Knowledge catalogue
arXiv:2608.01845v1 Announce Type: cross Abstract: Diffusion world models generate high-quality futures, but re- peated transformer evaluations make inference prohibitively slow. Existing caches reuse
arXiv:2510.10726v2 Announce Type: replace Abstract: We present WorldMirror, a unified feed-forward model for comprehensive 3D geometric prediction tasks. Unlike existing methods constrained to image-o
arXiv:2608.00935v1 Announce Type: new Abstract: Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning. International Classification of Diseases (ICD) cod
arXiv:2608.00544v1 Announce Type: new Abstract: Identifying the ``Inflamed'' immunophenotype in Gastric Adenocarcinoma predicts immunotherapy response but requires an expensive 10-gene RNA signature.
arXiv:2607.19335v2 Announce Type: replace-cross Abstract: Controlling the Lipschitz constant of a neural network is a standard way to promote robustness and stability. Most existing constraining strat
arXiv:2607.29077v1 Announce Type: new Abstract: Counterfactual explanations (CEs) enhance the interpretability of machine learning models by identifying the smallest change to an input required to obt
arXiv:2509.25778v3 Announce Type: replace Abstract: We presents a method for constructing neural networks intrinsically on statistical manifolds via the lognormal distribution. We demonstrate this app
arXiv:2607.08918v2 Announce Type: replace Abstract: Cyber-physical power systems are vulnerable to cascading failures caused by interdependencies between power and communication infrastructures. Becau
arXiv:2607.29530v1 Announce Type: new Abstract: Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale.
arXiv:2510.24598v2 Announce Type: replace Abstract: Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability. To address this,
arXiv:2607.28902v1 Announce Type: new Abstract: We develop a parallel framework that assembles static gradient methods to achieve better adaptivity. A static gradient method, denoted by GD(x_0,T), tak
Advanced reasoning and problem-solving, plus strong performance in Japanese language and Japan-specific context. Namazu is live on Merge Gateway now! https://x.com/SakanaAILabs/status/2084276852143919
arXiv:2607.29400v1 Announce Type: new Abstract: A routing decision can be revised at the next transaction, but a latched source exclusion persists across later decisions. We ask what evidence should a
All Chinese AI company media: AI will be a wondrous and wonderful and give you back precious time in your life to do things you love. All US AI company media: AI will take all your jobs, eat your chil
arXiv:2607.29343v1 Announce Type: new Abstract: Artificial intelligence is increasingly embedded in everyday software, making its integration into mobile apps inevitable. However, AI mobile app develo
arXiv:2505.20255v3 Announce Type: replace Abstract: Recent advances in video diffusion models have substantially enhanced character animation techniques. However, existing methods primarily depend on
arXiv:2607.09196v2 Announce Type: replace Abstract: Designing balanced adversaries is a central but labor-intensive task in tabletop role-playing game (TTRPG) development. In systems such as Pathfinde
arXiv:2607.29393v1 Announce Type: new Abstract: Underwater robots combine complementary sensors whose reliability changes abruptly with water visibility, viewpoint, and vehicle motion. We introduce Aq
arXiv:2607.28950v1 Announce Type: new Abstract: This paper proposes an automated classification method of chest CT volumes based on likelihood of COVID-19 cases. Novel coronavirus disease 2019 (COVID-
arXiv:2607.29625v1 Announce Type: new Abstract: The demand for humanoid loco-manipulation tasks with an object has recently increased, and most existing control approaches for stability in such tasks
arXiv:2607.11124v2 Announce Type: replace-cross Abstract: Music creation is fundamentally a process of revision. Yet symbolic music generation remains dominated by paradigms that produce complete sequ
arXiv:2307.10524v3 Announce Type: replace Abstract: We study the tradeoff between consistency and robustness in the context of a single-trajectory time-varying Markov Decision Process (MDP) with untru
arXiv:2607.29440v1 Announce Type: new Abstract: Long-term multimodal memory must support not only retrieving relevant information but also computing over observations accumulated across interactions.
arXiv:2607.28966v1 Announce Type: new Abstract: Large language models often improve task performance by generating long reasoning traces, but the resulting computation is frequently wasted on redundan
arXiv:2607.29310v1 Announce Type: new Abstract: Ambivalence and hesitancy (A/H) are subtle behavioural states that may be expressed through language, voice, facial activity, and other non-verbal cues.
