Knowledge catalogue
arXiv:2602.22913v2 Announce Type: replace-cross Abstract: With the rapid evolution of Large Language Models (LLMs), generative recommendation is gradually reshaping the paradigm of recommender systems
arXiv:2604.16842v1 Announce Type: cross Abstract: This thesis develops numerical and theoretical approaches for understanding and analyzing singularity formation in Partial Differential Equations (PDE
arXiv:2604.17350v1 Announce Type: cross Abstract: Time series forecasting is traditionally dominated by sequence-based architectures such as recurrent neural networks and attention mechanisms, which p
arXiv:2601.21159v2 Announce Type: replace Abstract: High-resolution remote sensing images contain densely distributed objects with pronounced scale variations and complex boundaries, which impose high
arXiv:2604.17385v1 Announce Type: new Abstract: Spatial intelligence, which refers to the ability to reason about geometric and physical structure from visual observations, remains a core challenge fo
arXiv:2603.27437v2 Announce Type: replace Abstract: Large vision-language models (VLMs) still struggle with reliable 3D spatial reasoning, a core capability for embodied and physical AI systems. This
arXiv:2512.21204v2 Announce Type: replace Abstract: Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compar
arXiv:2604.17358v1 Announce Type: new Abstract: While recent Spoken Language Models (SLMs) have been actively deployed in real-world scenarios, they lack the capability to discern Third-Party Interrup
arXiv:2601.06116v2 Announce Type: replace-cross Abstract: Generative AI models reproduce the biases in the training data and can further amplify them through mode collapse. We refer to the resulting h
arXiv:2603.18361v2 Announce Type: replace Abstract: Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also considering m
arXiv:2412.08812v2 Announce Type: replace Abstract: Reward models trained on aggregate preferences often fail to capture individual users' values, but existing adaptation methods such as fine-tuning o
arXiv:2509.07024v2 Announce Type: replace-cross Abstract: The Trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulation
arXiv:2507.01936v3 Announce Type: replace Abstract: Large language models (LLMs) are excellent at maintaining high-level, convincing dialogue, but it remains unclear whether their persuasive success r
arXiv:2604.16502v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning, while recent extensions that incorpo
arXiv:2604.16888v1 Announce Type: new Abstract: Parameter-free stochastic optimization aims to design algorithms that are agnostic to the underlying problem parameters while still achieving convergenc
arXiv:2604.17320v1 Announce Type: new Abstract: Deploying Vision-Language Models (VLMs) under aggressive low-bit inference remains challenging because inference cost is dominated by the long visual-to
arXiv:2604.18067v1 Announce Type: new Abstract: The miniaturisation of neural processing units (NPUs) and other low-power accelerators has enabled their integration into microcontroller-scale wearable
arXiv:2604.17658v1 Announce Type: cross Abstract: Large Language Model (LLM)-based Multi-Agent Systems (MAS) enable complex problem-solving but introduce significant debugging challenges, characterize
arXiv:2508.11281v3 Announce Type: replace Abstract: Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in
arXiv:2604.18539v1 Announce Type: new Abstract: This paper studies how empirical dialogue-flow statistics can be incorporated into Next Dialogue Act Prediction (NDAP). A KL regularization term is prop
arXiv:2604.17480v1 Announce Type: new Abstract: In principle, deep generative models can be used to perform domain adaptation; i.e. align the input feature representations of test data with that of a
arXiv:2604.18001v1 Announce Type: new Abstract: Super-resolution (SR) models are attracting growing interest for enhancing minimally invasive surgery and diagnostic videos under hardware constraints.
