Knowledge catalogue
arXiv:2602.03762v3 Announce Type: replace-cross Abstract: Visually-guided acoustic highlighting seeks to rebalance audio in alignment with the accompanying video, creating a coherent audio-visual expe
arXiv:2602.21357v2 Announce Type: replace-cross Abstract: Bayesian inference for inverse problems involves computing expectations under posterior distributions--e.g., posterior means, variances, or pr
arXiv:2602.11973v2 Announce Type: replace Abstract: In critical decision support systems based on medical imaging, the reliability of AI-assisted decision-making is as relevant as predictive accuracy.
arXiv:2606.22659v1 Announce Type: cross Abstract: Prompt-injection detectors are deployed as guards: a model scores an input and a downstream system trusts or blocks it on that score. I study the conf
arXiv:2606.21752v1 Announce Type: cross Abstract: Histopathologic cancer detection is challenging due to tissue variability, staining differences, and subtle visual distinctions between disease classe
arXiv:2606.21577v1 Announce Type: new Abstract: Quadratic programs (QPs) using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are widely used for safe control in reach-and-avoi
arXiv:2507.15741v2 Announce Type: replace-cross Abstract: This paper introduces a framework for uncertainty quantification in regression models defined on metric spaces. Using a proposed notion of hom
arXiv:2606.21116v1 Announce Type: new Abstract: Proofreading--correcting segmentation errors in 3D brain reconstructions--is the rate-limiting step in synapse-resolution connectomics. We release Conne
arXiv:2606.21234v1 Announce Type: new Abstract: To generate natural and accurate sentence-level sign language, synthesizing the 'gloss', the fundamental semantic unit, is essential. However, most curr
arXiv:2606.21900v1 Announce Type: cross Abstract: Continuous authentication for mobile and zero-trust systems requires nonintrusive evidence confirming the enrolled user-device context remains valid a
arXiv:2606.21021v1 Announce Type: new Abstract: Long-horizon spacecraft trajectory forecasting suffers from error accumulation due to the absence of corrective observations in the forecast regime, mak
arXiv:2606.21156v1 Announce Type: new Abstract: The high cost of spatial transcriptomics (ST) has driven extensive studies into predicting gene expression directly from H&E histology images. However,
arXiv:2606.22945v1 Announce Type: cross Abstract: Realistic integration of user-specified textures into scene images is a fundamental task in computer graphics and image editing. While existing materi
arXiv:2606.22259v1 Announce Type: cross Abstract: This paper investigates convergence properties of regularized Nystrom subsampling applied to the unsupervised domain adaptation problem under covariat
arXiv:2606.23364v1 Announce Type: new Abstract: Training dynamics is central to understanding neural networks, yet its theoretical analysis remains difficult even for simple architectures and becomes
arXiv:2411.07724v2 Announce Type: replace Abstract: The LION (evoLved sIgn mOmeNtum) optimizer for deep neural network training was found by Google via program search, with the simple sign update yet
arXiv:2606.22757v1 Announce Type: new Abstract: Multi-Agent Path Finding (MAPF) is a problem that requires computing collision-free paths for a set of agents from their start locations to designated g
arXiv:2606.23680v1 Announce Type: cross Abstract: Humanoid loco-manipulation is often simplified into a stop-and-go process: walking to an object, stopping to manipulate it, and then resuming locomoti
arXiv:2606.21935v1 Announce Type: new Abstract: Diffusion models excel at capturing multi-modal action distributions in robot imitation learning. However, in multi-task and long-horizon scenarios, mon
arXiv:2606.21932v1 Announce Type: new Abstract: Remote sensing change detection (CD) from bi-temporal imagery is critical for applications such as urban monitoring, disaster assessment, and environmen
arXiv:2606.21627v1 Announce Type: cross Abstract: As agentic systems tackle increasingly complex multi-step tasks, evaluating their trajectories presents a major bottleneck - human annotation of a sin
arXiv:2606.23015v1 Announce Type: new Abstract: Optimizing instructional policies in Intelligent Tutoring Systems (ITS) typically requires costly online experimentation or student simulators that may
arXiv:2606.20739v1 Announce Type: new Abstract: We present a unified framework that turns a set of 6-DoF inspection viewpoints into a time-optimal, collision-free route for a 9-DoF robotic system. Unl
arXiv:2606.20608v1 Announce Type: cross Abstract: Generative text-to-video systems can produce visually fluent educational clips, but they rarely encode the pedagogical content knowledge (PCK) needed
arXiv:2606.22804v1 Announce Type: new Abstract: Long, continuous video streams are an increasingly critical driver of multimedia intelligence. Existing efforts often handle long videos with a sample-e
arXiv:2510.15623v2 Announce Type: replace Abstract: Complex query answering (CQA) goes beyond the widely studied link prediction task by addressing more sophisticated queries that require multi-hop re
arXiv:2606.20376v2 Announce Type: replace Abstract: Safety is a core concern for deploying reinforcement learning (RL) agents in real-world domains such as robotics and autonomous driving. While bench
arXiv:2509.23340v4 Announce Type: replace-cross Abstract: Automatically assessing the credibility of online sources presents an invaluable tool for navigating today's information ecosystem. However, e
arXiv:2606.21613v1 Announce Type: new Abstract: Scaling wildlife monitoring for real-world conservation deployments requires automated analysis of smart sensors that operate under severe annotation sc
