Knowledge catalogue
arXiv:2608.18270v1 Announce Type: new Abstract: Reliable tool--tissue contact detection can support interaction-aware control and downstream force estimation in robot-assisted surgery. Most existing m
arXiv:2608.18687v1 Announce Type: new Abstract: Heart disease remains the leading cause of mortality globally, necessitating early and accurate detection to improve patient outcomes. This research foc
arXiv:2608.18655v1 Announce Type: new Abstract: The rapid progress in Artificial Intelligence has largely bypassed African languages, creating a digital divide that limits AI adoption on the continent
arXiv:2608.18932v1 Announce Type: new Abstract: Estimating causal effects under network interference typically assumes that the network used for training and the network used for deployment coincide.
arXiv:2601.17178v3 Announce Type: replace-cross Abstract: Hardware Trojans (HTs) remain a critical threat because learning-based detectors often overfit to narrow trigger/payload patterns and small, s
arXiv:2608.18178v1 Announce Type: new Abstract: Trust assessment is a fundamental component of cooperative and connected vehicle systems. However, existing approaches operate primarily at the level of
arXiv:2608.18386v1 Announce Type: cross Abstract: Large video-language models (LVLMs) have shown remarkable performance on multimodal tasks like multimodal emotion recognition (ER) in the wild. ER is
arXiv:2608.19125v1 Announce Type: new Abstract: When an expert corrects an LLM assistant's error, the correction usually dies with the session, and the error class returns. I argue this is an operatio
arXiv:2608.18504v1 Announce Type: new Abstract: Universal multimodal retrieval aims to support diverse instruction-aware retrieval tasks, demanding both efficient corpus-scale matching and fine-graine
arXiv:2608.18984v1 Announce Type: new Abstract: Museum and archival datasets do not mirror historical artistic production, but materialize the contingent histories of collecting, preservation, catalog
arXiv:2608.18825v1 Announce Type: cross Abstract: Medical automatic speech recognition (MedASR) requires adaptation to specialised terminology, limited annotated clinical data, and multilingual use ca
arXiv:2608.19036v1 Announce Type: new Abstract: Spatial representation learning for autonomous driving aims to map raw visual signals into structured 3D scene representations, where object-centric bou
arXiv:2608.18607v1 Announce Type: new Abstract: Using reinforcement learning to post-train joint video-audio generation models requires a reward signal. Existing methods construct this reward by combi
arXiv:2608.18404v1 Announce Type: cross Abstract: We answer this question with Vector-Symbolic Policy Gradient (VSPG), a discrete-action actor that represents each action by a unit-norm hypervector an
arXiv:2608.18258v1 Announce Type: new Abstract: Robot-Assisted Feeding (RAF) systems are essential for assisting individuals with disabilities or motor impairments in eating tasks. Manipulating granul
arXiv:2608.18836v1 Announce Type: new Abstract: Utilities lose a substantial share of treated water to leakage, yet rarely trust artificial-intelligence localizers to dispatch crews: guessing everywhe
arXiv:2608.18671v1 Announce Type: new Abstract: Egocentric video captures activities from the wearer's perspective, providing a direct view of human attention, hand--object interaction, and goal-direc
arXiv:2608.18727v1 Announce Type: new Abstract: Applying machine learning to web pages is challenging due to the need to interpret HTML together with associated resources and perform rendering to obta
arXiv:2608.18246v1 Announce Type: cross Abstract: Fine-grained wildlife re-identification remains a challenging area in research. Current state-of-the-art approaches apply a detection and re-identific
arXiv:2601.02945v2 Announce Type: replace Abstract: With the rapid development of e-commerce and digital fashion, image-based virtual try-on (VTON) has attracted increasing attention. However, existin
arXiv:2505.24313v3 Announce Type: replace Abstract: Weak-to-strong generalization (W2SG) is the phenomenon in which a powerful student model, trained on labels produced by a weaker teacher, ultimately
arXiv:2603.09194v2 Announce Type: replace Abstract: Local wind conditions strongly influence drone performance: headwinds increase flight time, crosswinds and wind shear hinder agility in cluttered sp
arXiv:2608.18186v1 Announce Type: cross Abstract: In the past few years, machine learning (ML) has been widely (and to an extent, successfully) implemented in medicine. However, uncertainties surround
arXiv:2608.18399v1 Announce Type: new Abstract: Vision transformers (ViTs) trained to copy a pretrained teacher's attention maps recover most of fine-tuning's in-distribution accuracy yet fall measura
arXiv:2608.19072v1 Announce Type: new Abstract: Large language model (LLM) agents can now post-train an LLM end-to-end. They can write code, launch training, evaluate checkpoints, and improve downstre
arXiv:2608.18280v1 Announce Type: cross Abstract: Background. Advances in agentic systems are simultaneously, and rapidly, saturating benchmarks. Despite this often discussed phenomena, benchmark scor
arXiv:2608.18397v1 Announce Type: new Abstract: Wearable stress classifiers can achieve strong average performance while failing completely for a particular individual. On WESAD, a Random Forest reach
arXiv:2608.18158v1 Announce Type: cross Abstract: LLMs have been increasingly used to catch data quality issues automatically, but we know very little about how consistent these judgments actually are
arXiv:2608.18330v1 Announce Type: new Abstract: Whether input-dependent ('dynamic') combination of a regression model pool beats the best static blend depends on the shift and is rarely known before d
arXiv:2608.19083v1 Announce Type: cross Abstract: Readable AI output can leave an evaluability gap: even when the source is shown, an overall-quality judgment may not reflect what an output preserves.
