Search: “synthesis”

arXiv:2609.16804v1 Announce Type: cross Abstract: Time-series foundation models have demonstrated strong cross-domain transfer, yet their common architectural assumptions remain poorly aligned with we

arXiv:2511.21265v2 Announce Type: replace Abstract: Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) ena

arXiv:2609.13453v1 Announce Type: new Abstract: Earth Observation (EO) data are increasingly organized as spatio-temporal data cubes, while machine learning (ML) methods operate on tabular feature mat

arXiv:2609.13602v1 Announce Type: new Abstract: Voice agents often need to collect names, addresses, identifiers, dates, and times exactly, yet end-to-end benchmarks obscure where capture fails. We in

arXiv:2609.13860v1 Announce Type: new Abstract: Querying clinical trial registries remains a manual and error-prone process, requiring researchers to navigate large volumes of semi-structured data wit

arXiv:2609.15973v1 Announce Type: new Abstract: Foundation models have progressed from learning and reasoning over existing knowledge, to increasingly learning through action, tool use, and outcome fe

arXiv:2609.14770v1 Announce Type: cross Abstract: Generalisations are common in scientific communication, even though they are semantically ambiguous. An automated method is needed to identify and cat

arXiv:2602.08370v2 Announce Type: replace-cross Abstract: Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unli

arXiv:2609.14533v1 Announce Type: cross Abstract: Automated theorem proving seeks to use computational systems to prove or disprove mathematical and logical statements [1, 2]. It underpins a wide rang

arXiv:2609.14377v1 Announce Type: new Abstract: Camouflage image generation (CIG) aims to synthesize realistic camouflaged images by blending foreground objects into concealment-compatible background

arXiv:2608.25536v2 Announce Type: replace-cross Abstract: In this work, we propose a view on electronic Neuromorphic Design Automation (eNDA), which we see as a design automation flow that bridges com

arXiv:2609.13995v1 Announce Type: new Abstract: Debate over synthetic data in marketing research has polarized between claims that large language models (LLMs) make human respondents obsolete and call

arXiv:2609.13226v1 Announce Type: new Abstract: Machine learning for neglected tropical diseases is limited by data, not algorithms: public annotated image sets for leprosy (Hansen's disease) number i

arXiv:2609.15503v1 Announce Type: new Abstract: Deep learning models have become the standard tool for document rectification and illumination correction, yet their performance is fundamentally bound

arXiv:2609.13838v1 Announce Type: new Abstract: Purpose: To develop and evaluate TotalSynth, a reusable pretrained framework for whole-body synthetic CT (sCT) generation from MRI and cone-beam CT (CBC

arXiv:2601.16982v2 Announce Type: replace Abstract: Modern generative video models excel at producing convincing, high-quality outputs, but struggle to maintain multi-view and spatiotemporal consisten

arXiv:2609.12298v1 Announce Type: new Abstract: Finger-level motor decoding is important for naturalistic brain-computer interface (BCI) control, yet individual-finger decoding from scalp electroencep

arXiv:2609.12850v1 Announce Type: new Abstract: Accurate head modeling requires a stable yet expressive geometric representation. Existing Gaussian-based head avatars commonly rely on parametric templ

arXiv:2609.12691v1 Announce Type: new Abstract: Instruction-guided image editing has a training-time blind spot. Generative editors are never required to semantically verify whether their outputs actu

arXiv:2603.02638v2 Announce Type: replace-cross Abstract: Machine learning for mobile network analysis, planning, and optimization is often limited by the lack of large, comprehensive real-world datas

arXiv:2609.12342v1 Announce Type: new Abstract: The conditional generation of 3D motion has emerged as a key research topic due to its wide applicability across robotics, AR/VR, gaming, and content cr

arXiv:2506.14045v2 Announce Type: replace Abstract: Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI

arXiv:2609.11709v1 Announce Type: new Abstract: When multiple LLM agents yield conflicting answers, the decision-making process dictates whether agent diversity improves performance or merely compound

arXiv:2609.11262v1 Announce Type: cross Abstract: Achieving high combustion efficiency in flare stacks is crucial for adhering to regulatory standards and controlling the release of hydrocarbons into

arXiv:2609.11545v1 Announce Type: new Abstract: Low-resource multilingual text-to-speech (TTS) systems have expanded language coverage, but their robustness under complex text inputs remains insuffici

