Knowledge catalogue
arXiv:2511.18486v2 Announce Type: replace Abstract: Electromagnetic navigation systems (eMNS) enable a number of magnetically guided surgical procedures. A challenge in magnetically manipulating surgi
arXiv:2609.22601v1 Announce Type: cross Abstract: The increasing reliance of autonomous AI agents on external and distributed knowledge sources introduces a fundamental challenge for decentralized inf
arXiv:2609.26377v1 Announce Type: new Abstract: Fairness-aware distributed learning prioritizes clients with large losses to reduce performance disparities, but label poisoning can create large losses
arXiv:2602.14285v2 Announce Type: replace-cross Abstract: Automated scholarly paper review (ASPR) has entered the coexistence phase with traditional peer review, where artificial intelligence (AI) sys
arXiv:2609.26299v2 Announce Type: new Abstract: Existing latent world models are typically optimized for future predictability, yet the resulting representations are not necessarily useful for plannin
arXiv:2609.26146v1 Announce Type: new Abstract: Risk monitoring in multi-agent systems is commonly built on a per-state primitive that scores each state independently and selects the top K. Under crow
arXiv:2609.25034v1 Announce Type: new Abstract: Central bank press conferences are not merely information releases --- they are structured narratives. We study whether the shape of sentiment within a
Full transcript: Minister Barrot, members of the Security Council, thank you for the invitation. As a French national who moved to the US 15 years ago, with a Brazilian wife and two American daughters
arXiv:2609.25741v1 Announce Type: new Abstract: Generative models have advanced image-conditioned 3D content creation, yet generating controllable and executable 3D scenes from a single image remains
arXiv:2609.26118v1 Announce Type: new Abstract: Latent action models (LAMs) learn action-related representations from action-free videos via self-supervised future prediction, offering a scalable para
arXiv:2609.25338v1 Announce Type: cross Abstract: Neural inertial odometry increasingly uses networks as learned measurements inside filtering pipelines. Such measurements should transform consistentl
arXiv:2609.25558v1 Announce Type: cross Abstract: Vision-language-action policies benefit from geometric supervision, but current-frame geometry alone does not explicitly describe the changes associat
arXiv:2609.26793v1 Announce Type: new Abstract: Compositional 3D scene reconstruction has recently been explored from two directions: agentic reasoning that provides semantic understanding of spatial
arXiv:2609.25363v1 Announce Type: new Abstract: Object transportation is a fundamental capability for humanoid robots operating in real-world, human-centric environments, yet existing methods struggle
arXiv:2609.25686v1 Announce Type: cross Abstract: Long-horizon assigned work requires an LLM agent to track the state of a task: which steps are done, blocked, cancelled, or open to repetition. Agent
arXiv:2609.26352v1 Announce Type: cross Abstract: Concept drift, driven by the rapid evolution of Android malware, severely degrades the performance of machine learning detectors. Current adaptation s
arXiv:2609.26672v1 Announce Type: new Abstract: Training vision-language-action (VLA) models for high-precision manipulation typically requires task-specific, high-quality data (e.g., teleoperation),
arXiv:2609.26257v1 Announce Type: new Abstract: Continual learning (CL) aims to incrementally acquire knowledge from sequential data while avoiding catastrophic forgetting. Recently, prompt tuning has
arXiv:2609.25322v1 Announce Type: new Abstract: Coordinated bimanual manipulation is challenging because the motion of either arm can alter the shared 3D scene and thereby affect the other arm. Yet mo
arXiv:2609.26375v1 Announce Type: new Abstract: Commercial image editing requires product identity preservation, accurate text rendering, and user appeal alongside general editing quality. We present
arXiv:2609.24346v1 Announce Type: new Abstract: Graph Retrieval-Augmented Generation (GraphRAG) has remarkably enhanced large language models on complex reasoning by leveraging structured entity topol
arXiv:2609.26564v1 Announce Type: new Abstract: Passive-wheeled terrestrial-aerial bimodal vehicles (TABVs) combine aerial mobility with energy-efficient ground locomotion. However, reliable air-groun
arXiv:2603.29608v2 Announce Type: replace Abstract: Acute poly-substance intoxication requires rapid, life-saving decisions under substantial uncertainty, as clinicians must rely on incomplete ingesti
arXiv:2609.25274v1 Announce Type: new Abstract: Robot assistants for older adults and people with disabilities need to perform collaborative tasks with users effectively. The core component of these s
arXiv:2609.25860v1 Announce Type: cross Abstract: Sparse instance representations provide a compact interface for spatial LiDAR-camera and temporal past-current interaction in multimodal perception an
arXiv:2609.26520v1 Announce Type: new Abstract: Humanoid robots require diverse embodied experiences to acquire complex loco-manipulation and collaborative skills. However, existing humanoid data pipe
arXiv:2609.26010v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) commonly trains decentralized policies from scratch, requiring agents to acquire individual task competence
arXiv:2609.26378v1 Announce Type: cross Abstract: Successful mobile manipulation requires coordinated base and arm motion while maintaining accurate spatial positioning. However, demonstration-trained
arXiv:2602.23607v2 Announce Type: replace Abstract: Developing learning-based autonomy for microrobotic manipulation is difficult because physical trials are slow, variable, and poorly suited to large
arXiv:2609.25331v1 Announce Type: new Abstract: Low-light image enhancement (LLIE) is an im- portant component of visual sensing systems operating under degraded illumination, including nighttime surv
