Knowledge catalogue
arXiv:2602.19574v2 Announce Type: replace-cross Abstract: Large-language-model (LLM)-based text-to-speech (TTS) systems can generate natural speech, but most are not designed for low-latency dual-stre
arXiv:2609.06189v1 Announce Type: new Abstract: This study examines how Customer Relationship Management (CRM), Master Data Management (MDM), and Customer Knowledge Management (CKM) jointly constitute
arXiv:2609.06126v1 Announce Type: new Abstract: We introduce Personalized and Reliable Popular Science Writing, a novel task that requires adapting scientific explanations to audiences with different
arXiv:2609.10350v1 Announce Type: cross Abstract: The banking system now depends on a small set of shared artificial intelligence vendors for fraud screening, credit decisioning, anti-money-laundering
arXiv:2503.12497v2 Announce Type: replace-cross Abstract: Malicious users attempt to replicate commercial models functionally at low cost by training a clone model with query responses. Timely prevent
arXiv:2609.06059v1 Announce Type: new Abstract: As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to as
arXiv:2609.05529v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) predominantly rely on centralized orchestration and lack formal verification mechanisms for
arXiv:2609.10113v1 Announce Type: new Abstract: Financial text, textbooks, and question-answer pairs are abundant, but only a small fraction is directly usable for reasoning-focused post-training. Exi
arXiv:2609.10377v1 Announce Type: cross Abstract: Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities.
arXiv:2509.06285v2 Announce Type: cross Abstract: LiDAR point cloud registration is fundamental to robotic perception and navigation. In geometrically degenerate environments (e.g., corridors), regist
arXiv:2606.05957v2 Announce Type: replace Abstract: Singular learning theory and information geometry study the same spaces: the former in resolved coordinates, the latter in original coordinates unde
arXiv:2609.09413v1 Announce Type: cross Abstract: Choosing a recovery process for scale-up requires connecting laboratory results with product requirements, process costs, and scale effects. We analyz
arXiv:2609.09885v1 Announce Type: cross Abstract: This paper studies unmanned aerial vehicle (UAV)-mouted reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication with st
arXiv:2609.10317v1 Announce Type: new Abstract: Streaming talking-head generation produces each frame as its driving audio arrives, yet fidelity and efficiency have so far pulled in opposite direction
arXiv:2609.09901v1 Announce Type: new Abstract: Large language models often repeatedly select the same answer even when many alternatives are plausible. Prior work treats this concentration as bias, b
arXiv:2609.10479v1 Announce Type: cross Abstract: More Electric Aircraft require fast and reliable monitoring of high-frequency electrical networks, yet most power quality disturbance and fault diagno
arXiv:2609.09971v1 Announce Type: new Abstract: Finger-specific motor intent is a clinically meaningful control signal for post-stroke neurorehabilitation, where residual muscle activity may remain me
arXiv:2609.10308v1 Announce Type: new Abstract: Manipulating deformable objects (DOs) is challenging due to their high-dimensional state space, underactuated dynamics, and partial observability. In th
arXiv:2609.09359v1 Announce Type: new Abstract: Polarimetric vision is gaining increasing attention because it provides physical cues about scene shape, material, and reflection that are difficult to
arXiv:2609.09217v1 Announce Type: new Abstract: Legged robots are versatile on land, but their use in underwater environments remains limited. Extending quadruped locomotion to water enables amphibiou
arXiv:2609.10022v1 Announce Type: cross Abstract: Modern TTS systems approach human quality for high-resource languages but degrade when clean speech data is scarce. Modern Greek exemplifies this, lac
arXiv:2609.06674v1 Announce Type: cross Abstract: We present a new attack that reconstructs the text generated by locally hosted LLMs by observing CPU cache activity during detokenization. Unlike prio
arXiv:2609.09912v1 Announce Type: new Abstract: Machine learning models used in engineering are typically trained within limited operating ranges, yet reliable predictions are often required beyond th
arXiv:2510.17072v2 Announce Type: replace-cross Abstract: Regression with non-Euclidean responses---e.g., probability distributions, networks, symmetric positive-definite matrices, and compositions---
arXiv:2609.07316v2 Announce Type: new Abstract: Due to the proliferation of vehicle trajectory data enabled by advanced sensing technologies, path representation learning has become a pivotal task in
arXiv:2609.05776v1 Announce Type: new Abstract: Evaluating enterprise agents on domain-specific benchmarks is critical, yet public benchmarks rarely evaluate whether agents can integrate business know
arXiv:2609.06181v1 Announce Type: cross Abstract: Efficiently aggregating and orchestrating computing power across heterogeneous clusters for HPC workflows faces four practical challenges: preserving
arXiv:2609.09536v1 Announce Type: cross Abstract: Average treatment effect (ATE) estimation in observational studies is a fundamental statistical tool used frequently in social science, medicine, and
arXiv:2609.09254v1 Announce Type: new Abstract: Field-programmable gate arrays (FPGAs) enable efficient neural-network inference, but most deployment flows either accelerate multiply-accumulate operat
