Knowledge catalogue
arXiv:2604.08598v1 Announce Type: cross Abstract: Text-based person search faces inherent limitations due to data scarcity, driven by stringent privacy constraints and the high cost of manual annotati
arXiv:2610.19743v1 Announce Type: new Abstract: Discriminative models are trained with task-specific objectives such as classification, detection, or representation learning, yet their practical utili
arXiv:2610.19492v1 Announce Type: new Abstract: Streaming Inverse Text Normalization (ITN) is vital for converting spoken-form outputs from streaming Automatic Speech Recognition into formatted writte
arXiv:2506.17585v4 Announce Type: replace-cross Abstract: Large language models can answer questions correctly and still hallucinate their citations. Most citation systems address this by retrieving d
arXiv:2609.37116v1 Announce Type: cross Abstract: Accurate perceptual quality assessment is essential for evaluating and optimizing spatial audio, where perceived quality depends on both signal fideli
arXiv:2609.36624v1 Announce Type: new Abstract: Joint speech-gesture synthesis must coordinate two modalities despite limited paired data. Existing approaches often lack bidirectional interaction, hav
arXiv:2609.33586v1 Announce Type: new Abstract: On NVIDIA Blackwell B200, tensor-core throughput outpaces special-function exponential throughput by more than two orders of magnitude, exposing exponen
arXiv:2609.23574v1 Announce Type: cross Abstract: In high-dimensional tabular learning, feature screening provides a lightweight, model-agnostic way to remove irrelevant features before model fitting.
arXiv:2609.22607v1 Announce Type: new Abstract: We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas, i
arXiv:2506.08641v3 Announce Type: replace-cross Abstract: Adapting vision models for time series analysis is compelling, yet all existing approaches are falling short of dedicated time series foundati
arXiv:2609.22606v1 Announce Type: new Abstract: Vision-Tactile-Language-Action (VTLA) models have demonstrated clear advantages over Vision-Language-Action (VLA) models in contact-rich manipulation. H
arXiv:2510.02538v2 Announce Type: replace Abstract: We are interested in solving the problem of imitation learning with a limited amount of real-world expert data. Existing offline imitation methods o
arXiv:2609.22184v1 Announce Type: new Abstract: Drug-target relation prediction supports candidate screening, drug repositioning, and mechanism analysis. Existing models often use incomplete drug or p
arXiv:2609.23463v1 Announce Type: new Abstract: Federated learning lets multiple parties train a shared model without pooling their data, but a client with far less data than the others can end up poo
arXiv:2609.14926v1 Announce Type: cross Abstract: We propose a linearized Physics-Informed Neural Network (lPINN), a reduced-order neural basis method for forward and inverse differential equations. I
arXiv:2608.24115v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) can integrate long visual histories, reason under partial observability, and infer behavior from a few exampl
arXiv:2508.21041v4 Announce Type: replace-cross Abstract: Atypical mitotic figures (AMFs) indicate abnormal cell division associated with poor prognosis. Their detection remains difficult due to low p
arXiv:2608.08749v1 Announce Type: new Abstract: Long-horizon robot manipulation requires policies to track completed subtasks and critical interaction events. However, existing memory mechanisms heavi
arXiv:2608.02662v1 Announce Type: cross Abstract: Reliable forecasting of nonlinear physical systems underpins scientific discovery and engineering decision-making. Yet high-fidelity simulations are p
arXiv:2607.27919v1 Announce Type: new Abstract: Decoder-only language models entangle long-term memory and reasoning in a single parameter set, making it difficult to scale memory capacity independent
arXiv:2607.13192v1 Announce Type: new Abstract: Self-supervision is a powerful technique for learning visual representations from unlabeled data. Existing techniques primarily adopt a two-stage approa
arXiv:2607.00249v1 Announce Type: new Abstract: New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a p
arXiv:2607.00777v1 Announce Type: cross Abstract: Recognizing jazz standards from audio is a challenging form of tune-level music retrieval: different performances of the same standard may vary in tem
arXiv:2509.18068v2 Announce Type: replace Abstract: Millimeter-wave radar provides robust perception in fog, smoke, dust, and low light, making it attractive for size-, weight-, and power-constrained
arXiv:2510.17700v2 Announce Type: replace Abstract: Vision foundation models achieve remarkable performance but are only available in a limited set of pre-determined sizes, forcing sub-optimal deploym
arXiv:2605.31173v1 Announce Type: cross Abstract: Reconstructing continuous speech from non-invasive neural recordings is a fundamental problem for probing human auditory perception and building safe,
arXiv:2603.26885v2 Announce Type: replace Abstract: Convolutional neural networks (CNNs) achieve state-of-the-art performance in medical image analysis yet remain opaque, limiting adoption in high-sta
arXiv:2605.30877v1 Announce Type: new Abstract: Large-scale Vision-Language-Action (VLA) pretraining is increasingly adopted as the foundation for robot policies, yet the evidence for pretrained VLAs
arXiv:2605.25352v1 Announce Type: cross Abstract: Deep learning models are vulnerable to adversarial perturbations, raising important concerns for safety-critical deployment. Empirical defenses can ac