arXiv:2609.26617v1 Announce Type: cross Abstract: Clinical decision making heavily relies on predicting the disease progression trajectory by seeking to understand patient's health status which is cha
arXiv:2609.26355v1 Announce Type: new Abstract: Reinforcement learning has become a central component of large language model (LLM) post-training, yet token-level credit lacks a generally accepted mat
arXiv:2609.25630v1 Announce Type: new Abstract: Real-world reinforcement learning (RL) systems still struggle with the demands of contact-rich industrial manipulation, including micrometer-level preci
arXiv:2609.25653v1 Announce Type: new Abstract: Large-scale manipulation demonstrations are essential for learning robust visuomotor policies, yet real-world data collection is expensive and difficult
arXiv:2609.25754v1 Announce Type: new Abstract: Humanoid motion tracking policies rely on dense frame-by-frame references, limiting their use as high-level motion controllers for planning and interact
arXiv:2410.14788v4 Announce Type: replace-cross Abstract: Neural operator (NO) architectures learn nonlinear maps between infinite-dimensional function spaces and are widely used to accelerate simulat
arXiv:2609.25969v1 Announce Type: new Abstract: Legged robots under sparse waypoint guidance must avoid moving obstacles using partial, rapidly changing LiDAR observations. We present LOOP (Latent-rec
arXiv:2609.22746v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently been introduced into traffic signal control (TSC) as decision agents due to their strengths in human-readable
arXiv:2609.25048v1 Announce Type: new Abstract: How many prompts does on-policy distillation (OPD) need, and how does the answer depend on the student policies that generate its training responses? We
arXiv:2609.25577v1 Announce Type: new Abstract: Most minimally invasive procedures are still performed with hand-held laparoscopic instruments, yet only the endoscopic video is retained; the instrumen
arXiv:2609.26457v1 Announce Type: cross Abstract: AI agents are beginning to automate research and development across the AI stack, from improving training efficiency to optimizing inference. A natura
arXiv:2609.25890v1 Announce Type: new Abstract: Short-to-long training is a simple curriculum for speech models, but its gains can be difficult to interpret. In speech token language models, length-ba
arXiv:2609.25506v1 Announce Type: new Abstract: Robot manipulation policies must generalize across visual shifts while preserving scene context relevant to action. General-purpose vision encoders are
arXiv:2609.26467v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide broad manipulation competence, but often struggle during the precision-critical stages that dominate contact
arXiv:2609.26420v1 Announce Type: new Abstract: Text-to-motion models generate plausible human motion but do not model a robot's dynamics; whole-body tracking controllers execute robot references reli
arXiv:2609.25687v1 Announce Type: new Abstract: An animal with a weakened limb does not necessarily switch its gait, instead it unloads the affected limb, re-coordinates the remaining limbs, and scale
arXiv:2609.26007v1 Announce Type: new Abstract: Monocular drone navigation requires reaching a goal in an unseen environment from a single forward-facing camera, which offers few cues for depth and sc
arXiv:2609.26131v1 Announce Type: new Abstract: Large language models and vision-language models are increasingly used as high-level planners in robotic systems, using task goals and sensor summaries
arXiv:2609.24036v1 Announce Type: new Abstract: This paper presents an LLM-based system that translates natural-language access control policies (NLACPs) into executable Rego code for Open Policy Agen
arXiv:2609.26303v1 Announce Type: cross Abstract: Forecasters often score the same units per date against one standardized realized outcome. We show that every standardized forecast splits exactly int
arXiv:2609.25674v1 Announce Type: new Abstract: Lower cost open source robots and reinforcement learning (RL) simulation tools create new opportunities for precollege students to engage with contempor
arXiv:2609.26099v1 Announce Type: new Abstract: Anomalous video understanding aims to identify abnormal events in videos and interpret their semantic meanings beyond simple anomaly detection. Recent v
arXiv:2601.20900v4 Announce Type: replace-cross Abstract: Adapting large language model (LLM)-based automatic speech recognition (ASR) systems to new domains using text-only data is a significant yet
arXiv:2609.22882v1 Announce Type: cross Abstract: On June 12, 2026, the U.S. government ordered Anthropic to bar foreign nationals from two of its most capable models within ninety minutes. Unable to
arXiv:2604.02279v2 Announce Type: replace Abstract: Agentic AI shifts the investor's role from analytical execution to oversight. We present an agentic strategic asset allocation pipeline in which 44
arXiv:2609.25987v1 Announce Type: new Abstract: Equivariant convolutional neural networks are usually built from a group acting globally on the space of signals. This hypothesis is inappropriate for m
arXiv:2509.26200v2 Announce Type: replace-cross Abstract: Agentic artificial intelligence is a candidate enabler of Level-4 autonomy in sixth-generation (6G) networks, but agents reasoning over a shar
arXiv:2609.26708v1 Announce Type: new Abstract: Quantization-aware distillation (QAD) restores much of the short-form question-answering performance lost to sub-3-bit quantization, yet leaves mathemat
arXiv:2609.26536v1 Announce Type: new Abstract: In LLM-based speech translation, transcription-based chain-of-thought (CoT) suffers from a mismatch between reference transcripts used in supervised fin
arXiv:2609.25066v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to simulate human survey responses and behavioral reactions, yet unreliable simulations can mislead s