Knowledge catalogue
arXiv:2505.20562v2 Announce Type: replace Abstract: Robotic surgery represents a major breakthrough in medical interventions, which has revolutionized surgical procedures. However, the high cost and l
arXiv:2604.19566v1 Announce Type: cross Abstract: Reliable biomedical and clinical retrieval requires more than strong ranking performance: it requires a practical way to find systematic model failure
arXiv:2602.05993v2 Announce Type: replace-cross Abstract: Flow and diffusion models produce high-quality samples, but adapting them to user preferences or constraints post-training remains costly and
arXiv:2604.19135v1 Announce Type: new Abstract: This paper presents the first exploration of text-to-image diffusion models for zero-shot sketch-based 3D shape retrieval (ZS-SBSR). Existing sketch-bas
arXiv:2604.19062v1 Announce Type: new Abstract: Satellite constellation design requires optimizing orbital parameters across multiple satellites to maximize mission specific metrics. For many types of
arXiv:2604.19432v1 Announce Type: new Abstract: Vision foundation models have shown great promise for open-set 3D object retrieval (3DOR) through efficient adaptation to multi-view images. Leveraging
arXiv:2604.19716v1 Announce Type: new Abstract: Large Language Models (LLMs) still struggle with multi-step logical reasoning. Existing approaches either purely refine the reasoning chain in natural l
arXiv:2604.18739v1 Announce Type: new Abstract: Masked diffusion large language models (dLLMs) are a promising alternative to autoregressive generation. While reinforcement learning (RL) methods have
arXiv:2604.19658v1 Announce Type: new Abstract: Damage identification is a core task in structural health monitoring. In practice, however, its reliability is often compromised by confounding non-dama
arXiv:2604.18942v1 Announce Type: new Abstract: Vision-language models (VLMs) exhibit affirmation bias: a systematic tendency to select positive captions ('X is present') even when the correct descrip
arXiv:2604.18963v1 Announce Type: cross Abstract: Knowledge distillation (KD) transfers capabilities from large language models (LLMs) to smaller students, yet it can fail unpredictably and also under
arXiv:2604.19339v1 Announce Type: new Abstract: Ultra-fine-grained visual categorization (Ultra-FGVC) aims to classify highly similar subcategories within fine-grained objects using limited training s
arXiv:2604.19354v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly proposed for autonomous cybersecurity tasks, but their capabilities in realistic offensive settings r
arXiv:2604.19125v1 Announce Type: new Abstract: Large language models have been extensively studied for emotion recognition and moral reasoning as distinct capabilities, yet the extent to which emotio
arXiv:2604.19459v1 Announce Type: new Abstract: Formal verification guarantees proof validity but not formalization faithfulness. For natural-language logical reasoning, where models construct axiom s
arXiv:2604.19395v1 Announce Type: new Abstract: While self-consistency is known to improve performance on symbolic reasoning, its effect on the recall of encyclopedic knowledge is unclear due to a lac
arXiv:2604.19118v1 Announce Type: cross Abstract: Modern distributed systems generate massive volumes of log data that are critical for detecting anomalies and cyber threats. However, in real world se
arXiv:2604.19264v1 Announce Type: new Abstract: Agentic multimodal models have garnered significant attention for their ability to leverage external tools to tackle complex tasks. However, it is obser
arXiv:2602.19790v2 Announce Type: replace Abstract: Concept drift -- the change of the distribution over time -- poses significant challenges for learning systems and is of central interest for monito
arXiv:2604.19544v1 Announce Type: new Abstract: Multimodal reward models (MRMs) play a crucial role in aligning Multimodal Large Language Models (MLLMs) with human preferences. Training a good MRM req
arXiv:2604.18603v1 Announce Type: cross Abstract: Bidirectional transformers are the foundation of many sequence modeling tasks across natural, biological, and chemical language domains, but they are
arXiv:2604.18829v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive performance on visual perception and reasoning tasks with RGB imagery, yet they remain
arXiv:2604.18964v1 Announce Type: new Abstract: This paper introduces DW-Bench, a new benchmark that evaluates large language models (LLMs) on graph-topology reasoning over data warehouse schemas, exp
arXiv:2603.04445v2 Announce Type: replace-cross Abstract: The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent mod
arXiv:2603.12592v2 Announce Type: replace-cross Abstract: Routing algorithms for public transport, particularly the widely used RAPTOR and its variants, often face performance bottlenecks during the t
arXiv:2604.18639v1 Announce Type: cross Abstract: Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entro
arXiv:2510.09477v2 Announce Type: replace-cross Abstract: Set-based transformer models for amortized probabilistic inference and meta-learning, such as neural processes, prior-fitted networks, and tab
arXiv:2604.18788v1 Announce Type: new Abstract: Apple Neural Engine (ANE) is a dedicated neural processing unit (NPU) present in every Apple Silicon chip. Mixture-of-Experts (MoE) LLMs improve inferen
arXiv:2604.18790v1 Announce Type: new Abstract: Depth completion from sparse LiDAR measurements and corresponding RGB images is a prerequisite for accurate 3D perception in robotic systems. Existing m
arXiv:2604.19105v1 Announce Type: new Abstract: Faithfully modeling human behavior in dynamic environments is a foundational challenge for embodied intelligence. While conditional motion synthesis has
arXiv:2604.19564v1 Announce Type: cross Abstract: Egocentric assistants often rely on first-person view data to capture user behavior and context for personalized services. Since different users exhib
arXiv:2604.17458v2 Announce Type: replace Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi-hop reasoning. While recen
arXiv:2604.19547v1 Announce Type: new Abstract: Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering caus
arXiv:2510.23156v2 Announce Type: replace-cross Abstract: The growing demand for smart home interfaces has increased interest in non-intrusive sensing methods like vibration-based gesture recognition.
