Knowledge catalogue
arXiv:2604.17512v1 Announce Type: new Abstract: Serialization formats designed for document interchange impose structural overhead that becomes prohibitive when large language models consume operation
arXiv:2511.21064v2 Announce Type: replace-cross Abstract: Open-Vocabulary Object Detection (OVOD) aims to enable detectors to generalize across categories by leveraging semantic information. Although
arXiv:2603.01332v2 Announce Type: replace Abstract: Multispectral demosaicing is crucial to reconstruct full-resolution spectral images from snapshot mosaiced measurements, enabling real-time imaging
arXiv:2604.17543v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable success in general-domain tasks, yet their direct application to the legal domain remains challeng
arXiv:2604.18575v1 Announce Type: new Abstract: Story Visualization aims to generate a sequence of images that faithfully depicts a textual narrative that preserve character identity, spatial configur
arXiv:2604.16677v1 Announce Type: new Abstract: Vision-language-action (VLA) models have emerged as generalist robotic controllers capable of mapping visual observations and natural language instructi
arXiv:2604.16775v1 Announce Type: new Abstract: Every prediction from a generative medical event model is bounded by how clinical events are tokenized, yet input representation is rarely isolated from
arXiv:2510.23116v3 Announce Type: replace Abstract: Diffusion bridge models establish probabilistic paths between arbitrary paired distributions and exhibit great potential for universal image restora
arXiv:2604.16424v1 Announce Type: cross Abstract: State-Space Models (SSMs) -- structured SSMs (S4, S4D, DSS, S5), selective SSMs (Mamba, Mamba-2), and hybrid architectures (Jamba) -- are deployed in
arXiv:2601.21159v2 Announce Type: replace Abstract: High-resolution remote sensing images contain densely distributed objects with pronounced scale variations and complex boundaries, which impose high
arXiv:2604.17287v1 Announce Type: new Abstract: Copy-move forgery, where a region within an image is duplicated to hide or fabricate content, remains a persistent threat to visual media integrity. We
arXiv:2604.17880v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have achieved great success on general robotic tasks, but still face challenges in fine-grained spatiotemporal man
arXiv:2604.18557v1 Announce Type: new Abstract: Controllable cooperative humanoid manipulation is a fundamental yet challenging problem for embodied intelligence, due to severe data scarcity, complexi
arXiv:2604.16926v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet
arXiv:2604.18518v1 Announce Type: new Abstract: Uniform Discrete Diffusion Model (UDM) has recently emerged as a promising paradigm for discrete generative modeling; however, its integration with rein
arXiv:2506.03157v4 Announce Type: replace-cross Abstract: Molecular Dynamics (MD) simulations are essential for understanding the atomic-level behavior of molecular systems, giving insights into their
arXiv:2604.17623v1 Announce Type: new Abstract: Kinematic rigs provide a structured interface for articulating 3D meshes, but they lack an inherent representation of the plausible manifold of joint co
arXiv:2604.16841v1 Announce Type: new Abstract: Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observation
arXiv:2604.17040v1 Announce Type: new Abstract: Spiking neural operators are appealing for neuromorphic edge computing because event-driven substrates can, in principle, translate sparse activity into
arXiv:2604.18484v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from complex
arXiv:2604.15631v1 Announce Type: new Abstract: VVI-ReID is a critical technique for all-day surveillance, where temporal information provides additional cues beyond static images. However, existing a
arXiv:2510.07774v3 Announce Type: replace Abstract: In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning abil
arXiv:2603.01098v2 Announce Type: replace-cross Abstract: Differential privacy (DP)'s effect in medical imaging is typically evaluated only through end-to-end performance, leaving the mechanism of pri
arXiv:2510.09033v3 Announce Type: replace Abstract: Recent work suggests that LLMs 'know what they don't know', positing that hallucinated and factually correct outputs arise from distinct internal pr
Earlier this year Yann LeCun left Meta because Mark Zuckerberg wouldn't bet the company on JEPA. Last week his group dropped the first JEPA that actually trains end-to-end from raw pixels. 15 million
arXiv:2604.16243v1 Announce Type: new Abstract: Reinforcement learning has advanced video reasoning in large multi-modal models, yet dominant pipelines either rely on on-policy self-exploration, which
arXiv:2506.19840v2 Announce Type: replace Abstract: Large-scale pre-trained video diffusion models have exhibited remarkable capabilities in diverse video generation. However, existing solutions face
arXiv:2604.15979v1 Announce Type: new Abstract: Gait recognition has emerged as a powerful biometric technique for identifying individuals at a distance without requiring user cooperation. Most existi
arXiv:2604.15694v1 Announce Type: new Abstract: Discrete diffusion models based on continuous-time Markov chains (CTMCs) have shown strong performance on language and discrete data generation, yet exi
arXiv:2604.16220v1 Announce Type: new Abstract: In computer vision and machine learning for geographic data, out-of-domain generalization is a pervasive challenge, arising from uneven global data cove