arXiv:2604.16976v1 Announce Type: new Abstract: Point cloud denoising is a fundamental and crucial challenge in real-world point cloud applications. Existing quantitative evaluation metrics for point
arXiv:2405.13068v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have revolutionized various applications, making robust safety alignment essential to prevent harmful outputs. Cu
arXiv:2507.21545v3 Announce Type: replace Abstract: Robotic task planning in real-world environments requires reasoning over implicit constraints from language and vision. While LLMs and VLMs offer st
arXiv:2604.18264v1 Announce Type: new Abstract: Zeroth-Order optimization presents a promising memory-efficient paradigm for fine-tuning Large Language Models by relying solely on forward passes. Howe
arXiv:2604.17525v1 Announce Type: cross Abstract: Medical imaging AI development is fundamentally dependent on annotated datasets, yet no existing standard provides machine-enforceable validation acro
arXiv:2604.17050v1 Announce Type: new Abstract: With the rapid development of embodied intelligence, robotics education faces a dual challenge: high computational barriers and cumbersome environment c
arXiv:2604.16916v1 Announce Type: new Abstract: Safety alignment in large language models (LLMs) is primarily evaluated under open-ended generation, where models can mitigate risk by refusing to respo
arXiv:2604.16841v1 Announce Type: new Abstract: Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observation
arXiv:2604.16369v1 Announce Type: cross Abstract: Global corporate AI investment reached $252.3 billion in 2024, yet only 6% of firms report significant earnings impact. This article argues that AI pr
arXiv:2604.16745v1 Announce Type: cross Abstract: Training-free token reduction methods for Vision Transformers (ToMe, ToFu, PiToMe, and MCTF) employ different scoring mechanisms, yet they share a clo
arXiv:2604.18331v1 Announce Type: new Abstract: The physicality of exercise makes the role of athletic trainers unique. Their physical presence allows them to guide a student through a motion, demonst
arXiv:2506.05760v2 Announce Type: replace Abstract: Recent advances in Large Language Models(LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited:
arXiv:2604.16917v1 Announce Type: new Abstract: Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single domi
arXiv:2604.15958v1 Announce Type: cross Abstract: Despite the considerable promise of Retrieval-Augmented Generation (RAG), many real-world use cases may create privacy concerns, where the purported u
arXiv:2602.17909v2 Announce Type: replace Abstract: Diffusion-based approaches have recently demonstrated strong performance for single-image novel view synthesis by conditioning generative models on
arXiv:2604.15617v1 Announce Type: cross Abstract: Hyperbolic conservation laws govern a wide range of transport-driven dynamics featuring shocks, contact discontinuities, and complex wave interactions
arXiv:2604.16239v1 Announce Type: cross Abstract: In multi-fidelity optimization, biased approximations of varying costs of the target function are available. This paper studies the problem of optimiz
arXiv:2602.00052v2 Announce Type: replace-cross Abstract: Increasing clinical trial protocol complexity, amendments, and challenges around knowledge management create significant burden for trial team
arXiv:2604.16207v1 Announce Type: cross Abstract: As forgery types continue to emerge consistently, Incremental Face Forgery Detection (IFFD) has become a crucial paradigm. However, existing methods t
arXiv:2604.15371v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance across many natural language processing tasks, yet their decision processes remain difficult t
arXiv:2509.01944v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models in autonomous driving systems have recently demonstrated transformative potential by integrating multimoda
arXiv:2604.15359v1 Announce Type: cross Abstract: Understanding communication behavior in modern system-on-chip (SoC) designs is critical for functional verification, performance analysis, and post-si
arXiv:2402.03038v2 Announce Type: replace-cross Abstract: In few-shot learning, the selection of samples has a significant impact on the performance of the model. While effective sample selection stra
arXiv:2604.15334v1 Announce Type: cross Abstract: The exponential growth of AI education has brought millions of learners to online platforms, yet this massive scale has simultaneously exposed critica
arXiv:2604.15821v1 Announce Type: cross Abstract: Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic mol
arXiv:2602.05523v2 Announce Type: replace-cross Abstract: Agentic large language models (LLMs) are increasingly evaluated on cybersecurity tasks using capture-the-flag (CTF) benchmarks, yet existing p
arXiv:2604.16060v1 Announce Type: cross Abstract: Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. Howe
arXiv:2604.15769v1 Announce Type: cross Abstract: Spiking transformers achieve competitive accuracy with conventional transformers while offering 38-57imes energy efficiency on neuromorphic hardware,
arXiv:2603.08899v2 Announce Type: replace Abstract: Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models to
arXiv:2604.15814v1 Announce Type: new Abstract: Hand-eye calibration through visual localization is a critical capability for robotic manipulation in open-world environments. However, most deep learni
arXiv:2604.15593v1 Announce Type: cross Abstract: Large language models compress heterogeneous knowledge into a single parameter space, allowing facts from different domains to interfere during genera
arXiv:2603.01098v2 Announce Type: replace-cross Abstract: Differential privacy (DP)'s effect in medical imaging is typically evaluated only through end-to-end performance, leaving the mechanism of pri
arXiv:2604.16015v1 Announce Type: cross Abstract: Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent
arXiv:2604.15842v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive capabilities, yet their internal mechanisms for handling reasoning-intensive tasks remain unde
arXiv:2604.15319v1 Announce Type: cross Abstract: Exploratory analysis of high-dimensional data relies on embedding the data into a low-dimensional space (typically 2D or 3D), based on which visualiza
arXiv:2604.15388v1 Announce Type: cross Abstract: Recent advances in large language models have improved code generation, but their use in hardware description languages is still limited. Moreover, tr
arXiv:2604.15805v1 Announce Type: cross Abstract: Learning robust robot policies in real-world environments requires diverse data augmentation, yet scaling real-world data collection is costly due to
arXiv:2602.01566v2 Announce Type: replace Abstract: Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research o