arXiv:2606.22094v1 Announce Type: new Abstract: Accurate yaw estimation is a bottleneck in cross-view localization between ground view and Bird's Eye View (BEV). Existing methods couple yaw with trans
arXiv:2207.14273v2 Announce Type: replace Abstract: We present Curve Distillation, CuDi, for efficient and controllable exposure adjustment without the requirement of paired or unpaired data during tr
arXiv:2603.02370v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) have grown increasingly powerful in recent years, but can also exhibit harmful biases. Prior studies investigat
arXiv:2606.21631v1 Announce Type: cross Abstract: Data curation is a critical part of post-training pipelines for large language models, yet existing tools often treat ingestion, deduplication, synthe
arXiv:2606.22317v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is widely viewed as a promising path toward continuously improving large language models. Recent w
arXiv:2606.22394v1 Announce Type: new Abstract: Consistency distillation has significantly accelerated the inference of diffusion models. In this work, we reveal an intriguing asymmetry: while Logit-N
arXiv:2606.22439v1 Announce Type: new Abstract: Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measureme
arXiv:2606.21608v1 Announce Type: new Abstract: Accurate segmentation of curvilinear structures remains challenging in biomedical imaging due to their thin geometry, complex topology, and sensitivity
arXiv:2606.22347v1 Announce Type: new Abstract: Identity-Preserving Text-to-Video Generation (IPT2V) seeks to synthesize a temporally coherent video from a reference image and a textual description, w
arXiv:2606.22476v1 Announce Type: new Abstract: Humans can effortlessly reason about scenes across different viewpoints, yet it remains unclear whether Vision-Language Models (VLMs) possess similar cr
arXiv:2606.20725v1 Announce Type: new Abstract: Accurate, up-to-date representations of road structures are critical for the safe operation of autonomous vehicles. Existing systems rely either on cost
arXiv:2603.14837v2 Announce Type: replace Abstract: Analyzing street-view imagery with computer vision models offers a promising approach for rapid, hyperlocal disaster damage assessment, but existing
arXiv:2606.20622v1 Announce Type: cross Abstract: The goal of artificial intelligence is to create agents capable of general, adaptive behaviour in open-ended environments. Guided by the 'Bitter Lesso
arXiv:2511.20906v2 Announce Type: replace Abstract: Diffusion- and flow-based policies deliver state-of-the-art performance on long-horizon robotic manipulation and imitation learning tasks. However,
arXiv:2602.10155v2 Announce Type: replace-cross Abstract: Accurate compensation of brain deformation is critical for reliable image-guided neurosurgery. Surgical manipulation and tumor resection induc
arXiv:2606.22674v1 Announce Type: cross Abstract: This paper introduces Wittgenstein's Rule Following (WRF) data evolution, a framework in philomatics for evolving or generating a new dataset from a s
arXiv:2606.21916v1 Announce Type: new Abstract: The performance of deep learning models is affected by not only data quantity but also data quality. Data pruning is a process by which practitioners ca
arXiv:2606.23611v1 Announce Type: new Abstract: The availability of large amounts of clean data is paramount to training neural networks. However, at large scales, manual oversight is impractical, res
arXiv:2606.21337v1 Announce Type: new Abstract: Massive unstructured multimodal streams suffer from high 'data entropy,' impeding both efficient human knowledge acquisition and high-quality AI post-tr
arXiv:2505.09603v2 Announce Type: replace-cross Abstract: Recently, the robotics community has amassed ever larger and more diverse datasets to train generalist policies. However, while these policies
arXiv:2606.20765v1 Announce Type: cross Abstract: Deep learning for 3D medical image segmentation requires extensive manual annotations, a major bottleneck in volumetric medical imaging. Active learni
arXiv:2606.22829v1 Announce Type: new Abstract: Intraoperative Adverse Events (IAEs) detection is critical for improving surgical safety, with bleeding being among the most frequent events across many
arXiv:2606.21212v1 Announce Type: new Abstract: Causal discovery is critical for understanding complex data-generating mechanisms, yet traditional algorithms often struggle with highly non-linear and
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:2606.21158v1 Announce Type: new Abstract: Singular learning theory characterises the complexity of a deep network through the geometry of its loss singularities. The local learning coefficient (
arXiv:2606.23585v1 Announce Type: cross Abstract: As autonomous aircraft are introduced at scale and traffic density increases, centralized management becomes insufficient to coordinate the large numb
arXiv:2606.21773v1 Announce Type: new Abstract: Plugging predictions of unknown parameters into downstream optimization problems, often referred to as the ``predict-then-optimize'' paradigm, has long
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:2606.21496v1 Announce Type: cross Abstract: Deploying generalist robotic agents in the real world requires transferable skills. Specifically, a policy trained to clone a behavior from object-spe
arXiv:2603.14418v2 Announce Type: replace Abstract: Label variability is a major challenge for prostate lesion segmentation. In multi-site datasets, annotations often reflect centre-specific contourin
arXiv:2603.11909v2 Announce Type: replace Abstract: Reliable uncertainty quantification is paramount for forecasting multivariate time series and spatiotemporal data. While Transformer architectures e