Knowledge catalogue
arXiv:2312.06423v3 Announce Type: replace-cross Abstract: Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid prolif
arXiv:2605.05115v1 Announce Type: new Abstract: Neural representations carry rich geometric structure; but does that structure causally shape behavior? To address this question, we intervene along pat
arXiv:2605.04873v1 Announce Type: new Abstract: Recent advances in natural language processing have enabled increasingly accurate estimation of psychological traits from language. However, most existi
arXiv:2605.04116v1 Announce Type: cross Abstract: We show that remotely hosted applications employing in-context learning when augmented with a retrieval function to select in-context examples can be
arXiv:2605.04363v1 Announce Type: new Abstract: TabPFN has recently gained attention as a foundation model for tabular datasets, achieving strong performance by leveraging in-context learning on synth
arXiv:2605.04134v1 Announce Type: new Abstract: We consider the problem of learning data-driven replicas for stiff systems of ordinary differential equations arising in chemical kinetics that can be e
arXiv:2602.02282v2 Announce Type: replace Abstract: Inferring spatial transcriptomics (ST) from histology enables scalable histogenomic profiling, yet current methods are largely restricted to single-
arXiv:2605.04731v1 Announce Type: new Abstract: Building height (BH) and building footprint (BF) jointly describe the vertical and horizontal extent of the built environment and are required inputs fo
arXiv:2605.04108v1 Announce Type: new Abstract: Federated Learning enables decentralized training by aggregating model updates across clients without sharing raw data, while Split Federated Learning f
arXiv:2505.00835v2 Announce Type: replace-cross Abstract: Appropriate modelling of extreme skew surges is crucial, particularly for coastal risk management. Our study focuses on modelling extreme skew
arXiv:2605.04589v1 Announce Type: cross Abstract: A central challenge in dynamic network analysis is to represent temporal evolution in a way that is both geometrically meaningful and statistically id
arXiv:2605.04918v1 Announce Type: cross Abstract: Strichartz inequalities are a cornerstone of the modern theory of dispersive PDEs, but their extremizers are known explicitly only in a handful of sha
arXiv:2605.04550v1 Announce Type: cross Abstract: Computing pseudospectra of non-normal matrices is essential for understanding the stability and transient behavior of dynamical systems. Such analysis
arXiv:2602.02543v3 Announce Type: replace Abstract: Sequential Locate-and-Edit (L&E) model editing can fail abruptly after many edits. We identify and formalize this failure as a positive norm-feedbac
arXiv:2605.04713v1 Announce Type: new Abstract: Engagement recognition datasets are typically subject-indexed and often contain noisy, subjective supervision, making post-hoc dataset revision a practi
This article provides firsthand observations and insights into the operations, research directions, and technical capabilities of artificial intelligence laboratories within China. It likely covers de
arXiv:2511.09537v2 Announce Type: replace Abstract: We introduce negative space learning machine translation (NSL-MT), a training method for underresourced languages, that augments limited parallel da
Yann LeCun, a prominent AI researcher and Chief AI Scientist at Meta, is scheduled to deliver a commencement address to the NYU Tandon School of Engineering Class of 2026 on May 18th at the Barclays C
arXiv:2605.04325v1 Announce Type: new Abstract: We introduce a unified theoretical framework for the rigorous analysis and systematic construction of deep neural networks (DNNs). This framework addres
arXiv:2605.04954v1 Announce Type: cross Abstract: Per-instance algorithm selection (PIAS) takes advantage of complementarity between a set of algorithms by deciding which algorithm to run on a given i
arXiv:2601.07389v2 Announce Type: replace Abstract: Post-training of large language models routinely interleaves supervised fine-tuning (SFT) with reinforcement learning (RL). These two methods have d
arXiv:2605.05118v1 Announce Type: new Abstract: Recently, Deng et al. (2026) proposed Generative Modeling via Drifting (GMD), a novel framework for generative tasks. This note presents an analysis of
arXiv:2605.04364v1 Announce Type: new Abstract: We study online prediction for marginally stable, partially observed linear dynamical systems under nonstochastic disturbances. Our objective is to mini
arXiv:2605.04270v1 Announce Type: cross Abstract: Industrial workplace challenges range from musculoskeletal disorders -- a leading cause of occupational injury -- to suboptimal workstation layouts, i
arXiv:2605.04397v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) holds great promise for continuous heart-rate monitoring of drivers in intelligent vehicles. However, its performance
arXiv:2602.12095v3 Announce Type: replace Abstract: The automation of warehouse operations is crucial for improving productivity and reducing human exposure to hazardous environments. One operation fr
arXiv:2605.04838v1 Announce Type: cross Abstract: The standard constraint-based paradigm for causal discovery with incomplete data -- impute first, test second -- is frequently miscalibrated: any cons
arXiv:2605.05163v1 Announce Type: new Abstract: Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on
arXiv:2605.04247v1 Announce Type: new Abstract: The Linear Mixing Model (LMM) dominates spectral unmixing for its simplicity, but fails under multiple scattering; existing nonlinear models compensate
arXiv:2605.05120v1 Announce Type: new Abstract: An interpretable and scalable framework for decoding driving behaviors from multimodal physiological signals is proposed in this study. We utilize multi
