Knowledge catalogue
arXiv:2606.01954v1 Announce Type: new Abstract: Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space m
arXiv:2606.00620v1 Announce Type: new Abstract: Recent Large Multimodal Models (LMMs), primarily designed for offline settings, are ill-suited for the dynamic requirements of streaming video. While re
arXiv:2606.01352v1 Announce Type: new Abstract: Watch time has emerged as a pivotal metric for optimizing deep user engagement in short-video recommender systems. However, current methods of watch tim
arXiv:2606.02365v1 Announce Type: cross Abstract: Shampoo is attracting considerable attention for its superior performance on large-scale optimization benchmarks; yet it faces a significant practical
arXiv:2606.02090v1 Announce Type: new Abstract: Diffusion transformer (DiT) has been widely adopted in the generative diffusion field, advancing the denoising of query tokens through attention and Fee
arXiv:2602.03203v2 Announce Type: replace Abstract: Recently, large language models (LLMs) have shown remarkable reasoning abilities by producing long reasoning traces. However, as the sequence length
arXiv:2606.02332v1 Announce Type: new Abstract: Combining attention's global retrieval with the sequential importance signal of state space models (SSMs) is the open challenge of hybrid language model
arXiv:2606.00132v1 Announce Type: cross Abstract: While finetuning effectively adapts foundation models to specialized downstream tasks, it can degrade nontarget capabilities acquired during pretraini
arXiv:2602.03766v2 Announce Type: replace Abstract: Human vision is foveated, with variable resolution peaking at the center of a large field of view; this reflects an efficient trade-off for active s
arXiv:2606.00634v1 Announce Type: new Abstract: This article evaluates the integration of data extracted from a French syntactic lexicon, the Lexicon-Grammar (Gross, 1994), into a probabilistic parser
arXiv:2606.00728v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in long-term interactions with users, empathy has become an increasingly important capability.
arXiv:2606.02268v1 Announce Type: new Abstract: Geometric analysis fundamentally distinguishes between extit{extrinsic} and extit{intrinsic} perspectives. The dominant paradigm in current 3D represent
arXiv:2603.00021v2 Announce Type: replace Abstract: Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-rang
arXiv:2606.02559v1 Announce Type: cross Abstract: Post-training compression of Large Language Models (LLMs) removes entire architectural components, either deleting them or replacing them with fitted
arXiv:2602.11453v2 Announce Type: replace-cross Abstract: In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approac
arXiv:2606.01374v1 Announce Type: new Abstract: Observable performance is commonly used to characterize biological systems. In adaptive systems, however, similar performances may arise from distinct o
arXiv:2606.01123v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback. Existing offline
arXiv:2602.03018v2 Announce Type: replace Abstract: Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes al
arXiv:2606.02575v1 Announce Type: new Abstract: Autoregressive world models have emerged as a powerful paradigm for interactive video generation, allowing users to navigate dynamically generated envir
arXiv:2509.04631v2 Announce Type: replace Abstract: Transductive conformal prediction addresses the simultaneous prediction for multiple data points. Given a desired confidence level, the objective is
arXiv:2605.13136v2 Announce Type: replace Abstract: Distilling multi-step reasoning abilities from large language models (LLMs) into compact student models remains challenging due to noisy rationales,
arXiv:2606.02424v1 Announce Type: cross Abstract: Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images
arXiv:2506.11903v5 Announce Type: replace Abstract: Advances in transformer-based language models have highlighted the benefits of language-specific pre-training on high-quality corpora. In this conte
arXiv:2606.00110v1 Announce Type: new Abstract: Achieving robust generalization from limited data is a central challenge in embodied intelligence. Prevailing methods fail by regressing absolute coordi
arXiv:2606.01981v1 Announce Type: new Abstract: Vehicle re-identification focuses on retrieving images of the same vehicle from a gallery given a query image. Upon closer inspection of commonly used d
arXiv:2510.06028v3 Announce Type: replace Abstract: This paper provides data-dependent bounds on the expected error of the Gibbs algorithm in the overparameterized interpolation regime, where low trai
arXiv:2511.01064v3 Announce Type: replace-cross Abstract: Variational inference (VI) approximates a target density p by the best match q in a family of tractable distributions. The best variational ap
arXiv:2606.00514v1 Announce Type: cross Abstract: Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distribu
arXiv:2510.12049v4 Announce Type: replace-cross Abstract: We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomize
arXiv:2606.00248v1 Announce Type: new Abstract: Vector Symbolic Algebras (VSAs) enable robust neurosymbolic reasoning by encoding symbolic information into high-dimensional distributed representations
arXiv:2606.00139v1 Announce Type: cross Abstract: Curvature-penalized geodesic models have proven their effectiveness in image segmentation by computing globally optimal curves. Unfortunately, these m
arXiv:2606.00140v1 Announce Type: cross Abstract: While the rapid adoption of multimodal generative models offers immense potential, it has also increased the risks of harmful content synthesis, deepf
arXiv:2606.02248v1 Announce Type: new Abstract: Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive,
arXiv:2606.00447v1 Announce Type: cross Abstract: Open-vocabulary 3D scene segmentation usually assumes RGB-D video, calibrated multi-view imagery, or a reconstructed mesh. GeoSAM-3D studies a lighter
arXiv:2606.01560v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges
arXiv:2606.01487v1 Announce Type: cross Abstract: In this article, we establish the global convergence properties of the FilterDDP algorithm, which extends the discrete-time differential dynamic progr
arXiv:2505.10882v2 Announce Type: replace Abstract: Principal component analysis classically requires full d-dimensional samples, yet in various applications hardware limits acquisition to a few scala
arXiv:2602.03282v2 Announce Type: replace-cross Abstract: A common assumption in representation learning is that globally well-distributed embeddings support robust and generalizable representations.
