Knowledge catalogue
arXiv:2603.11164v2 Announce Type: replace-cross Abstract: We present a new self-supervised machine learning approach for symbolic simplification of complex mathematical expressions. Training data is g
arXiv:2511.18322v3 Announce Type: replace-cross Abstract: Learning soft continuum robot (SCR) dynamics from video offers flexibility but existing methods lack interpretability or rely on prior assumpt
arXiv:2604.09787v1 Announce Type: cross Abstract: Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a s
arXiv:2510.14884v3 Announce Type: replace-cross Abstract: In high-stakes AI applications, even a single action can cause irreparable damage. However, nearly all of sequential decision-making theory as
arXiv:2505.21996v3 Announce Type: replace-cross Abstract: Foundational world models must be both interactive and preserve spatiotemporal coherence for effective future planning with action choices. Ho
arXiv:2604.11504v1 Announce Type: new Abstract: This book provides a comprehensive and accessible introduction to the emerging field of AI for mathematics. It covers the core principles and diverse ap
arXiv:2604.11699v1 Announce Type: cross Abstract: This work aims to improve the generalization of logic-based legal reasoning systems by integrating recent advances in NLP with legal-domain adaptive f
arXiv:2604.09566v1 Announce Type: cross Abstract: The application of games as a therapeutic tool for cognitive training is beneficial for patients with cognitive impairments. However, effective game d
arXiv:2604.09635v1 Announce Type: cross Abstract: This study develops machine learning models to assess Media and Information Literacy (MIL) skills specifically in the context of disinformation among
arXiv:2604.10780v1 Announce Type: new Abstract: Three-dimensional (3D) point cloud analysis has become central to applications ranging from autonomous driving and robotics to forestry and ecological m
arXiv:2604.10677v1 Announce Type: cross Abstract: Scaling up robot learning is hindered by the scarcity of robotic demonstrations, whereas human videos offer a vast, untapped source of interaction dat
arXiv:2509.08193v2 Announce Type: replace-cross Abstract: We present FlexiFlow, a lifetime-aware design framework for item-level intelligence (ILI) where computation is integrated directly into dispos
arXiv:2502.14644v5 Announce Type: replace Abstract: Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine
arXiv:2604.11071v1 Announce Type: cross Abstract: We present a lightweight two-stage framework for low-light image enhancement (LLIE) that achieves competitive perceptual quality with significantly fe
arXiv:2604.09998v1 Announce Type: cross Abstract: Large language models (LLMs) have recently emerged as promising tools for augmenting Security Operations Center (SOC) workflows, with vendors increasi
arXiv:2604.11524v1 Announce Type: new Abstract: Surrogates provide a cheap solution evaluation and offer significant leverage for optimizing computationally expensive problems. Usually, surrogates onl
arXiv:2407.05087v2 Announce Type: replace-cross Abstract: Multiplicative noise widely exists in radar images, medical images and other important fields' images. Compared to normal noises, multiplicati
arXiv:2604.09555v1 Announce Type: new Abstract: Multi-criteria Analysis (MCA) is used to rank alternatives based on various criteria. Key MCA methods, such as Multiple Criteria Decision Making (MCDM)
arXiv:2506.14493v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown great promise but require substantial computational resources during inference. Attackers can ex
arXiv:2604.10063v1 Announce Type: new Abstract: Social media research on mental health has focused predominantly on detecting and diagnosing conditions at the individual level. In this work, we shift
arXiv:2604.02600v2 Announce Type: replace-cross Abstract: Developing a novel research idea is hard. It must be distinct enough from prior work to claim a contribution while also building on it. This r
arXiv:2511.14774v3 Announce Type: replace-cross Abstract: Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either
arXiv:2604.10927v1 Announce Type: new Abstract: We propose LiveGesture, the first fully streamable, speech-driven full-body gesture generation framework that operates with zero look-ahead and supports
arXiv:2602.15481v2 Announce Type: replace Abstract: LLM-as-a-judge has emerged as a cornerstone technique for evaluating large language models by leveraging LLM reasoning to score prompt-response pair
arXiv:2507.01264v2 Announce Type: replace-cross Abstract: Designing diverse and safety-critical driving scenarios is essential for evaluating autonomous driving systems. In this paper, we propose a no
arXiv:2604.09620v1 Announce Type: cross Abstract: Large language models are increasingly used to support organizational decisions from hiring to governance, raising fairness concerns in AI-assisted ev
arXiv:2601.10775v2 Announce Type: replace Abstract: We propose a novel LLM-based framework for reasoning in discrete, game-theoretic tasks, illustrated with Tic-Tac-Toe. The method integrates in-conte
arXiv:2604.10834v1 Announce Type: cross Abstract: [Background:] Thematic analysis of free-text justifications in human experiments provides significant qualitative insights. Yet, it is costly because
arXiv:2604.09604v1 Announce Type: new Abstract: Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language m
arXiv:2604.10557v1 Announce Type: cross Abstract: This paper argues that Large Language Models (LLMs) should incorporate explicit mechanisms for human empathy. As LLMs become increasingly deployed in
