Knowledge catalogue
arXiv:2605.17236v1 Announce Type: cross Abstract: Manual Pap smear analysis for cervical cancer screening is limited by inter-observer variability, time constraints, and restricted expert availability
arXiv:2605.15707v1 Announce Type: cross Abstract: Whole-heart multi-compartment CT segmentation is clinically important, but standard CNNs do not explicitly enforce anatomical plausibility. Based on s
arXiv:2605.15341v1 Announce Type: cross Abstract: LLMs are increasingly deployed in autonomous laboratories, under the assumption that their domain priors and reasoning over iterative feedback let the
arXiv:2605.15235v1 Announce Type: new Abstract: Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two fail
arXiv:2605.16052v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have significantly enhanced automated legal reasoning. Yet, it remains unclear whether their performance
arXiv:2601.13529v2 Announce Type: replace Abstract: Cervical cancer accounts for a significant portion of the global cancer burden among women. Interstitial brachytherapy (ISBT) is a standard procedur
arXiv:2601.18842v3 Announce Type: replace-cross Abstract: As GUI agents increasingly rely on screenshots to perceive and operate digital environments, they may inadvertently expose sensitive informati
arXiv:2605.11989v1 Announce Type: new Abstract: Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by
arXiv:2605.10947v1 Announce Type: new Abstract: EEG microstate analysis segments continuous brain electrical activity into brief, quasi-stable topographic configurations that reflect discrete function
arXiv:2605.09663v1 Announce Type: cross Abstract: Machine learning classifiers in dynamic environments face concept drift -- changes in the data-generating process that degrade performance. Convention
arXiv:2605.08730v1 Announce Type: new Abstract: Class-level machine unlearning aims to remove the influence of specified classes while preserving model utility on retained classes. Existing methods ar
arXiv:2605.08113v1 Announce Type: cross Abstract: Accurate predictions of smallholder maize yields across national boundaries are critical for food security planning in sub-Saharan Africa, yet most pu
arXiv:2605.09106v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in financial contexts, raising critical concerns about reliability, alignment, and susceptibility
arXiv:2602.22953v2 Announce Type: replace Abstract: General-purpose agents perform tasks in unfamiliar environments without domain-specific manual customization. Yet no study has systematically measur
arXiv:2411.04077v2 Announce Type: replace Abstract: By leveraging both texts and images, large vision language models (LVLMs) have shown significant progress in various multi-modal tasks. Nevertheless
arXiv:2601.03511v2 Announce Type: replace-cross Abstract: A major challenge for the operation of large language models (LLMs) is how to predict whether a specific LLM will produce sufficiently high-qu
arXiv:2605.08093v1 Announce Type: cross Abstract: The use of chatbots for various forms of companionship is growing rapidly, raising a myriad of questions about simulated relationships, emotional depe
arXiv:2605.09070v1 Announce Type: cross Abstract: Many jailbreak attack research papers report attack success rates for a limited number of parameter settings, even though there are many combinations
arXiv:2605.07988v1 Announce Type: new Abstract: The spine plays a crucial role in the dynamic locomotion of quadrupedal animals, improving the stability, speed, and efficiency of their gait, especiall
arXiv:2605.03135v1 Announce Type: new Abstract: Standard classification treats all errors equally, but in content moderation, medical screening, and safety-critical applications, mistakes on clear-cut
arXiv:2605.03514v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a wid
arXiv:2605.03678v1 Announce Type: new Abstract: Reliable localization in GPS-denied, visually degraded environments is critical for autonomous UAV opera- tions. This paper presents a systematic compar
arXiv:2605.01809v1 Announce Type: cross Abstract: Unified audio-visual generation is rapidly gaining industrial and creative relevance, enabling applications in virtual production and interactive medi
arXiv:2605.01449v1 Announce Type: cross Abstract: Universal adversarial attacks on aligned multimodal large language models are increasingly reported with attack success rates in the 60-80% range, sug
AI agents require specialized testing and debugging approaches that differ from traditional software due to their non-deterministic behavior and complex decision-making processes. This entry likely co
arXiv:2605.01451v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly being integrated into high-stakes public safety systems, including emergency call triage and dispatch decision
arXiv:2604.27593v1 Announce Type: cross Abstract: Recent technological advances in astronomy, particularly the growing popularity of smart telescopes for the general public, make it possible to develo
arXiv:2604.27405v1 Announce Type: cross Abstract: We adapted the Reliable Change Index (RCI; Jacobson and Truax, 1991) from clinical psychology to item-level LLM version comparison on 2,000 MMLU-Pro i
arXiv:2604.28087v1 Announce Type: cross Abstract: Rule-based systems remain central in safety-critical domains but often struggle with scalability, brittleness, and goal misspecification. These limita
arXiv:2604.28093v1 Announce Type: new Abstract: Terminal-agent benchmarks have become a primary signal for measuring the coding and system-administration capabilities of large language models. As the
