Knowledge catalogue
arXiv:2602.18934v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a specific data point was used during train
arXiv:2606.23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the up
arXiv:2606.23851v1 Announce Type: cross Abstract: This work investigates the implementation of artificial intelligence and machine learning (AI/ML) for real-time monitoring in laser powder bed fusion
arXiv:2506.10520v5 Announce Type: replace-cross Abstract: Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale gra
arXiv:2606.24151v1 Announce Type: cross Abstract: Self-evolving agents improve over time by distilling experience from past executions and reusing it in future tasks. Existing systems represent such e
arXiv:2606.23885v1 Announce Type: cross Abstract: Representation alignment has emerged as an effective approach to improve Multimodal Large Language Models (MLLMs) by regularizing their internal repre
arXiv:2606.24078v1 Announce Type: new Abstract: Imitation learning enables robots to acquire complex manipulation skills from demonstrations, but its effectiveness is limited by the cost of collecting
arXiv:2509.14001v5 Announce Type: replace-cross Abstract: Personalized object detection aims to adapt a general-purpose detector to recognize user-specific instances from only a few examples. Lightwei
arXiv:2606.24404v1 Announce Type: new Abstract: The incorporation of additional modalities into action recognition models increases their performance across a wide range of settings. However, how this
arXiv:2606.23694v1 Announce Type: new Abstract: Graph-based text classification models typically rely on local neighborhood aggregation and overlook global community structure, despite semantic docume
arXiv:2606.24214v1 Announce Type: new Abstract: Accurate pulmonary vessel segmentation remains challenging due to the sparse, tortuous, and multi-scale nature of vascular structures, where small branc
arXiv:2504.06138v3 Announce Type: replace-cross Abstract: Professional users need tools to help them gain actionable insights from large multimedia collections. Foundation models and AI agents have ra
arXiv:2606.23938v1 Announce Type: new Abstract: Driving VLA models incorporating Chain-of-Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose interme
arXiv:2606.23761v1 Announce Type: cross Abstract: Spiking neural network (SNN)-based neuromorphic speech enhancement has emerged as a promising paradigm due to its energy efficiency, yet it still unde
arXiv:2606.24087v1 Announce Type: new Abstract: Reconstructing continuous speech from scalp electroencephalography (EEG) remains fundamentally challenging. EEG provides a weak, spatially diffuse, and
arXiv:2606.24068v1 Announce Type: new Abstract: Embodied reasoning and exploration are increasingly considered crucial abilities for robots operating in complex and unfamiliar environments. To accompl
arXiv:2606.24447v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have revolutionized document parsing by enabling end-to-end mapping from images to structured text, imposing a significant
🧠 Paradigm II — Agent Foundation Model: world modeling as agent capability. Single-turn, non-agentic environment prediction → tested directly on multi-turn, tool-calling agent tasks. No agentic RL, no
arXiv:2606.24632v1 Announce Type: cross Abstract: Linear Quadratic (LQ) control problems are at the heart of linear control theory and Model Predictive Control (MPC). While performant, standard approa
arXiv:2601.11957v4 Announce Type: replace Abstract: Overlapping calendar invitations force busy professionals to repeatedly decide which meetings to attend, reschedule, or decline. We refer to this pr
arXiv:2606.23744v1 Announce Type: cross Abstract: Deep learning has become an important tool in computational pathology, enabling automated analysis of histopathological images. While convolutional ne
arXiv:2606.24346v1 Announce Type: cross Abstract: Petroleum-engineering search exposes a supervision gap for strong general retrievers: relevant evidence exists in public web text, but domain relevanc
arXiv:2306.05239v3 Announce Type: replace Abstract: Sampled point and voxel methods are usually employed to downsample the dense events into sparse ones. After that, one popular way is to leverage a g
arXiv:2606.24773v1 Announce Type: new Abstract: Non-autoregressive generation offers a powerful paradigm for iterative refinement, allowing models to recursively critique, erase and regenerate arbitra
arXiv:2605.27729v3 Announce Type: replace-cross Abstract: The 2024-2025 Nobel and Turing awards recognised AI and quantum science simultaneously. Yet no deployed system has brought these streams toget
arXiv:2606.23943v1 Announce Type: new Abstract: Tokenization is a foundational step in NLP pipelines, yet standard evaluation metrics such as fertility rate fail to capture morphological correctness f
arXiv:2505.24622v3 Announce Type: replace Abstract: Many high-stakes screening tasks require predicting rare outcomes from unstructured text, where errors are costly and decisions must be auditable. W
arXiv:2606.23992v1 Announce Type: cross Abstract: Clinical value sets define the standardized terminology codes used in quality measurement, phenotyping, cohort construction, and clinical decision sup
arXiv:2606.24851v1 Announce Type: new Abstract: Fourier Neural Operators (FNO) learn solution operators of partial differential equations by parameterizing global convolutions in the complex Fourier d
arXiv:2606.24330v1 Announce Type: new Abstract: Vision Foundation Models (VFMs) have significantly advanced dense feature matching, yet severe in-plane rotation remains a critical challenge. Existing