arXiv:2604.11789v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have achieved remarkable progress in general-purpose vision--language understanding, yet they remain limited in tasks req
arXiv:2510.12117v3 Announce Type: replace-cross Abstract: Chatbot service providers (e.g., OpenAI) rely on tiered subscription plans to generate revenue, offering black-box access to basic models for
arXiv:2604.09970v1 Announce Type: new Abstract: In the decentralized distributed learning, achieving fast convergence and low communication cost is essential for scalability and high efficiency. Adapt
arXiv:2604.10643v1 Announce Type: new Abstract: Reliable confidence estimation is critical when deploying vision models. We study error prediction: determining whether an image classifier's output is
arXiv:2604.11348v1 Announce Type: new Abstract: Efficient and explainable breast cancer (BC) risk prediction is critical for large-scale population-based screening. Breast MRI provides functional info
arXiv:2604.09729v1 Announce Type: cross Abstract: Short-form video platforms have become central to multimedia information dissemination, where comments play a critical role in driving engagement, pro
arXiv:2604.10103v1 Announce Type: new Abstract: Streaming video generation (SVG) distills a pretrained bidirectional video diffusion model into an autoregressive model equipped with sliding window att
arXiv:2512.06713v3 Announce Type: replace-cross Abstract: Current LLM-based frameworks for text anonymization usually rely on remote API services from powerful LLMs, which creates an inherent privacy
arXiv:2601.14706v3 Announce Type: replace Abstract: In this paper, we present LookBench (We use the term 'look' to reflect retrieval that mirrors how people shop -- finding the exact item, a close sub
arXiv:2504.14386v2 Announce Type: replace-cross Abstract: Positional embeddings (PE) play a crucial role in Vision Transformers (ViTs) by providing spatial information otherwise lost due to the permut
arXiv:2604.10044v1 Announce Type: new Abstract: Through systematic experiments on long-context generation, we observe a damaging failure mode in which decoding can collapse into persistent repetition
arXiv:2604.10556v1 Announce Type: new Abstract: While Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive paradigm comparable to autoregressive (AR) models, their fa
arXiv:2604.11792v1 Announce Type: new Abstract: Despite rapid progress in video generation, existing models are incapable of producing vector animation, a dominant and highly expressive form of multim
arXiv:2604.11207v1 Announce Type: new Abstract: This paper reviews the LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment. This challenge aims to raise a new direction, i.e., how
arXiv:2604.10655v1 Announce Type: cross Abstract: This paper presents a review of the LoViF 2026 Challenge on Weather Removal in Videos. The challenge encourages the development of methods for restori
arXiv:2604.11446v1 Announce Type: cross Abstract: Recently, scaling reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs) has emerged as an effective training paradigm
arXiv:2603.21045v4 Announce Type: replace-cross Abstract: Diffusion-based image super-resolution (SR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) observations, yet faces a
arXiv:2604.10862v1 Announce Type: new Abstract: The rapid advancement of diffusion-based generative models has made face forgery detection a critical challenge in digital forensics. Current detection
arXiv:2604.10994v1 Announce Type: new Abstract: In 3D reconstruction, the problem of inverse rendering, namely recovering the illumination of the scene and the material properties, is fundamental. Exi
arXiv:2604.10765v1 Announce Type: cross Abstract: Lung cancer, the second leading cause of cancer-related deaths, is primarily linked to long-term tobacco smoking (85% of cases). Surprisingly, 10-15%
arXiv:2604.10024v1 Announce Type: cross Abstract: Long video summarization presents significant challenges for current multimodal large language models (MLLMs), particularly in maintaining temporal fi
arXiv:2604.01306v2 Announce Type: replace Abstract: Evaluating scientific arguments requires assessing the strict consistency between a claim and its underlying multimodal evidence. However, existing
arXiv:2303.10894v3 Announce Type: replace Abstract: Accurate medical image segmentation is critical for early medical diagnosis. Most existing methods are based on U-shape structure and use element-wi
arXiv:2512.20136v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has recently been extended to multimodal settings, connecting multimodal large language models (MLLMs) wi
arXiv:2604.10534v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) is a new and emerging technology that extends the functionality of large language models, improving workflows but als
arXiv:2604.11513v1 Announce Type: cross Abstract: We review recent advances in machine-learning (ML) force-field methods for large-scale Landau-Lifshitz-Gilbert (LLG) simulations of metallic spin syst
arXiv:2604.11131v1 Announce Type: new Abstract: Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans mak
arXiv:2601.07208v2 Announce Type: replace-cross Abstract: Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), yet its efficacy is
arXiv:2604.10989v1 Announce Type: new Abstract: Emergency situations in scheduling systems often trigger local functional failures that undermine system stability and even cause system collapse. Exist
arXiv:2604.11081v1 Announce Type: new Abstract: High-definition (HD) mapping tasks, which perform lane detections and predictions, are extremely challenging due to non-ideal conditions such as view oc