Knowledge catalogue
arXiv:2606.20122v2 Announce Type: replace Abstract: Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The out
arXiv:2608.29153v1 Announce Type: new Abstract: Automated radiological report generation can alleviate clinical workloads and eliminate observer variability. However, standard free-text generation mod
arXiv:2608.29800v1 Announce Type: new Abstract: Tool-using agents are being proposed for medical imaging, and their behaviour when a tool returns a false finding is largely unmeasured. We audit whethe
arXiv:2608.29626v1 Announce Type: new Abstract: Salient object detection in optical remote sensing images (ORSI-SOD) requires dense predictions that preserve object completeness and structural continu
arXiv:2608.29992v1 Announce Type: new Abstract: This paper presents an end-to-end, agent-driven pipeline for the LoD3 reconstruction of facade openings in 3D city models, producing directly usable Cit
arXiv:2608.28823v1 Announce Type: cross Abstract: Artists coordinate human pose, illumination, and camera placement to convey narrative and emotion, but existing generative methods typically model the
arXiv:2608.30688v1 Announce Type: new Abstract: While face recognition systems are widely deployed, ensuring their demographic reliability and robustness under uncontrolled visual conditions remains a
arXiv:2608.28665v1 Announce Type: new Abstract: Recent open-vocabulary segmentation models have advanced semantic perception for UAVs, but predictions from moving aerial platforms can remain temporall
arXiv:2608.29635v1 Announce Type: new Abstract: We study unsupervised hypergraph alignment, where the goal is to infer node correspondences between two hypergraphs using only structural information, w
arXiv:2608.30125v1 Announce Type: cross Abstract: Current video-to-music (V2M) models lack semantic control and fail to penalize instruction violations, largely due to their reliance on reconstruction
arXiv:2608.30964v1 Announce Type: new Abstract: Pretrained vision embeddings are increasingly used as general-purpose representations for modelling how people appraise urban scenes, and are validated
arXiv:2608.28695v1 Announce Type: new Abstract: Image retrieval has traditionally been formulated as a point-wise matching problem, where each candidate image is scored in isolation. However, this ato
arXiv:2608.30725v1 Announce Type: new Abstract: Recent multilingual vision--language encoders cover hundreds of languages in a single model, yet on two state-of-the-art instances retrieval on low-reso
arXiv:2604.02194v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Language Models (RALMs) have shown strong potential in knowledge-intensive tasks, yet they remain vulnerable when retrieve
arXiv:2608.29411v1 Announce Type: new Abstract: Integer sequences from the On-Line Encyclopedia of Integer Sequences (OEIS) are increasingly used to benchmark mathematical reasoning in language models
arXiv:2608.30824v1 Announce Type: new Abstract: Combining whole-body magnetic resonance imaging (WB-MRI) with clinical variables has the potential to improve systemic disease diagnosis by leveraging c
arXiv:2608.29834v1 Announce Type: new Abstract: Collaborative conversations frequently contain references whose targets are indirect rather than named: resolving 'this looks like the fix discussed yes
arXiv:2603.17304v2 Announce Type: replace Abstract: Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual
arXiv:2608.28063v1 Announce Type: new Abstract: Multi-organ ultrasound classifiers increasingly combine attention, mixture-of-experts routing, uncertainty gating, and evidential deep learning (EDL) ob
arXiv:2608.28461v1 Announce Type: new Abstract: We present an anatomy-aware, promptable model for whole-body lesion segmentation in FDG and PSMA PET/CT, developed for the AUTOPET V challenge. The prop
arXiv:2608.27492v1 Announce Type: cross Abstract: Publicly-acceesible face-manipulation tools have made deepfake creation accessible to non-expert users. Against these, existing defenses are mostly po
arXiv:2608.28205v1 Announce Type: new Abstract: Pruning visual foundation models has attracted considerable attention. However, existing methods focus on rigid point-to-point token alignment on a sing
arXiv:2608.27713v1 Announce Type: new Abstract: Edge intelligence systems increasingly require model training and online inference to coexist on resource-constrained devices, while inference demand ca
arXiv:2608.28108v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs) allow users to specify manipulation tasks in natural language, but distinguishing a target or placement goal amon
arXiv:2608.28302v1 Announce Type: new Abstract: Diffusion-based inpainting models modify only a localized part of an image, while many AI-image detectors rely on global artifacts and do not localize.
