Knowledge catalogue
arXiv:2609.19607v1 Announce Type: cross Abstract: Coding-agent benchmarks are built for broad and comprehensive comparisons, not frequent development decisions. Individual runs vary, full suites are e
arXiv:2609.19428v1 Announce Type: cross Abstract: This paper proposes a structured approach to learning linear operators for control systems from data. We address both structural and learning-theoreti
arXiv:2609.19367v1 Announce Type: new Abstract: Ensuring intrinsic safety in physical human robot interaction (pHRI) is a critical requirement for social and service robots. While Series Elastic Actua
arXiv:2609.20104v1 Announce Type: new Abstract: We describe the architecture, training methodology and inference speedups of Granite 5.0 Turbo CTC, a 470 million parameter encoder-only model with an e
arXiv:2609.20143v1 Announce Type: cross Abstract: Cognitive offloading to AI can reduce opportunities to practice skills, creating risks of deskilling. However, it remains unclear how to prevent deski
arXiv:2609.20336v1 Announce Type: cross Abstract: Deceptive online job advertisements have emerged as a primary pathway into forced labour, yet systematic detection methods remain underdeveloped due t
arXiv:2609.19531v1 Announce Type: cross Abstract: We propose PaRID (PaRallel Instruction Duplication), a software-directed soft error detection framework that requires only compile-time effort for mul
arXiv:2609.20649v1 Announce Type: cross Abstract: Learning predictive models of contact-rich dexterous manipulation requires dense tactile interaction, but such data are costly to scale on real robots
arXiv:2609.20304v1 Announce Type: new Abstract: A deployed control policy can conceal consequential dynamics changes: an actuator may lose effectiveness without affecting the current task when the pol
arXiv:2609.19805v1 Announce Type: new Abstract: Grapheme-to-phoneme (G2P) conversion turns raw text into its phonemic form and is an essential part of both text-to-speech (TTS) and automatic speech re
arXiv:2609.19913v1 Announce Type: new Abstract: The study of opinion dynamics in social networks is one of the key challenges in computational social science with direct relevance to understanding pol
arXiv:2609.19927v1 Announce Type: new Abstract: Optical motion capture delivers high-fidelity human motion, but its reliance on strict marker layouts and clean trajectories severely limits its real-wo
arXiv:2609.20248v1 Announce Type: new Abstract: Deploying a deep object detector in a new setting is limited less by architecture than by the cost of annotating data from that setting. Active learning
arXiv:2509.12390v3 Announce Type: replace Abstract: This paper addresses collaborative formation control for multi-agent systems with limited resources. We consider a team of robots tasked with achiev
arXiv:2609.19912v1 Announce Type: new Abstract: Teams of mobile robots rely on continuous communication with their neighbors for coordination, yet most distributed model predictive control (DMPC) sche
arXiv:2609.20501v1 Announce Type: new Abstract: Federated learning trains a shared model from private client data. In practice, data-generating distributions may differ, and the true mixture across cl
arXiv:2609.19196v1 Announce Type: new Abstract: Collecting data for manipulation with high-DOF hands is challenging, as interfaces must capture rich hand motion while rendering the contact interaction
arXiv:2609.19387v1 Announce Type: new Abstract: Agents are increasingly asked to design hardware, and increasingly reported to succeed. Such reports establish that a design improved; they cannot estab
arXiv:2609.20574v1 Announce Type: new Abstract: Answer grounding in document visual question answering remains an open challenge: most benchmarks lack grounding annotations or provide limited-quality
arXiv:2609.20715v1 Announce Type: cross Abstract: Agent trajectories record what an agent does and what happens next. Yet standard supervised fine-tuning (SFT) applies loss only to agent-authored acti
arXiv:2609.20751v1 Announce Type: new Abstract: Adapting a pretrained autoregressive (AR) model is a cost-efficient route to a diffusion language model (DLM). While nearly all such adaptations start f
arXiv:2609.20035v1 Announce Type: new Abstract: This paper presents dynamics-relaxed model predictive control (DR-MPC), a novel MPC formulation for legged locomotion, and a tailored interior-point met
arXiv:2603.07998v2 Announce Type: replace-cross Abstract: Aerodynamic promptness quantifies how rotor-speed variations generate multirotor wrench variations, but its Euclidean formulation assigns the
arXiv:2609.19830v1 Announce Type: new Abstract: Reinforcement learning for LLM agents involves two distinct optimization di- mensions: how environment feedback is exploited within a trajectory, and ho
arXiv:2609.20008v1 Announce Type: cross Abstract: Gromov--Wasserstein optimal transport (GW-OT) extends classical optimal transport by introducing structure-aware transport cost. This is particularly
arXiv:2609.19336v1 Announce Type: new Abstract: This paper proposes Dynamic-LIVO, a dynamic-aware LiDAR-Inertial-Visual Odometry (LIVO) system for robust state estimation and static colored mapping in
arXiv:2409.18457v2 Announce Type: replace Abstract: This paper addresses a special Perspective-n-Point (PnP) problem: estimating the optimal pose to align 3D and 2D shapes in real-time without corresp
arXiv:2609.20001v1 Announce Type: new Abstract: Automated video interview assessment integrates verbal content, acoustic delivery, and visual behavior, yet numerical predictions alone provide limited
arXiv:2609.20662v1 Announce Type: new Abstract: Earth surface anomalies, driven by escalating climate change, and expanding human activities, are increasing in both frequency and diversity, yet their
arXiv:2609.19523v1 Announce Type: new Abstract: Web agents often revisit the same sites, yet most evaluations discard the procedures learned in earlier successful interactions. We introduce EconSkills
