Knowledge catalogue
arXiv:2411.14833v4 Announce Type: replace-cross Abstract: Conventional multi-stage cell tracking approaches rely heavily on detection or segmentation in each frame as a prerequisite, requiring substan
arXiv:2607.05325v1 Announce Type: new Abstract: Cerebral microbleeds (CMBs) are MRI markers of small vessel disease and the microbleed component of amyloid related imaging abnormalities (ARIA-H), but
arXiv:2607.04123v1 Announce Type: new Abstract: Inverse design of mechanical metamaterials seeks a periodic unit cell whose homogenized elastic properties meet a prescribed target, but current learnin
arXiv:2607.03819v1 Announce Type: cross Abstract: Challenges remain in ego-centric 3D scene generation due to limited view overlap and the dominant influence of individual perspectives on scene interp
arXiv:2508.21010v3 Announce Type: replace-cross Abstract: Existing Causal-Why Video Question Answering (VideoQA) models often struggle with higher-order reasoning, relying on opaque, monolithic pipeli
arXiv:2607.04218v1 Announce Type: new Abstract: The growing demand for privacy-preserving, data-intensive applications such as IoT, augmented reality, and autonomous systems positions Federated Learni
arXiv:2607.04566v1 Announce Type: new Abstract: Adolescence is a critical developmental period marked by heightened emotional sensitivity, social stress, and vulnerability to substance use. However, t
arXiv:2506.07468v4 Announce Type: replace-cross Abstract: Conventional large language model (LLM) safety alignment relies on a reactive, disjoint loop: attackers exploit a static model, then defenders
arXiv:2607.05290v1 Announce Type: new Abstract: Large Language Models (LLMs) can produce detailed answers to complex queries, but these answers are typically presented as dense linear text, which make
arXiv:2607.05103v1 Announce Type: new Abstract: Parallel ensemble methods were compared on 56 small-to-medium tabular classification tasks drawn from OpenML CC18. A set of ``best practice'' recommenda
arXiv:2604.03553v2 Announce Type: replace Abstract: AI is increasingly supporting, accelerating, and automating scientific discovery across subjects. Yet, the adoption of AI in historical research rem
arXiv:2607.03803v1 Announce Type: cross Abstract: The growing demand for image-to-video creation on mobile devices has increasingly focused on cinematic motion effects like bullet time, dolly zoom, sl
arXiv:2607.02596v1 Announce Type: new Abstract: Deep learning models for medical diagnosis frequently exhibit substantial performance disparities across sensitive subgroups (e.g., race, sex), even whe
arXiv:2606.15198v2 Announce Type: replace Abstract: City landscapes viewed through home windows influence quality of life, yet perceptions of actual window views at the urban scale remain understudied
arXiv:2607.03771v1 Announce Type: new Abstract: Multi-view 3D surface reconstruction is a longstanding challenge in computer vision. Although recent large-scale reconstruction methods based on 3D Gaus
arXiv:2607.02930v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel in diverse vision tasks, but full-parameter retraining is computationally expensive as real-world knowled
arXiv:2607.02638v1 Announce Type: new Abstract: Neuroimaging research requires manipulating heterogeneous data structures, including raw MRI volumes, volumetric parcellations, cortical surface meshes,
arXiv:2607.05150v1 Announce Type: new Abstract: In this paper, we introduce Claim-Level Rubric Rewards (CuRe), a structured reward framework designed to address the reward-design bottleneck in reinfor
arXiv:2512.08029v3 Announce Type: replace-cross Abstract: Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform. While
arXiv:2607.05185v1 Announce Type: new Abstract: Compositional generalization, the ability to understand and produce novel combinations of known components, remains a fundamental challenge for modern a
arXiv:2607.02580v1 Announce Type: new Abstract: Classroom behavior monitoring plays a vital role in evaluating student engagement and improving teaching effectiveness. Traditional observation methods
arXiv:2601.15141v2 Announce Type: replace Abstract: Agentic Reinforcement Learning (RL) has empowered Large Language Models (LLMs) to utilize tools like Python interpreters for complex problem-solving
arXiv:2507.08150v4 Announce Type: replace-cross Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling. Existing methods typically address either aleatoric uncertai
arXiv:2607.02841v1 Announce Type: cross Abstract: End-to-end autonomous driving (E2E-AD) aims to directly map raw sensor information to driving actions. Recently, with the rapid advancement of multi-m
arXiv:2607.03650v1 Announce Type: cross Abstract: Extracting textual information from scanned medical documents, such as external laboratory reports and manually filled forms, has been a major challen
arXiv:2607.03338v1 Announce Type: new Abstract: Closed-loop (or feedback) error-state Kalman filters with their relatives and offspring are the state-of-the-art in modern aided inertial navigation res
arXiv:2511.19277v2 Announce Type: replace Abstract: Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning. Existing emissions datasets provide critical foundat
arXiv:2607.04940v1 Announce Type: new Abstract: Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities but remain difficult to train the control policies that can
arXiv:2603.06921v2 Announce Type: replace-cross Abstract: Safe navigation of autonomous robots remains one of the core challenges in the field, especially in dynamic and uncertain environments. One pr
arXiv:2607.03522v1 Announce Type: new Abstract: Fine-tuning a single low-rank adapter on many domains at once is multi-task learning: the domains must be co-learned, and how they share the adapter dec
