Knowledge catalogue
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:2604.27239v1 Announce Type: new Abstract: Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted
arXiv:2604.27644v1 Announce Type: cross Abstract: We propose a paradigm shift from learning to answer to learning to question: can a language model generate verifiable problems, solve them, and turn t
arXiv:2604.27102v1 Announce Type: cross Abstract: Soil contamination by heavy metals poses a persistent environmental and public health concern in rapidly urbanising regions of Ghana, particularly at
arXiv:2604.27550v1 Announce Type: cross Abstract: Privacy policies are essential for users to understand how service providers handle their personal data. However, these documents are often long and c
arXiv:2604.27543v1 Announce Type: new Abstract: Evaluating English ASR systems for conversational AI applications remains difficult, as many publicly available corpora are either pre-segmented into sh
arXiv:2604.28022v1 Announce Type: new Abstract: Current DeepFake detection scenarios are mostly binary, yet data manipulation can vary across audio, video, or both, whose variability is not captured i
arXiv:2604.27582v1 Announce Type: new Abstract: Surgical resection remains the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC), and eligibility depends on accurate asse
arXiv:2604.28065v1 Announce Type: cross Abstract: Although intersections are the most complex parts of the roadway network, pedestrian crashes at non-intersection locations are disproportionately freq
arXiv:2604.27947v1 Announce Type: cross Abstract: In this paper an attractor FCM is created, tested, and analyzed. This FCM is neither a hebbian based nor agentic, nor a hybrid; it rather is a gradien
arXiv:2604.27218v1 Announce Type: new Abstract: Person re-identification (ReID) systems that match individuals across images or video frames are essential in many real-world applications. However, exi
arXiv:2604.27720v1 Announce Type: new Abstract: Deploying vision-language models (VLMs) in clinical settings demands auditable behavior under realistic failure conditions, yet the failure landscape of
arXiv:2604.28109v1 Announce Type: new Abstract: Model merging has attracted attention as an effective path toward multi-task adaptation by integrating knowledge from multiple task-specific models. Amo
arXiv:2604.27105v1 Announce Type: new Abstract: Analyzing mutual gaze (MG) and joint attention (JA) is critical in developmental psychology but traditionally relies on labor-intensive manual coding. A
arXiv:2604.27011v1 Announce Type: cross Abstract: AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation. Most Au
arXiv:2604.27753v1 Announce Type: new Abstract: This article outlines a new framework of traffic light optimization through a digital twin of the transport infrastructure, managed by agentic AI to ens
arXiv:2604.27266v1 Announce Type: new Abstract: This paper introduces AutoREC, an open-source Python package for developing reinforcement learning (RL) agents to automatically generate equivalent circ
arXiv:2603.01444v2 Announce Type: replace Abstract: Synthetic data generation is an important capability for privacy-preserving data sharing, system benchmarking and test data provisioning. For mixed-
arXiv:2604.27089v1 Announce Type: new Abstract: Large-language-models (LLMs) demonstrate enormous utility in long-context tasks which require processing prompts that consist of tens to hundreds of tho
arXiv:2604.27253v1 Announce Type: new Abstract: Recent advances in multimodal large language models (LLMs) have revolutionized web agents that can automate complex tasks on websites. However, their ac
arXiv:2507.12414v2 Announce Type: replace-cross Abstract: Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations su
arXiv:2512.22046v2 Announce Type: replace Abstract: Prompt-driven Video Segmentation Foundation Models (VSFMs), such as SAM2, are increasingly used in applications including autonomous driving and dig
arXiv:2604.27576v1 Announce Type: cross Abstract: We present BAss (BDD-based ADF symbolic solver), a novel analysis tool for Abstract Dialectical Frameworks (ADFs) based on Binary Decision Diagrams (B
arXiv:2604.26986v1 Announce Type: new Abstract: We introduce a novel task of digital battery passport (DBP) conformance classification and introduce the first public benchmark for the task: BatteryPas
arXiv:2603.10252v2 Announce Type: replace-cross Abstract: Bayesian hierarchical models are frequently used in practical data analysis contexts. One interpretation of these models is that they provide
arXiv:2604.27563v1 Announce Type: new Abstract: Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. Convention
arXiv:2604.27394v1 Announce Type: cross Abstract: Conditional Average Treatment Effect (CATE) estimation in practice demands three properties simultaneously: heterogeneous effects au(x), calibrated un
arXiv:2604.27536v1 Announce Type: new Abstract: In black-box large language model (LLM) services, response reliability is often only partially observable at decision time, while stronger inference pat
arXiv:2409.16808v1 Announce Type: cross Abstract: Modern applications, such as autonomous vehicles, require deploying deep learning algorithms on resource-constrained edge devices for real-time image
arXiv:2604.27124v1 Announce Type: new Abstract: Training stable biological foundation models requires rethinking attention mechanisms: we find that using sigmoid attention as a drop in replacement for
