Knowledge catalogue
arXiv:2307.02719v5 Announce Type: replace Abstract: Uncertainty sampling is a classical active-learning strategy, yet the statistical objective induced by its query rule is often implicit. We introduc
arXiv:2511.13032v2 Announce Type: replace Abstract: We present Uni-Inter, a unified framework for human motion generation that supports a wide range of interaction scenarios: including human-human, hu
arXiv:2609.06729v1 Announce Type: new Abstract: Accurate 3D brain tumour segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for clinical diagnosis and treatment planning. Exis
arXiv:2609.07420v1 Announce Type: new Abstract: Vision cues are available and informative for pedestrian action prediction, but obtaining stable target-centric representations from video frames remain
arXiv:2603.22851v2 Announce Type: replace Abstract: We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images. Existing feedforwa
arXiv:2609.05636v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are able to achieve impressive zero-shot classification performance by aligning visual and textual representations, but ea
arXiv:2609.05541v1 Announce Type: new Abstract: Implicit Neural Representations (INR) provide a compact and content-adaptive paradigm for video compression, typically representing a video through shar
arXiv:2609.05537v1 Announce Type: new Abstract: Tracking social media posts can help spot early signs of depression. Recent studies show that combining text and images works better for detecting depre
arXiv:2609.06410v1 Announce Type: cross Abstract: Existing approaches to visual attribute value extraction (AVE) primarily rely on static product images, failing to capture temporal cues, multi-angle
arXiv:2609.07178v1 Announce Type: new Abstract: Deep learning models for CT scan analysis are often limited by the scarcity of precise pixel-level annotations, which require significant radiologist ef
arXiv:2609.06786v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of designated training data while preserving model utility, but its behavior on tabular data remains und
arXiv:2609.07525v1 Announce Type: new Abstract: Incomplete multi-view multi-label learning requires not only robust semantic aggregation from partially observed views, but also label-aware exploitatio
arXiv:2609.09055v1 Announce Type: new Abstract: Vision transformers (ViTs) have achieved remarkable generalization across visual domains, yet little is known about how they internally represent the st
arXiv:2609.08182v1 Announce Type: new Abstract: Because dense frame-level annotation of colonoscopy videos is costly, we propose WSPolypNet, a weakly supervised framework for polyp localization using
arXiv:2609.07724v1 Announce Type: new Abstract: Accurate 3D plant organ segmentation is fundamental to automated phenotyping. Existing approaches rely on annotated training data or species-specific mo
🤯 For the Navier-Stokes Millennium Prize Problem solution, OpenAI says they used a next-generation model that is significantly more capable than GPT-6 Astra. If GPT-6 Astra is this good already, imagi
Google LLC’s DeepMind research unit said today it has used its AlphaGenome artificial intelligence model to predict the biological consequences of the more than 9 billion possible single-letter change
arXiv:2609.04345v1 Announce Type: cross Abstract: Large-scale logistics networks require synthetic data generation capabilities to support scenario-based planning under novel conditions-such as networ
arXiv:2609.04267v1 Announce Type: new Abstract: Aerodynamic surrogate models trained on high-fidelity CFD data reproduce numerical predictions of both scalar outputs and entire fields accurately, yet
arXiv:2609.04695v1 Announce Type: cross Abstract: Large-volume neutrino telescopes infer neutrino properties from Cherenkov light, but simulating the transport of billions of photons through highly sc
arXiv:2609.04354v1 Announce Type: new Abstract: We introduce a lightweight Quantum Prototypical Recurrent Unit (QPRU) that requires significantly fewer parameters than both classical recurrent archite
arXiv:2609.04428v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to annotate cultural texts at scales that are impractical for human coders. However, before their out
arXiv:2609.04461v1 Announce Type: cross Abstract: Foundation models are beginning to reshape brain-signal analysis by moving the field beyond task-specific decoding pipelines toward reusable models pr
arXiv:2609.04819v1 Announce Type: new Abstract: Multilingual language models develop shared cross-lingual representations, and various interpretability methods claim to quantify this sharing. These me
arXiv:2609.04981v1 Announce Type: new Abstract: Recent structured RAG methods leverage tree- or graph-based reasoning structures to improve multi-hop QA. However, they face key limitations in evidence
arXiv:2609.05228v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures provide an efficient paradigm for scaling large language models (LLMs), yet fixed top-k routing activates the sam
arXiv:2609.04464v1 Announce Type: new Abstract: Sampling-based motion planning algorithms are a popular class of trajectory planning algorithm due to their speed in complex, high-dimensional environme
arXiv:2609.04901v1 Announce Type: cross Abstract: Tabular foundation models (TabFMs) achieve strong performance on structured data, particularly for standard classification and regression problems. Ye
arXiv:2609.05331v1 Announce Type: new Abstract: Robotic systems increasingly operate in dynamic, uncertain, and open-ended environments, where design-time assumptions may no longer hold, and adaptatio
