Knowledge catalogue
arXiv:2604.24443v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated strong performance on textbook-style physics problems, yet they frequently fail when confronted with dyn
arXiv:2604.22823v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) rely on multimodal pre-training over diverse data sources, where different datasets often induce complementar
arXiv:2604.06094v2 Announce Type: replace-cross Abstract: Convolutional neural networks owe much of their success to hard-coding translation equivariance. Quantum convolutional neural networks (QCNNs)
arXiv:2501.02548v2 Announce Type: replace-cross Abstract: Deploying learned decision-making systems often requires transferring to new sites where the sensing pipeline differs. In such cases, observat
arXiv:2604.24171v1 Announce Type: new Abstract: Current visual text generation models struggle with the trade-off between text accuracy and overall image coherence. We find that achieving high text ac
arXiv:2604.24524v1 Announce Type: new Abstract: Clinical fusion of Single Photon Emission Computed Tomography Myocardial Perfusion Imaging (SPECT MPI) and Computed Tomography Angiography (CTA) remains
arXiv:2604.24586v1 Announce Type: new Abstract: Single-image point cloud reconstruction must infer complete 3D geometry, including occluded parts, from a single RGB image. While diffusion-based recons
arXiv:2604.24169v1 Announce Type: new Abstract: 3D point cloud perception remains tightly coupled to custom CUDA operators for spatial operations, limiting portability and efficiency on non-NVIDIA, AM
arXiv:2509.25424v5 Announce Type: replace-cross Abstract: Reinforcement learning fine-tuning (RLFT) is a dominant paradigm for improving pretrained policies for downstream tasks. These pretrained poli
arXiv:2510.17881v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. W
arXiv:2505.01700v3 Announce Type: replace Abstract: Existing protein-ligand docking studies typically focus on the self-docking scenario, which is less practical in real applications. Moreover, some s
arXiv:2604.04177v2 Announce Type: replace Abstract: As large language models (LLMs) are increasing integrated into fact-checking pipelines, formal logic is often proposed as a rigorous means by which
arXiv:2604.24020v1 Announce Type: cross Abstract: Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering, yet
arXiv:2511.19339v2 Announce Type: replace Abstract: In computer vision, machine unlearning aims to remove the influence of specific visual concepts or training images without retraining from scratch.
arXiv:2512.22113v2 Announce Type: replace-cross Abstract: Unresolved production cloud incidents cost an average of over $2M per hour. This paper introduces PRAXIS, an orchestrator that manages and dep
arXiv:2511.16839v3 Announce Type: replace-cross Abstract: Heart failure (HF) discharge planning depends on identifying patients at risk of deterioration or death, yet accurate prediction from routinel
arXiv:2604.22882v1 Announce Type: new Abstract: Modern container ships face higher wind loads due to increased windage areas, making accurate predictions of wind loads essential for mooring design. Ex
arXiv:2604.23543v1 Announce Type: cross Abstract: Test-time alignment methods offer a promising alternative to fine-tuning by steering the outputs of large language models (LLMs) at inference time wit
arXiv:2602.03886v3 Announce Type: replace-cross Abstract: Prenatal psychological stress affects 15-25% of pregnancies and increases risks of preterm birth, low birth weight, and adverse neurodevelopme
arXiv:2604.23036v1 Announce Type: cross Abstract: Despite MoE models leading many benchmarks, supervised fine-tuning (SFT) for the MoE architectures remains difficult because its router layers are fra
arXiv:2604.24356v1 Announce Type: cross Abstract: What do recurrent neural networks, polynomial ODEs, and discrete polynomial maps each bring to computation, and what do they lack? All three operate o
arXiv:2604.24517v1 Announce Type: new Abstract: Robust forecast aggregation combines the predictions of multiple information sources to perform well in the worst case across all possible information s
arXiv:2604.23514v1 Announce Type: cross Abstract: The health condition of components in civil infrastructures can be described by various discrete states according to their performance degradation. In
arXiv:2602.11079v3 Announce Type: replace-cross Abstract: We propose probe-based data attribution, a method that traces behavioral changes in post-trained language models to responsible training datap
arXiv:2604.24642v1 Announce Type: new Abstract: The dream of instantly creating rich 360-degree panoramic worlds from text is rapidly becoming a reality, yet a crucial gap exists in our ability to rel
arXiv:2604.22868v1 Announce Type: cross Abstract: Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation. Yet, in m
arXiv:2604.23333v1 Announce Type: cross Abstract: Scaling test-time computation with reinforcement learning (RL) has emerged as a reliable path to improve large language models (LLM) reasoning ability
arXiv:2604.23099v1 Announce Type: cross Abstract: Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models
arXiv:2604.24154v1 Announce Type: cross Abstract: The Universal Approximation Theorem (UAT) guarantees universal function approximation but does not explain how residual models distribute approximatio
arXiv:2604.24295v1 Announce Type: new Abstract: Inefficient driving behaviors, such as overly conservative yielding, remain a key obstacle to deployment of autonomous vehicles (AVs). Instantaneous dri