arXiv:2608.18628v1 Announce Type: cross Abstract: Aligned vision-language models (VLMs) are designed to balance grounded visual reasoning with safe generation behavior. However, we observe a striking
arXiv:2608.18979v1 Announce Type: new Abstract: Semantic segmentation demands a careful balance between accuracy, efficiency, and scalability, which remains difficult to achieve for high-resolution im
arXiv:2608.19063v1 Announce Type: new Abstract: PSMA and FDG PET/CT visualise complementary biological information in prostate cancer. Combining both tracers could capture heterogeneous tumour phenoty
arXiv:2608.18521v1 Announce Type: new Abstract: Dense-caption retrieval has recently been improved by introducing segmentation, edge maps, LLM-filtered captions, and cross-modal modules into contrasti
arXiv:2608.18486v1 Announce Type: new Abstract: In a Transformer, each layer attends to past tokens only through KV produced at its own depth, despite the presence of deeper representations during aut
arXiv:2602.19816v3 Announce Type: replace-cross Abstract: For computational efficiency, modern language models are typically trained on independently sampled fixed-length sequences. Symbolic music lan
arXiv:2603.29865v2 Announce Type: replace-cross Abstract: Wildfires cause major losses worldwide, and the frequency of fire-weather conditions is likely to increase in many regions. We study the alloc
arXiv:2608.18986v1 Announce Type: new Abstract: Digital subtraction angiography (DSA) is the reference standard for leptomeningeal collateral (LMC) assessment, providing critical prognostic insights t
arXiv:2608.18661v1 Announce Type: new Abstract: Streaming text-to-speech is essential for low-latency spoken dialogue systems, yet many systems wait for sentence-level text and are therefore only pseu
arXiv:2608.18309v1 Announce Type: new Abstract: Registering images acquired with different microscopy modalities is essential for relating complementary measurements of the same specimen. In correlati
arXiv:2608.18116v1 Announce Type: new Abstract: Vision-language models enable zero-shot classification through natural language prompts, but performance is sensitive to prompt formulation, especially
arXiv:2608.18544v1 Announce Type: new Abstract: Automated recognition of ancient cuneiform script poses a compound signal-degradation problem: the three-dimensional relief of clay tablets creates spat
arXiv:2608.18240v1 Announce Type: new Abstract: Despite the rapid industrialization of the touch sensor manufacturing process, most of these sensors are still handmade in research laboratories. This c
arXiv:2608.17298v1 Announce Type: cross Abstract: 3D Gaussian Splatting has made Gaussian primitives a highly efficient representation for real-time novel view synthesis, but its rasterisation-based f
arXiv:2608.16947v1 Announce Type: cross Abstract: Huang, Lou, and Xiao introduced Dynamic Mixture-of-Experts Serving and gave an O(sqrt(log k))-competitive randomized algorithm for its integral primal
arXiv:2608.17124v1 Announce Type: new Abstract: Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by
arXiv:2510.14000v2 Announce Type: replace Abstract: The growing demand for parking has increased the need for automated parking planning methods that can operate reliably in confined spaces. In restri
arXiv:2608.16893v1 Announce Type: cross Abstract: Expert surveys are widely used in security research to study practitioner workows and decision-making, yet recruiting domain experts - especially in S
arXiv:2608.17096v1 Announce Type: new Abstract: The Voynich manuscript (Beinecke MS 408) is usually analysed on three unstated assumptions: that its glyphs are letters, that the strings between blanks
arXiv:2511.17954v2 Announce Type: replace-cross Abstract: Incorporating spatial information, particularly when related to climate, weather, and demographic factors, is crucial for improving underwriti
arXiv:2608.17894v1 Announce Type: cross Abstract: This paper investigates the feasibility of using residual learning to improve unsteady aerodynamic load prediction for aeroelastic applications. The m
arXiv:2608.17928v1 Announce Type: cross Abstract: In the Lifelong Multi-Agent Path Finding (L-MAPF) problem, agents must repeatedly move from one destination to another while avoiding obstacles and in
arXiv:2608.17286v1 Announce Type: new Abstract: Compute-optimal scaling laws guide the training of frontier language models yet remain largely unexplored for visual generation. We present a systematic
arXiv:2608.17711v1 Announce Type: new Abstract: Prediction cascades significantly reduce energy consumption of Artificial Intelligence (AI) models while maintaining high predictive performance. The id
arXiv:2606.12499v2 Announce Type: replace Abstract: We present AEM, an Action-Effect Memory pretraining framework for robot manipulation that learns compact temporal representations from vision-action
arXiv:2608.17834v1 Announce Type: cross Abstract: Large language models are pushing data science toward increasingly autonomous and agentic workflows, with recent systems already supporting multi-step
arXiv:2608.17929v1 Announce Type: new Abstract: Robust Markov decision processes optimize one policy against a set of plausible transition functions. This can be conservative when the unknown dynamics
arXiv:2608.17250v1 Announce Type: cross Abstract: This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of spatio-temp
arXiv:2608.17975v1 Announce Type: cross Abstract: Programmatic representations provide a compelling paradigm for 3D content creation, enabling fine-grained edits, interpretability, and explicit struct
arXiv:2608.16902v1 Announce Type: cross Abstract: The increasing integration of machine learning in healthcare has highlighted critical challenges related to fairness, transparency, and health equity.