arXiv:2605.04127v1 Announce Type: cross Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern
arXiv:2502.12370v3 Announce Type: replace Abstract: Recent advancements in transformer-based models have greatly improved time series analysis, providing robust solutions for tasks such as forecasting
arXiv:2601.06839v2 Announce Type: replace Abstract: We present PRISM, a novel color-guided stratified sampling method for RGB-LiDAR point clouds. Our approach is motivated by the observation that uniq
arXiv:2605.04286v1 Announce Type: new Abstract: Climate classification plays a vital role in agricultural planning, hydrological studies, and climate science. One of the most widely used systems for c
arXiv:2605.03101v1 Announce Type: new Abstract: Symbolic regression (SR), the task of discovering mathematical expressions that best describe a given dataset, remains a fundamental challenge in scient
arXiv:2601.14180v3 Announce Type: replace Abstract: Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence
arXiv:2605.04844v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as an advanced technique for real-time novel view synthesis by representing scene geometry and appearance using
arXiv:2605.04267v1 Announce Type: new Abstract: Interactive multi-objective optimization systems face a budget allocation dilemma: one can spend resources on expensive objective evaluations or on elic
arXiv:2605.04523v1 Announce Type: new Abstract: We present our winning system for Task~B (generation with reference passages) in SemEval-2026 Task~8: MTRAGEval. Our method is a heterogeneous ensemble
arXiv:2307.03017v5 Announce Type: replace Abstract: With the rise of Extended Reality (XR) technology, there is a growing need for real-time light field reconstruction from sparse view inputs. Existin
arXiv:2605.05053v1 Announce Type: cross Abstract: Tactile perception is key to dexterous manipulation, yet simulating high-resolution elastomer deformation remains computationally prohibitive. Finite
arXiv:2605.04832v1 Announce Type: new Abstract: Neural operators generally demonstrate strong predictive performance on in-distribution (ID) problems. However, a critical limitation of existing method
arXiv:2605.04075v1 Announce Type: cross Abstract: Multimodal Large Language Models face severe challenges in computational efficiency and memory consumption due to the substantial expansion of the vis
arXiv:2605.04531v1 Announce Type: new Abstract: Open-vocabulary object detection with vision-language models (VLMs) such as Grounding DINO suffers from performance degradation under test-time distribu
arXiv:2605.04607v1 Announce Type: new Abstract: This paper presents a multi-phase whole-body model predictive control approach for bipedal walking, combining a detailed whole-body model in the near ho
ROCm has reached production-ready status for PyTorch and vLLM workloads in 2026. PyTorch runs well and MI300X benchmarks are competitive, though the ecosystem gap with CUDA remains real. AMD's MI355X
arXiv:2511.19513v2 Announce Type: replace Abstract: Decentralized learning often involves a weighted global loss with heterogeneous node weights lambda. We revisit two natural strategies for incorpora
日経デジタルガバナンスにて、Sakana AIの「事後学習」技術と Sakana Chat / Namazuモデルに関する記事が掲載されました。 Namazuモデル開発をリードしたリサーチサイエンティストの菅沼雅徳とチーフサイエンティストの秋葉拓哉に取材いただきました。 記事: https://www.nikkei.com/prime/digital-governance/article/DGXZ
arXiv:2605.02897v1 Announce Type: cross Abstract: LLM assistant personalities play a critical role in user experience and perceived response quality. We present a large-scale experiment of frontier LL
arXiv:2605.04560v1 Announce Type: new Abstract: Perceptual image compression focuses on preserving high visual quality under low-bitrate constraints. Most existing approaches to perceptual compression
arXiv:2605.04496v1 Announce Type: new Abstract: Long-Text Understanding (LTU) at million-token scale requires balancing reasoning fidelity with computational efficiency. Frontier long-context LLMs can
arXiv:2605.05057v1 Announce Type: new Abstract: Open-vocabulary human-object interaction (HOI) detection requires recognizing interaction phrases that may not appear as annotated categories during tra
arXiv:2508.00049v2 Announce Type: cross Abstract: The formation of dark-matter halos from small cosmological perturbations generated in the early universe is a highly non-linear process typically mode
arXiv:2605.04887v1 Announce Type: new Abstract: The exponential expansion of digital commerce in Indonesia has significantly shifted consumer interactions toward video-centric social networks, particu
arXiv:2605.04126v1 Announce Type: new Abstract: This paper develops convolutional neural network (CNN) methods for simultaneous approximation and elliptic boundary value problems on compact Riemannian
arXiv:2602.03204v2 Announce Type: replace Abstract: While Mixture-of-Experts (MoE) architectures define the state-of-the-art, their theoretical success is often attributed to heuristic efficiency rath
arXiv:2605.03202v1 Announce Type: new Abstract: Large language models offer a tempting solution to address the peer review crisis. This position paper argues that today's AI systems should not be used
arXiv:2605.04412v1 Announce Type: new Abstract: 3D asset generation plays a pivotal role in fields such as gaming and virtual reality, enabling the rapid synthesis of high-fidelity 3D objects from a s
arXiv:2605.05151v1 Announce Type: new Abstract: Transformer architectures have been widely adopted for time series forecasting, yet whether the representational mechanisms that make them powerful in N
arXiv:2605.05207v1 Announce Type: new Abstract: Dense 3D reconstruction and tracking of dynamic scenes from monocular video remains an important open challenge in computer vision. Progress in this are