Knowledge catalogue
arXiv:2607.07420v1 Announce Type: new Abstract: Safety for generalist robots is usually discussed in terms of motion or dialogue. We argue a third question is missing: should the robot take its first
arXiv:2607.06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy withou
arXiv:2607.07343v1 Announce Type: cross Abstract: Online truckload bid acceptance is a closed-loop stochastic decision problem in which a carrier or broker must, in real time, accept or reject a tende
arXiv:2607.06922v1 Announce Type: cross Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding t
arXiv:2607.06839v1 Announce Type: cross Abstract: Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain
arXiv:2607.06623v1 Announce Type: cross Abstract: Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure online. Labeled data are sc
arXiv:2607.06757v1 Announce Type: new Abstract: Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making. However, ABMs
arXiv:2607.06845v1 Announce Type: new Abstract: African American English (AAE), a rule-governed dialect spoken by over 30 million people, is routinely misinterpreted and 'corrected' by large language
arXiv:2607.06918v1 Announce Type: cross Abstract: Pre-trained Vision Foundation Models (VFMs) provide strong visual representations for diverse downstream tasks. The key challenge of VFM adaptation st
arXiv:2607.06719v1 Announce Type: cross Abstract: This paper introduces a graph-theoretic approach for predicting market regimes in foreign exchange (FX) currency prices. Specifically, the proposed mo
arXiv:2607.06929v1 Announce Type: cross Abstract: Music aesthetic assessment is a challenging yet underexplored problem, requiring models to capture fine-grained, multi-dimensional human perceptual ju
arXiv:2607.07051v1 Announce Type: cross Abstract: Conversational image editing requires preserving not only visible content, but also content that temporarily disappears across turns. When newly added
arXiv:2607.07040v1 Announce Type: new Abstract: How can we measure intelligence beyond human capability? Human-authored benchmarks saturate, and above human capability, examiners may not know which ta
arXiv:2607.07316v1 Announce Type: new Abstract: This article offers a comprehensive overview of mechanistic interpretability, an emerging field that seeks to reverse-engineer the internal algorithms o
arXiv:2607.06974v1 Announce Type: new Abstract: Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in
arXiv:2607.07383v1 Announce Type: new Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires models to retrieve visual entities matching the query image from large-scale encyclopedic kn
arXiv:2510.25370v2 Announce Type: replace Abstract: Forecasting transformative technologies remains a critical but challenging task, particularly in fast-evolving domains such as Information and Commu
arXiv:2607.07016v1 Announce Type: cross Abstract: Accurate forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mo
arXiv:2607.07294v1 Announce Type: cross Abstract: Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot
arXiv:2607.07264v1 Announce Type: new Abstract: Deep neural networks are widely deployed in high-stakes visual applications where interpretability is critical, yet existing explanations face a trade-o
arXiv:2607.07077v1 Announce Type: cross Abstract: Functional brain networks exhibit a hierarchical organization across ROI, community, and whole-brain levels, supporting local processing, inter-commun
arXiv:2607.06607v1 Announce Type: cross Abstract: Accurate long-term forecasting in complex systems is frequently compromised by dataset-level distribution shifts, where diverse underlying behavioral
arXiv:2607.07682v1 Announce Type: new Abstract: The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-co
arXiv:2607.07168v1 Announce Type: new Abstract: Pose-Free Feed-forward 3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for fast scene reconstruction. However, its performance
arXiv:2603.17057v2 Announce Type: replace-cross Abstract: Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while re
arXiv:2607.06637v1 Announce Type: new Abstract: In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers. Central to our metho
arXiv:2607.06954v1 Announce Type: new Abstract: Wearable and mobile sensing technologies have demonstrated strong potential for health inference; however, most sensor models are designed for specific
arXiv:2607.07313v1 Announce Type: cross Abstract: Pairwise comparison (PC) via pairwise reciprocal matrices (PRMs) is central to the Analytic Hierarchy Process (AHP). Although the traditional eigenvec
arXiv:2607.07076v1 Announce Type: new Abstract: Imitation learning has enabled remarkable progress in robotic manipulation, especially with diffusion and flow-based policies that generate complex visu
arXiv:2607.07281v1 Announce Type: new Abstract: Modular miniature robots could provide scalable function in constrained environments, but coordinating many imperfect modules remains difficult when com
arXiv:2607.06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or
arXiv:2607.07170v1 Announce Type: new Abstract: Online 3D scene graph generation builds a persistent, structured representation of a scene by incrementally fusing 2D observations into a global 3D grap
