research

arXiv:2606.15667v2 Announce Type: replace Abstract: Long-term mortality rates after endovascular aneurysm repair (EVAR) remain elevated due to post-EVAR rupture caused by loss of seal in stent graft s

arXiv:2607.06587v1 Announce Type: new Abstract: We present CoFINN (Conservation Flux Informed Neural Networks), a physics-informed deep learning framework for predicting compressible flow fields gover

CoFrGeNets are a novel architecture proposed by IBM Research that replaces the core structural components ('bones') of transformer-based models with a more efficient design. This approach aims to impr

arXiv:2607.07565v1 Announce Type: cross Abstract: One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so wit

arXiv:2607.07119v1 Announce Type: new Abstract: Color transfer aims to align the color distribution of a source image with that of a reference image while preserving structural and semantic consistenc

arXiv:2607.06919v1 Announce Type: new Abstract: Artificial intelligence (AI) analysis of micro-ultrasound (muUS) has shown promise for prostate cancer (PCa) detection. However, most existing AI method

arXiv:2607.06940v1 Announce Type: cross Abstract: The remarkable performance of large language models (LLMs) in linguistic tasks underscores an urgent need for comprehensive evaluation of their respon

arXiv:2607.06893v1 Announce Type: cross Abstract: The Stochastic-Oracle Turing Machine (SOTM) framework models AI-augmented computation as the interaction of a probabilistic Turing machine with an ora

arXiv:2607.07026v1 Announce Type: new Abstract: Constrained decoding is essential for serving LLMs, ensuring that generated outputs follow specific structures such as JSON schema-formatted function ca

arXiv:2607.07209v1 Announce Type: cross Abstract: Modern federated and streaming learning systems often release intermediate models, so privacy must hold for the full trajectory under adaptive interac

arXiv:2607.07622v1 Announce Type: new Abstract: The elephant trunk is a dexterous and versatile manipulator whose performance is still unmatched in robotics. In previous works, modularity was prioriti

arXiv:2607.06887v1 Announce Type: new Abstract: Most self-supervised image clustering models, actually almost all deep learning approaches, are based on gradient descent: In order to calculate the los

arXiv:2607.07521v1 Announce Type: cross Abstract: AI agents that generate final answers based on user input often do not meet the needs of creative fields. Fields such as structural design and archite

arXiv:2606.15967v2 Announce Type: replace Abstract: Anisotropic volumetric acquisitions are common in clinical MRI and volume electron microscopy (vEM), where sparse through-plane sampling creates thi

arXiv:2607.06628v1 Announce Type: cross Abstract: Which properties of a partially trained network are causally portable to a different, independently trained network? Single-trajectory interventions s

【採用情報】Data Engineer (Product) のポジションをオープンしました🐟 https://sakana.ai/careers/data-engineer-product/ プロダクトチーム初のデータエンジニアとして、AIが自律的にデータ分析を行うためのデータ基盤をゼロから構築していただくポジションです。 ・データパイプラインの構築・運用 ・プロダクトおよび ビジネス指標のデータ

arXiv:2412.03405v3 Announce Type: replace-cross Abstract: Motivated by dynamic risk measures and conditional g-expectations, in this work we propose a numerical method to approximate the solution oper

arXiv:2607.07195v1 Announce Type: new Abstract: Perceptual quality enhancement of severely compressed videos remains challenging due to complex artifact patterns and substantial information loss. Rece

arXiv:2607.06646v1 Announce Type: cross Abstract: This paper introduces Diffusion Semi-Relaxed Fused Gromov-Wasserstein (DsrFGW), a novel method for graph comparison that unifies node features and str

arXiv:2607.06681v1 Announce Type: cross Abstract: Knowledge workers switch between applications thousands of times per day, spending nearly a tenth of the work year transitioning between digital appli

arXiv:2607.07232v1 Announce Type: cross Abstract: Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models

arXiv:2607.07003v1 Announce Type: cross Abstract: Large Language Models (LLMs) frequently exhibit sycophancy, where they agree with a user's statement even when incorrect. While sycophancy is often tr

