Knowledge catalogue
arXiv:2607.29237v1 Announce Type: new Abstract: LiDAR scene flow estimation has settled into a monoculture: nearly all recent methods share the same feed-forward architecture and the same family of se
arXiv:2607.29355v1 Announce Type: cross Abstract: Cross-lingual transfer is central to low-resource machine translation, but its behavior within closely related language families remains insufficientl
arXiv:2607.29120v1 Announce Type: new Abstract: Relational Prior-Data Fitted Networks (PFNs) such as RDB-PFN approximate Bayesian inference over multi-table relational databases by pretraining on mill
arXiv:2509.22874v2 Announce Type: replace Abstract: Deformable image registration (DIR) is central to medical image analysis, supporting spatial alignment for longitudinal studies and multi-modal fusi
arXiv:2607.28707v1 Announce Type: new Abstract: Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test t
arXiv:2607.29188v1 Announce Type: new Abstract: Migrants traversing geographically distinct routes such as the Trans-Saharan and Balkan corridors often recount strikingly parallel lived experiences: p
arXiv:2607.29657v1 Announce Type: new Abstract: Fault detection and diagnosis (FDD) technology is essential for improving HVAC system reliability, energy efficiency, and maintenance effectiveness. How
arXiv:2607.29038v1 Announce Type: new Abstract: Fractional scientific machine learning requires numerical operators that can be differentiated, batched, accelerated, and composed with neural networks.
arXiv:2607.28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space. However, the decision boun
arXiv:2503.03156v4 Announce Type: replace-cross Abstract: We propose DiRe, a force-directed dimensionality reduction framework designed to preserve global structure and homological features while rema
arXiv:2607.28971v1 Announce Type: cross Abstract: Contrastive learning is widely used for user modeling in large-scale recommender systems, where standard in-batch negatives implicitly assume universa
arXiv:2607.29112v1 Announce Type: cross Abstract: Audio-visual speech recognition (AVSR) relies on effective fusion of audio and visual modalities, yet existing approaches treat cross-modal interactio
arXiv:2607.29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circu
arXiv:2607.29337v1 Announce Type: cross Abstract: Background and Objective: Generating realistic medical images with anatomically accurate segmentation masks helps address the shortage of annotated da
arXiv:2607.29036v1 Announce Type: new Abstract: Sparse identification of nonlinear dynamics (SINDy) and PDE functional identification (PDE-FIND) recover parsimonious ordinary and partial differential
arXiv:2604.08342v2 Announce Type: replace Abstract: Long context egocentric video understanding has recently attracted significant research attention, with augmented reality (AR) highlighted as one of
arXiv:2607.29058v1 Announce Type: new Abstract: Professional-document review is a constraint-checking problem in which decisions depend on relations among text, geometry, pages, and document revisions
arXiv:2607.29484v1 Announce Type: new Abstract: Interventional data is widely regarded as the gold standard for teaching models causal reasoning. We test this assumption in a fully controlled syntheti
arXiv:2607.29581v1 Announce Type: new Abstract: A marketplace review photograph is a document: platforms approve refunds on it, and generative models drove the cost of forging one to zero. We study th
arXiv:2607.29079v1 Announce Type: new Abstract: Training-free acceleration makes diffusion-based multimodal large language models (dMLLMs) more deployable, but it may silently change generated content
arXiv:2607.29235v1 Announce Type: cross Abstract: Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands
arXiv:2607.29182v1 Announce Type: cross Abstract: Transcranial focused ultrasound (tFUS) is a non-invasive technique that delivers focused acoustic energy through the skull for neuromodulation and the
arXiv:2607.29596v1 Announce Type: cross Abstract: Vision-language-action models (VLAs), which leverage the cognition of multimodal information to infer physical-world actions, provide a generalized so
arXiv:2607.29132v1 Announce Type: new Abstract: Surgical gauze is an essential part of surgical procedures, primarily used for controlling bleeding and absorbing bodily fluids. The post-surgical reten
arXiv:2510.06945v2 Announce Type: replace Abstract: Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evalua
arXiv:2607.28698v1 Announce Type: new Abstract: Flow matching assumes fully observed training data, which many real-world applications rarely provide. We propose Missing-Data Flow Matching, which trea
arXiv:2607.28834v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) is evolving from one-time reconstruction into deliverable, inspectable, and maintainable visual assets. Existing workflows
arXiv:2607.29516v1 Announce Type: cross Abstract: AI coding agents are generating code at volumes that exceed the capacity of traditional peer review. At the same time, existing AI code review tools o
arXiv:2607.28916v1 Announce Type: cross Abstract: Multistep credit assignment is critical for sample-efficient reinforcement learning, yet managing off-policy bias in Q-learning remains a fundamental
arXiv:2603.29888v2 Announce Type: replace-cross Abstract: In collaboration with Alibaba, we study how a generative AI assistant affects service performance in e-commerce after-sales operations. In a l
arXiv:2607.29019v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, but existing pipelines typically operate on p