arXiv:2609.20484v1 Announce Type: cross Abstract: Despite the widely recognized potential of AI in education, most prior work has focused on individualized student assistance. In contrast, the majorit
arXiv:2512.06660v3 Announce Type: replace-cross Abstract: Analysts in Security Operations Centers query massive telemetry streams using Kusto Query Language (KQL), but writing correct KQL demands spec
arXiv:2609.19399v1 Announce Type: cross Abstract: Computing Nash equilibria of simulation-based cybersecurity games with policy-space response oracles (PSRO) is bottlenecked by payoff estimation: ever
arXiv:2609.19451v1 Announce Type: new Abstract: The Mobile Unified Multimodal Understanding (MUMU) Challenge requires a single efficient model to jointly perform multi-concept image tagging, open-voca
arXiv:2609.19972v1 Announce Type: cross Abstract: Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federati
arXiv:2609.19491v1 Announce Type: cross Abstract: Modern LLMs and AI agents increasingly support data engineering workflows that integrate evidence from unstructured sources. Such pipelines typically
arXiv:2601.22488v3 Announce Type: replace Abstract: Modern sequence models are typically trained at a fixed computational capacity, while real-world applications require deployment across platforms wi
arXiv:2609.20348v1 Announce Type: new Abstract: Conventional 3D Gaussian Splatting assumes a closed set of observations and long optimization schedules. Continual RGB-D mapping in contrast poses the p
arXiv:2609.20821v1 Announce Type: new Abstract: Embedding spaces define notions of semantic similarity and distance. We study whether those embeddings reflect physical measurements of mass, distance,
arXiv:2609.19659v1 Announce Type: cross Abstract: Training embodied foundation models typically requires massive-scale datasets and extensive computational resources, yet often suffers from three crit
arXiv:2507.22148v3 Announce Type: replace-cross Abstract: Artificial intelligence and robotic systems are increasingly deployed as interacting collectives of learning agents. This raises the question
arXiv:2506.11747v2 Announce Type: replace-cross Abstract: Longform audio recordings obtained with microphones worn by children-also known as child-centered daylong recordings-have become a standard me
arXiv:2609.19466v1 Announce Type: new Abstract: Accurate prediction of nitrous oxide (N2O) emissions from agriculture is important for assessing environmental impacts and supporting sustainable farmin
arXiv:2609.19964v1 Announce Type: new Abstract: Detection Transformers (DETRs) achieve strong performance in object detection but remain challenging to deploy on edge devices due to their high computa
arXiv:2609.19375v1 Announce Type: new Abstract: Safety and comfort in human-robot physical in-teractions are essential aspects in the development of social technologies, where natural gestures, such a
arXiv:2609.20676v1 Announce Type: new Abstract: Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions neces
arXiv:2609.20004v1 Announce Type: cross Abstract: Reward-based reinforcement learning for language models, exemplified by Group Relative Policy Optimization (GRPO), collapses an entire stochastic traj
arXiv:2609.19742v1 Announce Type: new Abstract: Autonomous Underwater Vehicles (AUVs) navigating without GPS typically fuse inertial measurements with acoustic Doppler Velocity Log (DVL) velocities an
arXiv:2609.20815v1 Announce Type: cross Abstract: Hereditary polyposis syndromes can be precursor lesions to colorectal cancer and are associated with a broad spectrum of extracolonic tumors. Early id
arXiv:2609.19937v1 Announce Type: cross Abstract: Recent studies have shown that smooth functions can be well approximated by ReLU neural networks with path norm constraint on the weights. We extend t
arXiv:2403.15038v3 Announce Type: replace-cross Abstract: We endeavour to estimate numerous multi-dimensional means of various probability distributions on a common space based on independent samples.
arXiv:2609.19885v1 Announce Type: new Abstract: Interpreter agents built on machine translation (MT) increasingly mediate live conversation between people who do not share a language, yet we still eva
arXiv:2609.20058v1 Announce Type: new Abstract: Explanations of machine learning models are usually judged by criteria that are hard to compare. We propose a simpler test: if an explanation really des
arXiv:2609.20198v1 Announce Type: new Abstract: Financial sentiment measures are widely used in empirical finance, but it remains unclear whether general-purpose large language models (LLMs) improve o
arXiv:2508.06734v3 Announce Type: replace-cross Abstract: While graph-based Android malware classifiers report strong benchmark accuracy of over 94%, their performance sharply decreases up to 45% when
arXiv:2609.20313v1 Announce Type: new Abstract: Point-of-Interest (POI) recommendation is a core task in location-based services, yet most existing methods assume a fixed user population and POI catal
arXiv:2609.19799v1 Announce Type: cross Abstract: LLM-driven evolutionary search finds programs by launching seeds and iterating each one. Papers report a single budget setting, usually one seed run f
arXiv:2609.19946v1 Announce Type: new Abstract: A geometrically valid placement can still be difficult to execute because the selected grasp changes the required end-effector pose, collision geometry,
arXiv:2609.19858v1 Announce Type: new Abstract: The problem of multiobjective optimization under uncertainties is often approached by taking the expectation of each objective. In this work, we propose
arXiv:2609.20217v1 Announce Type: new Abstract: Short-term traffic forecasting supports real-time monitoring and control of road networks, and graph attention networks (GAT) are the standard means of