arXiv:2606.02498v1 Announce Type: new Abstract: This study introduces an automated deep learning framework for predicting brain injury (BI) in preterm infants from T2-weighted MRI (dHCP dataset). We p
arXiv:2012.02110v2 Announce Type: replace Abstract: Pre-trained language models have significantly advanced natural language processing (NLP), especially with the introduction of BERT and its optimize
arXiv:2606.01412v1 Announce Type: new Abstract: Post-training quantization is widely used for compressing large neural networks, but aggressive low-bit quantization can significantly degrade model qua
arXiv:2504.17471v2 Announce Type: replace-cross Abstract: Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighborin
arXiv:2508.06588v3 Announce Type: replace-cross Abstract: Vector Quantization (VQ) has recently emerged as a promising approach for learning compressed and discrete representations for graph-structure
arXiv:2606.00716v1 Announce Type: new Abstract: Inference and control in engineered physical systems pay a heavy physics cost at deployment: state estimators, inverse-problem solvers, model-predictive
arXiv:2606.00432v1 Announce Type: new Abstract: As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence. Large lang
arXiv:2601.22651v2 Announce Type: replace-cross Abstract: Training-data attribution for vision generative models aims to identify which training data influenced a given output. While most methods scor
arXiv:2606.02331v1 Announce Type: new Abstract: Diffusion-based inverse problem solvers can produce realistic reconstructions, but realism alone does not ensure that the recovered details are supporte
arXiv:2606.00349v1 Announce Type: cross Abstract: Reconstructing spatiotemporal fields from partial observations is fundamental to scientific inference, from inferring atmospheric states from satellit
arXiv:2606.00751v1 Announce Type: new Abstract: Visual Speech Recognition (VSR) aims to recognize speech from visual cues such as lip movements, but its performance is fundamentally limited by viseme
arXiv:2606.00642v1 Announce Type: new Abstract: Reasoning traces have become a valuable form of learning signals for improving and transferring the capabilities of large language models. In particular
arXiv:2502.15411v4 Announce Type: replace-cross Abstract: Accurate tagging of earnings reports can yield significant short-term returns for stakeholders. The machine-readable inline eXtensible Busines
arXiv:2601.13574v2 Announce Type: replace Abstract: Reconstructing the three-dimensional (3D) geometry of object surfaces is essential for robot perception, yet vision-based approaches degrade under l
arXiv:2606.00352v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become the standard for real-time novel view synthesis on commodity GPUs. Its pipeline ties spatial partitioning and ra
arXiv:2606.00082v1 Announce Type: cross Abstract: Explainability of deep learning algorithms is critical for computer-vision applications with high-stake decisions. Concept bottleneck models (CBM) hav
arXiv:2606.02379v1 Announce Type: new Abstract: Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from
arXiv:2602.13906v2 Announce Type: replace-cross Abstract: Stochastic approximation (SA) is a method for finding the root of an operator perturbed by noise. The focus of this paper is studying the dist
arXiv:2606.01107v1 Announce Type: cross Abstract: We study fitting problems, sometimes called ``training problems'', where we have a finite sample consisting of inputs and outputs, and we want to know
arXiv:2606.00356v1 Announce Type: new Abstract: Sparse autoencoder (SAE) features are increasingly used to interpret language models, with auto-generated natural-language labels serving as the primary
arXiv:2606.00371v1 Announce Type: new Abstract: Muon optimizers improve neural-network training by replacing ill-conditioned momentum updates with approximately semi-orthogonal updates. This motivates
arXiv:2606.00635v1 Announce Type: new Abstract: Modern VAEs are rarely trained with the pointwise likelihood implied by the standard eta-VAE objective. In practice, pointwise reconstruction is often c