arXiv:2604.23974v1 Announce Type: new Abstract: Fake news generally refers to false information that is spread deliberately to deceive people, which has detrimental social effects. Existing fake news
arXiv:2604.23238v1 Announce Type: cross Abstract: Frontier models push the boundaries of what is learnable at extreme computational costs, yet distillation via sampling reasoning traces exposes closed
arXiv:2604.24126v1 Announce Type: new Abstract: Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data sca
arXiv:2604.24674v1 Announce Type: new Abstract: Radar offers unique advantages for localization in unstructured environments, including robustness to weather, lighting, and airborne particulates. Whil
arXiv:2604.23407v1 Announce Type: cross Abstract: Large vision-language models (VLMs) can recognize extit{what} happens in video but fail to count extit{how many} times. We introduce extbf{PushupBench
arXiv:2604.24021v1 Announce Type: new Abstract: We explore a central question in AI for mathematics: can AI systems produce original, nontrivial proofs for open research problems? Despite strong bench
arXiv:2604.24052v1 Announce Type: cross Abstract: Video-to-text summarization remains underexplored in terms of comprehensive evaluation methods. Traditional n-gram overlap-based metrics and recent la
arXiv:2503.05587v3 Announce Type: replace-cross Abstract: Robustness has become a critical attribute for the deployment of RAG systems in real-world applications. Existing research focuses on robustne
arXiv:2604.22891v1 Announce Type: cross Abstract: LLM-as-a-Judge has become a dominant approach in automated evaluation systems, playing critical roles in model alignment, leaderboard construction, qu
arXiv:2604.22760v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly operate as autonomous agents that reason over external APIs to perform complex tasks. However, their reliabi
arXiv:2604.24597v1 Announce Type: cross Abstract: We provide evidence of quantum kernel advantage under noiseless simulation in binary insurance classification on MIMIC-CXR chest radiographs using qua
arXiv:2604.23972v1 Announce Type: cross Abstract: Knowledge graphs (KGs) are increasingly used to support large lan guage model (LLM) reasoning, but standard triplet-based KGs treat each relation as g
arXiv:2604.23720v1 Announce Type: new Abstract: Metanetworks are neural architectures designed to operate directly on pretrained weights to perform downstream tasks. However, the parameter space serve
arXiv:2604.23922v1 Announce Type: cross Abstract: In this paper, we introduce the Quasi-Quadratic Gradient (QQG), a novel search direction designed to accelerate the BFGS method within the quasi-Newto
arXiv:2604.23816v1 Announce Type: cross Abstract: Software documentation frequently becomes outdated or fails to exist entirely, yet developers need focused views of their codebase to understand compl
arXiv:2510.11541v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge so
arXiv:2604.23702v1 Announce Type: new Abstract: Humanoid robots operating in human-centered environments (e.g., homes, hospitals, and offices) must mitigate foot--ground impact transients, as impact-i
arXiv:2604.24543v1 Announce Type: new Abstract: RGB-Thermal (T) crowd counting aims to integrate visible-spectrum and thermal infrared information to improve the robustness of crowd density estimation
arXiv:2603.19670v3 Announce Type: replace Abstract: Nonasymptotic diffusion analyses often decompose sampling error into score estimation, continuous reverse-time propagation, discretization, and term
arXiv:2604.22755v1 Announce Type: cross Abstract: Reliable decision support in nuclear engineering requires traceable, domain-grounded knowledge retrieval, yet safety and risk analysis workflows remai
arXiv:2604.24230v1 Announce Type: new Abstract: Skull-base meningiomas are often characterized by favorable long-term prognosis, yet their anatomical complexity and proximity to critical neurovascular
arXiv:2604.23681v1 Announce Type: cross Abstract: A widely cited result by Dong et al. (2021) showed that Transformers built from self-attention alone, without skip connections or feed-forward layers,
arXiv:2604.24278v1 Announce Type: cross Abstract: Automatic speech recognition systems often produce confident yet incorrect transcriptions under noisy or ambiguous conditions, which can be misleading
arXiv:2604.23190v1 Announce Type: cross Abstract: Automating repository-level software engineering tasks is a foundational challenge for autonomous code agents, largely due to the difficulty of config
arXiv:2604.23644v1 Announce Type: cross Abstract: Intelligent document processing pipelines extract structured entities (tables, images, and text) from documents for use in downstream systems such as
arXiv:2604.22800v1 Announce Type: cross Abstract: Motivation: Structural Biologists have contributed more than 245,000 experimentally determined three-dimensional structures of biological macromolecul
arXiv:2604.23685v1 Announce Type: new Abstract: Accurate text recognition in low-light environments is essential for intelligent systems in applications ranging from autonomous vehicles to smart surve
arXiv:2604.23696v1 Announce Type: new Abstract: Haptic feedback has been a long-missed feature in robotic-assisted surgery, one that would allow surgeons to perceive tissue properties and apply contro
arXiv:2604.24628v1 Announce Type: new Abstract: Proprietary design in commercial windrow-detection systems restricts transparency and limits progress in open autonomous forage-harvesting research. We
arXiv:2602.07096v2 Announce Type: replace-cross Abstract: Reliable financial reasoning requires knowing not only how to answer, but also when an answer cannot be justified. In real financial practice,