arXiv:2607.07634v1 Announce Type: cross Abstract: Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges. A ma
arXiv:2607.07338v1 Announce Type: cross Abstract: Nonlinear dynamics is ubiquitous in nature, ranging from chemical pattern formation to ocean circulation, yet its simulation on quantum computers is f
arXiv:2607.07159v1 Announce Type: cross Abstract: In partially exploratory factor analysis (PEFA), the loading structure and factor numbers are weakly specified. The regularized variational approximat
arXiv:2607.06950v1 Announce Type: cross Abstract: Sampling-based model predictive control methods handle nonlinear dynamics and complex cost landscapes through Monte Carlo rollouts, yet typically empl
arXiv:2607.06843v1 Announce Type: new Abstract: Diffusion-based text-to-motion models synthesize realistic human motions but often exhibit semantic drift from the input text. Motion is inherently temp
arXiv:2607.06626v1 Announce Type: new Abstract: Recent Vision-Language Models capture increasingly complex aspects of human cognition. Here we ask whether this alignment extends to reward valuation, w
arXiv:2607.06699v1 Announce Type: new Abstract: Recovering real-world scenes as interactive simulation environments can enable generalizable robot learning and reproducible policy evaluation. However,
arXiv:2607.06979v1 Announce Type: new Abstract: Federated Learning (FL) enables training shared models on private, on-device data, but production deployments remain constrained to slow, multi-day refr
arXiv:2607.07252v1 Announce Type: new Abstract: Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems
arXiv:2607.07675v1 Announce Type: new Abstract: Despite the recent promise in robot control, video generative models suffer from a domain mismatch due to their primary focus on content creation. For e
Ollama announced an upcoming event or release scheduled for the next day via their official X (Twitter) account. The post likely teases new features, updates, or announcements related to their open-so
arXiv:2607.06909v1 Announce Type: new Abstract: Despite rapid advances in chest X-ray (CXR) foundation models, most radiology report generation (RRG) systems still rely on heavily downsampled inputs (
arXiv:2607.07508v1 Announce Type: cross Abstract: Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mos
arXiv:2607.07676v1 Announce Type: new Abstract: Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs n
arXiv:2607.06620v1 Announce Type: cross Abstract: Recent Multimodal Large Language Models (MLLMs) struggle to bridge the representational gap between 2D semantic understanding and 3D spatial geometry.
arXiv:2607.06978v1 Announce Type: new Abstract: Robot imitation learning for manipulation should preserve demonstrated task geometry while producing dynamically admissible robot motions. Existing pipe
arXiv:2607.06949v1 Announce Type: new Abstract: Training robust handwriting recognition (HTR) systems requires massive amounts of annotated data, which is often difficult to acquire. While synthetic h
arXiv:2607.07173v1 Announce Type: new Abstract: Subject-driven personalized text-to-image generation requires a pretrained diffusion model to acquire a specific subject from a few reference images whi
arXiv:2502.00037v4 Announce Type: replace-cross Abstract: We introduce Superstate Quantum Mechanics (SQM), a theory that considers states in Hilbert space subject to multiple quadratic constraints, wi
arXiv:2607.07469v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) for e-commerce attribute extraction requires labeled data representative across thousands of product types, a
arXiv:2601.22879v2 Announce Type: replace Abstract: Time series data are essential for a wide range of applications, yet access to high-quality datasets is often constrained by privacy concerns, acqui
arXiv:2511.16107v3 Announce Type: replace-cross Abstract: Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training. Recent
arXiv:2607.07008v1 Announce Type: cross Abstract: A common method for the representation and analysis of time-series data is the hidden Markov model (HMM), where each observation is associated with a
arXiv:2607.06642v1 Announce Type: new Abstract: Active learning studies the fundamental question: what data should we choose to observe? The greedy algorithm in optimal experiment design is a common h
arXiv:2607.07706v1 Announce Type: new Abstract: The quadratic cost of causal self-attention severely bottlenecks long-context transformer inference. While numerous post hoc linearization pipelines exi
arXiv:2607.06643v1 Announce Type: cross Abstract: Backdoor attacks severely threaten large-scale AI models. When model owners delegate training to external compute providers within a decentralized tra
arXiv:2606.12629v3 Announce Type: replace-cross Abstract: The standard basis of transformer hidden states is a training-free, architecture-general feature basis for detecting concepts and, in language
arXiv:2601.21476v2 Announce Type: replace Abstract: On-policy reinforcement learning (RL) for language model post-training suffers from a fundamental tension: as training progresses, policy entropy co