arXiv:2608.28375v1 Announce Type: cross Abstract: Global goodness-of-fit and discrepancy statistics can establish that a sample departs from a reference distribution without identifying which observat
arXiv:2512.17581v2 Announce Type: replace Abstract: Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shap
arXiv:2511.07011v2 Announce Type: replace Abstract: Background: Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research to date
arXiv:2608.28378v1 Announce Type: new Abstract: Large language models are increasingly used as agentic workflow executors, yet existing training data and benchmarks largely assume informationally comp
arXiv:2608.27826v1 Announce Type: cross Abstract: Federated recommendation (FedRec) enables personalized modeling without centralizing users' interaction histories, but most existing methods assume a
arXiv:2608.27988v1 Announce Type: new Abstract: Smooth speaker transitions are fundamental to effective conversation and rely on an interlocutor's ability to predict when to enter the conversation. Th
arXiv:2608.27584v1 Announce Type: new Abstract: Autonomous systems rely on extracting information from light, yet remain brittle in extreme environments, from nighttime navigation to high-speed roboti
arXiv:2608.27462v1 Announce Type: new Abstract: Unlike explicit attacks with obvious profanity, implicit hate speech hides malice within seemingly compliant expressions through metaphors and contextua
arXiv:2608.28524v1 Announce Type: new Abstract: Texture image classification plays a significant role in computer vision applications, including industrial inspection, medical image analysis, remote s
arXiv:2512.12066v3 Announce Type: replace-cross Abstract: Current safety evaluations of large language models rely on single-shot testing, implicitly assuming that model responses are deterministic an
arXiv:2511.16695v2 Announce Type: replace Abstract: Art is a deeply personal and expressive medium, where each artist brings their own style, technique, and cultural background into their work. Tradit
arXiv:2608.27658v1 Announce Type: new Abstract: Subword tokenization hinders low-resource language processing by imposing frequency patterns from dominant languages onto script-sharing variants. Byte-
arXiv:2608.27391v1 Announce Type: new Abstract: LLMs are increasingly able to answer complex questions about enterprise-scale document collections. But evaluation is hard: companies don't want to shar
arXiv:2608.26989v1 Announce Type: new Abstract: This paper investigates decentralized multitask learning over networks when the underlying task relationships are unknown. While existing graph-regulari
arXiv:2608.26757v1 Announce Type: new Abstract: Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities
arXiv:2608.27110v1 Announce Type: new Abstract: Reliable medical conversational AI requires authentic expert--patient interaction data, yet such datasets remain scarce, especially for low-resource lan
arXiv:2608.26901v1 Announce Type: cross Abstract: Automating prostate radiotherapy treatment planning is dosimetrically complex, particularly for extreme hypofractionated regimens. In this study, we i
arXiv:2608.26153v1 Announce Type: new Abstract: Clinical electroencephalography (EEG) reporting remains largely manual and time-consuming, and current EEG software ecosystems do not produce the struct
arXiv:2608.26743v1 Announce Type: new Abstract: Enterprises fine-tune language models on proprietary data that may later require removal due to privacy, contractual, or compliance obligations. Selecti
arXiv:2509.25160v2 Announce Type: replace-cross Abstract: Mathematical reasoning is a key capability for vision-language models (VLMs), yet current benchmarks mainly evaluate text-based or explicitly
Impressive new work from Google DeepMind showing real-world applications of agents for scientific research. Impressive new paper from Google DeepMind. (bookmark it) It takes Co-Scientist out of simula
arXiv:2608.26189v1 Announce Type: new Abstract: Tool-using agents fail two ways: choosing the wrong tool, or forming wrong arguments, and an early failure of either kind can silently corrupt everythin
arXiv:2608.26806v1 Announce Type: new Abstract: With the growing demand for processing multiple image sequences in real-world applications, various visual token pruning methods have emerged to mitigat
arXiv:2606.18801v2 Announce Type: replace-cross Abstract: With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for g
arXiv:2608.26722v1 Announce Type: new Abstract: Text-guided drone geo-localization aims to identify a target region in a large-scale image gallery from a natural-language description. Existing methods
arXiv:2604.03403v2 Announce Type: replace-cross Abstract: Dense retrieval systems increasingly face an asymmetry between complex instruction-like queries and relatively simple, static document collect
arXiv:2608.25520v1 Announce Type: new Abstract: Audio-visual cross-modal Fine-Grained Visual Categorization (FGVC) aims to identify fine-grained categories by jointly leveraging visual and auditory in
arXiv:2608.25465v1 Announce Type: new Abstract: Visual inspection of welded assemblies remains one of the least automated stages in many industrial production processes, still depending largely on the
model: add Qwen3.8-Flash-Next (qwen4exp) (#27742) gguf: add qwen4exp (Qwen3.8-Flash-Next) arch and converter Adds the GGUF-side plumbing for HF model_type qwen4_exp: MODEL_ARCH.QWEN4EXP plus tensors f
arXiv:2502.02118v2 Announce Type: replace-cross Abstract: Self-supervised learning has been a powerful approach for learning meaningful representations from unlabeled data across various domains, redu
arXiv:2608.25635v1 Announce Type: new Abstract: Industrial e-commerce search systems ultimately aim to optimize the user-level long-term objective, such as n-day cumulative purchases or gross merchand
arXiv:2608.25138v1 Announce Type: new Abstract: Stochastic masking, cropping, or modality removal makes deterministic reconstruction an incomplete target: one observation can admit many clean completi
arXiv:2608.25575v1 Announce Type: new Abstract: Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and rela
arXiv:2608.25662v1 Announce Type: new Abstract: In 2026, we held the fourth iteration of the SHROOM Shared Task series: SHROOM-Visions (extbf{S}hared-task on extbf{H}allucinations and extbf{R}elated e
arXiv:2608.24905v1 Announce Type: cross Abstract: Service robots may encounter ambiguous user requests that require context-aware inference. Users may also have unique preferences with certain tasks w