extensive safety alignment, Large Language Models (LLMs) remain vulnerable to jailbreak attacks. However, existing methods generally l
arXiv:2508.14913v4 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated significant capabilities in solving mathematical problems expressed in natural language. However, mul
arXiv:2510.24074v2 Announce Type: replace-cross Abstract: The Heston stochastic volatility model is a widely used tool in financial mathematics for pricing European options. However, its calibration r
arXiv:2604.18052v1 Announce Type: cross Abstract: Intrusion detection systems (IDSs) for 5G networks must handle complex, high-volume traffic. Although opaque 'black-box' models can achieve high accur
arXiv:2604.18083v1 Announce Type: new Abstract: Reconstructing continuous environmental fields from sparse and irregular observations remains a central challenge in environmental modelling and biodive
arXiv:2506.03178v2 Announce Type: replace-cross Abstract: Automated radiology report generation holds significant potential to reduce radiologists' workload and enhance diagnostic accuracy. However, g
arXiv:2604.16313v1 Announce Type: cross Abstract: Retrieval-based multimodal document QA aims to identify and integrate relevant information from visually rich documents with complex multimodal struct
arXiv:2604.16493v1 Announce Type: cross Abstract: Natural Language to SQL (NL2SQL) technology empowers non-expert users to query relational databases without requiring SQL expertise. While large langu
arXiv:2604.18145v1 Announce Type: new Abstract: Automated medical report generation for 3D PET/CT imaging is fundamentally challenged by the high-dimensional nature of volumetric data and a critical s
arXiv:2604.17328v1 Announce Type: new Abstract: This paper investigates the length problem in sequence-level relative reinforcement learning. We observe that, although existing methods partially allev
arXiv:2604.17769v1 Announce Type: new Abstract: Ensuring the safety of large language models (LLMs) requires robust red teaming, yet the systematic synthesis of high-quality toxic data remains under-e
arXiv:2602.19549v2 Announce Type: replace Abstract: Visual Document Retrieval (VDR), which aims to retrieve relevant pages within vast corpora of visually-rich documents, is of significance in current
arXiv:2604.16848v1 Announce Type: new Abstract: Fine-grained semantic segmentation of transmission-corridor point clouds is fundamental for intelligent power-line inspection. However, current progress
arXiv:2510.21804v2 Announce Type: replace Abstract: Autoregressive neural surrogates offer computational acceleration for fluid dynamics but inherently suffer from error accumulation and non-physical
arXiv:2604.15341v1 Announce Type: cross Abstract: Mixed Reality (MR) offers immersive and multimodal opportunities for education but remains difficult for teachers to author without technical expertis
arXiv:2604.15475v1 Announce Type: new Abstract: Deploying learned multi-robot models on heterogeneous robots remains challenging due to hardware heterogeneity, communication constraints, and the lack
arXiv:2604.15490v1 Announce Type: new Abstract: Recent developments in reasoning capabilities have enabled large language models to solve increasingly complex mathematical, symbolic, and logical tasks
arXiv:2604.15938v1 Announce Type: new Abstract: Diffusion policies are becoming mainstream in robotic manipulation but suffer from hard negative class imbalance due to uniform sampling and lack of sam
arXiv:2502.20689v4 Announce Type: replace Abstract: Large Language Models (LLMs) offer promising opportunities to support mental healthcare workflows, yet they often lack the structured clinical reaso
arXiv:2604.14882v1 Announce Type: cross Abstract: Rapid urbanization and continuous population growth have made municipal solid waste management increasingly challenging. These challenges highlight th
arXiv:2604.13048v1 Announce Type: cross Abstract: Modern cloud-native platforms expose thousands of time series metrics through systems like Prometheus, yet formulating correct queries in domain-speci
arXiv:2604.14583v1 Announce Type: new Abstract: Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to
arXiv:2604.15134v1 Announce Type: new Abstract: Reliable procedural monitoring in video requires exposure to naturally occurring human errors and the recoveries that follow. In egocentric recordings,
arXiv:2604.14216v1 Announce Type: cross Abstract: Predicting post-surgical seizure outcomes in pharmacoresistant epilepsy is a clinical challenge. Conventional deep-learning approaches operate on stat
arXiv:2604.14352v1 Announce Type: cross Abstract: Online A/B testing at scale relies on proxy metrics -- short-term, easily-measured signals used in place of slow-moving long-term outcomes. When the p
arXiv:2604.14787v1 Announce Type: cross Abstract: Network Digital Twins (NDTs) enable safe what-if analysis for 6G cloud-edge infrastructures, but adoption is often limited by fragmented workflows fro
arXiv:2604.13658v1 Announce Type: new Abstract: Advanced deep learning methods have shown remarkable success in power quality disturbance (PQD) classification. To enhance model transparency, explainab
