Knowledge catalogue
arXiv:2604.14974v1 Announce Type: new Abstract: You are a robot and you live in a Markov decision process (MDP) with a finite or an infinite number of transitions from state-action to next states. You
arXiv:2604.15124v1 Announce Type: new Abstract: Continuous glucose monitoring (CGM) is central to diabetes care, but explaining CGM patterns clearly and empathetically remains time-intensive. Evidence
arXiv:2604.15173v1 Announce Type: new Abstract: Temporal action segmentation (TAS) demands dense temporal supervision, yet most of the annotation cost in untrimmed videos is spent identifying and refi
arXiv:2510.20151v2 Announce Type: replace Abstract: Structured texts refer to texts containing structured elements beyond plain texts, such as code snippets and placeholders. Such structured texts inc
arXiv:2411.05472v2 Announce Type: replace Abstract: The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-en
arXiv:2604.15088v1 Announce Type: new Abstract: Building extraction from optical Remote Sensing (RS) imagery suffers from performance degradation under real-world hazy and low-light conditions. Howeve
arXiv:2604.13101v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into aviation safety decision-making represents a significant technological advancement, yet their sta
arXiv:2603.24992v2 Announce Type: replace Abstract: Accurate segmentation of the left atrial (LA) wall in 3D late gadolinium-enhanced MRI (LGE-MRI) is essential for wall thickness mapping and fibrosis
arXiv:2604.14251v1 Announce Type: new Abstract: Monitoring LLM safety at scale requires balancing cost and accuracy: a cheap latent-space probe can screen every input, but hard cases should be escalat
arXiv:2604.14961v1 Announce Type: new Abstract: Contextual bandit algorithms suffer from high regret during cold-start, when the learner has insufficient data to distinguish good arms from bad. We pro
arXiv:2604.14691v1 Announce Type: cross Abstract: LLM-empowered agent simulations are increasingly used to study social emergence, yet the micro-to-macro causal mechanisms behind macro outcomes often
arXiv:2604.14161v1 Announce Type: new Abstract: Reliable evaluation is essential in machine learning research, yet methodological flaws-particularly data leakage-continue to undermine the validity of
arXiv:2604.14892v1 Announce Type: new Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudi
arXiv:2511.21025v2 Announce Type: replace Abstract: Image captions serve as efficient surrogates for visual content in multimodal systems such as retrieval, recommendation, and multi-step agentic infe
arXiv:2604.14344v1 Announce Type: new Abstract: Animals in nature combine multiple modalities, such as sight and feel, to perceive terrain and develop an understanding of how to walk on uneven terrain
arXiv:2604.14726v1 Announce Type: new Abstract: Online anomaly detection (OAD) plays a pivotal role in real-time analytics and decision-making for evolving data streams. However, existing methods ofte
arXiv:2604.14602v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate toxic content, posing significant risks for safe deployment. Current mitigation strategies often degrad
arXiv:2601.21262v3 Announce Type: replace Abstract: Although Multimodal Large Language Models (MLLMs) have shown remarkable potential in Visual Document Retrieval (VDR) through generating high-quality
arXiv:2604.15052v1 Announce Type: new Abstract: Autonomous robots operating in natural karstic caves face perception and navigation challenges that are qualitatively distinct from those encountered in
arXiv:2604.13102v1 Announce Type: cross Abstract: Enterprise software organizations face an escalating challenge in maintaining the integrity, security, and freshness of codebases that span hundreds o
arXiv:2603.06431v2 Announce Type: replace-cross Abstract: Neural network methods for PDEs require reliable error control in function space norms. However, trained neural networks can typically only be
arXiv:2604.14692v1 Announce Type: new Abstract: Video understanding requires identifying and reasoning over semantically discriminative visual objects across frames, yet existing object-agnostic solut
arXiv:2604.14520v1 Announce Type: new Abstract: Omni-modal Large Language Models (Omni-MLLMs) promise a unified integration of diverse sensory streams. However, recent evaluations reveal a critical pe
arXiv:2602.08698v2 Announce Type: replace Abstract: This study examines the practical applications and methodological implications of Machine Translation in Indian Languages, specifically Bangla, Mala
arXiv:2604.14645v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) often exhibit poor generalisation in limited training data scenarios due to overfitting and insufficient feature di
arXiv:2604.02811v2 Announce Type: replace-cross Abstract: Functional verification consumes over 50% of the IC development lifecycle, where SystemVerilog Assertions (SVAs) are indispensable for formal
arXiv:2604.14167v1 Announce Type: new Abstract: Rhetoric recognition is a critical component in automated essay scoring. By identifying rhetorical elements in student writing, AI systems can better as
arXiv:2604.14210v1 Announce Type: new Abstract: A claim has been circulating on social media and practitioner forums that Chinese prompts are more token-efficient than English for LLM coding tasks, po
arXiv:2604.14169v1 Announce Type: new Abstract: In large-scale construction projects, the continuous evolution of decisions generates extensive records, most often captured in meeting minutes. Since d
arXiv:2604.14519v1 Announce Type: cross Abstract: Catastrophic forgetting remains a fundamental challenge in continual learning, in which models often forget previous knowledge when fine-tuned on a ne