arXiv:2609.04603v1 Announce Type: new Abstract: Recent generative image-editing Diffusion Transformers (DiTs) demonstrate impressive semantic editing capabilities but still struggle with spatially con
arXiv:2602.14941v2 Announce Type: replace-cross Abstract: Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory
arXiv:2609.05161v1 Announce Type: cross Abstract: Rigid Body Dynamics (RBD) forms the computational core of real-time robotic control, but its immense computational complexity creates a performance bo
arXiv:2609.04917v1 Announce Type: new Abstract: Artificial intelligence (AI) now supports investment workflows from data and prediction through research, portfolios, execution, and tool use. Technical
arXiv:2609.04292v1 Announce Type: new Abstract: Human mobility predictability concerns the best prediction performance attainable from a given target and input information, but its ground truth is not
arXiv:2609.04425v1 Announce Type: new Abstract: Forecasting-model selection remains difficult in heterogeneous demand because the most suitable decision rule may vary with demand structure, data avail
arXiv:2609.05210v1 Announce Type: new Abstract: Recent hybrid Structure-from-Motion (SfM) systems combine the robustness of feed-forward 3D reconstruction with the accuracy of traditional bundle adjus
arXiv:2609.04356v1 Announce Type: cross Abstract: This paper analyzes the implementation of blockchain-based integrity mechanisms in Greek Fiscal Electronic Mechanisms (FEMs) and the central tax infor
arXiv:2609.04647v1 Announce Type: new Abstract: Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, assembling context sets that may be individ
arXiv:2609.04792v1 Announce Type: cross Abstract: Activation steering has shown promise for controlling LLM generation along well-defined attributes, but it remains unclear whether it can handle the m
arXiv:2609.04578v1 Announce Type: cross Abstract: We study stochastic gradient descent with random reshuffling for finite sums [ F(x)=frac1nsum_{i=1}^n f_i(x). ] For fresh reshuffling with a constant
arXiv:2609.04870v1 Announce Type: new Abstract: Multiplayer Online Battle Arena (MOBA) games rely on matchmaking to maintain competitive balance. Our prior work, CUPID, framed matchmaking as an assign
arXiv:2609.04475v1 Announce Type: cross Abstract: Federated unlearning aims to remove a client's data from a shared model without retraining from scratch. Some efficient systems make deletion exact by
arXiv:2609.04784v1 Announce Type: new Abstract: Zero-shot 6D pose estimation pipelines increasingly rely on strong downstream pose solvers, but their performance is often limited by the front-end: obj
arXiv:2609.05126v1 Announce Type: new Abstract: Overparameterized neural networks carry far more hidden units than a task nominally requires, raising the question of which neurons are essential and wh
arXiv:2609.04781v1 Announce Type: new Abstract: Multi-modal medical images and clinical reports provide complementary anatomical, functional, and semantic information for medical image segmentation. E
arXiv:2609.05104v1 Announce Type: new Abstract: Biological agents navigate familiar environments not by re-solving routes for each new goal, but by reusing a learned map built once and read off as goa
arXiv:2602.13784v2 Announce Type: replace-cross Abstract: Explaining with examples is an intuitive way to justify AI decisions. However, it is challenging to understand how a decision value should cha
arXiv:2609.05152v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances language models with external knowledge, but the lengthy retrieved context inflates the input and degrades
arXiv:2609.05165v1 Announce Type: cross Abstract: Despite its introduction more than a quarter century ago, Conformal Prediction (CP) has seen surprisingly few applications to the cyber security world
arXiv:2609.05073v1 Announce Type: new Abstract: Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. The
arXiv:2506.07309v3 Announce Type: replace Abstract: Can Large Language Models (LLMs) be trained to avoid hallucinating factual statements, and can Retrieval-Augmented Generation (RAG) be triggered onl
arXiv:2609.05182v1 Announce Type: new Abstract: Pathology foundation models integrated with multiple instance learning achieve competitive accuracy within single-cancer cohorts, yet cross-cancer gener
arXiv:2609.04809v1 Announce Type: new Abstract: Comparing intelligent systems under deployment constraints requires more than predictiveaccuracy.This paper develops Compression-Prediction-Resource Int
arXiv:2505.23043v2 Announce Type: replace-cross Abstract: Unified vision-language models (VLMs) aim to support both visual understanding and generation within a single framework, but it remains unclea
arXiv:2609.04541v1 Announce Type: new Abstract: Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective r
arXiv:2509.14029v2 Announce Type: replace Abstract: Nanopore-based single-molecule sensing is a promising route to fast, low-cost disease diagnosis and protein sequencing: as an analyte such as a pept
arXiv:2609.05081v1 Announce Type: new Abstract: This paper introduces Deep Microcompression (DMC), a hardware-aware pipeline for deep learning inference on bare-metal microcontrollers. DMC integrates
arXiv:2609.04390v1 Announce Type: cross Abstract: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 30% of the general population. Ultrasound-based imaging, includ
arXiv:2609.05404v1 Announce Type: cross Abstract: Diffusion TV is an interactive AI art installation that offers a tangible and embodied experience of diffusion models through a modified CRT TV. By ph
arXiv:2609.05214v1 Announce Type: new Abstract: The Appropriately Combined Edge-length (ACE) sequence in A-BLiN depends on the zooming dimension d_z. This note removes that dependence. The next edge l