Knowledge catalogue
arXiv:2605.00342v1 Announce Type: new Abstract: Tree-based speculative decoding accelerates autoregressive generation by verifying multiple draft candidates in parallel, but this advantage weakens for
arXiv:2605.00781v1 Announce Type: new Abstract: 3D world generation is essential for applications such as immersive content creation or autonomous driving simulation. Recent advances in 3D world gener
arXiv:2605.00201v1 Announce Type: cross Abstract: The standard oracle model for matroid algorithms assumes that each independence query can be answered in constant time, regardless of the size of the
arXiv:2501.14660v2 Announce Type: replace-cross Abstract: In this work, we study the asymptotic behavior of Mixture of Experts (MoE) trained via gradient flow on supervised learning problems. Our main
arXiv:2604.01707v2 Announce Type: replace Abstract: Memory emerges as the core module in the large language model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game pla
arXiv:2510.17281v5 Announce Type: replace Abstract: Scaling up data, parameters, and test-time computation has been the mainstream methods to improve LLM systems (LLMsys), but their upper bounds are a
arXiv:2605.00356v1 Announce Type: new Abstract: Long-term conversational agents must decide which turns to store in external memory, yet recent systems rely on autoregressive LLM generation at every t
arXiv:2605.00762v1 Announce Type: new Abstract: We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-ba
arXiv:2605.00394v1 Announce Type: new Abstract: We present Mesh Field Theory (MeshFT) and its neural realization, MeshFT-Net: a structure-preserving framework for mesh-based continuum physics that cle
arXiv:2510.23557v2 Announce Type: replace-cross Abstract: Training or fine-tuning large language model (LLM)-based systems often requires costly human feedback, yet there is limited understanding of h
arXiv:2605.00397v1 Announce Type: new Abstract: We present MiniVLA-Nav v1, a simulation dataset for Language-Conditioned Object Approach (LCOA) navigation: given a short natural-language instruction,
arXiv:2505.23723v2 Announce Type: replace Abstract: The emergence of large language model (LLM)-based agents has significantly advanced the development of autonomous machine learning (ML) engineering.
arXiv:2605.00689v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in cross-linguistic contexts, ensuring safety in diverse regulatory and cultural environments
arXiv:2605.00495v1 Announce Type: cross Abstract: Recent advances in multimodal generation have enabled high-quality audio generation from silent videos. Practical applications, such as sound producti
arXiv:2605.00431v1 Announce Type: cross Abstract: Although recent video-to-audio (V2A) models excelled at synthesizing semantically plausible sounds from visual inputs, they do not explicitly model ro
arXiv:2605.00025v1 Announce Type: cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communicatio
arXiv:2605.00393v1 Announce Type: new Abstract: Reinforcement learning (RL) in large environments often suffers from severe computational bottlenecks, as conventional regret minimization algorithms re
arXiv:2605.00764v1 Announce Type: new Abstract: Urban perception describes how people subjectively evaluate urban environments, shaping how cities are experienced and understood. Existing computationa
arXiv:2605.00475v1 Announce Type: cross Abstract: Real-world fine manipulation, particularly in bimanual manipulation, typically requires low-latency control and stable visual localization, while coll
arXiv:2605.00527v1 Announce Type: cross Abstract: Lissajous confocal laser endomicroscopy (CLE) is a promising solution for high speed in vivo optical biopsy for handheld scenarios. However, Lissajous
arXiv:2405.13693v4 Announce Type: replace Abstract: Testing for individual discrimination involves deriving a profile, the comparator, similar to the one making the discrimination claim, the complaina
arXiv:2605.00473v1 Announce Type: new Abstract: Multi-task learning (MTL) has emerged as a pivotal paradigm in machine learning by leveraging shared structures across multiple related tasks. Despite i
arXiv:2605.00169v1 Announce Type: cross Abstract: Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reli
arXiv:2605.00260v1 Announce Type: new Abstract: Nonlinear Parametric Optimization Network (NLPOpt-Net) is an unsupervised learning architecture to solve constrained nonlinear programs (NLP). Given the
arXiv:2605.00751v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) scales poorly in cooperative multi-agent domains because expansion must consider an exponentially large set of joint acti
arXiv:2605.00086v1 Announce Type: new Abstract: High-quality corpora are essential for advancing Natural Language Processing (NLP) in Portuguese. Building on previous encoder-only models such as BERTi
arXiv:2512.16762v3 Announce Type: replace Abstract: Generative Pre-trained Transformer (GPT) architectures are the most popular design for language modeling. Energy-based modeling is a different parad
arXiv:2605.00778v1 Announce Type: new Abstract: In biomechanical systems, observable performance is often used as a proxy for underlying system organization. However, this assumption implicitly presum
arXiv:2605.00347v1 Announce Type: cross Abstract: Given the rapidly growing capabilities of vision-language models (VLMs), extending them to interactive decision-making tasks such as video games has e
arXiv:2603.25860v2 Announce Type: replace-cross Abstract: Transformer architectures have achieved remarkable empirical success in modeling contextual relations, yet a clear understanding of their expr
arXiv:2605.00440v1 Announce Type: cross Abstract: The evolution of artificial intelligence (AI) has rendered the boundary between humanity and computational machinery increasingly ambiguous. In the pr
arXiv:2602.14474v2 Announce Type: replace Abstract: We study K-armed Multiarmed Bandit (MAB) problem with M heterogeneous data sources, each exhibiting unknown and distinct noise variances {sigma_j^2}
arXiv:2605.00323v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) often suffer from hallucinations, generating descriptions that include visual details absent from the input image.
