Knowledge catalogue
arXiv:2609.22608v1 Announce Type: new Abstract: Projectile interception is a challenging dynamic manipulation problem. Intercepting a thrown object with a robot arm requires reaching a point on the ob
arXiv:2609.22633v1 Announce Type: new Abstract: Bilingual language models (LMs) offer a controlled setting for studying how training conditions shape second-language (L2) behaviour, but prior work typ
arXiv:2609.22583v1 Announce Type: new Abstract: Predictive maintenance in Industry 4.0 refers to using data from sensors, machines, and production systems to estimate when equipment is likely to fail,
arXiv:2609.24215v1 Announce Type: new Abstract: Although text-to-image models can accurately depict subjects and scenes, creators still struggle to specify the fine-grained emotions an image should co
arXiv:2609.22767v1 Announce Type: new Abstract: The IEEE BigData Cup benchmark combines three prediction problems with different output structures: ordinal suicide-risk classification, multi-label psy
arXiv:2609.23646v1 Announce Type: cross Abstract: E-commerce search requires distinguishing products that are merely related to a query from those that directly satisfy the user's shopping intent. We
arXiv:2609.22178v1 Announce Type: cross Abstract: Computer-use agents (CUAs) have made rapid progress in completing complex tasks through graphical user interfaces, yet post-training centered on task
arXiv:2609.22111v1 Announce Type: new Abstract: Large language model agents are increasingly capable of conducting research autonomously, producing research documents alongside the code and experiment
arXiv:2609.23387v1 Announce Type: new Abstract: Before a machine learning model can learn a thermodynamic equation of state, it must discover what its measurements represent: which channels scale with
arXiv:2609.22282v1 Announce Type: new Abstract: Reconstructing visual stimuli from electroencephalography (EEG) is difficult because scalp measurements have high temporal but limited spatial resolutio
arXiv:2506.12576v3 Announce Type: replace Abstract: Sparse autoencoders (SAEs) can enable inference-time topic steering by modifying latent feature activations, but existing steering methods often fai
arXiv:2609.22222v1 Announce Type: cross Abstract: Large language models can generate executable data-analysis code, but successful execution is not equivalent to a valid official-statistics result. Th
arXiv:2609.24118v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies achieve strong performance in robotic manipulation but remain brittle once execution deviates from nominal traject
arXiv:2609.24112v1 Announce Type: cross Abstract: Causal Bayesian Optimization (CBO) combines causal inference with Bayesian optimization to enable sample-efficient intervention selection in systems w
arXiv:2609.22260v1 Announce Type: cross Abstract: A large audio language model (LALM) attaches a speech front end to a text language model (LM) that is already safety-aligned. When such a model refuse
arXiv:2609.22247v1 Announce Type: new Abstract: Search agents are usually trained under a single harness. But once an agent is deployed in a real application, its harness is frequently updated (e.g.,
arXiv:2609.23178v1 Announce Type: new Abstract: Language models are appealing tools for research on the past. But to trust the evidence a model provides, researchers need to know whether its responses
arXiv:2609.22632v1 Announce Type: new Abstract: We study binary classification with abstention under separate class-conditional error constraints, with the objective of minimizing abstention while kee
arXiv:2609.24287v1 Announce Type: new Abstract: Classifier-free guidance (CFG) improves conditional generation in Flow Matching, but strong guidance can distort the generated distribution and reduce d
arXiv:2609.22166v1 Announce Type: new Abstract: The rapid advancement of Internet of Things (IoT) technology has led to the widespread deployment of smart, interconnected devices across a range of dom
arXiv:2609.23486v1 Announce Type: cross Abstract: Robots operating in human-centered environments are typically designed to execute explicit instructions, and most robot-learning datasets likewise pai
arXiv:2609.23883v1 Announce Type: new Abstract: We present a collaborative streaming anomaly detection system for high-speed data streams that explicitly integrates human analysts into the decision lo
arXiv:2609.22896v1 Announce Type: new Abstract: Autonomous vehicles operating in open-world scenarios are inevitably confronted with previously unknown objects, such as exotic animals or loose cargo.