arXiv:2607.28796v1 Announce Type: new Abstract: High-throughput phenotyping requires AI-enabled computer vision models that generalize across genotypes, locations, and growing seasons, yet such models
arXiv:2607.28864v1 Announce Type: cross Abstract: Tree-based diffusion models fit flexible conditional predictive distributions for tabular regression without a neural density estimator, but they inhe
arXiv:2607.29553v1 Announce Type: new Abstract: Organizations increasingly define operational metrics in structured, machine-readable formats to monitor systems, processes, and compliance. These metri
arXiv:2607.29237v1 Announce Type: new Abstract: LiDAR scene flow estimation has settled into a monoculture: nearly all recent methods share the same feed-forward architecture and the same family of se
arXiv:2607.29355v1 Announce Type: cross Abstract: Cross-lingual transfer is central to low-resource machine translation, but its behavior within closely related language families remains insufficientl
arXiv:2607.29365v1 Announce Type: new Abstract: Graph Domain Adaptation (GDA) transfers predictive knowledge from labeled source graphs to unlabeled target graphs under distribution shift. Existing me
arXiv:2607.29009v1 Announce Type: new Abstract: Multi-robot systems, particularly heterogeneous robot swarms, can improve the efficiency of complex task execution through parallel collaboration and co
arXiv:2607.28707v1 Announce Type: new Abstract: Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test t
arXiv:2607.29394v1 Announce Type: cross Abstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast ag
arXiv:2607.29657v1 Announce Type: new Abstract: Fault detection and diagnosis (FDD) technology is essential for improving HVAC system reliability, energy efficiency, and maintenance effectiveness. How
arXiv:2607.29687v1 Announce Type: new Abstract: Sequential robot manipulation requires policies to execute novel combinations of familiar instruction components. However, collecting demonstrations for
arXiv:2606.13303v2 Announce Type: replace Abstract: Recent diffusion editors perform diverse instruction-based edits while conditioning on the source image at every denoising step. Yet persistent sour
arXiv:2511.02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO). However, most existing HPO methods only incorporate exper
arXiv:2607.28959v1 Announce Type: cross Abstract: Adversarial training is one of the most effective defenses against adversarial attacks, yet the computational cost remains prohibitive at modern scale
arXiv:2512.17303v2 Announce Type: replace Abstract: In diffusion and flow-matching generative models, guidance techniques are widely used to improve sample quality and consistency. Classifier-free gui
arXiv:2607.28659v1 Announce Type: new Abstract: Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains. Recentl
arXiv:2602.15751v2 Announce Type: replace-cross Abstract: This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which coul
arXiv:2510.12953v4 Announce Type: replace-cross Abstract: Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection. However, most are ada
arXiv:2603.24917v3 Announce Type: replace Abstract: Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across seque
arXiv:2607.29058v1 Announce Type: new Abstract: Professional-document review is a constraint-checking problem in which decisions depend on relations among text, geometry, pages, and document revisions
arXiv:2607.29484v1 Announce Type: new Abstract: Interventional data is widely regarded as the gold standard for teaching models causal reasoning. We test this assumption in a fully controlled syntheti
arXiv:2607.29642v1 Announce Type: new Abstract: The iterated learning model was introduced to investigate language evolution: the way in which the characteristic properties of human languages have bee
arXiv:2607.29581v1 Announce Type: new Abstract: A marketplace review photograph is a document: platforms approve refunds on it, and generative models drove the cost of forging one to zero. We study th
arXiv:2607.29419v1 Announce Type: cross Abstract: In reinforcement learning, exploration with sparse and delayed rewards presents a significant challenge due to the limited feedback available for guid
arXiv:2607.29383v1 Announce Type: new Abstract: In recent years, with the development of big data technology, increasingly more companies use HDFS for data processing and storage. As a result, the mai
arXiv:2607.28762v1 Announce Type: new Abstract: This work embeds feature interaction modules derived from factorization machines (FMs) into physics-informed neural networks (PINNs) and neural operator
arXiv:2607.29284v1 Announce Type: new Abstract: 4D Gaussian Splatting (4DGS) can render dynamic scenes photorealistically. However, with limited viewpoint coverage, some spatiotemporal regions remain
arXiv:2601.22146v3 Announce Type: replace Abstract: Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised 'predict the next word' object
arXiv:2607.28834v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) is evolving from one-time reconstruction into deliverable, inspectable, and maintainable visual assets. Existing workflows
arXiv:2607.29106v1 Announce Type: new Abstract: Reconstructing animatable 3D human avatars from a single image remains particularly challenging for loose garments, whose geometry and motion cannot be
arXiv:2607.28671v1 Announce Type: cross Abstract: Accurate fracture risk prediction is important for osteoporosis management, but commonly used clinical tools may not fully use information available i
arXiv:2607.29665v1 Announce Type: new Abstract: PDE discovery from sparse observations requires reconstructing a continuous field and selecting the correct differential terms. Our analysis of optimiza
arXiv:2607.29019v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, but existing pipelines typically operate on p
arXiv:2504.19683v4 Announce Type: replace Abstract: Fine-grained robotic manipulation often fails when inaccurate initial grasps propagate errors and necessitate complex pose correction. We propose Gr
arXiv:2607.28667v1 Announce Type: cross Abstract: Recent work has shown that classifying large language models (LLMs)' responses can be distinguished by modeling token embeddings as trajectories of a