arXiv:2607.02911v1 Announce Type: cross Abstract: LLM-based coding agents solve software-engineering tasks through iterative interactions with development environments, where returned observations acc
arXiv:2501.10711v5 Announce Type: replace-cross Abstract: Code-related benchmarks play a critical role in evaluating large language models (LLMs), yet their quality fundamentally shapes how the commun
arXiv:2607.02746v1 Announce Type: cross Abstract: Machine learning has shown speedups for numerical methods in physics applications, but integrating Python-based libraries into high-performance C++ so
arXiv:2509.15330v2 Announce Type: replace Abstract: Recent advances in pre-training vision-language models (VLMs), e.g., contrastive language-image pre-training (CLIP) methods, have shown great potent
arXiv:2607.02928v1 Announce Type: new Abstract: Cold-start drug-drug interaction (DDI) prediction for new drugs is critical for minimizing unexpected adverse drug reactions. The key challenge is to ca
arXiv:2607.03731v1 Announce Type: cross Abstract: Creating 3D assets for virtual reality requires modeling expertise, which restricts the authorship of immersive experiences. Existing generative AI to
arXiv:2607.03853v1 Announce Type: new Abstract: Automated radiology report generation (RRG) can ease radiologist workload, yet most existing systems produce a report in a single forward pass, with no
arXiv:2503.12755v3 Announce Type: replace Abstract: Artificial intelligence (AI) has significantly improved medical screening accuracy, particularly in cancer detection and risk assessment. However, t
arXiv:2607.05046v1 Announce Type: new Abstract: Evaluating generative AI models is a routine, but resource-intensive, process that is conducted over and over again during the course of model developme
arXiv:2509.24181v2 Announce Type: replace Abstract: Active learning (AL) aims to build high-quality labeled datasets by iteratively selecting the most informative samples from an unlabeled pool under
arXiv:2607.02622v1 Announce Type: cross Abstract: Multiplex CRISPR-Cas9 gene editing requires selecting one guide RNA per target gene subject to cross-gene interactions: a constrained combinatorial pr
arXiv:2607.05378v1 Announce Type: new Abstract: Long-horizon agentic LLMs are increasingly limited by finite context windows, as extended interaction trajectories can exceed the maximum context length
arXiv:2607.05008v1 Announce Type: cross Abstract: Echocardiography is the first imaging modality used for assessing cardiac function, and accurate segmentation of cardiac structures is essential for d
arXiv:2607.03278v1 Announce Type: cross Abstract: Topological data analysis (TDA) is a machine learning technique that uses topology to extract patterns from data and has shown the potential to exhibi
arXiv:2607.02034v2 Announce Type: replace Abstract: Physics-based Human-Scene Interaction (HSI) imitation learning is crucial for embodied intelligence as it bridges the gap between kinematic 3D motio
arXiv:2509.19228v2 Announce Type: replace Abstract: Large Language Models (LLMs) face significant computational challenges when processing long contexts due to the quadratic complexity of self-attenti
arXiv:2602.24264v2 Announce Type: replace Abstract: Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems. Although mod
arXiv:2607.04800v1 Announce Type: new Abstract: Neural networks are thought to represent concepts as directions in their activation space, and superposition lets them encode more concepts than they ha
arXiv:2607.04606v1 Announce Type: cross Abstract: This report presents our solutions to the QoMEX 2026 Grand Challenge on Video Quality Assessment for Asymmetric Encoded Videos, comprising a full-refe
arXiv:2607.04508v1 Announce Type: new Abstract: Agentic AI-for-Science can automate ideation, planning, and analysis, but final validation still depends on real experiments. A self-driving lab (SDL) c
arXiv:2607.05078v1 Announce Type: new Abstract: Monetary risk measures have gained popularity for expressing decision-makers' risk aversion. Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR), i
arXiv:2510.14007v2 Announce Type: replace-cross Abstract: We introduce Conditional Clifford-Steerable CNNs (C-CSCNNs), a unified framework that incorporates equivariance to arbitrary pseudo-Euclidean
arXiv:2607.03154v1 Announce Type: cross Abstract: Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support K
arXiv:2607.04124v1 Announce Type: cross Abstract: Employing multiple manipulators can boost efficiency and accomplish tasks that a single manipulator cannot do. However, real-time planning for multipl
arXiv:2602.20512v2 Announce Type: replace Abstract: This paper investigates a problem called Multi-Agent Path Finding with Elevators (MAPF-E), which seeks conflict-free paths for multiple agents from
arXiv:2607.02998v1 Announce Type: cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simul
arXiv:2607.03783v1 Announce Type: new Abstract: Cross-subject generalization remains a fundamental challenge in surface electromyography (sEMG)-based gesture recognition. Although deep learning method
arXiv:2607.05056v1 Announce Type: new Abstract: Recently, diffusion models have achieved considerable success in the text-guided video editing domain. However, existing works often struggle to balance
arXiv:2607.03882v1 Announce Type: cross Abstract: LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilis
arXiv:2607.04513v1 Announce Type: cross Abstract: Flow matching is a powerful tool for generative modeling, but emerging applications in robotics, planning, and physics require inference-time constrai