Do large language models actually understand the world, or are they just very good at pretending? Does it even matter? Our recent special issue in the Royal Society, “World Models in Natural and Artif

arXiv:2609.09980v1 Announce Type: cross Abstract: High Performance Computing-Quantum Computing (HPCQC) platforms expose multiple Quantum Processing Units (QPUs) that may differ in size, topology, nati

arXiv:2609.11312v1 Announce Type: new Abstract: Lung cancer causes more deaths than any other malignancy, and low-dose CT screening is the main pathway to early diagnosis. That pathway hinges on the s

arXiv:2603.26934v2 Announce Type: replace Abstract: Recent advances in photorealistic avatar generation have enabled highly realistic talking-head avatars, raising security concerns regarding identity

arXiv:2609.10914v1 Announce Type: cross Abstract: Label-free virtual staining offers a compelling, non-destructive alternative to standard histopathology; however, its clinical adoption is hindered by

arXiv:2604.00280v2 Announce Type: replace-cross Abstract: Formal specifications play a central role in ensuring software reliability, yet automatically synthesizing high-quality specifications remains

arXiv:2601.15645v2 Announce Type: replace Abstract: Large-scale language models (LLMs) often offer clinical judgments based on incomplete information, increasing the risk of misdiagnosis. Existing stu

arXiv:2601.12723v3 Announce Type: replace-cross Abstract: Optimization benchmarks play a fundamental role in assessing algorithm performance; however, existing artificial benchmarks often fail to capt

arXiv:2609.08729v1 Announce Type: new Abstract: The transition from single-core to multi-core architectures in safety-critical embedded systems introduces significant challenges due to inter-core inte

arXiv:2609.08435v2 Announce Type: new Abstract: In persistent interactions, long contexts may encode an evolving process rather than a fixed record: later events can revise or revoke earlier informati

arXiv:2609.06071v1 Announce Type: new Abstract: PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that share the same predic

arXiv:2609.08139v1 Announce Type: cross Abstract: We present the IGT system for PolyFiQA Task 2 of the FinMMEval Lab at CLEF 2026, a multilingual financial question answering task over English SEC fil

arXiv:2609.09852v1 Announce Type: new Abstract: The advance of multimodal large language models (MLLMs) has fundamentally reshaped the paradigm of human-computer interaction, especially speech interac

arXiv:2601.08026v5 Announce Type: replace Abstract: Scientific compound figures combine multiple labeled panels into a single image, and downstream pretraining and retrieval require panel-aligned visu

arXiv:2609.06823v1 Announce Type: new Abstract: The next generation of artificial intelligence systems will likely be natively temporal and multimodal in both inputs and outputs: able to converse, per

arXiv:2609.08041v1 Announce Type: new Abstract: Many pedestrian trajectory prediction algorithms have been proposed to improve the safety of navigation for mobile robots working in human-robot coexist

arXiv:2609.06360v1 Announce Type: new Abstract: Training-free zero-shot video anomaly detection (ZS-VAD) leverages vision-language models (VLMs) to localize anomaly instances from a predefined anomaly

arXiv:2609.06099v1 Announce Type: new Abstract: Reconstructing 4D scenes from casually captured monocular video is vital for applications in virtual reality (VR) and embodied AI. Recent advances in 4D

arXiv:2609.07229v1 Announce Type: new Abstract: Vision-language models offer a promising approach for zero-shot anomaly detection (ZSAD). However, due to object-centric bias, normal and anomalous text

arXiv:2609.04249v1 Announce Type: cross Abstract: Existing audio-video generation methods produce well-synchronized clips but are limited to short durations, while long-video generation methods extend

arXiv:2609.04274v1 Announce Type: cross Abstract: Overfitted codecs have demonstrated promising performance for image and video compression. In particular, for image compression, the Cool-chic family