Knowledge catalogue
arXiv:2508.06869v4 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) demonstrate exceptional performance in vision-language tasks, yet their processing of long videos is
arXiv:2604.08558v1 Announce Type: cross Abstract: Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically
arXiv:2604.09048v1 Announce Type: cross Abstract: While the large energy consumption of Large Language Models (LLMs) is recognized by the community, system operators lack guidance for energy-efficient
arXiv:2604.08763v1 Announce Type: cross Abstract: We extend the Weak Adversarial Neural Pushforward Method to the Wigner transport equation governing the phase-space dynamics of quantum systems. The c
arXiv:2510.06499v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved remarkable success through imitation learning on vast text corpora, but this paradigm creates a tra
arXiv:2604.08716v1 Announce Type: new Abstract: Virtual Try-On (VTON) has seen rapid advancements, providing a strong foundation for generative fashion tasks. However, the inverse problem, Virtual Try
arXiv:2602.17667v2 Announce Type: replace-cross Abstract: In video search systems, user historical behaviors provide rich context for identifying search intent and resolving ambiguity. However, tradit
arXiv:2510.07517v5 Announce Type: replace Abstract: Multi-agent debate (MAD) aims to improve large language model (LLM) reasoning by letting multiple agents exchange answers and then aggregate their o
arXiv:2602.22918v2 Announce Type: replace Abstract: Vision-language models (VLMs) can read text from images, but where does this optical character recognition (OCR) information enter the language proc
arXiv:2512.12641v2 Announce Type: replace Abstract: The Unigram tokenization algorithm offers a probabilistic alternative to the greedy heuristics of Byte-Pair Encoding. Despite its theoretical elegan
arXiv:2603.03099v5 Announce Type: replace-cross Abstract: Despite Adam demonstrating faster empirical convergence than SGD in many applications, much of the existing theory yields guarantees essential
arXiv:2604.08626v1 Announce Type: new Abstract: Understanding objects in 3D from a single image is a cornerstone of spatial intelligence. A key step toward this goal is monocular 3D object detection--
arXiv:2604.08728v1 Announce Type: new Abstract: Cooperation in multi-agent reinforcement learning (MARL) benefits from inter-agent communication, yet most approaches assume idealized channels and exis
arXiv:2604.08958v1 Announce Type: cross Abstract: Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to
arXiv:2604.09489v1 Announce Type: cross Abstract: Model poisoning attacks pose a significant security threat to Federated Learning (FL). Most existing model poisoning attacks rely on collusion, requir
arXiv:2604.09413v1 Announce Type: cross Abstract: This paper argues that a one-size-fits-all approach to specifying consent for the use of creative works in generative AI is insufficient. Real-world o
arXiv:2604.09501v1 Announce Type: new Abstract: Norm, the formal theoretical linguist, and Claudette, the computational language scientist, have a lovely time discussing whether modern language models
arXiv:2603.15757v2 Announce Type: replace-cross Abstract: What happens when a pretrained generative robot policy is provided a constant initial noise as input, rather than repeatedly sampling it from
arXiv:2602.00821v2 Announce Type: replace Abstract: The secure analysis of dermatological images in clinical environments is fundamentally restricted by the critical trade-off between patient privacy
arXiv:2604.08042v1 Announce Type: new Abstract: Sketching in 3D space enables expressive reasoning about shape, structure, and spatial relationships, yet generating 3D sketches through natural languag
arXiv:2604.06227v1 Announce Type: new Abstract: Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developi
arXiv:2604.04614v2 Announce Type: replace-cross Abstract: Deep learning-based modeling of multimodal Electronic Health Records (EHRs) has become an important approach for clinical diagnosis and risk p
arXiv:2604.07258v1 Announce Type: new Abstract: In this growing age of data and technology, large black-box models are becoming the norm due to their ability to handle vast amounts of data and learn i
arXiv:2604.06207v1 Announce Type: cross Abstract: This paper investigates demonstration selection strategies for predicting a user's next point-of-interest (POI) using large language models (LLMs), ai
arXiv:2501.16150v3 Announce Type: replace Abstract: Agents for computer use (ACUs) are an emerging class of systems capable of executing complex tasks on digital devices -- such as desktops, mobile ph
arXiv:2604.06864v1 Announce Type: cross Abstract: Clustering in high-dimensional settings with severe feature noise remains challenging, especially when only a small subset of dimensions is informativ
arXiv:2604.07981v1 Announce Type: new Abstract: Long-context reasoning is essential for complex real-world applications, yet remains a significant challenge for Large Language Models (LLMs). Despite t
arXiv:2604.06940v1 Announce Type: cross Abstract: Most neural solvers for the Traveling Salesperson Problem (TSP) are trained to output a single solution, even though practitioners rarely stop there:
arXiv:2604.08381v1 Announce Type: new Abstract: Sarcasm is a rhetorical device that expresses criticism or emphasizes characteristics of certain individuals or situations through exaggeration, irony,
arXiv:2604.06531v2 Announce Type: cross Abstract: The mean-field Schrodinger bridge (MFSB) problem concerns designing a minimum-effort controller that guides a diffusion process with nonlocal intera
arXiv:2402.12797v4 Announce Type: replace Abstract: We introduce a novel approach for the reconstruction of tubular shapes from skeletal representations. Our method processes all skeletal points as a
arXiv:2504.20906v4 Announce Type: replace-cross Abstract: The continuous monitoring of the interactions between cyber-physical components of any industrial control system (ICS) is required to secure a
arXiv:2604.06184v1 Announce Type: cross Abstract: We propose a personalized chatbot designed for elderly individuals. The chatbot initiates discussions based on family photos, encouraging users to int
arXiv:2604.06666v1 Announce Type: cross Abstract: Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations. Existing methods incorporating
arXiv:2602.03604v3 Announce Type: replace-cross Abstract: We present EB-JEPA, an open-source library for learning representations and world models using Joint-Embedding Predictive Architectures (JEPAs
arXiv:2604.07017v1 Announce Type: new Abstract: AI assistants that interact with users over time need to interpret the user's current emotional state in order to respond appropriately and personally.
arXiv:2604.06327v1 Announce Type: cross Abstract: Recent diffusion-based text-to-speech (TTS) models achieve high naturalness and expressiveness, yet often suffer from speaker drift, a subtle, gradual
arXiv:2604.06650v1 Announce Type: cross Abstract: Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at
arXiv:2604.07395v1 Announce Type: cross Abstract: Robotic manipulation systems that follow language instructions often execute grasp primitives in a largely single-shot manner: a model proposes an act
arXiv:2604.06365v1 Announce Type: cross Abstract: Arabic medical text generation is increasingly needed to help users interpret symptoms and access general health guidance in their native language. Ne
arXiv:2604.08528v1 Announce Type: new Abstract: Reliable in-hand manipulation requires accurate real-time estimation of slip between a gripper and a grasped object. Existing tactile sensing approaches
arXiv:2604.08443v1 Announce Type: new Abstract: The potential of Animal-Robot Interaction (ARI) in welfare applications depends on how much an animal perceives a robotic agent as socially relevant, no
arXiv:2604.07129v1 Announce Type: cross Abstract: Numerical simulation of wave propagation and run-up is a cornerstone of coastal engineering and tsunami hazard assessment. However, applying these for
arXiv:2510.04628v2 Announce Type: replace Abstract: Deep learning-based methods have achieved significant success in remote sensing Earth observation data analysis. Numerous feature fusion techniques
arXiv:2503.17181v3 Announce Type: replace-cross Abstract: Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntact
arXiv:2408.05086v3 Announce Type: replace Abstract: Neural language models (LMs) have been shown to capture complex linguistic patterns, yet their utility in understanding human language and more broa
arXiv:2604.07274v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in medical question answering; however, purely parametric models often suffer from
arXiv:2604.08341v1 Announce Type: new Abstract: Current Human-Robot Interaction (HRI) systems for skill teaching are fragmented, and existing approaches in the literature do not offer a cohesive frame
arXiv:2604.08050v1 Announce Type: new Abstract: In this study, we focus on video captioning by fully open multimodal large language models (MLLMs). The comprehension of visual sequences is challenging
arXiv:2604.06561v1 Announce Type: cross Abstract: High-dimensional hyperspectral imaging (HSI) enables the visualization of ultrafast molecular dynamics and complex, heterogeneous spectra. However, ap
arXiv:2604.07122v1 Announce Type: cross Abstract: In semantic segmentation, the creation of pixel-level labels for training data incurs significant costs. To address this problem, semi-supervised lear
arXiv:2604.07775v1 Announce Type: cross Abstract: Collaboration and information sharing empower Multi-Agent Systems (MAS) but also introduce a critical security risk known as Agent Cascading Injection
arXiv:2604.08545v1 Announce Type: new Abstract: The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a pro
arXiv:2511.20162v2 Announce Type: replace Abstract: Large multi-modal models (LMMs) show increasing performance in realistic visual tasks for images and, more recently, for videos. For example, given
arXiv:2604.07480v1 Announce Type: new Abstract: Reward machines are automaton-like structures that capture the memory required to accomplish a multi-stage task. When combined with reinforcement learni
arXiv:2403.03208v3 Announce Type: replace-cross Abstract: Inspired by the concept of active learning, we propose active inferencenicode{x2013}a methodology for statistical inference with machine-l
arXiv:2604.08534v1 Announce Type: new Abstract: Large-scale real-world robot data collection is a prerequisite for bringing robots into everyday deployment. However, existing pipelines often rely on s
arXiv:2604.07055v1 Announce Type: new Abstract: We give a computer-assisted counterexample to the open question, posed by Rudin, Schapire, and Daubechies in COLT 2012, of whether exhaustive AdaBoost a
arXiv:2604.07615v1 Announce Type: new Abstract: In language model interpretability research, extbf{circuit tracing} aims to identify which internal features causally contributed to a particular outp
arXiv:2411.02622v3 Announce Type: replace-cross Abstract: Machine unlearning, enabling a trained model to forget specific data, is crucial for addressing erroneous data and adhering to privacy regulat