Knowledge catalogue
arXiv:2607.22302v1 Announce Type: new Abstract: Reconstructing dynamic human faces from brain activity provides a powerful way to study how the mind perceives identity, expression, and facial motion.
arXiv:2411.19715v4 Announce Type: replace Abstract: We describe Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector. Although CLI
arXiv:2607.21615v1 Announce Type: cross Abstract: The rapid deployment of generative AI has amplified the critical need for Training Data Attribution to ensure transparency and accountability. However
arXiv:2607.22182v1 Announce Type: new Abstract: Large language model (LLM) evaluation spans diverse tasks and benchmarks, yet evidence remains organized around tasks rather than the capabilities they
arXiv:2607.22255v1 Announce Type: new Abstract: The segmentation of multiple degradations has been a challenging problem in the field of image segmentation. Existing level set approaches commonly adop
arXiv:2607.21608v1 Announce Type: cross Abstract: With the EU AI Act entering into force, organizations developing or operating AI systems face new obligations on transparency, risk management, and tr
arXiv:2607.21999v1 Announce Type: new Abstract: Long-tailed learning couples two sources of poor generalization: head classes dominate training exposure, while under-represented classes often converge
arXiv:2607.22199v1 Announce Type: new Abstract: Score-based diffusion models have achieved remarkable empirical success in generative modeling, yet their approximation-theoretic foundations remain inc
arXiv:2607.21785v1 Announce Type: cross Abstract: Roadblocks in Bolivia are a social conflict phenomenon with devastating economic impacts, estimated at losses equivalent to 4% of the national Gross D
arXiv:2607.22075v1 Announce Type: new Abstract: Continual Learning for Named Entity Recognition (CLNER) enable models to incrementally learn new entity types without forgetting previously acquired one
arXiv:2607.22268v1 Announce Type: cross Abstract: Remaining useful life (RUL) prediction and failure-mode classification are central tasks in predictive maintenance. Many data-driven pipelines use fix
arXiv:2408.02295v4 Announce Type: replace Abstract: Conventional uncertainty-aware temporal difference (TD) learning often models TD errors as zero-mean Gaussian. This assumption can miss the heavy-ta
arXiv:2607.21660v1 Announce Type: cross Abstract: Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with
arXiv:2601.03499v2 Announce Type: replace-cross Abstract: Synthetic aperture radar (SAR) image generation can mitigate data scarcity, but controllablegeneration under sparse observation angles remains
arXiv:2607.22325v1 Announce Type: new Abstract: In production processes for consumer products, assembly instructions are essential not only for planning but also for executing the production process.
arXiv:2506.13553v4 Announce Type: replace Abstract: Road topology reasoning is fundamental for autonomous driving, requiring both accurate perception of road elements and understanding of their comple
arXiv:2607.22135v1 Announce Type: new Abstract: Existing BraTS-GLI datasets provide a widely used benchmark for adult glioma MRI segmentation, but their task definition focuses on tumor subregions and
arXiv:2512.06143v2 Announce Type: replace Abstract: Despite a large corpus of recent work on scaling up Gaussian processes, a stubborn trade-off between computational speed, prediction and uncertainty
arXiv:2607.21661v1 Announce Type: new Abstract: Diffusion policies generate multimodal robot action sequences from demonstrations, but steering them toward deployment-time constraints typically relies
arXiv:2607.22436v1 Announce Type: cross Abstract: This work addresses the generation of theoretical correlation matrices with prescribed sparsity patterns associated to graph structures. We propose a
arXiv:2607.22456v1 Announce Type: new Abstract: Existing lexical distance, similarity, and evaluation metrics operate on Unicode code points, which can misrepresent errors in writing systems where a s
arXiv:2607.21962v1 Announce Type: new Abstract: Benchmarks for LLM-agent memory typically generate conversations first and extract answer keys afterwards -- with documented label-error and contaminati
arXiv:2607.21675v1 Announce Type: cross Abstract: Neural Radiance Fields (NeRF) have revolutionized novel view synthesis, yet their classical implementations remain computationally intensive for high-
arXiv:2509.14617v4 Announce Type: replace Abstract: Efficient medical data classification is essential for modern disease screening, particularly in resource-constrained environments where power budge
arXiv:2412.01101v2 Announce Type: replace Abstract: Face-swapping DeepFakes have become an escalating societal concern, attracting increasing attention in recent years. To counter this, we investigate
arXiv:2603.19139v2 Announce Type: replace Abstract: Learning systems must balance generalization across experiences with discrimination of task-relevant details. Effective learning therefore requires
arXiv:2607.22389v1 Announce Type: cross Abstract: With the rapid adoption of long-context large language models (LLMs), the continuously growing KV cache during decoding has become the critical memory
arXiv:2607.22299v1 Announce Type: cross Abstract: Forecasting multiple time-series with high-dimensional covariates presents a core challenge: unifying common temporal patterns while retaining meaning
arXiv:2607.21832v1 Announce Type: cross Abstract: Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agen