arXiv:2602.16385v4 Announce Type: replace Abstract: Independent indoor mobility remains a critical challenge for individuals with visual impairments, largely due to the limited capability of existing
arXiv:2604.13409v1 Announce Type: new Abstract: In clinical practice, the robustness of deep learning models for multimodal brain tumor segmentation is severely compromised by incomplete MRI data. Thi
arXiv:2604.13183v1 Announce Type: new Abstract: Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. Its cor
arXiv:2512.10696v2 Announce Type: replace-cross Abstract: Procedural memory enables large language model (LLM) agents to internalize 'how-to' knowledge, theoretically reducing redundant trial-and-erro
arXiv:2604.13236v1 Announce Type: new Abstract: Semiconductor failure analysis (FA) requires engineers to examine inspection images, correlate equipment telemetry, consult historical defect records, a
arXiv:2505.24869v3 Announce Type: replace Abstract: Recent advances in test-time optimization have led to remarkable reasoning capabilities in Large Language Models (LLMs), enabling them to solve high
arXiv:2604.13055v1 Announce Type: new Abstract: Today's evolving labor markets rely increasingly on recommender systems for hiring, talent management, and workforce analytics, with natural language pr
arXiv:2604.12543v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to translate the technical outputs of eXplainable Artificial Intelligence (XAI) methods into accessib
arXiv:2604.12686v1 Announce Type: cross Abstract: Recent advances in deep learning underscore the need for systems that can not only acquire new knowledge through Continual Learning (CL) but also remo
arXiv:2604.12436v1 Announce Type: new Abstract: Efficient and scalable 3D occupancy mapping is essential for autonomous robot applications in unknown environments. However, traditional occupancy grid
arXiv:2604.12306v1 Announce Type: cross Abstract: Climate decision-making in the Gulf increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guida
arXiv:2604.12805v1 Announce Type: new Abstract: Image-to-image translation (I2I) is a fundamental task in computer vision, focused on mapping an input image from a source domain to a corresponding ima
arXiv:2604.12481v1 Announce Type: new Abstract: Text-to-image (T2I) generative models achieve impressive visual fidelity but inherit and amplify demographic imbalances and cultural biases embedded in
arXiv:2604.12216v1 Announce Type: cross Abstract: The widespread use of Large Language Models (LLMs) in text generation has raised increasing concerns about intellectual property disputes. Watermarkin
arXiv:2604.12909v1 Announce Type: new Abstract: As reinforcement learning for humanoid robots evolves from single-task to multi-skill paradigms, efficiently expanding new skills while avoiding catastr
arXiv:2604.09932v1 Announce Type: cross Abstract: Hybrid approaches that combine data-driven learning with physics-based insight have shown promise for improving the reliability of industrial conditio
arXiv:2604.10177v1 Announce Type: cross Abstract: We study the piecewise-stationary restless multi-armed bandit (PS-RMAB) problem, where each arm evolves as a Markov chain but mean rewards may change
arXiv:2604.09565v1 Announce Type: cross Abstract: This paper introduces a unified, hardware-independent baremetal runtime architecture designed to enable high-performance machine learning (ML) inferen
arXiv:2604.11801v1 Announce Type: new Abstract: With the recent progress of Large Language Models (LLMs), there is a growing interest in applying these models to solve complex and challenging problems
arXiv:2604.11584v1 Announce Type: cross Abstract: We study computational aspects of a key problem in robust statistics -- the penalized least trimmed squares (LTS) regression problem, a robust estimat
arXiv:2512.20642v2 Announce Type: replace-cross Abstract: Particle image velocimetry (PIV) and related optical-flow methods are widely used to quantify fluid motion, but their development and evaluati
arXiv:2604.10470v1 Announce Type: cross Abstract: Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-qu
arXiv:2604.10955v1 Announce Type: new Abstract: Hypergraph neural networks (HGNNs) have shown remarkable potential in modeling high-order relationships that naturally arise in many real-world data dom
arXiv:2602.05467v2 Announce Type: replace-cross Abstract: Visual Language Navigation (VLN) is one of the fundamental capabilities for embodied intelligence and a critical challenge that urgently needs
arXiv:2507.20997v4 Announce Type: replace-cross Abstract: In real-world machine learning deployments, models must be continually updated, composed, and when required, selectively undone. However, exis
arXiv:2604.11017v1 Announce Type: cross Abstract: Cloud native architecture is about building and running scalable microservice applications to take full advantage of the cloud environments. Managed K
arXiv:2604.11070v1 Announce Type: new Abstract: Current approaches to AI safety define red lines at the case level: specific prompts, specific outputs, specific harms. This paper argues that red lines
arXiv:2508.04436v2 Announce Type: replace Abstract: Autonomous highway driving involves high-speed safety risks due to limited reaction time, where rare but dangerous events may lead to severe consequ