arXiv:2609.22284v1 Announce Type: new Abstract: Talking-face (TF) deepfakes are detected unevenly by rPPG-based methods across generators. We study two lightweight visual-only cues, rPPG-derived wavef
arXiv:2609.22919v1 Announce Type: new Abstract: Reinforcement learning in real-life applications requires safety guarantees during exploration. Typical reinforcement learning algorithms do not provide
arXiv:2609.22614v1 Announce Type: new Abstract: Process model forecasting (PMF) aims to predict the process model that will characterize a future period, thereby providing a process-level view of how
arXiv:2609.23815v1 Announce Type: new Abstract: Medical image segmentation models typically rely on large amounts of densely annotated volumetric data, limiting their scalability across tasks and imag
arXiv:2606.20002v2 Announce Type: replace-cross Abstract: This work presents a general framework for training large language models (LLMs) to 'Connect the Dots' (CoD), a meta-capability required by lo
arXiv:2609.22983v1 Announce Type: cross Abstract: Robotic grasping is typically formulated as the problem of identifying successful actions from a space of candidate grasp poses. However, the organiza
arXiv:2609.23742v1 Announce Type: new Abstract: Small open-source large language models (LLMs) in the 0.6B-4B parameter range are increasingly deployed for structured output generation (JSON, function
arXiv:2609.23800v1 Announce Type: new Abstract: High-precision connector insertion remains challenging for robotic systems due to tight mechanical tolerances, partial observability during contact, and
arXiv:2609.22360v1 Announce Type: new Abstract: Electrical submersible pumps (ESPs) are essential in offshore oil production, where unexpected failures can result in significant operational and financ
arXiv:2609.22175v1 Announce Type: cross Abstract: World models trained via pixel reconstruction can struggle in visually complex environments, where irrelevant information dominates the objective and
arXiv:2609.22487v1 Announce Type: new Abstract: Matrix-valued optimizer states may contain relational structure that is not captured by treating their entries independently. We study whether relations
arXiv:2601.17556v2 Announce Type: replace Abstract: We consider the problem of vision-based pose estimation for autonomous systems. While deep neural networks have been successfully used for vision-ba
arXiv:2609.22108v1 Announce Type: cross Abstract: Learning-enabled perception is important in many autonomous systems. Unlike traditional sensors, the boundary where ML perception does or does not wor
arXiv:2609.24651v1 Announce Type: new Abstract: Diffusion and flow models, as promising generative paradigms for speech enhancement, face a training--inference mismatch: training uses analytical path
arXiv:2609.22187v1 Announce Type: cross Abstract: Spatial transcriptomics (ST) provides spatially resolved gene expression profiling but remains expensive, motivating the prediction of ST from histolo
arXiv:2609.24021v1 Announce Type: cross Abstract: Neural operators are data-driven models that learn mappings from inputs that parameterize partial differential equations, such as spatially varying co
arXiv:2609.24382v1 Announce Type: new Abstract: The intensification of climate change poses a growing threat to food security, especially in vulnerable communities. This study employs an observational
arXiv:2609.23012v1 Announce Type: new Abstract: Generative vision-language models (VLMs) offer a counting paradigm in which one model produces both a count and a natural-language account of the scene,
arXiv:2609.22862v1 Announce Type: new Abstract: Graph neural networks are widely used for drug--target affinity (DTA) prediction, and discrete Ricci curvature has recently been used to characterize mo
arXiv:2609.24625v1 Announce Type: cross Abstract: How should natural language processing models be selected and adapted for global health literature in environments where annotated data and computatio
arXiv:2609.22752v1 Announce Type: new Abstract: Diffusion models have demonstrated strong power in generative modeling tasks across multiple domains, exhibiting a remarkable capability of learning com
arXiv:2609.24749v1 Announce Type: cross Abstract: Latent world models predict the consequences of actions, but accurate prediction does not guarantee that latent distance reflects which candidate will
arXiv:2609.22726v1 Announce Type: new Abstract: Efficient coordination under limited communication remains a key challenge in decentralized multi-robot exploration. While centralized approaches benefi
arXiv:2609.23535v1 Announce Type: new Abstract: Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on
arXiv:2609.23926v1 Announce Type: cross Abstract: Density-Ratio Rescoring (DRR) augments a classifier trained at the original class prior with a survey-raking dual score. Raking reweights the majority
arXiv:2609.23507v1 Announce Type: new Abstract: The increasing reliance on mobile phones has made phone-induced pedestrian distraction increasingly prevalent. Activities such as texting, watching vide
arXiv:2609.22267v1 Announce Type: new Abstract: Camera motion often reflects directorial intent and requires professional equipment, making it a high value form of intellectual property. However, gene
arXiv:2411.05005v2 Announce Type: replace Abstract: Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most exi
DigitalOcean Managed Agents are here! They work with major harnesses like Claude Code and Codex. 16,000+ tools. Idle agents stop using CPU. Optimized to help builders scale agents in production. Worth
arXiv:2609.22256v1 Announce Type: new Abstract: Effective workplace negotiation requires balancing multiple objectives, including achieving task goals, preserving professional relationships, and resol
arXiv:2609.22245v1 Announce Type: new Abstract: Large language models can produce fluent explanations for chess moves, but plausible language does not necessarily reflect the reasoning behind a decisi
arXiv:2609.22566v1 Announce Type: new Abstract: Knowledge distillation (KD) aims to compress high-performance teacher LLMs into lightweight students. However, distilled students often exhibit substant
arXiv:2609.24868v1 Announce Type: new Abstract: World Action Models (WAMs) jointly generate robot actions and predict future world states, transferring priors from video pretraining to robot control.
arXiv:2609.24531v1 Announce Type: new Abstract: Thermography plays a vital role in military and broader thermal analysis applications. Recent progress in 3D thermal reconstruction has extended tempera
arXiv:2609.22150v1 Announce Type: cross Abstract: Content promotion spans a broad set of skills, from understanding content to forecasting its market reception. However, LLMs' ability to support such
arXiv:2404.06135v4 Announce Type: replace Abstract: The Transformer architecture has achieved remarkable success in natural language processing and high-level vision tasks over the past few years. How
arXiv:2609.22508v1 Announce Type: new Abstract: Generative Adversarial Networks (GANs) have demonstrated strong performance in generating high-quality synthetic data. However, they are limited by no f