Knowledge catalogue
arXiv:2606.00275v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have demonstrated impressive performance on multimodal tasks through scaled architectures and extensive training.
arXiv:2602.11554v3 Announce Type: replace-cross Abstract: How far can 3D object detection go using 4D radar alone? Despite offering weather-robust and velocity-aware sensing for autonomous perception,
arXiv:2506.21278v3 Announce Type: replace-cross Abstract: We propose spherical Cauchy (spCauchy) latent variables for variational autoencoders on hyperspherical latent spaces. The spCauchy family has
arXiv:2512.07192v2 Announce Type: replace Abstract: Vector Quantization (VQ) based generative image compression has achieved remarkable perceptual quality. However, existing VQ codecs suffer from two
arXiv:2606.00971v1 Announce Type: new Abstract: Biomedical question answering with large language models is commonly evaluated using answer accuracy, but answer accuracy alone does not indicate whethe
arXiv:2606.00906v1 Announce Type: new Abstract: Compact Vision Transformers are attractive for medical imaging in low-data and resource-constrained settings, but most existing variants assume that Euc
arXiv:2606.00497v1 Announce Type: cross Abstract: Deceptive web content, widely instantiated across the internet and commonly known as extit{social-engineering attacks}, manipulates autonomous web age
arXiv:2606.00750v1 Announce Type: new Abstract: Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, existing
arXiv:2606.00875v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for tasks involving creative problem solving and idea generation. However, there is a lack of consens
arXiv:2503.07154v3 Announce Type: replace-cross Abstract: Generative pre-training is often framed through a false dichotomy between autoregressive models for discrete signals and diffusion models for
arXiv:2606.02231v1 Announce Type: cross Abstract: Temporal systems often exhibit non-stationary behaviour, such as seasonal climate variation or glucose fluctuations in patients with type-1 diabetes.
arXiv:2606.01584v1 Announce Type: cross Abstract: Conversational tutoring agents have been shown to improve learning engagement and student outcomes, and large language models (LLMs) are increasingly
arXiv:2602.19066v2 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have recently achieved strong results in text generation. However, their multi-step sampling leads to slow in
arXiv:2507.23277v3 Announce Type: replace Abstract: Feed-forward 3D modeling has emerged as a promising approach for rapid and high-quality 3D reconstruction. In particular, directly generating explic
arXiv:2512.21472v2 Announce Type: replace Abstract: Multi-annotator medical image segmentation is an important research problem, but requires annotated datasets that are expensive to collect. Dermosco
arXiv:2606.02519v1 Announce Type: new Abstract: Agricultural robots are serving as powerful assistants across a wide range of agricultural tasks, nevertheless, still heavily relying on manual operatio
arXiv:2606.00872v1 Announce Type: new Abstract: AI-generated image detection is a moving-target problem: detectors trained on one generator often fail when a new generator appears, and only a few labe
arXiv:2606.01205v1 Announce Type: new Abstract: Vision-language navigation (VLN) for UAVs demands grounding free-form instructions into 6-DoF flight under partial observability. While Vision-Language-
arXiv:2602.13937v2 Announce Type: replace Abstract: Automated Machine Learning (AutoML) has improved access to machine learning, yet existing techniques often remain limited in flexibility, transparen
arXiv:2606.01098v1 Announce Type: cross Abstract: Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequen
arXiv:2606.01204v1 Announce Type: cross Abstract: We investigate whether large language models produce different medical triage recommendations for identical symptoms based solely on the language of t
arXiv:2411.11436v2 Announce Type: replace-cross Abstract: In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit
arXiv:2606.00077v1 Announce Type: cross Abstract: Recently, Belief-Attention ite{Guoqiang25BeliefAttention} has been proposed by first performing an orthogonal projection of the softmax-based weighted
arXiv:2606.02532v1 Announce Type: new Abstract: Analyzing microstructural defects in transmission electron microscopy (TEM) images, particularly in irradiated metal alloys, is often limited by the ava
arXiv:2603.02650v2 Announce Type: replace-cross Abstract: Diffusion planners are a strong approach for offline reinforcement learning, but they can fail when value-guided selection favours trajectorie
arXiv:2606.00041v1 Announce Type: cross Abstract: This study analyzes COVID-19 care pathways using the COVID Data for Shared Learning dataset. We build a transparent, reproducible pipeline that transf
arXiv:2606.00161v1 Announce Type: cross Abstract: The detection of intrusions in IoT-based networks poses challenges that cannot be overcome using traditional machine learning methods. Perhaps the big
arXiv:2606.00556v1 Announce Type: new Abstract: Visual grounding aims to locate image regions that correspond to natural language descriptions and is a key component of interpretable vision systems. I
arXiv:2606.00583v1 Announce Type: cross Abstract: Recent diffusion transformers have demonstrated strong image synthesis capabilities but remain inefficient to train due to weak alignment between gene
arXiv:2606.01711v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks, yet the quadratic computation