arXiv:2604.26382v1 Announce Type: cross Abstract: Most enterprise document AI today is a pipeline. Parse, index, retrieve, generate. Each of those stages has been studied to death on its own -- what's
arXiv:2506.21444v4 Announce Type: replace Abstract: Atypical mitosis marks a deviation in the cell division process that has been shown be an independent prognostic marker for tumor malignancy. Howeve
arXiv:2604.25924v1 Announce Type: cross Abstract: Large Language Models have been increasingly employed in the creation of Virtual Assistants due to their ability to generate human-like text and handl
arXiv:2604.26347v1 Announce Type: cross Abstract: Objective metrics for emotional expressiveness are vital for speech generation, particularly in expressive synthesis and voice conversion requiring em
arXiv:2505.22897v2 Announce Type: replace Abstract: While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less atten
arXiv:2604.25685v1 Announce Type: cross Abstract: Foundation segmentation models such as the Segment Anything Model (SAM) have demonstrated strong generalization across natural images; however, their
arXiv:2604.24589v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly proposed as general-purpose tools for scientific data interpretation, yet their reliability on real astro
arXiv:2603.10926v1 Announce Type: cross Abstract: Time-series anomaly detectors are commonly compared on workstation-class hardware under unconstrained execution. In-vehicle monitoring, however, requi
arXiv:2604.24609v1 Announce Type: new Abstract: Many sign language translation (SLT) systems operate on pose sequences instead of raw video to reduce input dimensionality, improve portability, and par
arXiv:2603.17717v3 Announce Type: replace-cross Abstract: Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS). Nowadays, in a pivotal
arXiv:2604.22296v1 Announce Type: new Abstract: Synthetic image generation is one of the crucial input for planetary missions. It enables researchers and engineers to visualize planned planetary missi
arXiv:2604.21937v1 Announce Type: new Abstract: Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specializ
arXiv:2506.05038v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning-intensive tasks. However, these models exhibit unexpecte
TuneForge is an MCP (Model Context Protocol) server that enables coding agents like Claude and Cursor to perform machine learning operations directly within chat interfaces, including dataset generati
arXiv:2603.27189v2 Announce Type: replace-cross Abstract: Conformal prediction provides rigorous distribution-free finite-sample guarantees for marginal coverage under the assumption of exchangeabilit
arXiv:2604.21421v1 Announce Type: cross Abstract: Protecting patient privacy in clinical narratives is essential for enabling secondary use of healthcare data under regulations such as GDPR and HIPAA.
arXiv:2106.01254v3 Announce Type: replace Abstract: In many classification tasks, there is no definitive ground truth, only human judgments that may disagree. We address two challenges that arise in s
arXiv:2604.19776v1 Announce Type: new Abstract: Tuberculosis (TB) is one of the world's deadliest infectious diseases, and in South Africa, it contributes a significant burden to the country's health
arXiv:2604.19829v1 Announce Type: new Abstract: Tactile graphics require careful expert validation before reaching blind and visually impaired (BVI) learners, yet existing datasets provide only coarse
arXiv:2604.19502v1 Announce Type: new Abstract: The rapid adoption of Large Language Models (LLMs) has spurred interest in automated peer review; however, progress is currently stifled by benchmarks t
arXiv:2604.19533v1 Announce Type: cross Abstract: We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of th
arXiv:2604.19354v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly proposed for autonomous cybersecurity tasks, but their capabilities in realistic offensive settings r
arXiv:2604.19054v1 Announce Type: new Abstract: The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy wit
Coding agents can update prompts, wire in tools, and change application logic across your codebase in a single run. The hard part isn’t getting the agent to make changes, but... The post How to add an
arXiv:2601.22737v2 Announce Type: replace Abstract: The robust safety of Vision-Language Large Models (VLLMs) against joint multilingual and multimodal threats remains severely underexplored. Current
arXiv:2604.17046v1 Announce Type: new Abstract: Collisions between cyclists and pedestrians at urban intersections remain a persistent source of injuries, yet few systems attempt real-time warnings to
arXiv:2509.24478v2 Announce Type: replace Abstract: Modern neural networks have greatly improved performance across speech recognition benchmarks. However, gains are often driven by frequent words wit
arXiv:2604.17718v1 Announce Type: new Abstract: Many benchmarks show that large language models can answer direct questions about culture. We study a different question: do they also change how they s
arXiv:2604.17344v1 Announce Type: cross Abstract: When task-specific labels are not available, it becomes difficult to select an embedding model for a specific target corpus. Existing labelless measur
arXiv:2601.04609v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly used to make visual content accessible via text-based descriptions. In current systems, however, desc