arXiv:2606.24437v1 Announce Type: new Abstract: Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines. However, existi
arXiv:2511.20892v4 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed. Efficient and accurate knowledge updates without cost
arXiv:2606.24338v1 Announce Type: new Abstract: Despite rapid progress, embodied reasoning under real-world variability remains challenging. Existing approaches rely on demonstration-driven sequential
arXiv:2606.24441v1 Announce Type: new Abstract: We present S1-Omni-Image, an open-weight unified multimodal model for scientific image understanding, generation, and editing. Unlike general-purpose im
Sakana AI CEO David Ha (@hardmaru) appeared on TBS CROSS DIG’s “1on1 Tech.” He talks about our founding story, latest research and technical vision, product launches including Sakana Fugu, Japan’s AI
arXiv:2606.24138v1 Announce Type: new Abstract: Generating explicit 3D city assets from a single satellite image is important for digital twins, urban simulation, and geospatial intelligence. Unlike s
arXiv:2606.24747v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance across a growing range of domains, yet their scale poses deployment challenges in applications w
arXiv:2606.19623v2 Announce Type: replace Abstract: Graph neural networks (GNNs) offer a flexible framework for learning from scientific data with physical, biological, or functional associations. One
arXiv:2505.10457v3 Announce Type: replace-cross Abstract: Incremental learning is a machine learning paradigm where a model learns from a sequential stream of tasks. This setting poses a key challenge
arXiv:2606.24063v1 Announce Type: cross Abstract: Modern spoken language understanding (SLU) systems are increasingly deployed in real-world settings, where specific functionalities may need to be rem
arXiv:2606.23697v1 Announce Type: cross Abstract: Semantic segmentation of code written in a C-family language remains a challenging problem, due to the language's complex syntax, macro expansion, and
arXiv:2606.24449v1 Announce Type: new Abstract: We revisit the memory update mechanism in SAM2-based visual object tracking and identify confidence-only mask selection as the dominant cause of drift u
arXiv:2606.24726v1 Announce Type: new Abstract: Video MLLMs often struggle with fine-grained spatio-temporal reasoning, sometimes generating correct answers based on irrelevant frames or objects. Alth
arXiv:2512.10244v2 Announce Type: replace Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled
arXiv:2606.24235v1 Announce Type: new Abstract: Spatial proteomics enables single-cell-resolution characterization of protein expression within tissue architecture, playing a critical role in understa
arXiv:2606.24165v1 Announce Type: new Abstract: Reducing visual token redundancy is critical for accelerating Multimodal Large Language Models (MLLMs) without degrading cross-modal reasoning performan
arXiv:2606.23757v1 Announce Type: cross Abstract: Extracting interpretable governing equations from sparse, noisy chemical time-series data remains difficult because discrete reaction topology and con
arXiv:2606.23821v1 Announce Type: cross Abstract: Accurate numerical eigenvalues are often difficult to certify, especially in singular or non-normal settings. This article reports a human--AI collabo
arXiv:2606.24598v1 Announce Type: cross Abstract: While expert-validated 'LLM + script' workflows deliver significant value, they remain static: they encode hard-won domain knowledge yet fail to adapt
arXiv:2606.24237v1 Announce Type: new Abstract: Federated Graph Learning facilitates collaborative graph modeling across distributed clients while preserving data privacy. However, real-world data cat
arXiv:2606.24331v1 Announce Type: new Abstract: Transformer-based language models have become the default substrate for natural language processing and the pace of new releases has made it hard for pr
arXiv:2606.24302v1 Announce Type: new Abstract: Fine-tuning transformer-based handwritten text recognition (HTR) models on medieval manuscripts is challenging because these models are pre-trained on m
arXiv:2507.01752v4 Announce Type: replace-cross Abstract: Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation. However, exposing
arXiv:2606.24759v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still fac
arXiv:2606.24282v1 Announce Type: new Abstract: High frame-rate RGB-D videos are crucial for a variety of downstream tasks, including motion analysis, dynamic scene understanding, and 3D reconstructio
arXiv:2606.24333v1 Announce Type: new Abstract: In-Image Machine Translation (IIMT) aims to translate scene text in an image and render the translated text back into the original regions while preserv
arXiv:2606.24344v1 Announce Type: cross Abstract: ODRL policy evaluators produce verdicts, but say nothing about the normative positions a policy brings into existence, the authority structures those
arXiv:2606.23706v1 Announce Type: cross Abstract: The development of generalizable electroencephalography (EEG) decoding models is essential for robust brain-computer interfaces (BCI) and objective ne
arXiv:2606.24178v1 Announce Type: cross Abstract: Pretrained vision models often misclassify inputs that are rotated, scaled, or sheared, even though these affine transformations leave the object clas
arXiv:2606.24320v1 Announce Type: cross Abstract: We present ZONOS2 8B, our latest TTS model, which achieves state-of-the-art naturalness, prosody, and voice cloning fidelity. We improve upon Zonos-v0