Knowledge catalogue
arXiv:2603.11625v2 Announce Type: replace-cross Abstract: While specialized Medical Vision-Language Models (VLMs) have achieved remarkable success in interpreting 2D and 3D medical modalities, their d
arXiv:2505.12343v2 Announce Type: replace-cross Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucinations, where generated conten
arXiv:2507.04949v3 Announce Type: replace Abstract: Many robotic tasks, such as inverse kinematics, motion planning, and contact-rich manipulation, can be formulated as optimization problems. Solving
arXiv:2606.26188v1 Announce Type: new Abstract: Logarithmic spirals are ubiquitous in biological appendages and provide an attractive morphology for continuum manipulators capable of reaching, wrappin
arXiv:2606.27306v1 Announce Type: new Abstract: Translation cascades for reasoning translate the query from another language to English, reason in English, and translate the answer back to the origina
My friend the brilliant @kevin_volpp dropped dead He survived (read abt it: https://tinyurl.com/ypcpdzpz) But every year 300-400,000 in the US don't This week: our @nature article (+A.Schubert J.Ross
arXiv:2305.06315v3 Announce Type: replace-cross Abstract: For deep learning problems on graph-structured data, pooling layers are important for down sampling, reducing computational cost, and to minim
Nous Research is conducting benchmarking tests to evaluate whether combinations of open-source models can achieve performance comparable to Anthropic's Claude Opus while maintaining significantly lowe
arXiv:2606.27018v1 Announce Type: cross Abstract: Remote Sensing Foundation Models (RSFMs) have emerged as a powerful alternative to supervised models for Earth Observation, allowing satellites to aut
arXiv:2602.01969v2 Announce Type: replace Abstract: Complex tables with multi-level headers, merged cells and heterogeneous layouts pose persistent challenges for LLMs in both understanding and reason
arXiv:2606.26533v1 Announce Type: new Abstract: Scenario-Based Testing predominantly relies on the legacy ASAM OpenSCENARIO 1.x XML standard because existing continuous simulation frameworks lack nati
arXiv:2606.27071v1 Announce Type: new Abstract: Panoramic sensing offers wide field-of-view coverage, yet 3D reconstruction from sparse panoramas remains challenging under rotation-dominant, weak-para
arXiv:2606.26360v1 Announce Type: new Abstract: The neutral, or floating, tone of Mandarin Chinese is a tone with an enigmatic set of properties. It has been described as a reduced tone, or as a tone
arXiv:2606.26916v1 Announce Type: new Abstract: Developing physically aware video generation models remains a significant challenge due to the difficulty in capturing diverse physical phenomena, such
arXiv:2606.26631v1 Announce Type: new Abstract: Interleaved multimodal reasoning improves visual grounding by revisiting visual evidence during multi-step generation, yet existing methods typically re
arXiv:2606.26165v1 Announce Type: new Abstract: Fruit spoilage is a significant issue in agriculture, leading to substantial economic losses. Addressing this, our study introduces a hybrid approach co
arXiv:2606.26413v1 Announce Type: cross Abstract: Belief-space planning under motion uncertainty and state and control constraints remains a fundamental challenge, largely due to the difficulty of est
arXiv:2602.17683v3 Announce Type: replace-cross Abstract: Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture. Normalized Differenc
arXiv:2606.27345v1 Announce Type: new Abstract: Modern video diffusion transformers position their tokens through RoPE on the (u,v,t) axes -- a description of the camera's sampling grid that says noth
arXiv:2606.26797v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) on a small, high-quality set of long reasoning traces is an effective approach for eliciting strong reasoning capabilities
arXiv:2606.27285v1 Announce Type: new Abstract: Learning governing equations from observed solution data is a fundamental challenge in scientific machine learning ite{bruntonDiscoveringGoverningEquati
arXiv:2606.26236v1 Announce Type: cross Abstract: The objective of this paper is to improve radiological gradings measured on MRIs of spines, by resampling scans so that the new view planes are better
arXiv:2606.26769v1 Announce Type: new Abstract: The adoption of powerful diffusion models is hindered by their significant inference latency. Recent ``cache-then-forecast'' schemes alleviate this issu
arXiv:2606.26431v1 Announce Type: cross Abstract: Mammogram-based deep learning models have improved breast cancer risk prediction, but the learned imaging patterns remain underexplored. Existing inte
arXiv:2602.14486v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that representations from neural networks are converging to a common statistical model of real
arXiv:2606.26213v1 Announce Type: new Abstract: RoboTales is a low-cost robotic storytelling system that animates narratives using expressive sock puppetry. Implemented autonomously on a Baxter robot
arXiv:2606.27339v1 Announce Type: new Abstract: Despite impressive advances in image matting, video matting remains challenging due to the inherent gap between high-level tracking, which requires fram
arXiv:2606.26389v1 Announce Type: cross Abstract: Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain com
arXiv:2606.26873v1 Announce Type: cross Abstract: Graphs provide a natural language for relational data in chemistry, biology and optimisation. Graph neural networks (GNNs) have driven much of the rec
