Knowledge catalogue
arXiv:2605.00496v1 Announce Type: new Abstract: Understanding human actions from visual observations is essential for human--robot interaction, particularly when semantic interpretation of unfamiliar
arXiv:2605.00113v1 Announce Type: new Abstract: We examine if frontier chat-based large language models (LLMs) adjust their outputs based on neurodivergence (ND) context in system prompts and describe
arXiv:2605.00269v1 Announce Type: new Abstract: Recent white-box OOD detection methods for LLMs -- including CED, RAUQ, and WildGuard confidence scores -- appear effective, but we show they are struct
arXiv:2507.01955v3 Announce Type: replace Abstract: Multimodal foundation models (MFMs), such as GPT-4o, have recently made remarkable progress. However, their detailed visual understanding beyond que
arXiv:2605.00068v1 Announce Type: new Abstract: Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively hi
arXiv:2605.00820v1 Announce Type: cross Abstract: We introduce HyCOP, a modular framework that learns parametric PDE solution operators by composing simple modules (advection, diffusion, learned closu
arXiv:2605.00351v1 Announce Type: new Abstract: Root cause localization in cloud native microservice systems requires modeling complex service dependencies, irregular temporal dynamics, and heterogene
arXiv:2605.00082v1 Announce Type: new Abstract: The Forward-Forward (FF) algorithm presents a compelling, bio-inspired alternative to backpropagation. However, while efficient in training, it has a co
arXiv:2605.00526v1 Announce Type: new Abstract: Suspect face generation remains a technical challenge in crime investigations. Traditional sketch-drawing workflows suffer from low efficiency and quali
arXiv:2408.11349v2 Announce Type: replace Abstract: Mercari is the largest C2C e-commerce marketplace in Japan, having more than 20 million active monthly users. Search being the fundamental way to di
arXiv:2605.00436v1 Announce Type: new Abstract: Concerns with the safety and reliability of applying large-language models (LLMs) in unpredictable real-world applications motivate this study, which ex
arXiv:2509.20098v2 Announce Type: replace Abstract: Learning physical dynamics from data is a fundamental challenge in machine learning and scientific modeling. Real-world observational data are inher
arXiv:2605.00250v1 Announce Type: cross Abstract: Standard diffusion models for graph generation typically rely on uniform time-stepping, an approach that overlooks the non-homogeneous dynamics of dis
arXiv:2605.00064v1 Announce Type: new Abstract: Information-theoretic generalization bounds analyze stochastic optimization by relating expected generalization error to the mutual information between
arXiv:2605.00664v1 Announce Type: new Abstract: We present a training-free approach for controllable 3D inpainting based on initial noise optimization. In the structured 3D latent diffusion framework,
arXiv:2510.16450v2 Announce Type: replace Abstract: Annotation-efficient segmentation of the numerous mitochondria instances from various electron microscopy (EM) images is highly valuable for biologi
arXiv:2605.00324v1 Announce Type: cross Abstract: Large-scale ranking systems depend on thousands of features derived from user behavior across multiple time horizons. Typically requires model retrain
arXiv:2508.07630v2 Announce Type: replace Abstract: We introduce InterChart, a diagnostic benchmark that evaluates how well vision-language models (VLMs) reason across multiple related charts, a task
arXiv:2605.00591v1 Announce Type: new Abstract: Contrastive vision-language models like CLIP exhibit remarkable zero-shot generalization. However, prompt tuning remains highly sensitive to label noise
arXiv:2605.00184v1 Announce Type: new Abstract: With the growing integration of human-computer interaction into everyday life, advances in machine learning have enabled systems to better perceive and
arXiv:2605.00618v1 Announce Type: new Abstract: We investigate the extent to which cosine similarity between paragraph embeddings is invariant under machine translation, using the Manifesto Corpus of
arXiv:2601.00090v2 Announce Type: replace Abstract: Contemporary text-to-image models exhibit a surprising degree of mode collapse, as can be seen when sampling several images given the same text prom
arXiv:2605.00583v1 Announce Type: new Abstract: The visual modality of vision-language models (VLMs) is an underexplored attack surface for bypassing safety alignment. We introduce four jailbreak atta
arXiv:2605.00267v1 Announce Type: new Abstract: As language model safeguards become more robust, attackers are pushed toward developing increasingly complex jailbreaks. Prior work has found that this
arXiv:2509.21514v3 Announce Type: replace-cross Abstract: Research on Knowledge Tracing (KT) models traditionally focuses on improving predictive accuracy. However, responsible real-world deployment r
arXiv:2605.00640v1 Announce Type: new Abstract: Prevailing machine-learned interatomic potential (MLIP) uncertainty-quantification methods rely on ensembles of independently trained backbones. These m
arXiv:2403.02290v2 Announce Type: replace-cross Abstract: The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman equation, are ubiquitous in reinforcement learning and control theor
arXiv:2605.00501v1 Announce Type: new Abstract: In financial predictions, the performance of machine learning models is often assessed by Rank IC, which is the Spearman rank correlation between the mo
arXiv:2511.08156v2 Announce Type: replace Abstract: Land Use and Land Cover (LULC) mapping is a fundamental task in Earth Observation (EO). However, current LULC models are typically developed for a s
