Knowledge catalogue
arXiv:2605.04495v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) depends on document ranking to provide useful evidence for generation, but conventional reranking methods mainly op
arXiv:2506.22226v2 Announce Type: replace-cross Abstract: Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilita
arXiv:2605.04641v1 Announce Type: new Abstract: Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviat
arXiv:2603.20684v2 Announce Type: replace Abstract: Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectivenes
arXiv:2505.05880v2 Announce Type: replace-cross Abstract: Monitoring and analyzing process traces is a critical task for modern companies and organizations. In scenarios where there is a gap between t
arXiv:2605.03067v1 Announce Type: new Abstract: Approval-based committee voting has received significant attention in the social choice community. Among the studied rules, Thiele rules, and especially
arXiv:2605.05124v1 Announce Type: new Abstract: We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electr
arXiv:2504.11101v4 Announce Type: replace Abstract: Optical Character Recognition (OCR) is fundamental to Vision-Language Models (VLMs) and high-quality data generation for LLM training. Yet, despite
arXiv:2605.04662v1 Announce Type: new Abstract: Generating realistic reactive motions, in which one person reacts to the fixed motions of others, is challenging due to strict interaction constraints a
arXiv:2605.04400v1 Announce Type: cross Abstract: While Separate Source-Channel Coding (SSCC) retains the practical benefits of modular system design, its effectiveness in noisy text transmission is f
arXiv:2605.04059v1 Announce Type: cross Abstract: Deep learning models continue to scale, with some requiring more storage than many large-scale datasets. Thus, we introduce a new paradigm: Continual
arXiv:2605.05097v1 Announce Type: cross Abstract: LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicit
arXiv:2605.04396v1 Announce Type: new Abstract: Recent work has shown that Transformers' compositional generalization is governed by complexity control, initialization scale and weight decay, which st
arXiv:2512.14954v2 Announce Type: replace Abstract: Computing next-token likelihood ratios between two language models (LMs) is a standard task in training paradigms such as knowledge distillation. Si
arXiv:2410.23222v3 Announce Type: replace Abstract: Recent advancements in foundation models have been successfully extended to the time series (TS) domain, facilitated by the emergence of large-scale
arXiv:2605.03882v1 Announce Type: cross Abstract: Individuals frequently form deep attachments to physical objects (e.g., plush toys) that usually cannot sense or respond to their emotions. While AI c
arXiv:2605.03420v1 Announce Type: cross Abstract: This paper describes a submission to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) 2026, which addresses component-level
arXiv:2605.04324v1 Announce Type: new Abstract: Decentralized multi-source domain adaptation seeks to transfer knowledge from multiple heterogeneous and related source domains to an unlabeled target d
arXiv:2605.04418v1 Announce Type: new Abstract: The empirical success of large language model (LLM) pre-training relies heavily on heuristic stabilization techniques, such as explicit normalization la
arXiv:2602.19651v2 Announce Type: replace-cross Abstract: Learning-based methods commonly treat state estimation in robotics as a sequence modeling problem. While this paradigm can be effective at max
arXiv:2605.04239v1 Announce Type: new Abstract: Optical satellite image time series are extensively used in many Earth observation applications, including agriculture, climate monitoring, and land sur
arXiv:2605.04405v1 Announce Type: new Abstract: Driven by the rapid development of generative AI models, deepfake detectors are compelled to undergo periodic recalibration to capture newly developed s
arXiv:2605.05025v1 Announce Type: new Abstract: We propose a lightweight and single-pass uncertainty quantification method for detecting hallucinations in Large Language Models. The method uses attent
arXiv:2512.20773v4 Announce Type: replace Abstract: Realistic user simulation is crucial for training and evaluating multi-turn dialogue systems, yet creating simulators that accurately replicate huma
arXiv:2603.29917v2 Announce Type: replace Abstract: This work presents a robust multi-class classification framework for handwritten digits that combines diffusion-driven feature denoising with a hybr
Recent advances in diffusion-based language models enable controllable sequence generation, but applying them to structured code remains challenging, prompting exploration of syntax-aware diffusion fr
arXiv:2605.04234v1 Announce Type: new Abstract: Implicit neural representations (INRs) have emerged as a powerful paradigm for medical imaging via physics-informed unsupervised learning. Classical INR
arXiv:2602.16233v2 Announce Type: replace-cross Abstract: Circuit cutting decomposes a large quantum circuit into smaller subcircuits whose outputs are classically reconstructed to recover original ex
arXiv:2404.13649v2 Announce Type: replace-cross Abstract: Dimension reduction techniques usually lose information in the sense that reconstructed data are not identical to the original data. However,
arXiv:2603.26114v2 Announce Type: replace Abstract: DPD-Cancer is a graph-attention deep learning framework for predicting small-molecule DPD-Cancer is a graph-attention deep learning framework for pr
