Knowledge catalogue
arXiv:2606.06967v1 Announce Type: new Abstract: Generative policies provide expressive and multimodal action distributions, making them attractive for reinforcement learning (RL) in complex continuous
arXiv:2606.07151v1 Announce Type: new Abstract: Traditional change point detection in dynamic networks assumes abrupt transitions between stationary states, overlooking scenarios of continuous evoluti
arXiv:2606.07280v1 Announce Type: new Abstract: Novel class discovery in point cloud segmentation aims to transfer knowledge from known classes to automatically identify and segment unlabeled novel cl
arXiv:2606.06550v1 Announce Type: cross Abstract: Self-supervised learning (SSL) yields powerful, context-rich representations for speech emotion recognition (SER), yet aggregating these representatio
arXiv:2606.07183v1 Announce Type: new Abstract: This work examines the semantic geometry underlying NLP models. We compare supervised vector embeddings, such as CamemBERT, with lexical co-occurrence g
arXiv:2606.06881v1 Announce Type: new Abstract: Blood glucose forecasting models are foundational for modern diabetes management systems, as reliable short-term predictions can enable proactive interv
arXiv:2606.06532v1 Announce Type: new Abstract: Despite significant progress in agentic long video understanding, existing methods still lack detailed motion comprehension coupled with an efficient me
arXiv:2606.07102v1 Announce Type: cross Abstract: We propose GP-Adapter, a training-free framework that augments CLIP (Contrastive Language-Image Pre-training) with Gaussian Process (GP) uncertainty m
arXiv:2606.07475v1 Announce Type: cross Abstract: Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis. GNNs achieve high-accuracy node class
arXiv:2606.06892v1 Announce Type: new Abstract: Scalable data attribution methods typically assign isolated utility scores to individual training examples. This prevalent additive assumption fundament
arXiv:2512.09084v3 Announce Type: replace Abstract: The Kolmogorov-Arnold representation theorem offers a theoretical alternative to Multi-Layer Perceptrons (MLPs) by placing learnable univariate func
arXiv:2606.07024v1 Announce Type: new Abstract: Multi-modal approaches used for 3D CAD generation require substantial computational resources, necessitating efficient training. To address this, we pro
arXiv:2603.20967v2 Announce Type: replace-cross Abstract: One of the most common machine learning setups is logistic regression. In many classification models, including neural networks, the final pre
arXiv:2606.06833v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transc
arXiv:2209.00188v4 Announce Type: replace-cross Abstract: Long-latency load requests continue to limit the performance of high-performance processors. To increase the latency tolerance of a processor,
arXiv:2606.06986v1 Announce Type: new Abstract: While green finance has become a key instrument for low-carbon city transitions, its actual decarbonization effects and transmission mechanisms remain u
arXiv:2606.07316v1 Announce Type: cross Abstract: Byzantine collaboration among large-language-model agents requires a finality-control primitive: given delivered stochastic, structured natural-langua
arXiv:2606.07044v1 Announce Type: new Abstract: Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations. Passenger demand has a hierarchical structure i
arXiv:2606.07033v1 Announce Type: new Abstract: Open-vocabulary audio-visual event localization (OV-AVEL) jointly models audio-visual cues to recognize and temporally localize events, including catego
arXiv:2606.06679v1 Announce Type: cross Abstract: Court judgments are central to legal practice and jurisprudence, yet discourse analysis of Hong Kong judgments has received limited attention, owing l
arXiv:2606.07218v1 Announce Type: cross Abstract: Multi-hop RAG poses a data-engineering problem beyond passage matching: under fixed retrieval budgets, a system must organize retrieved text into evid
arXiv:2506.02622v2 Announce Type: replace Abstract: Mixed Reality (MR) interfaces have been extensively explored for controlling mobile robots, but there is limited research on their application to ma
arXiv:2606.07489v1 Announce Type: new Abstract: Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute t
arXiv:2606.07334v1 Announce Type: cross Abstract: Harmony is a compact symbolic layer where mathematical pitch relations, acoustic consonance, and musical convention meet. This report treats chord-sym
arXiv:2606.06635v1 Announce Type: cross Abstract: Failures in language model reasoning emerge through distinct processes that leave identifiable signatures in the reasoning trace. We characterize thes
arXiv:2606.07515v1 Announce Type: cross Abstract: We investigate the probabilistic reasoning capabilities of large language models through a controlled benchmarking study on discrete probability probl
arXiv:2606.06743v1 Announce Type: cross Abstract: The popularity of neural audio codecs as speech tokenizers has surged with the advent of Multimodal Large Language Models. New codec architectures wit
arXiv:2606.06727v1 Announce Type: new Abstract: Autonomous cyber-physical systems (CPS) sit at the intersection of Model-Based Systems Engineering (MBSE) and data-driven Machine Learning and Artificia
arXiv:2408.08973v3 Announce Type: replace Abstract: Purpose: A major barrier to the implementation of artificial intelligence for medical applications is automated CNNs' lack of explainability and hig