arXiv:2605.00373v1 Announce Type: new Abstract: In line with the Global Communication Plan 2025, we have pursued the development of multilingual translation technologies to realize a language-barrier-
arXiv:2602.04212v2 Announce Type: replace Abstract: Though large language models (LLMs) have enabled great success across a wide variety of tasks, they still appear to fall short of one of the loftier
arXiv:2605.00777v1 Announce Type: cross Abstract: A speaker encoder used in multilingual voice cloning should treat the same speaker identically regardless of which script the audio was uttered in. Of
arXiv:2510.22819v2 Announce Type: replace Abstract: The convergence analysis of online learning algorithms is central to machine learning theory, where the last-iterate convergence is particularly imp
arXiv:2408.11513v2 Announce Type: replace Abstract: This paper focuses on learning a Constrained Markov Decision Process (CMDP) via general parameterized policies. We propose a Primal-Dual based Regul
arXiv:2505.13007v2 Announce Type: replace Abstract: The ability to accurately model random fields plays a critical role in science and engineering for problems involving uncertain, spatially-varying q
arXiv:2605.00642v1 Announce Type: cross Abstract: Graphical User Interface (GUI) grounding maps natural language instructions to the visual coordinates of target elements and serves as a core capabili
arXiv:2605.00718v1 Announce Type: new Abstract: Knee osteoarthritis (OA) assessment involves a natural but often underused label hierarchy: a coarse binary OA decision and a fine-grained Kellgren--Law
arXiv:2605.00130v1 Announce Type: new Abstract: Learning meaningful representations from medical time series (MedTS) such as ECG or EEG signals is a critical challenge. These signals are often high-di
arXiv:2605.00448v1 Announce Type: new Abstract: The deployment of artificial intelligence in medical imaging is hindered by high computational complexity and resource-intensive processing of volumetri
arXiv:2510.19897v2 Announce Type: replace Abstract: We investigate how agents built on pretrained large language models (LLMs) can learn target classification functions from labeled examples without p
arXiv:2605.00051v1 Announce Type: new Abstract: Anticipating traffic accidents is a critical yet unresolved problem for autonomous driving, hindered by the inherent complexity of modeling interactions
arXiv:2605.00702v1 Announce Type: new Abstract: Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving pre
arXiv:2412.00452v2 Announce Type: replace-cross Abstract: Conventioanl federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the fed
arXiv:2605.00644v1 Announce Type: new Abstract: Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. Howeve
arXiv:2605.00050v1 Announce Type: cross Abstract: Traffic accidents are routinely documented in textual reports, yet physically grounded accident reconstruction remains difficult because detailed scen
arXiv:2605.00760v1 Announce Type: new Abstract: This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network based on the De
arXiv:2605.00416v1 Announce Type: new Abstract: Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. De
arXiv:2605.00809v1 Announce Type: new Abstract: In this paper, we present extbf{Gen}erative extbf{L}anguage-extbf{I}mage extbf{P}re-training (GenLIP), a minimalist generative pretraining framework for
arXiv:2605.00480v1 Announce Type: new Abstract: Active learning aims to reduce annotation cost by selectively querying informative samples for supervision under a limited labeling budget. In this work
arXiv:2505.22003v2 Announce Type: replace Abstract: In India, access to legal assistance for the general public has been observed to have a critical gap, as many citizens are not able to take full adv
arXiv:2605.00434v1 Announce Type: new Abstract: Real-world multimodal learning is often hindered by missing modalities. While Incomplete Multimodal Learning (IML) has gained traction, existing methods
arXiv:2605.00556v1 Announce Type: cross Abstract: Partial driving automation creates a tension: drivers remain legally responsible for vehicle behaviour, yet their active control is significantly redu
arXiv:2509.24496v3 Announce Type: replace Abstract: The explosive growth of large language models (LLMs) has created a vast but opaque landscape: millions of models exist, yet their evolutionary relat
arXiv:2605.00505v1 Announce Type: cross Abstract: Modern information retrieval (IR) is no longer consumed primarily by humans but increasingly by large language models (LLMs) via retrieval-augmented g
arXiv:2512.04341v3 Announce Type: replace Abstract: Popular offline reinforcement learning (RL) methods rely on explicit conservatism, penalizing out-of-dataset actions or restricting rollout horizons
arXiv:2605.00253v1 Announce Type: new Abstract: Mamba's recurrent state h_t is, by construction, a compressed summary of every token seen so far. This raises a tempting hypothesis: if we extract token
arXiv:2605.00244v1 Announce Type: cross Abstract: We introduce Lucid-XR, a generative data engine for creating diverse and realistic-looking multi-modal data to train real-world robotic systems. At th
arXiv:2605.00398v1 Announce Type: new Abstract: Causal graph discovery for space-time systems is challenging in high-dimensional gridded data, which often has many more grid cells than temporal observ
arXiv:2605.00242v1 Announce Type: new Abstract: Millimetre-wave (mmWave) radar offers a more privacy-preserving alternative to RGB-based human pose estimation. However, existing methods typically rely
arXiv:2605.00789v1 Announce Type: new Abstract: Key-Value (KV) cache has become a de facto component of modern Large Vision-Language Models (LVLMs) for inference. While it enhances decoding efficiency