arXiv:2509.25300v4 Announce Type: replace-cross Abstract: While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement lear
arXiv:2604.16197v1 Announce Type: new Abstract: Data attribution and valuation are critical for understanding data-model synergy for Large Language Models (LLMs), yet existing gradient-based methods s
arXiv:2602.05638v3 Announce Type: replace Abstract: While foundation models have advanced surgical video analysis, current approaches rely predominantly on pixel-level reconstruction objectives that w
arXiv:2604.15542v1 Announce Type: new Abstract: Tristructural isotropic (TRISO)-coated particle fuels undergo dimensional changes and chemical reactions during high-temperature neutron irradiation. Po
arXiv:2604.16177v1 Announce Type: new Abstract: We present a three-stage progressive shadow-removal pipeline for the CVPR2026 NTIRE WSRD+ challenge. Built on OmniSR, our method treats deshadowing as i
arXiv:2604.15096v1 Announce Type: new Abstract: Echocardiography is a widely used modality for cardiac assessment due to its non-invasive and cost-effective nature, but the sparse and heterogeneous sp
arXiv:2604.14914v1 Announce Type: new Abstract: Text-driven inversion of generative models is a core paradigm for manipulating 2D or 3D content, unlocking numerous applications such as text-based edit
arXiv:2604.15166v1 Announce Type: new Abstract: Machine unlearning aims to remove targeted knowledge from a trained model without the cost of retraining from scratch. In class unlearning, however, red
arXiv:2604.14506v1 Announce Type: new Abstract: Masked image modeling (MIM) is a highly effective self-supervised learning (SSL) approach to extract useful feature representations from unannotated dat
arXiv:2604.14489v1 Announce Type: new Abstract: Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision. Neural approaches achieve strong performance but req
arXiv:2512.08230v2 Announce Type: replace Abstract: Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have pro
arXiv:2604.14267v1 Announce Type: new Abstract: Search agents extend Large Language Models (LLMs) beyond static parametric knowledge by enabling access to up-to-date and long-tail information unavaila
arXiv:2604.15284v1 Announce Type: new Abstract: The efficient spatial allocation of primitives serves as the foundation of 3D Gaussian Splatting, as it directly dictates the synergy between representa
arXiv:2604.15141v1 Announce Type: new Abstract: Higher-order learning is fundamentally rooted in exploiting compositional features. It clearly hinges on enriching the representation by more elaborate
arXiv:2604.14717v1 Announce Type: cross Abstract: Persistent language-model agents increasingly combine tool use, tiered memory, reflective prompting, and runtime adaptation. In such systems, behavior
arXiv:2604.14206v1 Announce Type: new Abstract: This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty. We construct
arXiv:2604.14547v1 Announce Type: new Abstract: Objective: Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Early prediction of P
arXiv:2604.13392v1 Announce Type: new Abstract: Tabular data remains prevalent in high-stakes domains such as healthcare and finance, where predictive models are expected to provide both high accuracy
arXiv:2604.14616v1 Announce Type: new Abstract: Clinical value set authoring -- the task of identifying all codes in a standardized vocabulary that define a clinical concept -- is a recurring bottlene
arXiv:2604.14973v1 Announce Type: cross Abstract: A vision foundation model outputs an embedding vector for an image, which can be affected by common editing operations (e.g., JPEG compression, bright
arXiv:2604.15271v1 Announce Type: new Abstract: Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decisio
arXiv:2604.14339v1 Announce Type: new Abstract: Large language models (LLMs) increasingly operate in settings that require reliable long-context understanding, such as retrieval-augmented generation a
arXiv:2604.15310v1 Announce Type: new Abstract: This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We
arXiv:2603.20997v2 Announce Type: replace Abstract: We identify a routing paradox in hybrid sequence models: content-based routing - deciding which tokens deserve expensive attention - requires pairwi
arXiv:2604.05672v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have emerged as a powerful paradigm for open-world robot manipulation, but their practical deployment is often c
arXiv:2604.06168v2 Announce Type: replace Abstract: World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model t
arXiv:2604.13313v1 Announce Type: new Abstract: Vision-Language Models demonstrate remarkable capabilities but often struggle with compositional reasoning, exhibiting vulnerabilities regarding word or
arXiv:2406.12632v3 Announce Type: replace-cross Abstract: Positron emission tomography (PET) provides molecular biomarkers for Alzheimer's disease and related dementias (ADRD) and is increasingly used
arXiv:2604.13307v1 Announce Type: new Abstract: An unsupervised framework for hyperspectral image (HSI) clustering is proposed that incorporates masked deep representation learning with diffusion-base
arXiv:2604.13841v1 Announce Type: new Abstract: Text-conditioned image editing has greatly benefitted from the advancements in Image Diffusion Models. However, extending these techniques to facial vid