arXiv:2509.12057v3 Announce Type: replace Abstract: The study of optimal decision trees has gained increasing attention in recent years; however, despite substantial progress, it still suffers from tw
arXiv:2605.00432v1 Announce Type: new Abstract: Online Conformal Prediction (CP) struggles to balance temporal adaptability and structural stability. Feedback-driven methods (e.g., Adaptive Conformal
arXiv:2603.19397v2 Announce Type: replace Abstract: Non-pharmaceutical interventions (NPIs), such as diagnostic testing and quarantine, are crucial for controlling infectious disease outbreaks but are
arXiv:2605.00193v1 Announce Type: new Abstract: Many machine learning systems make constrained decisions by optimizing factorized objectives, but the context-specific objective is often treated as fix
arXiv:2605.00634v1 Announce Type: cross Abstract: We introduce Paired-CSLiDAR (CSLiDAR), a cross-source aerial-ground LiDAR benchmark for single-scan pose refinement: refining a ground-scan pose withi
arXiv:2605.00466v1 Announce Type: new Abstract: Real-world time series forecasting faces the fundamental challenge of non-stationary statistical properties, including shifts in mean and variance over
arXiv:2605.00647v1 Announce Type: new Abstract: Automated pediatric electrocardiogram (ECG) diagnosis remains challenging because models trained predominantly on adult data suffer from substantial cro
arXiv:2601.05833v2 Announce Type: replace Abstract: Pretokenization is a crucial, sequential pass in Byte-level BPE tokenizers, yet little work has been done to optimize it for edge-side inference. Ou
arXiv:2605.00814v1 Announce Type: new Abstract: While autoregressive Large Vision-Language Models (LVLMs) demonstrate remarkable proficiency in multimodal tasks, they face a 'Visual Signal Dilution' p
arXiv:2605.00227v1 Announce Type: new Abstract: There are growing concerns about the risks posed by AI companion applications designed for emotional engagement. Existing safety evaluations often rely
arXiv:2605.00264v1 Announce Type: new Abstract: Offline multi-agent reinforcement learning in general-sum settings is challenged by the distribution shift between logged datasets and target equilibriu
arXiv:2605.00707v1 Announce Type: new Abstract: Image editing instructions are heterogeneous: a color swap, an object insertion, and a physical-action edit all demand different spatial coverage and di
arXiv:2605.00412v1 Announce Type: cross Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement lea
arXiv:2605.00517v1 Announce Type: new Abstract: Despite substantial progress in text-driven 3D human motion synthesis, generating realistic multi-person interaction sequences remains challenging. Nota
arXiv:2605.00385v1 Announce Type: new Abstract: Physics-Informed Neural Networks have become a powerful mesh-free method for solving partial differential equations, but their performance is often limi
arXiv:2605.00265v1 Announce Type: new Abstract: Real-world knowledge is often organized as hierarchies such as product taxonomies, medical ontologies, and label trees, yet learning hierarchical repres
arXiv:2510.26020v2 Announce Type: replace Abstract: Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls t
arXiv:2605.00345v1 Announce Type: new Abstract: Generating pose-aligned 3D objects is challenging due to the spatial mismatches and transformation ambiguities inherent in decoupled canonical-then-rota
arXiv:2605.00742v1 Announce Type: cross Abstract: LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which to
arXiv:2605.00600v1 Announce Type: cross Abstract: Deep neural networks achieve impressive results across diverse applications, yet their overconfidence on unseen inputs necessitates reliable epistemic
arXiv:2605.00825v1 Announce Type: new Abstract: Flow matching (FM) trains a time-dependent vector field that transports samples from a simple prior to a complex data distribution. However, for high-di
arXiv:2411.02327v4 Announce Type: replace Abstract: In the past year, video-based large language models (Video LLMs) have achieved impressive progress, particularly in their ability to process long vi
arXiv:2605.00665v1 Announce Type: new Abstract: The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker s
arXiv:2605.00121v1 Announce Type: new Abstract: We have seen tremendous recent progress in our ability to build 'spatio-semantic' representations that enable robots to perform complex reasoning across
arXiv:2602.19423v3 Announce Type: replace Abstract: Domain adaptive segmentation (DAS) is a promising paradigm for delineating intracellular structures from various large-scale electron microscopy (EM
arXiv:2412.02125v2 Announce Type: replace-cross Abstract: Goal-conditioned policies enable decision-making models to execute diverse behaviors based on specified goals, yet their downstream performanc
arXiv:2605.00384v1 Announce Type: new Abstract: Preference-based reinforcement learning offers a scalable alternative to manual reward engineering by learning reward structures from comparative feedba