arXiv:2607.07504v1 Announce Type: new Abstract: Product data scientists often ask LLM-based agents to help with recurring execution tasks such as cleaning data, writing SQL, choosing statistical tests

arXiv:2607.07670v1 Announce Type: new Abstract: Large language models hallucinate most about entities they have never seen. We ask whether a model's activations betray entity familiarity before a sing

arXiv:2603.19742v2 Announce Type: replace-cross Abstract: Understanding the internal mechanisms of transformer-based large language models (LLMs) is crucial for their reliable deployment and effective

arXiv:2508.21556v3 Announce Type: replace Abstract: Modeling human-object interactions (HOI) from an egocentric perspective is a critical yet challenging task, particularly when relying on sparse sign

arXiv:2607.06982v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of

arXiv:2607.06776v1 Announce Type: new Abstract: We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive pre

arXiv:2607.06651v1 Announce Type: new Abstract: Federated learning (FL) over mobile and edge devices increasingly involves multimodal models in which clients differ in both sensing capability and comp

arXiv:2607.07302v1 Announce Type: new Abstract: This paper reports an empirical study evaluating the relevance of several RAG metrics. The experiment is based on a question-answering dataset created b

arXiv:2603.09385v2 Announce Type: replace Abstract: Event cameras offer superior sensitivity to high-speed motion and extreme lighting, making event-based monocular depth estimation a promising approa

arXiv:2607.06644v1 Announce Type: cross Abstract: Determinantal point processes have recently emerged as a kernel-based alternative to standard independent sampling for constructing efficient minibatc

arXiv:2407.11217v4 Announce Type: replace-cross Abstract: Clustering problems such as k-means and k-median are staples of unsupervised learning, and many algorithmic techniques have been developed to

arXiv:2607.06648v1 Announce Type: cross Abstract: Latent reasoning methods perform multi-step inference entirely in the model's continuous hidden states, promising more compact and efficient reasoning

arXiv:2607.06883v1 Announce Type: cross Abstract: We consider the problem of finding stationary points for stochastic convex optimization problems. Rather than surrogates to stationarity, such as a pr

arXiv:2607.06621v1 Announce Type: new Abstract: The pre-softmax score of an attention head is a bilinear form score(i,j) = x_i^T M x_j in a learned operator M = W_q^T W_k. Because M is generally non-s

I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump ad

arXiv:2607.07478v1 Announce Type: cross Abstract: FFT-based spectral preprocessing of learned query-key (Q/K) projections substantially improves transformer attention on character-level language model

arXiv:2506.04985v2 Announce Type: replace Abstract: Large language models (LLMs) require substantial compute, and thus energy, at inference time. While quantizing weights and activations is effective

arXiv:2607.07144v1 Announce Type: new Abstract: The key-value (KV) cache dominates the memory cost of long-context autoregressive inference, and a growing body of work compresses it through quantizati

arXiv:2607.07039v1 Announce Type: cross Abstract: Mobile C-arm cone-beam computed tomography (CBCT) has been widely used for real-time intraoperative 3D imaging. However, current practice often mechan

arXiv:2607.06782v1 Announce Type: cross Abstract: Global localization from 3D point clouds remains challenging under limited or asymmetric fields of view (FOV), which fail to provide the dense, symmet

arXiv:2607.07032v1 Announce Type: new Abstract: Spectral positional encodings (PEs) for directed graphs face two obstacles: magnetic Laplacians require an O(n^3) Hermitian eigendecomposition per poten

arXiv:2607.07533v1 Announce Type: new Abstract: Personalizing exoskeleton assistance requires user-specific gait data across many locomotor tasks, yet collecting this data demands repeated motion capt

arXiv:2607.06871v1 Announce Type: new Abstract: Recent progress in large-scale self-supervised learning has improved dense geometric prediction, but it remains unclear whether such scaling yields infe

arXiv:2512.05044v2 Announce Type: replace Abstract: Generating interactive and dynamic 4D scenes from a single static image remains a core challenge. Most existing generate-then-reconstruct and recons