arXiv:2605.20267v1 Announce Type: new Abstract: Synthetic PET images are valuable for quantitative imaging workflow development, scalable virtual imaging trials, and deep learning model training, but
arXiv:2605.19568v1 Announce Type: new Abstract: Embedding models are pivotal in industrial information retrieval systems like search and advertising. However, existing pretrained models often exhibit
arXiv:2605.13010v1 Announce Type: cross Abstract: We study image inpainting with generative diffusion models. Existing methods typically either train dedicated task-specific models, or adapt a pretrai
arXiv:2605.08276v1 Announce Type: new Abstract: Cell-level dense prediction is central to computational pathology, but remains challenging due to fine-grained histological structures, strong domain sh
arXiv:2605.04291v1 Announce Type: new Abstract: We present a discrete diffusion-based language model using Glauber dynamics from statistical physics. Our main insight is that instead of trying to trai
arXiv:2604.10985v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have rapidly advanced by leveraging powerful pre-trained Large Language Models (LLMs) as core reasoning backbones. As new
arXiv:2604.11576v1 Announce Type: new Abstract: Despite their impressive zero-shot abilities, vision-language models such as CLIP have been shown to be susceptible to adversarial attacks. To enhance i
arXiv:2610.11214v1 Announce Type: cross Abstract: KV-cache quantization and linear attention are two representative approaches to tackling the storage and computational costs of Transformers. KV-cache
arXiv:2610.10984v1 Announce Type: new Abstract: We present Fluid-Gen-Zero, a training-free framework for physics-aware fluid-object interaction video generation that decouples physical reasoning from
arXiv:2606.15568v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models perform well in generalist robot manipulation but remain brittle to out-of-distribution perturbations, while ful
arXiv:2610.07028v1 Announce Type: new Abstract: Diffusion-based world models can generate and predict trajectories in high-dimensional dynamical systems, but predictive accuracy does not imply that th
arXiv:2610.05649v1 Announce Type: new Abstract: We investigate the scaling laws and compute-optimal training of physiological waveform foundation models (FMs). We train Aether, a family of over one hu
arXiv:2307.12226v3 Announce Type: replace-cross Abstract: Machine learning models -- including prominent zero-shot models -- are often trained on datasets whose labels are only a small proportion of a
arXiv:2610.01425v1 Announce Type: new Abstract: Train-validation separation is the evolving difference between performance on observed training examples and a finite held-out validation set. We propos
arXiv:2609.39784v1 Announce Type: new Abstract: Can heterogeneous physical degradation systems benefit from joint pretraining and move beyond system-specific prognostics toward reusable cross-system r
arXiv:2609.03824v1 Announce Type: new Abstract: 3D foundation models (3DFMs) excel at predicting camera poses and dense depth from multiple views of a scene, showcasing strong zero-shot generalization
arXiv:2608.30305v1 Announce Type: new Abstract: Image restoration models are typically trained with a fixed set of capabilities. When new restoration requirements emerge, existing solutions usually tr
arXiv:2608.27370v1 Announce Type: new Abstract: Language model pretraining has become almost synonymous with prohibitive cost, placing it out of reach for much of the academic and open-source communit
arXiv:2608.25410v1 Announce Type: cross Abstract: Token communications realize the semantic communication principle at the granularity of transformer tokens, providing a promising direction for client
arXiv:2608.22272v1 Announce Type: cross Abstract: Generative adversarial networks (GANs) can provide efficient image generation, while diffusion models offer high-quality image restoration but require
arXiv:2405.03728v3 Announce Type: replace-cross Abstract: Zero-shot optimization involves optimizing a target task that was not seen during training, aiming to provide the optimal solution without or
arXiv:2608.00383v1 Announce Type: cross Abstract: Logical rule induction seeks interpretable rules that transfer across propositional schemas. This requires respecting symmetries: atom naming, example
arXiv:2607.28776v1 Announce Type: new Abstract: Generative machine learning is increasingly used for inorganic crystal structure generation. Most models and the corresponding evaluation approaches rel
arXiv:2607.07885v1 Announce Type: cross Abstract: Dynamic obstacle avoidance in unstructured outdoor environments remains a critical challenge for autonomous mobile robots, particularly when large-sca
arXiv:2607.07294v1 Announce Type: cross Abstract: Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot
arXiv:2603.21210v3 Announce Type: replace Abstract: Designing urban spaces that provide pedestrian wind comfort and safety requires time-resolved Computational Fluid Dynamics (CFD) simulations, but th
arXiv:2606.27371v1 Announce Type: new Abstract: State-of-the-art flow models generate stunning images from text or image prompts. However, they suffer from diversity collapse when generating multiple
arXiv:2606.25197v1 Announce Type: new Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximat
arXiv:2606.15678v2 Announce Type: replace Abstract: A feasibility and dynamics study of the Reservoir Attention Network (RAN), an architecture that injects a fixed, randomly-initialized reservoir into
arXiv:2606.09430v1 Announce Type: cross Abstract: Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data str
arXiv:2602.16061v2 Announce Type: replace-cross Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedba