Knowledge catalogue
arXiv:2608.16181v1 Announce Type: cross Abstract: Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science wo
arXiv:2608.14767v1 Announce Type: cross Abstract: Automated vehicles must explain their decisions in ways that passengers can understand, monitor, and trust. Existing language-annotated driving datase
arXiv:2608.15956v1 Announce Type: new Abstract: Agentic retrieval workflows produce query, retrieval, and stopping traces as a byproduct of answering questions. We study how these traces can adapt a d
arXiv:2510.16084v3 Announce Type: replace Abstract: Backpropagation learning algorithm, the workhorse of modern artificial intelligence, is notoriously difficult to implement in physical neural networ
arXiv:2509.16586v2 Announce Type: replace Abstract: Recent advances have significantly improved our understanding of the sample complexity of learning in average-reward Markov decision processes (AMDP
arXiv:2608.16503v1 Announce Type: cross Abstract: Real-world deployment of Vision-Language-Action (VLA) models is often bottlenecked by efficiency-performance trade-offs, cross-embodiment generalizati
arXiv:2608.16794v1 Announce Type: cross Abstract: Language and vision-language models generate plausible embodied plans but do not guarantee executability, as their outputs can violate environment dyn
arXiv:2608.15438v1 Announce Type: cross Abstract: Building approximate nearest neighbor (ANN) indexes at billion scale is often dominated by expensive global clustering or graph construction, making t
arXiv:2608.16104v1 Announce Type: new Abstract: Diffusion and flow matching models have made significant progress in text-to-image generation, yet high computation, quadratic complexity, and large mem
arXiv:2608.16038v1 Announce Type: cross Abstract: Post-hoc Graph Neural Network (GNN) explainers commonly follow a Perturb-Query paradigm, inferring the importance of graph elements based on queried p
arXiv:2608.15286v1 Announce Type: cross Abstract: We introduce AgentRelBench, an environment-agnostic reliability instrument that computes ground-truth, severity-priced damage from database state diff
arXiv:2608.15919v1 Announce Type: cross Abstract: Retrieval-Augmented Generation over knowledge graphs (Graph-RAG) has emerged as a powerful paradigm for grounding large language models in domain-spec
arXiv:2608.16864v1 Announce Type: cross Abstract: In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based sum
arXiv:2608.15459v1 Announce Type: cross Abstract: Attention mechanisms have driven machine learning for a decade, from neural machine translation to language models that do general-purpose reasoning.
arXiv:2608.15573v1 Announce Type: new Abstract: Reliable long-horizon planning remains a key challenge in end-to-end autonomous driving. By accounting for future motion evolution and potential consequ
arXiv:2603.06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentia
arXiv:2608.15002v1 Announce Type: cross Abstract: When a robot policy is trained for a new task or dataset, its visual encoder can be frozen and only its action generation module trained, reducing tra
arXiv:2608.14776v1 Announce Type: new Abstract: Collegiate running in the United States generates thousands of race results annually in cross country and track and field, yet no large-scale dataset ha
arXiv:2608.15425v1 Announce Type: cross Abstract: Vision-language models (VLMs) achieve strong performance on high-level multimodal tasks, yet numerosity perception, a cognitive ability that emerges i
arXiv:2608.16442v1 Announce Type: new Abstract: Inspection is a core capability in many mobile robotics applications, including industrial facility monitoring, infrastructure maintenance, agriculture,
arXiv:2608.16499v1 Announce Type: cross Abstract: Active 3D Gaussian reconstruction fundamentally relies on selecting informative next-best views under limited sensing budgets. Existing active 3DGS me
arXiv:2608.16373v1 Announce Type: cross Abstract: Despite comprising over 70% of its surface, the world's oceans are critically underobserved compared to the land surface or the atmosphere.Understandi
arXiv:2608.16070v1 Announce Type: cross Abstract: Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecast
arXiv:2307.15691v4 Announce Type: replace-cross Abstract: ODTlearn is an open source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptiv
arXiv:2608.14677v1 Announce Type: cross Abstract: Most mobile image-generation applications are thin clients over cloud services, leaving outputs hard to audit. We present an Android latent-diffusion
arXiv:2608.16482v1 Announce Type: new Abstract: The dosing of intravenous fluids and vasopressors in sepsis is a sequential decision made under uncertainty and guided largely by clinical judgment, whi
arXiv:2608.14580v1 Announce Type: new Abstract: OGX (Open GenAI Stack) is an open-source AI application server and Python library that implements the APIs of major frontier labs (OpenAI, Anthropic, Go
arXiv:2608.14705v1 Announce Type: new Abstract: Hyperparameter optimization (HPO) can materially affect the performance of deep learning (DL) image classifiers, but there is little empirical guidance
arXiv:2512.22284v2 Announce Type: replace-cross Abstract: Nature rarely reveals her secrets bluntly, yet in the Fibonacci sequence she grants us a glimpse of her quiet architecture of growth, harmony,
arXiv:2608.15649v1 Announce Type: cross Abstract: The popular CART algorithm for regression trees combines a greedy splitting rule with a stopping rule, but while the splitting rule has been well stud
arXiv:2504.19952v2 Announce Type: replace-cross Abstract: We present two general lower bounds for stopping times of sequential tests between arbitrary composite nulls mathcal P and alternatives mathca
arXiv:2608.15267v1 Announce Type: new Abstract: Semantic change detection (SCD) is a bitemporal dense-prediction task that jointly identifies changed regions and their semantic states before and after
arXiv:2608.16760v1 Announce Type: new Abstract: Reliable optimization is central to neural network (NN) training, yet Adam, the default optimizer for modern LLMs, rests on a fragile foundation. This t
arXiv:2608.16542v1 Announce Type: new Abstract: Continuous wrist-worn hand sensing for gesture interfaces and motor symptom monitoring needs an always-on front end that fits inside a coin-cell power b
arXiv:2608.14683v1 Announce Type: new Abstract: Given a patient's clinical findings, a diagnostic system ranks possible diseases and must decide when to endorse its first prediction or defer it for re
arXiv:2505.20532v2 Announce Type: replace Abstract: This paper studies robust one-shot aggregation for distributed and federated Independent Component Analysis (ICA). In this setting, each client comp
arXiv:2608.15050v1 Announce Type: new Abstract: We study online convex optimization with dueling (pairwise comparison) feedback, where the learner observes only a binary preference between two queried
arXiv:2602.18094v2 Announce Type: replace-cross Abstract: Existing Visual-Language Models (VLMs) have achieved significant progress by being trained on massive-scale datasets, typically under the assu
arXiv:2509.24900v2 Announce Type: replace-cross Abstract: The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensivenes
arXiv:2608.15982v1 Announce Type: new Abstract: We develop operator-theoretic generalization bounds for deep multi-output function classes by representing network layers as Koopman composition operato
arXiv:2608.15472v1 Announce Type: cross Abstract: The problem of networked information aggregation, studied in Kearns et al. (2026), involves a group of learners situated on the vertices of a directed
arXiv:2608.14906v1 Announce Type: cross Abstract: Watermarking provides a principled way to authenticate text generated by large language models (LLMs). In practice, however, the final text may be mix
arXiv:2608.16238v1 Announce Type: new Abstract: The increasing share of renewable energy in power systems creates a need for fast-response and flexible resources to maintain system stability. With the
arXiv:2608.16651v1 Announce Type: cross Abstract: Satellite agents for on-orbit navigation tasks need to predict collision risks using limited onboard observations. However, conventional planners ofte
arXiv:2608.14557v1 Announce Type: cross Abstract: Low Earth Orbit (LEO) computing is emerging for low-latency, globally distributed AI services, enabled by advances in satellite constellations and reu
arXiv:2608.15436v1 Announce Type: new Abstract: Frontier AI models have advanced rapidly, but they still struggle with telecom-specific tasks. We present Open Telco (OTel), an open telecom AI resource
arXiv:2608.14835v1 Announce Type: new Abstract: Dynamic scene graphs (DSGs) capture spatio-temporal interactions across videos as langlesubject, predicate, objectrangle triplets, and underpin downstre
arXiv:2608.14774v1 Announce Type: new Abstract: Modern sequence models heavily rely on massive memory footprints and large-batch stochastic optimization, barriers that restrict sample efficiency and c
arXiv:2608.14656v1 Announce Type: cross Abstract: Photoplethysmography (PPG) is widely used in consumer wearables because of its low cost and ease of acquisition. However, unlike electrocardiography (
arXiv:2608.15026v1 Announce Type: new Abstract: Post-training of vision-language-action (VLA) models typically relies on expert demonstrations and policy interaction trajectories. However, in long-hor
arXiv:2608.14650v1 Announce Type: new Abstract: Existing adaptive-inference and world-action-model systems use cheap-stage outputs or predicted futures to allocate additional computation. We study a n
arXiv:2608.16477v1 Announce Type: new Abstract: AI-RAN brings large language model (LLM) serving close to mobile users, but cellular handover can separate an active request from its inference state: t
arXiv:2608.16620v1 Announce Type: cross Abstract: Palmyra x6 is a large language model optimized for use with enterprise-oriented agentic tasks. The model was built by post-training a Mixture-of-Exper
arXiv:2608.14742v1 Announce Type: cross Abstract: Pandas has emerged as the de facto library for data processing and machine learning, widely used for tasks, such as data loading, transformation, and
arXiv:2608.16425v1 Announce Type: new Abstract: Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows
arXiv:2608.14995v1 Announce Type: cross Abstract: Quantum federated learning (QFL) lets multiple quantum clients collaboratively train quantum neural networks (QNNs) without sharing private local data
arXiv:2608.14924v1 Announce Type: cross Abstract: Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images wi
arXiv:2608.14710v1 Announce Type: cross Abstract: Predicting spatial gene expression from hematoxylin and eosin (H&E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST)
arXiv:2608.14951v1 Announce Type: new Abstract: Low-rank matrix decompositions can uncover patterns and structure in data and have a number of different applications across many disciplines. Extension
arXiv:2608.16650v1 Announce Type: new Abstract: Large language models (LLMs) exhibit sycophancy, a tendency to agree with user beliefs regardless of factual accuracy. This can reinforce misconceptions