Knowledge catalogue
arXiv:2606.22779v1 Announce Type: cross Abstract: Vision language models (VLMs) employ both visual and textual modalities to enable advanced vision-language inference. However, incorporating visual mo
arXiv:2606.21678v1 Announce Type: new Abstract: Language models can generate plausible rationales for their predictions, but these explanations may not faithfully represent the model's internal reason
arXiv:2606.23213v1 Announce Type: new Abstract: Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monitore
arXiv:2606.21475v1 Announce Type: new Abstract: Accurate soil moisture estimation in semi-arid agricultural regions requires integrating remote sensing and meteorological information while accounting
arXiv:2606.22566v1 Announce Type: cross Abstract: Piezoelectric composites are widely used in sensors, actuators, transducers, and energy-harvesting devices because their effective electromechanical p
arXiv:2606.16246v2 Announce Type: replace Abstract: As AI labs approach a data ceiling where compute capacity outpaces the rate of new high-quality text generation, language model pretraining is shift
arXiv:2606.21956v1 Announce Type: new Abstract: Infrared small target detection (IRSTD) in high-resolution images is crucial for many practical applications, such as surveillance of unmanned aerial ve
arXiv:2606.20976v1 Announce Type: new Abstract: Radio-frequency (RF) monitoring is essential for space domain awareness, but it often generates large, variable, and sparsely populated datasets with fe
arXiv:2606.23500v1 Announce Type: cross Abstract: Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research
arXiv:2606.21940v1 Announce Type: new Abstract: Understanding how a nervous system wires itself from birth to adulthood is a fundamental challenge in developmental neuroscience. We present DevoTG, a t
arXiv:2606.23627v1 Announce Type: cross Abstract: Diffusion models are known to exploit unknown low-dimensional structure to accelerate sampling. However, existing convergence theory under low-dimensi
arXiv:2606.22866v1 Announce Type: new Abstract: We introduce Human-AI Co-discovery system (HACO) for scientific algorithm discovery through cross-domain search and sparse human steering. Starting from
arXiv:2606.23609v1 Announce Type: cross Abstract: Machine learning models exploit spurious correlations, achieving high average accuracy but failing disproportionately on underrepresented subgroups. E
arXiv:2606.21205v1 Announce Type: cross Abstract: In this paper, we present a fully discrete approach for the accurate and numerically efficient dynamical modeling and state estimation of continuum ro
arXiv:2510.10854v3 Announce Type: replace Abstract: Diffusion models have demonstrated remarkable performance in generating high-dimensional samples across domains such as vision, language, and the sc
arXiv:2606.21795v1 Announce Type: new Abstract: Despite their widespread use, the role of reward models in shaping reinforcement learning is poorly understood. Reward models offer a tempting promise:
arXiv:2603.08206v5 Announce Type: replace Abstract: Modern tabular foundation models such as TabPFN and TabICL naturally produce full predictive distributions, while the benchmarks used to evaluate th
arXiv:2503.12947v3 Announce Type: replace Abstract: Neural Radiance Field (NeRF) has shown remarkable performance in novel view synthesis but requires numerous multi-view images, limiting its practica
arXiv:2606.23047v1 Announce Type: cross Abstract: The deployment of data-driven models in 6G wireless networks is increasingly challenged by frequent distribution shifts that degrade performance over
arXiv:2606.22952v1 Announce Type: cross Abstract: This technical report presents submission systems for Task 7(domain-incremental audio classification) of the DCASE 2026 Challenge. The main obstacle i
arXiv:2601.09166v3 Announce Type: replace Abstract: Differentially private federated learning (DP-FL) often suffers from slow convergence under tight privacy budgets because the noise required for pri
arXiv:2606.21159v1 Announce Type: cross Abstract: Automated floor plan generation is an important problem in computational architectural design. The goal is to construct a floor plan from user-defined
arXiv:2507.07855v4 Announce Type: replace Abstract: Normative theories allow one to elicit key parts of a ML algorithm from first principles, which is crucial at a time of championed scrutiny for ML w
arXiv:2606.22182v1 Announce Type: new Abstract: This paper explores a novel brain decoding model for 3D shape perception through a dual pathway architecture mirroring biological vision. Our bio-inspir
arXiv:2606.23655v1 Announce Type: new Abstract: We consider the problem of sequentially approximating functions of each element in a slowly-varying sequence, i.e. one where the magnitude alpha_i of th
arXiv:2601.20904v3 Announce Type: replace-cross Abstract: Cardiac Magnetic Resonance (CMR) imaging provides a comprehensive assessment of cardiac structure and function but remains constrained by high
arXiv:2512.10324v2 Announce Type: replace Abstract: Audio-Visual Large Language Models (AV-LLMs) face prohibitive computational costs of processing massive, redundant audio-visual tokens. Existing uni
arXiv:2606.23210v1 Announce Type: new Abstract: Embedded global navigation satellite system (GNSS) interference monitoring requires fast and memory-efficient inference to process large volumes of raw
India is home to about 60% of the world’s wild Asian elephants, and around 80% of the animals’ habitat lies outside protected areas, according to the Ministry of Environment, Forest, and Climate Chang
arXiv:2606.21728v1 Announce Type: new Abstract: Machine learning models are increasingly used to model chemical process systems, yet they often lack principled uncertainty quantification and mechanism
