Knowledge catalogue
arXiv:2508.07101v2 Announce Type: replace Abstract: Large reasoning models achieve strong performance through test-time scaling, but this incurs substantial computational overhead due to long decoding
arXiv:2601.03136v2 Announce Type: replace Abstract: Language plays a critical role in Vision-Language-Action (VLA) models, yet the linguistic characteristics of the datasets used to train and evaluate
arXiv:2604.24907v1 Announce Type: cross Abstract: We introduce a new family of temporal logics intended for specifications in motion planning (MP). It builds upon the signal temporal logic (STL), whic
arXiv:2604.25817v1 Announce Type: new Abstract: Magnification shift is a major obstacle to robust histopathology classification, because models trained on one imaging scale often generalize poorly to
arXiv:2604.25200v1 Announce Type: cross Abstract: Public agencies are beginning to consider large language models (LLMs) as decision-support tools for grant evaluation. This creates a practical govern
arXiv:2604.25578v1 Announce Type: new Abstract: We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only
arXiv:2604.25765v1 Announce Type: new Abstract: The quality of training data is critical to the performance of machine learning models. In this paper, the Error Sensitivity Profile (ESP) is proposed.
arXiv:2508.03941v3 Announce Type: replace-cross Abstract: The typical offline protocol to evaluate recommendation algorithms is to collect a dataset of user-item interactions and then use a part of th
arXiv:2511.20496v3 Announce Type: replace Abstract: Accurate state estimation for flexible robotic systems poses significant challenges, particularly for platforms with dynamically deforming structure
arXiv:2604.25152v1 Announce Type: cross Abstract: We present MGTEVAL, an extensible platform for systematic evaluation of Machine-Generated Text (MGT) detectors. Despite rapid progress in MGT detectio
arXiv:2604.25674v1 Announce Type: new Abstract: Modeling the emergence of human-like lexicons in computational systems has advanced through the use of interacting neural agents, which simulate both le
arXiv:2604.24880v1 Announce Type: cross Abstract: This study proposes an anomaly-detection framework for monitoring exposure-length variations in submarine free-span cables using Distributed Acoustic
arXiv:2602.05330v2 Announce Type: replace Abstract: Comprehensive panoramic scene understanding is critical for immersive applications, yet it remains challenging due to the scarcity of high-resolutio
arXiv:2602.04872v2 Announce Type: replace-cross Abstract: Recent progress has rapidly advanced our understanding of the mechanisms underlying in-context learning in modern attention-based neural netwo
arXiv:2412.07584v2 Announce Type: replace Abstract: Current video retrieval systems, especially those used in competitions, primarily focus on querying individual keyframes or images rather than encod
arXiv:2604.25819v1 Announce Type: new Abstract: In this work, we propose Mutual Forcing, a framework for fast autoregressive audio-video generation with long-horizon audio-video synchronization. Our a
arXiv:2508.18090v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable versatility across a wide range of natural language processing tasks and domains. On
arXiv:2508.18717v3 Announce Type: replace-cross Abstract: Modern multi-class image classification uses high-dimensional CNN features that incur large memory and computational costs and obscure the dat
日経クロステック @NIKKEIxTECH の連載記事「R&Dを根底から変革、普及始まる『AI科学者』」にて、Sakana AIのResearch Scientist、@RobertTLangeへの取材記事が掲載されました。 https://xtech.nikkei.com/atcl/nxt/column/18/03603/042600005/ 当社のAIサイエンティストについて、テーマ探索から論
arXiv:2412.10441v2 Announce Type: replace-cross Abstract: In the burgeoning field of medical imaging, precise computation of 3D volume holds a significant importance for subsequent qualitative analysi
arXiv:2604.25028v1 Announce Type: new Abstract: Recent work revisiting measurability in the fundamental theorem of statistical learning imposes Borel measurability of ghost-gap suprema. We show that,
arXiv:2110.12922v2 Announce Type: replace-cross Abstract: Laplace-type results characterize the limit of sequence of measures (pi_arepsilon)_{arepsilon >0} with density w.r.t the Lebesgue measure (d p
arXiv:2604.25269v1 Announce Type: new Abstract: Most work on sequential learning assumes a fixed set of actions that are available all the time. However, in practice, actions can consist of picking su
arXiv:2604.25271v1 Announce Type: cross Abstract: We consider adversarial multi-armed bandit problems where the learner is allowed to observe losses of a number of arms beside the arm that it actually
arXiv:2604.25284v1 Announce Type: new Abstract: We study cooperative shortest path planning for an unmanned ground vehicle (UGV) assisted by an unmanned aerial vehicle (UAV) in environments with unkno
arXiv:2604.25295v1 Announce Type: new Abstract: Continuous causal discovery typically couples representation learning with structural optimization via non-convex acyclicity penalties, which subjects s
arXiv:2604.25371v1 Announce Type: cross Abstract: Generating novel, biologically plausible three-dimensional morphological structures is a fundamental challenge in computational evolutionary biology,
arXiv:2602.17697v2 Announce Type: replace Abstract: Large Language Models (LLMs) are being increasingly used across a wide range of tasks. However, their substantial computational demands raise concer
arXiv:2604.25020v1 Announce Type: cross Abstract: Neural architectures trained with losses inspired by differential conditions are the basis for PINN models. Since many constructions in differential g
arXiv:2604.25599v1 Announce Type: cross Abstract: Code understanding models increasingly rely on pretrained language models (PLMs) and graph neural networks (GNNs), which capture complementary semanti
arXiv:2604.24994v1 Announce Type: cross Abstract: We introduce a differentiable 3D representation that unifies the ray tracing capabilities of foam-based ray tracing with the efficiency of modern rast
arXiv:2604.25642v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual-textual understanding, yet their reliability is critically undermined b
arXiv:2508.18473v3 Announce Type: replace Abstract: While Large Language Models (LLMs) have emerged as powerful foundational models to solve a variety of tasks, they have also been shown to be prone t
arXiv:2604.25859v1 Announce Type: new Abstract: World action models jointly predict future video and action during training, raising an open question about what role the future-prediction branch actua
arXiv:2604.25306v1 Announce Type: new Abstract: FlashAttention improves efficiency through tiling, but its online softmax still relies on floating-point arithmetic for numerical stability, making full
arXiv:2604.25137v1 Announce Type: cross Abstract: We solve the time-dependent Schrodinger equation by learning the score function, the gradient of the log-probability density, on Bohmian trajectories.