arXiv:2504.19683v4 Announce Type: replace Abstract: Fine-grained robotic manipulation often fails when inaccurate initial grasps propagate errors and necessitate complex pose correction. We propose Gr
arXiv:2601.07048v5 Announce Type: replace-cross Abstract: Approximate nearest neighbor search (ANNS) is a core problem in machine learning and information retrieval applications. GPUs offer a promisin
arXiv:2607.13683v2 Announce Type: replace Abstract: Large Language Models (LLMs) have enabled capable agents across diverse applications. Beyond the foundation model, the performance of an agent is go
arXiv:2607.29144v1 Announce Type: cross Abstract: Synthetic face datasets are increasingly used to reduce privacy exposure and data access constraints in biometric recognition. Yet the generators that
arXiv:2607.28652v1 Announce Type: cross Abstract: Early-life monitoring in laying hens remains constrained by fragmented single-modality sensing and the absence of formal system-level state representa
arXiv:2607.28670v1 Announce Type: new Abstract: A stochastic Gumbel-Top-K router defines, for every token of a mixture-of-experts (MoE) model, a routing law: a distribution over ordered expert lists a
arXiv:2607.29638v1 Announce Type: new Abstract: Multi-page document visual question answering requires locating sparse evidence at both the page and region levels. Existing approaches typically emphas
I have been working on this tool for months and there are a lot of new functionalities and tests that are going to be released in the next few weeks! The goal of the tool is to allow community members
arXiv:2607.29158v1 Announce Type: cross Abstract: We introduce implicit machine learning force fields (I-MLFFs), which replace explicit stacks of neural network layers with self-consistent fixed-point
arXiv:2607.29222v1 Announce Type: new Abstract: The zero-shot capabilities of multimodal large language models (MLLMs) are pushing salient object detection (SOD) beyond task-specific supervision. To d
arXiv:2512.13677v2 Announce Type: replace Abstract: In this paper, we present JoVA, a streamlined framework that unifies joint video-audio generation and editing. While existing methods often rely on
arXiv:2603.09956v2 Announce Type: replace Abstract: We present the KinoDynamic Motion Retargeting (KDMR) framework, a novel approach for humanoid locomotion that models the retargeting process as a mu
arXiv:2607.29185v1 Announce Type: new Abstract: Reward models (RMs) are central to aligning large language models with human preferences via reinforcement learning. Although traditional scalar RMs ena
arXiv:2607.28855v1 Announce Type: cross Abstract: Dense 3D sensors in various real-world fields produce point clouds that are geometrically redundant for real-time processing. In this paper, we propos
arXiv:2607.28876v1 Announce Type: cross Abstract: Music is often used as a medium for communicating emotion, with performers shaping perceived affect through interpretation. This study addresses the c
arXiv:2607.29559v1 Announce Type: new Abstract: Reinforcement Learning (RL) systems are typically trained using a single, well-specified scalar reward function. However, real-world decision-making tas
arXiv:2607.29192v1 Announce Type: new Abstract: Remote sensing scene classification is increasingly relying on foundation models pre-trained on large-scale Earth-observation data. Moreover, transducti
arXiv:2607.29125v1 Announce Type: new Abstract: Full-duplex spoken dialogue systems (FDSDSs) can listen while speaking, enabling natural behaviors such as smooth turn-taking, backchannel handling, and
arXiv:2607.29177v1 Announce Type: cross Abstract: Utility data (e.g., electricity, water, and gas consumption), collected by ubiquitous sensors and embedded devices, often contains substantial missing
arXiv:2607.28683v1 Announce Type: cross Abstract: Large language models benefit from elements in natural language, such as metaphors and analogies in training data and inference input to achieve gener
arXiv:2607.29093v1 Announce Type: cross Abstract: Metasignal is an open-source Python package for signal detection theory (SDT) and metacognitive measurement. It implements the 17 metacognitive measur
arXiv:2607.28954v1 Announce Type: new Abstract: Branch and Bound (BaB) aims to achieve complete verification of neural networks by adaptively partitioning the problem and applying off-the-shelf verifi
arXiv:2607.29002v1 Announce Type: new Abstract: Online shoppers increasingly turn to AI shopping assistants, using images and multi-turn dialogue to express and refine product needs that are difficult
arXiv:2505.21736v2 Announce Type: replace Abstract: Translation equivariance is a central reason convolutional neural networks have been successful in computer vision. Other symmetries, such as rotati
arXiv:2607.29462v1 Announce Type: cross Abstract: Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfi
arXiv:2607.28679v1 Announce Type: new Abstract: Multi-agent planning problems arise in a variety of engineering applications, such as multi-robot wildfire fighting and unmanned aerial inspection in fa
arXiv:2512.03438v3 Announce Type: replace Abstract: Agentic reasoning models trained with multimodal reinforcement learning (MMRL) have become increasingly capable, yet they are almost universally opt
arXiv:2607.28663v1 Announce Type: cross Abstract: Artificial Intelligence (AI) systems often perform well on isolated tasks but struggle under continual learning conditions, where training on new task
arXiv:2607.15776v2 Announce Type: replace Abstract: OWL ontologies provide a formal knowledge representation framework that enables semantic reasoning, and have been widely adopted across domains such