arXiv:2606.23179v1 Announce Type: new Abstract: The recently introduced EML (Exp-Minus-Log) function acts as continuous analogue of NAND gates, providing a compositional building block capable of repr
arXiv:2508.14600v4 Announce Type: replace Abstract: Non-Intrusive Load Monitoring (NILM) offers a cost-effective method to obtain fine-grained appliance-level energy consumption in smart homes and bui
A technology developed by Professor Sangeeta Bhatia, SM ’93, PhD ’97, and colleagues could offer new hope to the thousands of Americans with chronic liver disease who are waiting for an organ transpla
arXiv:2606.21107v1 Announce Type: new Abstract: Differential privacy (DP) has become the gold standard for ensuring the privacy protection of machine learning and statistical algorithms in recent deca
arXiv:2606.22433v1 Announce Type: new Abstract: Many central machine learning tasks, from entropy tuning in reinforcement learning to equilibrating generative adversarial networks, are fundamentally s
arXiv:2606.21622v1 Announce Type: cross Abstract: Person-level psychological assessment requires aggregating meaning across many messages from the same individual, a task that document-level training
arXiv:2606.22943v1 Announce Type: new Abstract: Self-supervised learning (SSL) is increasingly used in medical imaging to reduce annotation requirements, but representation quality is often judged usi
arXiv:2606.22765v1 Announce Type: cross Abstract: Biological systems maintain function in fluctuating environments by transforming past stimulation into internal dynamical states that support future-o
arXiv:2601.01405v2 Announce Type: replace-cross Abstract: Ball Mapper is a tool in topological data analysis that summarizes a finite metric dataset by covering it with metric balls and encoding their
arXiv:2606.22780v1 Announce Type: new Abstract: Mental illnesses among drug users are an increasing international issue, particularly in regions where early detection cannot be easily undertaken. The
arXiv:2408.07822v2 Announce Type: replace-cross Abstract: We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving attention
arXiv:2606.21687v1 Announce Type: new Abstract: Piecewise-affine neural networks (e.g., with ReLU or LeakyReLU activations) implement continuous piecewise-affine maps, and the number of affine regions
arXiv:2606.22149v1 Announce Type: cross Abstract: Failure analysis is being reshaped by heterogeneous integration, chiplet-based architectures, hybrid bonding, backside technologies, & increasingly bu
arXiv:2606.23354v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in Visual Question Answering (VQA), yet their 'black-box' nature hinders deplo
arXiv:2606.15476v2 Announce Type: replace Abstract: Robots operating in homes, warehouses, and other object-rich environments need memory systems that can find specific object instances on demand. Obj
arXiv:2508.18031v2 Announce Type: replace Abstract: Craniofacial reconstruction in forensics is one of the processes to identify victims of crime and natural disasters. Identifying an individual from
arXiv:2606.22618v1 Announce Type: new Abstract: Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sus
arXiv:2606.22875v1 Announce Type: new Abstract: Training Latent Diffusion Models (LDMs) within Federated Learning (FL) has attracted increasing attention due to its ability to combine the powerful gen
arXiv:2606.22131v1 Announce Type: new Abstract: 4D dynamics (3D geometry evolving over time) is a fundamental representation of the physical world and plays a crucial role in world modeling (e.g., ani
arXiv:2606.22487v1 Announce Type: new Abstract: Automated frame selection for fetal biometry remains under addressed, with most prior work targeting generic quality assessment or downstream measuremen
arXiv:2606.21267v1 Announce Type: new Abstract: Early detection of aphid infestation in crops is essential for preventing yield loss and reducing unnecessary pesticide use. Hyperspectral imaging combi
arXiv:2606.21910v1 Announce Type: new Abstract: Arbitrary-scale image super-resolution (ASISR) aims to reconstruct high-resolution images from low-resolution inputs over a continuous range of upscalin
arXiv:2606.20888v1 Announce Type: new Abstract: In this work, we propose methodname, an LLM-based model for fine-grained human motion understanding that represents motion as a sequence of skeletal pos
arXiv:2602.17431v2 Announce Type: replace-cross Abstract: Uncertainty quantification has emerged as an effective approach to closed-book hallucination detection for LLMs, but existing methods are larg
arXiv:2606.23370v1 Announce Type: cross Abstract: Device-side Large Language Models (LLMs) have grown explosively, offering stronger privacy and higher availability than their cloud-side counterparts.
arXiv:2606.22346v1 Announce Type: cross Abstract: Principled regression for stochastic processes is a long-standing challenge with deep connections to scientific inverse problems. We introduce Flow An
arXiv:2508.13313v5 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates a dynamical system's state from noisy observations. Recent generative models like the ensemble score filter (
arXiv:2606.19025v2 Announce Type: replace Abstract: Pre-training Large Language Models (LLMs) typically demands large-scale infrastructure with tightly coupled hardware accelerators. Mixture-of-Expert
arXiv:2602.06226v2 Announce Type: replace Abstract: The ubiquity of monocular videos capturing daily hand-object interactions presents a valuable resource for embodied intelligence. While 3D hand reco
arXiv:2606.22932v1 Announce Type: new Abstract: Reverse-mode differentiation computes every weight gradient, writes it to memory, and only then lets the optimizer read it back. This two-phase schedule