arXiv:2604.25755v1 Announce Type: cross Abstract: SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additiona
arXiv:2604.25310v1 Announce Type: new Abstract: This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from ex
arXiv:2604.24938v1 Announce Type: cross Abstract: Depth pruning improves the inference efficiency of large language models by removing Transformer blocks. Prior work has focused on importance criteria
arXiv:2506.20941v3 Announce Type: replace Abstract: Large language models are trained on massive corpora of web data, which may include private data, copyrighted material, factually inaccurate data, o
arXiv:2604.25404v1 Announce Type: new Abstract: Graph-based representations such as Scene Graphs enable localization in structured indoor environments by matching a locally observed graph, constructed
arXiv:2604.25315v1 Announce Type: new Abstract: Gradient-based saliency methods are widely used to interpret deep neural networks, yet they often produce noisy and unstable explanations that poorly al
Science is how we’ve cured diseases and how we just sent four humans around the Moon. It’s a big reason why we’re the greatest country on Earth, but firing the National Science Board shows just how mu
arXiv:2512.01510v3 Announce Type: replace Abstract: We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation, where we train a network on one domain
arXiv:2604.25766v1 Announce Type: new Abstract: This paper presents a sensitivity-based tube Nonlinear Model Predictive Control (NMPC) framework for cooperative aerial chains under bounded parametric
arXiv:2604.25065v1 Announce Type: new Abstract: Object recognition (OR) in humans relies heavily on shape cues and the ability to recognize objects across varying 3D viewpoints. Unlike humans, deep ne
arXiv:2603.26554v2 Announce Type: replace Abstract: Spectral optimizers such as Muon have recently shown strong empirical performance in large-scale language model training, but the source and extent
arXiv:2503.03426v2 Announce Type: replace Abstract: We study early-stopped mirror descent (ESMD) for high-dimensional Gaussian linear regression over arbitrary convex bodies and design matrices, where
arXiv:2604.25781v1 Announce Type: new Abstract: Articulation modeling aims to infer movable parts and their motion parameters for a 3D object, enabling interactive animation, simulation, and shape edi
arXiv:2604.25661v1 Announce Type: new Abstract: Robot-assisted Transcranial Magnetic Stimulation (Robo-TMS) is an image-guided robotic intervention that enhances the accuracy and reproducibility of co
arXiv:2604.25292v1 Announce Type: new Abstract: This paper addresses the problem of traffic congestion management in fixed-wing unmanned aerial vehicle (UAV) corridors by further developing a recently
arXiv:2512.09923v2 Announce Type: replace Abstract: Radiance field representations have recently been explored in the latent space of VAEs that are commonly used by diffusion models. This direction of
arXiv:2604.04973v2 Announce Type: replace-cross Abstract: This paper presents StrADiff, a Structured Source-Wise Adaptive Diffusion Framework for unsupervised blind source separation under linear and
arXiv:2604.25467v1 Announce Type: new Abstract: Federated learning increasingly operates in a large-model regime where communication, memory, and computation are all scarce. Typically, non-IID client
arXiv:2604.25904v1 Announce Type: new Abstract: Identity teacher forcing (ITF) enables stable training of deterministic recurrent surrogates for chaotic dynamical systems and has been highly effective
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. It’s time to make a plan for nuclear waste Today, nuclear ener
arXiv:2604.25096v1 Announce Type: new Abstract: There is growing concern that AI chatbots might fuel delusional beliefs in users. Some have suggested that humans and chatbots mutually reinforce false
arXiv:2604.25150v1 Announce Type: new Abstract: Overparameterization is central to the success of deep learning, yet the mechanisms by which it improves optimization remain incompletely understood. We
arXiv:2604.24811v1 Announce Type: new Abstract: Graph neural Ordinary Differential Equations (ODE) combine neural ODE with the message passing mechanism of Graph Neural Networks (GNN), providing a con
arXiv:2604.25545v1 Announce Type: new Abstract: Visual state-space models (SSMs) have shown strong potential for medical image segmentation, yet their effectiveness is often limited by two practical i