arXiv:2604.15166v1 Announce Type: new Abstract: Machine unlearning aims to remove targeted knowledge from a trained model without the cost of retraining from scratch. In class unlearning, however, red
arXiv:2604.14856v1 Announce Type: new Abstract: Understanding climate change requires reasoning over complex causal networks. Yet, existing causal discovery datasets predominantly capture explicit, di
arXiv:2604.14587v1 Announce Type: new Abstract: Lion optimizer is a popular learning-based optimization algorithm in machine learning, which shows impressive performance in training many deep learning
arXiv:2604.15269v1 Announce Type: cross Abstract: The impossibility of simultaneously cloning non-orthogonal states lies at the foundations of quantum theory. Even when allowing for approximation erro
arXiv:2604.14630v1 Announce Type: new Abstract: Recent advances in unsupervised video object segmentation have highlighted the potential of two-stream architectures that integrate appearance and motio
arXiv:2604.14506v1 Announce Type: new Abstract: Masked image modeling (MIM) is a highly effective self-supervised learning (SSL) approach to extract useful feature representations from unannotated dat
arXiv:2604.14489v1 Announce Type: new Abstract: Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision. Neural approaches achieve strong performance but req
arXiv:2511.18850v2 Announce Type: replace Abstract: Discovering effective predictive signals, or 'alphas,' from financial data with high dimensionality and extremely low signal-to-noise ratio remains
arXiv:2604.13814v1 Announce Type: cross Abstract: Recent advances in artificial intelligence (AI) have shown promise in automating key aspects of Agile project management, yet their impact on team cog
arXiv:2604.14305v1 Announce Type: cross Abstract: Targeted amplicon panels are widely used in oncology diagnostics, but providing per-gene performance guarantees for copy number variant (CNV) detectio
arXiv:2604.14907v1 Announce Type: new Abstract: Online hate speech and abusive language pose a growing challenge for content moderation, especially in multilingual settings and for low-resource langua
arXiv:2604.13567v1 Announce Type: cross Abstract: Heart sound signals, phonocardiography (PCG) signals, allow for the automatic diagnosis of potential cardiovascular pathology. Such classification tas
arXiv:2604.14156v1 Announce Type: new Abstract: Large language models deliver strong generative performance but at the cost of massive parameter counts, memory use, and decoding latency. Prior work ha
arXiv:2604.15153v1 Announce Type: new Abstract: Large Language Models (LLMs) incur significant computational and memory costs when processing long prompts, as full self-attention scales quadratically
arXiv:2604.14612v1 Announce Type: cross Abstract: Self-speculative decoding is an inference technique for large language models designed to speed up generation without sacrificing output quality. It c
arXiv:2603.02196v2 Announce Type: replace-cross Abstract: An agent must try new behaviors to explore and improve. In high-stakes environments, an agent that violates safety constraints may cause harm
arXiv:2509.01728v4 Announce Type: replace-cross Abstract: Recent advances in the development of robotic foundation models have led to promising end-to-end and general-purpose capabilities in robotic s
arXiv:2604.14769v1 Announce Type: new Abstract: The pre-training and fine-tuning paradigm has become the dominant approach for model adaptation. However, conventional pre-training typically yields mod
arXiv:2604.15224v1 Announce Type: cross Abstract: The extit{LLM-as-a-judge} paradigm has become the operational backbone of automated AI evaluation pipelines, yet rests on an unverified assumption: th
arXiv:2602.16716v3 Announce Type: replace Abstract: Contextuality is a central feature of quantum theory, traditionally understood as the impossibility of reproducing quantum measurement statistics us
arXiv:2604.14259v1 Announce Type: cross Abstract: Functional magnetic resonance imaging (fMRI) is widely used for studying and diagnosing brain disorders, with functional connectivity (FC) matrices pr
arXiv:2602.06930v2 Announce Type: replace Abstract: We study off-policy reinforcement learning for controlling continuous-time Markov diffusion processes with discrete-time observations and actions. W
arXiv:2604.13100v1 Announce Type: cross Abstract: The shift toward intent-driven software engineering (often termed 'Vibe Coding') exposes a critical Context-Fidelity Trade-off: vague user intents ove
arXiv:2603.20340v3 Announce Type: replace-cross Abstract: Self-generated skills for web agents are often unstable and can even hurt performance relative to direct acting. We argue that the key bottlen
arXiv:2604.15086v1 Announce Type: cross Abstract: Recent advances in video-to-audio (V2A) generation enable high-quality audio synthesis from visual content, yet achieving robust and fine-grained cont
arXiv:2604.14556v1 Announce Type: new Abstract: Video object insertion is a critical task for dynamically inserting new objects into existing environments. Previous video generation methods focus prim
arXiv:2604.14488v1 Announce Type: cross Abstract: In any domain where knowledge accumulates under formal authority -- law, drug regulation, software security -- a later document can formally void an e
arXiv:2604.14454v1 Announce Type: cross Abstract: Autonomous vehicles equipped with robust onboard perception, localization, and planning still face limitations in occlusion and non-line-of-sight (NLO
arXiv:2604.15267v1 Announce Type: cross Abstract: It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite tr
arXiv:2604.14773v1 Announce Type: new Abstract: While LLMs have demonstrated remarkable potential in Question Answering (QA), evaluating personalization remains a critical bottleneck. Existing paradig