arXiv:2606.01626v1 Announce Type: new Abstract: Planning with a learned latent world model is a promising route to control from raw pixels, but a strong world model alone is not enough. We show this e
arXiv:2606.00520v1 Announce Type: cross Abstract: Many stochastic gradient methods are believed not to converge when the noise in stochastic gradients has only a finite p-th moment for pinleft(1,2righ
arXiv:2603.28825v2 Announce Type: replace-cross Abstract: Using a stylised coordination problem drawn from inpatient capacity management, three archetypal forms of AI deployment are described: effort-
arXiv:2311.00260v2 Announce Type: replace-cross Abstract: In collaborative active learning, where multiple agents try to learn labels from a common hypothesis, we introduce an innovative framework for
arXiv:2606.01260v1 Announce Type: cross Abstract: Despite being home to more than 1300 ethnic groups and 700 indigenous languages, bias in Large Language Models has not been fully studied in Indonesia
arXiv:2606.00730v1 Announce Type: new Abstract: Imitation learning (IL) is an effective approach to train complex robotics policies. Recent works have introduced hard constraints into imitation-learni
arXiv:2512.10234v2 Announce Type: replace-cross Abstract: Even LLMs that appear safe during evaluation can still produce harmful responses in deployment. Because stochastic sampling yields different r
arXiv:2606.00241v1 Announce Type: cross Abstract: Measuring statistical dependency between high-dimensional random variables is a fundamental task in data science and machine learning. Neural mutual i
arXiv:2606.02161v1 Announce Type: cross Abstract: Video Large Language Models (Video-LLMs) achieve strong performance in video understanding, but their excessive visual tokens bring substantial comput
arXiv:2606.00703v1 Announce Type: cross Abstract: Low-precision pretraining (FP8, MXFP4, NVFP4) is now standard for frontier language models, yet the literature is almost entirely achievability -- alg
arXiv:2606.02453v1 Announce Type: cross Abstract: Despite the remarkable fidelity of generative models, they frequently suffer from mode collapse. Existing strategies for enhancing diversity predomina
arXiv:2606.00289v1 Announce Type: new Abstract: Quantization is a fundamental tool used to compress datasets, neural network weights, and memory usage in a range of computational tasks. Many downstrea
arXiv:2509.12263v3 Announce Type: replace Abstract: Large multimodal models (LMMs) encode physical laws observed during training, such as momentum conservation, as parametric knowledge. It allows LMMs
arXiv:2606.02171v1 Announce Type: new Abstract: Visual emotion understanding requires models not only to recognize emotional states, but also to why they arise and perform higher-level cognitive reaso
arXiv:2605.26102v2 Announce Type: replace Abstract: In this paper, we introduce InstructSAM, a unified and streamlined framework designed for multi-instance segmentation under arbitrary instructions.
arXiv:2606.00807v1 Announce Type: new Abstract: Traditional artificial intelligence has largely conceptualized intelligence as isolated computation occurring within bounded agents. Across classical AI
arXiv:2606.01051v1 Announce Type: new Abstract: Dynamic medical treatment requires deciding treatment intensity and intervention timing, while patient states evolve continuously and adverse events may
arXiv:2606.02486v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models generalize across static manipulation but fail when objects move during task execution. They map the current observa
arXiv:2606.00755v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards improves the reasoning ability of large language models, but often suffers from entropy collapse, in whic
arXiv:2602.02239v2 Announce Type: replace Abstract: Deep time series models continue to improve predictive performance, yet their deployment remains limited by their black-box nature. In response, exi
arXiv:2503.22939v4 Announce Type: replace Abstract: The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational mod
arXiv:2508.05852v2 Announce Type: replace Abstract: Driver gaze is commonly modeled as a spatial heatmap, but heatmaps alone are difficult for humans to interpret because they do not explain which roa
arXiv:2602.23694v3 Announce Type: replace-cross Abstract: Human operators are still frequently exposed to hazardous environments such as disaster zones and industrial facilities, where intuitive and r
arXiv:2606.00561v1 Announce Type: cross Abstract: Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to
arXiv:2509.24467v3 Announce Type: replace Abstract: Self-supervised learning (SSL) learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necess
arXiv:2606.00157v1 Announce Type: cross Abstract: We consider establishing the interpretability theory of deep learning through constructing a corresponding relationship between the renormalization gr
arXiv:2512.09730v3 Announce Type: replace Abstract: Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs. It provides two compleme
arXiv:2410.14483v3 Announce Type: replace-cross Abstract: Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an e
arXiv:2606.02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets (arepsilon_i) according to insti
arXiv:2601.00212v2 Announce Type: replace Abstract: Segmentation of vestibular schwannoma and cochlea from T2 MRI is clinically important yet annotation-intensive. Domain adaptation (DA) has been wide