arXiv:2606.27305v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) for 3D generation is now established across a number of works, but most existing pipelines optimise ex
arXiv:2606.26194v1 Announce Type: new Abstract: Urban tree biomass remains less spatially explicitly quantified than biomass in managed forests because many estimates rely on inventories or coarse pro
arXiv:2606.26383v1 Announce Type: cross Abstract: How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit? These questions are central to sof
something has definitely shifted in the past few weeks. seeing a huge uptick in large enterprises wanting to secure compute and post-train their own models in house, frequently on top of GLM-5.2. ever
arXiv:2606.26559v1 Announce Type: cross Abstract: Earth observation satellite networks generate massive volumes of high-resolution imagery, whereas inter-satellite and downlink resources remain limite
arXiv:2606.26723v1 Announce Type: cross Abstract: Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasi
arXiv:2606.27088v1 Announce Type: new Abstract: Mesh subdivision is a fundamental operation for converting coarse, editable meshes into high-resolution surfaces, with broad applications in digital ass
arXiv:2606.27029v1 Announce Type: new Abstract: Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample
arXiv:2606.27206v1 Announce Type: new Abstract: Garden path sentences present a processing difficulty for humans -- the sentence prefix leads the listener towards one interpretation, until the listene
arXiv:2606.26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning com
arXiv:2606.26615v1 Announce Type: new Abstract: While traditional image restoration focuses on perceptual quality, Task-Driven Image Restoration (TDIR) aims to maximize the performance of downstream h
arXiv:2606.26874v1 Announce Type: new Abstract: Transcatheter Aortic Valve Replacement (TAVR) planning requires meticulous multimodal reasoning. However, adapting Multimodal Large Language Models (MLL
arXiv:2606.26285v1 Announce Type: cross Abstract: Noise-based backdoor attacks on diffusion models typically rely on input-time trigger injection, untargeted activation, and out-of-distribution target
The decades-long prison sentences imposed on a group of Texas activists who were protesting against ICE, using the guise of 'terrorism,' are an outrage clearly designed to suppress such activism. http
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. Heat waves mess with your brain. Scientists are trying to figu
arXiv:2606.26117v1 Announce Type: cross Abstract: This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditi
arXiv:2606.27103v1 Announce Type: new Abstract: Humans flexibly adapt their reasoning strategies to the requirements of a given problem. Large language models (LLMs) have performed well on many cognit
arXiv:2606.26207v1 Announce Type: cross Abstract: Several theoretical works have tried to explain the adversarial vulnerability of deep neural networks through properties of high-dimensional geometry.
There will be more and more pressure and more money and lobbying behind open soon. It is not a 'two big companies have all the power in AI' situation like some imagine. The financial incentives are sh
This post discusses Tokyo Bay's land reclamation practices, specifically explaining why initial reclamation projects are constructed as isolated islands rather than connected to existing land—a strate
arXiv:2508.16159v2 Announce Type: replace-cross Abstract: Meta-learning aims to uniformly sample homogeneous support-query pairs, characterized by the same categories and similar attributes, and extra
arXiv:2606.27161v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong multimodal reasoning capabilities, but their efficiency is limited by the large number of
arXiv:2412.09959v5 Announce Type: replace Abstract: Dataset distillation provides an effective approach to reduce memory and computational costs by optimizing a compact dataset that achieves performan
arXiv:2606.26898v1 Announce Type: new Abstract: Diffusion MRI (dMRI) tractography enables non-invasive reconstruction of white-matter pathways, but its accuracy is fundamentally limited by indirect, l
arXiv:2606.27096v1 Announce Type: new Abstract: Transformer-based models have recently attracted increasing attention for Raman spectral classification. In this study, a transformer-based approach was
arXiv:2606.20605v2 Announce Type: replace-cross Abstract: Background: Generative artificial intelligence (GenAI) is increasingly used for health information, yet its influence on users' trust calibrat
arXiv:2603.01461v2 Announce Type: replace Abstract: Echocardiography is critical for diagnosing cardiovascular diseases, yet the shortage of skilled sonographers hinders timely patient care, due to hi
arXiv:2606.27001v1 Announce Type: new Abstract: Quantifying the evolution of uncertainty is critical to both probabilistic forecasting and data assimilation in numerical weather prediction. In this st
arXiv:2602.14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions. Recently proposed Causal Foundation Models
arXiv:2606.26485v1 Announce Type: new Abstract: Microblogging platforms generate massive amounts of short, noisy, and dispersed user content, making automatic keyphrase extraction (AKE) an important b
arXiv:2606.27313v1 Announce Type: new Abstract: A unified representation for text and vision is a natural pursuit, as it enables simpler multimodal modeling and more efficient training. However, repre