arXiv:2604.27006v1 Announce Type: cross Abstract: Context: Study screening in systematic literature reviews is costly, inconsistency-prone, and risk-asymmetric, since false negatives can compromise va
arXiv:2604.28122v1 Announce Type: new Abstract: Modern visual world modeling systems increasingly rely on high-capacity architectures and large-scale data to produce plausible motion, yet they often f
arXiv:2604.28136v1 Announce Type: new Abstract: Night Photography Rendering (NPR) poses a significant challenge due to the extreme contrast between dark and illuminated areas in scenes, stemming from
arXiv:2604.27920v1 Announce Type: cross Abstract: Preserving affective nuance remains a challenge in Machine Translation (MT), where semantic equivalence often takes precedence over emotional fidelity
arXiv:2604.27936v1 Announce Type: new Abstract: Animals hear and vocalize across frequency ranges that differ substantially from humans, often extending into the ultrasonic domain. Yet most computatio
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:2604.27551v1 Announce Type: cross Abstract: Large-scale transformers achieve impressive results on program synthesis benchmarks, yet their true generalization capabilities remain obscured by dat
arXiv:2602.01265v2 Announce Type: replace Abstract: Knowledge distillation (KD) is a machine learning framework that transfers knowledge from a teacher model to a student model. The vanilla KD propose
arXiv:2604.27007v1 Announce Type: new Abstract: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally define a BSNN and represent its spiking ac
arXiv:2604.27308v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) methods face a tradeoff between adapter size and expressivity: ultra-low-parameter adapters are confined to fix
arXiv:2604.27277v1 Announce Type: cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substa
arXiv:2604.27699v1 Announce Type: new Abstract: Current embodied agents are often limited to passive instruction-following or reactive need-satisfaction, lacking a stable, high-order value framework e
arXiv:2604.26981v1 Announce Type: cross Abstract: Large Language Models (LLMs) have revolutionized the field of natural language processing. However, they exhibit some limitations, including a lack of
arXiv:2604.27882v1 Announce Type: new Abstract: Recent advances in agentic AI are shifting automation from discrete tools to proactive multi-agent systems that coordinate multi-specialized capabilitie
arXiv:2604.27944v1 Announce Type: new Abstract: Large-scale IoT weather sensing networks require incentive mechanisms to sustain participation, yet determining how much value individual data contribut
arXiv:2604.27924v1 Announce Type: cross Abstract: Peer review is a multi-stage process involving reviews, rebuttals, meta-reviews, final decisions, and subsequent manuscript revisions. Recent advances
arXiv:2604.26956v1 Announce Type: cross Abstract: The growing use of generative AI raises ethical concerns about authorship and plagiarism. This study examines how people judge the reuse of AI-generat
arXiv:2602.10140v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can now synthesize non-trivial executable code from textual descriptions, raising an important question: can LLMs
arXiv:2604.27667v1 Announce Type: cross Abstract: Policy optimization in high-dimensional continuous control for robotics remains a challenging problem. Predominant methods are inherently local and of
arXiv:2604.26959v1 Announce Type: cross Abstract: Integrating large language models (LLMs) into patient-facing healthcare systems offers significant potential to improve access to medical information.
arXiv:2604.27361v1 Announce Type: new Abstract: Synthesizing realistic 3D indoor scenes remains challenging due to data scarcity and the difficulty of simultaneously enforcing global architectural con
arXiv:2604.27840v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have shown great promise in time series forecasting. However, most existing LLM-based forecasting methods still
arXiv:2602.07915v2 Announce Type: replace-cross Abstract: Causal discovery from time series is a fundamental task in machine learning. However, its widespread adoption is hindered by a reliance on unt
arXiv:2602.12652v2 Announce Type: replace Abstract: Clouds are a common phenomenon that distorts optical satellite imagery, which poses a challenge for remote sensing. However, in the literature cloud
arXiv:2604.28082v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on narrowly misaligned data generalizes to broadly misaligned behavior, a phenomenon termed emergent misalignme
arXiv:2604.27415v1 Announce Type: new Abstract: With the rapid advancement of semiconductor technology, Electronic Design Automation (EDA) has become an increasingly knowledge-intensive and document-d
arXiv:2504.12612v2 Announce Type: replace Abstract: Provenance is the chronological history of things, resonating with the fundamental pursuit to uncover origins, trace connections, and situate entiti
arXiv:2604.27043v1 Announce Type: new Abstract: Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for mode
arXiv:2602.00937v2 Announce Type: replace-cross Abstract: Leveraging pre-trained 2D image representations in behavior cloning policies has achieved great success and has become a standard approach for
arXiv:2604.28139v1 Announce Type: cross Abstract: LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks