Knowledge catalogue
arXiv:2608.17983v1 Announce Type: cross Abstract: Ultrasound tongue contour segmentation remains challenging under cross-dataset domain shift, where limited annotations, probe variability, and acquisi
arXiv:2608.18063v1 Announce Type: new Abstract: High-resolution image editing is increasingly demanded in professional workflows, yet existing diffusion-based models remain constrained to resolutions
arXiv:2608.17254v1 Announce Type: new Abstract: Intra-tumoural heterogeneity (ITH) reflects spatial variation in tumour biology and is an important determinant of tumour behaviour, prognosis, and trea
arXiv:2507.06185v2 Announce Type: replace-cross Abstract: In July 2025, 18 academic manuscripts on arXiv contained hidden instructions that manipulated AI-assisted peer review (indirect prompt injecti
arXiv:2608.17914v1 Announce Type: new Abstract: Static Timing Analysis (STA) is a critical step in the design flow of digital integrated circuits, however, obtaining accurate delay estimations can rep
arXiv:2608.17051v1 Announce Type: cross Abstract: Secondary use of electronic health records requires de-identification, yet existing systems miss institutionally situated protected health information
arXiv:2608.17994v1 Announce Type: new Abstract: Using LLMs as judges has become standard practice for evaluating model outputs at scale. This is particularly common for subjective, open-ended tasks su
arXiv:2602.01115v3 Announce Type: replace-cross Abstract: Diffusion-based visuomotor policies excel at modeling action distributions but are inference-inefficient, since recursively denoising from noi
arXiv:2603.26929v2 Announce Type: replace Abstract: Interactive video segmentation often requires many user interventions for robust performance in challenging scenarios (e.g., occlusions, object sepa
arXiv:2608.17635v1 Announce Type: new Abstract: Vision-LSTM (ViL) enables efficient global modeling, but its cost still scales with the number of spatial tokens, so existing segmenters confine ViL to
arXiv:2608.17704v1 Announce Type: new Abstract: Monitoring nature restoration at scale is an important but difficult ecological problem. Deep learning methods to analyze satellite image time series (S
arXiv:2606.13113v2 Announce Type: replace-cross Abstract: Underactuated spacecraft faces controllability limitations and heightened sensitivity to environmental disturbances, complicating attitude man
arXiv:2608.17289v1 Announce Type: new Abstract: Group-relative policy optimization has emerged as a key paradigm for training agentic large language models (LLMs) on multi-turn interactive tasks. Howe
arXiv:2608.17224v1 Announce Type: new Abstract: Training query-propagation end-to-end multi-object tracking (MOT) models requires dense bounding-box and identity annotations across video sequences, ma
arXiv:2608.17284v1 Announce Type: cross Abstract: Irregular time series forecasting is crucial in many domains, such as healthcare and meteorological observation. However, due to the inherent characte
arXiv:2608.16891v1 Announce Type: new Abstract: Agentic AI systems request tool actions that can modify files, send messages, launch jobs, or change workflow state. This shifts the safety problem from
Agentic artificial intelligence runs on data, so Salesforce Inc. today announced it’s launching Headless Data 360 for Model Context Protocol to provide that data directly to agents, allowing them acce
arXiv:2607.18046v2 Announce Type: replace Abstract: Graphical User Interface (GUI) agents powered by vision-language models hold promise for automating real-world mobile tasks. However, progress is li
arXiv:2608.17180v1 Announce Type: cross Abstract: Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related tasks. While pr
arXiv:2608.17398v1 Announce Type: new Abstract: Persistent clouds blind optical satellites during floods. While Synthetic Aperture Radar (SAR) penetrates clouds, its raw data is noisy and lacks clear
**Release Summary – v0.32.15 (GitHub Release)** Version 0.32.15 was published on **19 Aug**, adding a *model metadata cache* to reduce per‑request overhead in Ollama. The release builds on the previou
arXiv:2506.14934v2 Announce Type: replace Abstract: Distinguishing between quark- and gluon-initiated jets is a critical and challenging task in high-energy physics, pivotal for improving new physics
arXiv:2601.08024v2 Announce Type: replace Abstract: Maintaining or improving the performance of Deep Neural Networks (DNNs) through fine-tuning requires labeling newly collected inputs, a process that
arXiv:2608.14698v1 Announce Type: cross Abstract: Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require inv
arXiv:2608.15080v1 Announce Type: new Abstract: Modern input methods routinely rely on autocomplete to omit information that can be recovered from local context. Inspired by these autocomplete-assiste
arXiv:2608.16236v1 Announce Type: new Abstract: Collaborative inference (CI) splits a model between an edge device and a server, whereby the client computes an intermediate activation, transmits it, a
arXiv:2512.09005v2 Announce Type: replace Abstract: Body and face motion play an integral role in communication. They convey crucial information on the participants. Advances in generative modeling an
arXiv:2608.16158v1 Announce Type: cross Abstract: Phishing remains a persistent and evolving cybersecurity threat, with attack volumes reaching record levels. This growth is driven by the industrializ
arXiv:2608.15160v1 Announce Type: cross Abstract: Automatic modulation recognition (AMR) faces distinct representation bottlenecks under varying observation lengths, where a single model architecture
arXiv:2608.16319v1 Announce Type: new Abstract: This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we
arXiv:2608.15591v1 Announce Type: new Abstract: Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deployment time, whi
arXiv:2602.12276v2 Announce Type: replace Abstract: Test-time scaling has become a standard way to improve performance and boost reliability of neural network models. However, its behavior on agentic,
arXiv:2608.15949v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled their use as conversational recommender systems (CRS), demonstrating strong recommendatio
arXiv:2608.15757v1 Announce Type: new Abstract: Incomplete multi-view clustering (IMVC) aims to uncover shared cluster structures from data with partially observed views. Although recent imputation-fr
arXiv:2608.14813v1 Announce Type: new Abstract: Despite a strong interest on the part of the research community in the topic of trustworthy and safe AI, the composition of the text corpora that large
arXiv:2511.17006v2 Announce Type: replace Abstract: Scaling test-time computation has been extended from language model reasoning to tool-augmented agents, where scaling involves not only thinking in
arXiv:2608.16310v1 Announce Type: new Abstract: Residents' perception of the urban streetscape is an important factor in public health, active mobility, and social wellbeing. Street view imagery (SVI)
arXiv:2608.16564v1 Announce Type: new Abstract: Machine learning (ML) is a key technology driving innovation today, but ensuring ML safety remains a major challenge for safety-related applications. A
arXiv:2608.15780v1 Announce Type: cross Abstract: Stale recommendations are a pervasive challenge and a leading source of user complaints on large-scale content platforms. Items lose relevance through
arXiv:2608.15353v1 Announce Type: new Abstract: Whole-slide image (WSI) report generation requires recognizing spatially distributed pathological features and organizing them into a coherent diagnosti
arXiv:2608.16221v1 Announce Type: new Abstract: Reliable gas source localization (GSL) is critical to safety in industrial and urban environments, yet remains challenging indoors because walls and obs
arXiv:2608.16180v1 Announce Type: new Abstract: To combat oversmoothing in Graph Convolutional Networks, Sheaf Neural Networks (SNNs) were proposed as a generalization by equipping the graph with a sh
arXiv:2608.15303v1 Announce Type: new Abstract: Test-time compute can substantially improve Large Language Model (LLM) reasoning performance, yet how and when additional compute helps remains poorly u
arXiv:2608.15630v1 Announce Type: cross Abstract: The rapid development and growing deployment of large language models (LLMs) have made it increasingly important to understand their capabilities. A c
arXiv:2608.14731v1 Announce Type: new Abstract: Worldwide, millions of senior citizens are suffering from Alzheimer disease abbreviated as AD, a well- versed form of dementia. AD is featured by amnesi
arXiv:2608.15424v1 Announce Type: cross Abstract: The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patien
arXiv:2608.14651v1 Announce Type: new Abstract: Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the needs of individu
arXiv:2608.15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents. Existing methods typically extract knowle
arXiv:2608.15381v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of large language models, but its factorized parameterization creates a tension betwe
arXiv:2608.14550v1 Announce Type: new Abstract: AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environmental costs. W
arXiv:2608.14740v1 Announce Type: new Abstract: Unified image and video creation requires a model to follow diverse instructions while preserving identity, geometry, and temporal structure from visual
arXiv:2608.16721v1 Announce Type: new Abstract: The rapid evolution of text-to-image (T2I) generation models has effectively solved the foundational challenge of raw pixel synthesis, shifting the comm
arXiv:2509.11218v2 Announce Type: replace-cross Abstract: Spatial transformations such as rotation and scale obscure the morphological cues needed for accurate image classification. Careful considerat
arXiv:2608.15420v1 Announce Type: new Abstract: Relocalising archival photographs within a contemporary scene model is challenging because historic and modern views can differ in photographic appearan
I would like to publicly commend @sama and @OpenAI for this taking it at face value. It is very clear that strange and perhaps dangerous things are happening and our systems are not ready for this. Mo
arXiv:2608.15731v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandabl
arXiv:2608.14692v1 Announce Type: cross Abstract: Personalized, generative AI systems increasingly adapt their behavior to individual users over time, fundamentally changing model behavior. While exis
arXiv:2608.15400v1 Announce Type: new Abstract: Large Language Models (LLMs) are notorious for struggling with assessing their own uncertainty, detecting knowledge conflicts, or recognizing when probl
arXiv:2608.15910v1 Announce Type: cross Abstract: Expressive text-to-speech (TTS) systems that use explicit conditioning labels provide direct and interpretable control over expressive attributes, in
arXiv:2608.15395v1 Announce Type: new Abstract: Although text-to-image generative models produce impressive results, they struggle to generate densely detailed, high-resolution (HR) images. Current li