arXiv:2609.05779v1 Announce Type: cross Abstract: Large language models used for code editing can be trained and deployed in at least two output regimes: direct generation, where the model emits the e
arXiv:2609.10334v1 Announce Type: new Abstract: This paper addresses the issue of supervised classification in the context of hyperspectral satellite images. It deals with two fundamental aspects: dim
arXiv:2609.10052v1 Announce Type: new Abstract: LLM agents for sequential decision tasks are often post-trained with trajectory-level outcome labels, but such labels provide little supervision for pre
arXiv:2609.10253v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in globally used assistants, yet their default choices in culturally grounded everyday situations
arXiv:2609.07222v1 Announce Type: new Abstract: Transformer surrogates for 3D flow prediction compress an industrial mesh into a small set of tokens from which every prediction point reads. Two operat
arXiv:2609.09586v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are increasingly pre-trained on synthetically generated time series trajectories, where the data generating proc
arXiv:2609.09737v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) enhances the robustness of models against distribution shifts but faces two critical challenges: error accumulation from nois
arXiv:2609.05782v1 Announce Type: new Abstract: Vision-language models (VLMs) offer a promising alternative to conventional fire detection systems by reasoning about the semantic context of a scene an
arXiv:2609.09241v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling model capacity while preserving efficient inference in large fo
arXiv:2511.18031v1 Announce Type: cross Abstract: Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that is
arXiv:2609.07754v1 Announce Type: cross Abstract: AI coding assistants now select, install, and configure software, and attackers have exploited that position through invented package names, compromis
arXiv:2609.08189v1 Announce Type: new Abstract: Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, how
arXiv:2609.07879v1 Announce Type: new Abstract: Large Language Models (LLMs) are frequently confident, eloquent, and well versed. A natural question arises: do they know what they don't know? To answe
arXiv:2609.09363v1 Announce Type: new Abstract: Large language models (LLMs) are prone to hallucinating or misinterpreting facts, which impairs their usability in retrieval-augmented generation or dat
arXiv:2609.08574v1 Announce Type: cross Abstract: Long context language models now advertise windows of one million tokens, but two habits limit how much of that window is used. Attention heads with n
arXiv:2609.10434v1 Announce Type: new Abstract: Self-supervised speech foundation models are now used in a wide array of downstream applications, including traditional speech recognition and as the ba
arXiv:2602.05354v3 Announce Type: replace Abstract: Autonomous web agents are increasingly deployed in moderation and policy enforcement, where correct decisions often depend on evidence that is not i
arXiv:2609.08186v1 Announce Type: new Abstract: The emergence of Chain-of-Thought (CoT) has established a robust foundation for Large Reasoning Models (LRMs). While deep reasoning is widely believed t
arXiv:2601.13591v3 Announce Type: replace Abstract: Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-
arXiv:2609.10506v1 Announce Type: cross Abstract: Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However,
arXiv:2609.10028v1 Announce Type: cross Abstract: Multidimensional item response theory (MIRT) is a statistical test theory that precisely estimates multiple latent skills of learners from the respons
arXiv:2609.08609v2 Announce Type: replace Abstract: Tracking semantic change in low-resource languages across extensive historical timelines presents significant challenges due to data scarcity and th
arXiv:2609.05553v1 Announce Type: new Abstract: Agent memory allows LLM agents to use earlier interactions when answering new queries. Existing methods often compress interaction histories into summar
arXiv:2609.09425v1 Announce Type: new Abstract: Educational data filters have become a practical way to improve language-model pre-training, but most filters treat educational value as a single scalar
arXiv:2609.09728v1 Announce Type: new Abstract: Source-level analysis of interictal epileptiform discharges (IEDs) is relevant to presurgical evaluation and treatment planning because it helps charact
arXiv:2410.20487v5 Announce Type: replace Abstract: Experience replay is widely used to improve learning efficiency in reinforcement learning by leveraging past experiences. However, existing experien
arXiv:2609.09486v1 Announce Type: cross Abstract: Fairness auditing of text-to-image diffusion models often requires generating large numbers of images across sampling configurations, making comprehen
arXiv:2609.09708v1 Announce Type: cross Abstract: 6G wireless technology is poised to adopt higher and wider frequency bands, leveraging highly directional beamforming. However, the vast bandwidths am
arXiv:2609.09433v1 Announce Type: new Abstract: Leave-One-Subject-Out (LOSO) evaluation estimates generalisation performance for subject-based classification but makes Neural Architecture Search (NAS)
arXiv:2609.09721v1 Announce Type: new Abstract: Low-bit attention accelerates Transformer inference by moving the QK^op and PV matrix multiplications to FP8 or FP4 matrix engines. However, the softmax
arXiv:2609.07247v1 Announce Type: new Abstract: Scaling the interaction horizon-the maximum number of environment interactions per episode-improves LLM agents on long-horizon tasks, and curriculum-bas