arXiv:2606.07381v1 Announce Type: cross Abstract: Background and Purpose: Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise lesion-delineated MRI data, which ar
arXiv:2606.07498v1 Announce Type: new Abstract: Scientific observations generate large quantities of unlabeled data which is laborious to hand-label, making unsupervised learning techniques valuable f
arXiv:2606.06586v1 Announce Type: new Abstract: Large language models (LLMs) trained predominantly on English data encode substantial world knowledge, yet often fail to express it reliably in other la
arXiv:2602.06245v2 Announce Type: replace-cross Abstract: Neural-network techniques are often transferred across architecture families by analogy, but such transfer is valid only when the assumptions
arXiv:2606.06664v1 Announce Type: cross Abstract: Despite high accuracy, Vision Transformer (ViT) predictions can be driven by spurious cues, raising the need to understand their inner workings before
arXiv:2312.07762v3 Announce Type: replace Abstract: Psychiatry research seeks to understand the manifestations of psychopathology in behavior, as measured in questionnaire data, by identifying a small
arXiv:2606.06857v1 Announce Type: new Abstract: A central goal of cognitive neuroscience is to characterize the features that are represented by human language cortex. Artificial language models (LMs)
arXiv:2606.06804v1 Announce Type: new Abstract: Digital learning environments record learners' responses to individual items, making it possible to study the development of specific skills rather than
arXiv:2606.06559v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models allow voice agents to listen and speak concurrently, enabling natural interaction with real-time overlap. However,
arXiv:2512.23128v2 Announce Type: replace-cross Abstract: Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking. Their r
arXiv:2606.06671v1 Announce Type: new Abstract: Existing implicit neural representation (INR) approaches suffer from stochastic initialization that does not guarantee consistent or high-quality perfor
arXiv:2601.18510v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights a
arXiv:2606.07240v1 Announce Type: new Abstract: Cross-lingual voice cloning aims to generate speech in a target language while preserving speaker identity from a source-language reference. This task i
arXiv:2606.06797v1 Announce Type: new Abstract: Cultural-aspect work on large language models is dominated by a negative target: which outputs to suppress. We argue that a constructive counterpart is
arXiv:2603.02970v2 Announce Type: replace Abstract: We introduce LAGO, a LocAl-Global Optimization framework coupling Bayesian Optimization (BO) and gradient-based trust region local refinement throug
arXiv:2606.06805v1 Announce Type: cross Abstract: Lane changing entails simultaneous longitudinal and lateral motions that affect driving comfort and mobility efficiency. Because these motions are tig
arXiv:2606.07100v1 Announce Type: new Abstract: Visual-language action (VLA) models enable robots to predict actions directly from observations and language instructions, but their performance depends
arXiv:2605.21706v2 Announce Type: replace Abstract: Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal representat
arXiv:2606.06523v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence. Despite recen
arXiv:2606.06744v1 Announce Type: new Abstract: Two-sided matching markets often involve information that unfolds over time through interviews, repeated interaction, learning, and separation. Existing
arXiv:2606.06790v1 Announce Type: cross Abstract: This paper presents ERNEST, a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension that combines yaw an
arXiv:2606.07127v1 Announce Type: new Abstract: Interactive agents trained only against task return can achieve high scores while failing to represent the mechanisms that make their actions succeed. T
arXiv:2606.06830v1 Announce Type: cross Abstract: Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail. By learning demand models from customer fea
arXiv:2606.07123v1 Announce Type: new Abstract: Social meaning in language is inherently perspectival, varying across annotator backgrounds, demographics, and ideological positions. However, most NLP
arXiv:2601.23207v2 Announce Type: replace-cross Abstract: Understanding what graph neural networks can learn, especially their ability to learn to execute algorithms, remains a central theoretical cha
arXiv:2606.06899v1 Announce Type: new Abstract: Variations in illumination remain a major challenge for visual representation learning, as they induce substantial appearance changes both across and wi
arXiv:2508.17821v3 Announce Type: replace-cross Abstract: This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the i
arXiv:2606.06953v1 Announce Type: new Abstract: We argue that high-quality motion data can steer tracking policies toward better optimization trajectories early in training. In this work, we introduce
arXiv:2601.12375v3 Announce Type: replace-cross Abstract: Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Ne
arXiv:2606.06838v1 Announce Type: cross Abstract: Automatic decompilers produce functionally correct but often unreadable C code. This paper addresses one stage of the reverse engineering workflow: im
arXiv:2603.11333v2 Announce Type: replace Abstract: Short-video platforms are closed-loop, human-in-the-loop ecosystems where platform policy, creator incentives, and user behavior co-evolve. This fee