arXiv:2509.14640v2 Announce Type: replace Abstract: Existing positional encoding methods in transformers are fundamentally signal-agnostic, deriving positional information solely from sequence indices
arXiv:2605.04062v1 Announce Type: new Abstract: Recent years have witnessed an increasing interest in deploying LLMs on resource-constrained devices, among which quantization has emerged as a promisin
arXiv:2605.04079v1 Announce Type: new Abstract: Early and reliable detection of Alzheimer's disease (AD) is crucial for timely clinical intervention and improved patient management. It also supports t
arXiv:2605.04164v1 Announce Type: new Abstract: Wildfires are a major producer of fine particulate matter, impacting human health and the electrical grid. Accurately forecasting smoke impacts over lon
arXiv:2605.04727v1 Announce Type: new Abstract: Programming Knowledge Tracing (PKT) has recently advanced through hybrid approaches that integrate attention-based feature modeling for code representat
arXiv:2605.04255v1 Announce Type: cross Abstract: Many machine learning problems involve data supported on curved spaces such as spheres, rotation groups, hyperbolic spaces, and general Riemannian man
arXiv:2605.03227v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning. However, their ability to perform ex
arXiv:2605.04664v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic ano
arXiv:2605.05082v1 Announce Type: cross Abstract: We externally validated three deep learning models (DenseNet121, ViT-B/32, and ResNet50) for predicting mammographic breast density from breast ultras
arXiv:2605.04651v1 Announce Type: cross Abstract: Adapting pretrained models typically involves a trade-off between the high training costs of backpropagation and the heavy inference overhead of memor
arXiv:2605.04702v1 Announce Type: new Abstract: Identity-preserving text-to-video generation (IPT2V) empowers users to produce diverse and imaginative videos with consistent human facial identity. Des
arXiv:2605.04666v1 Announce Type: new Abstract: The objective of this paper is to understand what characteristics and features of clinical data influence physician's decision about ordering laboratory
arXiv:2605.04993v1 Announce Type: new Abstract: Accurate forecasting of electric vehicle (EV) charging demand is critical for grid stability, infrastructure planning, and real-time charging optimizati
arXiv:2605.04157v1 Announce Type: new Abstract: SemEval-2026 Task 13 investigates machine-generated code detection across multiple programming languages and application scenarios, asking participating
arXiv:2605.03205v1 Announce Type: cross Abstract: Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowled
arXiv:2605.02902v1 Announce Type: cross Abstract: Recommendation feeds work well when people are simply browsing, and search works well when they can formulate a query. Between these two cases is a co
arXiv:2605.04678v1 Announce Type: cross Abstract: Latent actions serve as an intermediate representation that enables consistent modeling of vision-language-action (VLA) models across heterogeneous da
arXiv:2605.04515v1 Announce Type: new Abstract: While Video Large Language Models (Video-LLMs) excel in general understanding, they exhibit systematic deficits in fine-grained physical reasoning. Exis
arXiv:2605.04535v1 Announce Type: new Abstract: Inferring continuum models directly from video is hampered by two facts: the recorded field is uncalibrated image intensity rather than a physical state
arXiv:2605.05062v1 Announce Type: new Abstract: As time-to-market is crucial in the Integrated Circuit (IC) industry, speeding up layout manufacturability verifi-cation is essential. Chemical-Mechanic
arXiv:2501.14171v3 Announce Type: replace-cross Abstract: Multi-modal brain MRI provides essential complementary information for clinical diagnosis. However, acquiring all modalities in practice is of
arXiv:2604.07401v2 Announce Type: replace-cross Abstract: We study the thermodynamic memory capacity of modern Hopfield networks (Dense Associative Memory models) with continuous states under geometri
arXiv:2605.04474v1 Announce Type: new Abstract: Geometry is central to PDE-governed systems, motivating shape optimization and inversion. Classical pipelines conduct costly forward simulation with geo
arXiv:2605.04581v1 Announce Type: new Abstract: Light field (LF) image super-resolution benefits from Epipolar Plane Images (EPIs), whose line slopes explicitly encode disparity. However, existing Tra
arXiv:2605.04759v1 Announce Type: new Abstract: Transformer based pre-trained large language models have become ubiquitous. There is increasing evidence to suggest that even with large scale pre-train
arXiv:2605.05113v1 Announce Type: new Abstract: We study signal propagation in linear recurrent models at finite width. While existing signal propagation theory relies predominantly on the infinite-wi
arXiv:2605.05144v1 Announce Type: new Abstract: This paper reflects on a AI research project carried out by a team of high-school and early-undergraduate students under the mentorship of graduate rese
arXiv:2605.04853v1 Announce Type: new Abstract: We propose HIN-LRI, a hybrid framework that augments a classical numerical solver with a neural operator trained to correct the solver's structured trun
arXiv:2605.04506v1 Announce Type: new Abstract: We introduce Ilov3Splat, a novel framework for instance-level open-vocabulary 3D scene understanding built on 3D Gaussian Splatting (3D-GS). Most prior
arXiv:2605.05197v1 Announce Type: new Abstract: Grammaticality and likelihood are distinct notions in human language. Pretrained language models (LMs), which are probabilistic models of language fitte