arXiv:2507.01918v3 Announce Type: replace-cross Abstract: We develop a rotation-invariant neural network that provides the global minimum-variance portfolio by jointly learning how to lag-transform hi
arXiv:2509.03726v2 Announce Type: replace-cross Abstract: Sampling from unnormalized target distributions, e.g. Boltzmann distributions mu_{ext{target}}(x) propto exp(-E(x)/T), is fundamental to many
arXiv:2602.11631v2 Announce Type: replace-cross Abstract: Accurate and efficient wavefield modeling underpins seismic structure and source studies. Traditional methods comply with physical laws but ar
arXiv:2604.19559v1 Announce Type: new Abstract: Construction workers are highly vulnerable to heat stress, yet tools that translate real-time physiological data into actionable safety intelligence rem
arXiv:2604.19505v1 Announce Type: cross Abstract: Automatic keyword extraction from academic papers is a key area of interest in natural language processing and information retrieval. Although previou
arXiv:2510.18358v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) is essential for deploying deep neural networks in safety-critical settings. Although methods like Deep Ensemb
arXiv:2604.19652v1 Announce Type: cross Abstract: In this paper, we propose a deep-learning framework for environmental sound deepfake detection (ESDD) -- the task of identifying whether the sound sce
arXiv:2604.19699v1 Announce Type: new Abstract: The pursuit of truth is central to democratic deliberation and governance, yet political discourse reflects varying epistemic orientations, ranging from
arXiv:2504.01733v4 Announce Type: replace Abstract: This paper presents a class of epistemic logics that captures the dynamics of acquiring knowledge and descending into oblivion, while incorporating
arXiv:2411.16312v2 Announce Type: replace Abstract: Leveraging the overfitting property of deep neural networks (DNNs) is trending in video delivery systems to enhance video quality within bandwidth l
arXiv:2604.18916v1 Announce Type: new Abstract: In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be
arXiv:2512.20817v2 Announce Type: replace Abstract: Automated essay scoring (AES) has advanced significantly with neural language models, yet most systems remain opaque, offering little visibility int
arXiv:2604.18660v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior work
arXiv:2604.19510v1 Announce Type: new Abstract: Variability in illumination is a primary factor limiting deep learning robustness for field-based plant disease detection. This study evaluates Histogra
arXiv:2604.19331v1 Announce Type: new Abstract: Understanding how policy is debated and justified in parliament is a fundamental aspect of the democratic process. However, the volume and complexity of
arXiv:2604.19526v1 Announce Type: cross Abstract: Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious
arXiv:2604.19341v1 Announce Type: cross Abstract: Language models are increasingly used in scientific discovery to generate hypotheses, propose candidate solutions, implement systems, and iteratively
arXiv:2604.19054v1 Announce Type: new Abstract: The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy wit
arXiv:2604.17406v2 Announce Type: replace Abstract: The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. While the scientific method is
arXiv:2604.19485v1 Announce Type: cross Abstract: Reinforcement learning (RL) for LLM post-training faces a fundamental design choice: whether to use a learned critic as a baseline for policy optimiza
arXiv:2604.18786v1 Announce Type: cross Abstract: Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or ref
arXiv:2603.15956v2 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data. While human demonstrations (
arXiv:2604.19278v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) show promise on complex tasks but remain prone to coordination failures such as goal drift, error cascades, and misa
arXiv:2604.19034v1 Announce Type: new Abstract: Constructing structured spatial memory is essential for enabling long-horizon reasoning in complex embodied navigation tasks. Current memory constructio
arXiv:2604.19678v1 Announce Type: new Abstract: Function vectors (FVs) are vector representations of tasks extracted from model activations during in-context learning. While prior work has shown that
arXiv:2604.19702v1 Announce Type: new Abstract: Accurate reconstruction and tracking of dynamic human faces from image sequences is challenging because non-rigid deformations, expression changes, and