arXiv:2607.21758v1 Announce Type: new Abstract: Teachers, conference chairs, and public readers all judge writing from limited evidence, seeing only a finished document and not the process that produc
arXiv:2607.22444v1 Announce Type: new Abstract: For scale-invariant deep networks, Hyperball-style optimizers have shown strong performance in large-scale training by fixing the norms of matrix-valued
arXiv:2607.22251v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning method for large language models, but its performance depends strongly on ho
arXiv:2607.22030v1 Announce Type: new Abstract: Ship-borne manipulators operating in maritime environments are subject to stochastic wave-induced base motions that introduce kinematic disturbances and
arXiv:2507.18311v3 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) have shown impressive capabilities across a range of tasks that integrate visual and textual understanding, suc
arXiv:2607.22361v1 Announce Type: new Abstract: We study information bottlenecks in modern deep-learning architectures -- RNNs, softmax transformers, linear-attention transformers and state-space mode
arXiv:2607.22153v1 Announce Type: cross Abstract: Industrial health management increasingly relies on heterogeneous information sources, including condition monitoring systems, supervisory control and
arXiv:2607.22101v1 Announce Type: new Abstract: Diffusion models have recently achieved remarkable success in high-fidelity image synthesis, yet their application to visual text generation and editing
arXiv:2607.22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platfor
arXiv:2510.24668v2 Announce Type: replace Abstract: Language agents have demonstrated remarkable potential in web search and information retrieval. However, many search-agent benchmarks assume that us
arXiv:2607.22367v1 Announce Type: new Abstract: Feature-attribution methods assign scores relating input variables to a model's output, but do not by themselves characterize how explicitly defined int
arXiv:2607.16811v2 Announce Type: replace Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributiona
arXiv:2506.06616v2 Announce Type: replace Abstract: Accurate and interpretable detection of depressive language in social media can support early identification of mental health conditions and inform
arXiv:2607.22508v1 Announce Type: new Abstract: Electroencephalography (EEG) is widely used to diagnose neurological conditions, but its analysis usually relies on either predefined spectral features
arXiv:2607.22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is
arXiv:2607.22380v1 Announce Type: new Abstract: Efficient processing is becoming increasingly important in infrared remote sensing, where satellite constellations produce large volumes of observations
arXiv:2607.21897v1 Announce Type: new Abstract: The rapid advancement of generative models has spurred the critical need to evaluate the worst-case robustness of deepfake detectors. In this paper, we
arXiv:2607.21981v1 Announce Type: new Abstract: Chain-of-thought prompting improves language-model reasoning by carrying intermediate states across successive computation steps. However, relying on na
arXiv:2508.01782v4 Announce Type: replace-cross Abstract: Recently, large language models (LLMs) have driven promising progress in lossless image compression. However, directly adopting existing parad
arXiv:2607.22172v1 Announce Type: new Abstract: Accurate yet low-latency depth is essential for radar-camera perception in autonomous systems. Cameras provide rich appearance but lack metric scale, wh
arXiv:2607.22489v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted technique for efficient neural network fine-tuning, decomposing model updates into low-rank matri
arXiv:2607.21780v1 Announce Type: new Abstract: Document packets, multiple documents concatenated into a single file, are common in government and administrative workflows, yet splitting them into the
arXiv:2603.07025v2 Announce Type: replace Abstract: Speech Large Language Models (LLMs) that understand and follow instructions in many languages are useful for real-world interaction, but are difficu
arXiv:2508.13826v4 Announce Type: replace-cross Abstract: Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limit
arXiv:2607.22215v1 Announce Type: new Abstract: In this study, we introduce latent PDE mapping, a broadly applicable physics-informed learning technique designed to enable efficient geometric generali
arXiv:2607.21941v1 Announce Type: new Abstract: Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and
arXiv:2602.05988v2 Announce Type: replace Abstract: Pre-training Large Language Models (LLMs) on web-scale datasets becomes fundamental for advancing general-purpose AI. In contrast, enhancing their p
arXiv:2607.22200v1 Announce Type: new Abstract: End-to-end OCR systems based on vision-language models have achieved strong performance in complex document OCR, but their efficiency is limited by the
arXiv:2607.21856v1 Announce Type: cross Abstract: Modern reasoning models depend on reasoning data, today sourced from human annotations or distilled from stronger LLMs. However, a rich and largely un
arXiv:2607.21896v1 Announce Type: new Abstract: Semantic 3D Gaussians provide a compact representation for 3D semantic occupancy prediction by rendering semantic primitives into a voxel volume under v
arXiv:2607.22313v1 Announce Type: cross Abstract: Estimating contemporaneous bidirectional interactions from observational data is difficult because each outcome is endogenous to the other, while flex