arXiv:2509.15237v2 Announce Type: replace Abstract: Industrial workflows demand adaptive and trustworthy assistance that can operate under limited computing, connectivity, and strict privacy constraints. In this work, we present MICA (Multi-Agent Industrial Coordination Assistant), a perception-grounded and speech-interactive system that delivers real-time guidance for assembly, troubleshooting, part queries, and maintenance. MICA coordinates five role-specialized language agents, […]
MERIT Feedback Elicits Better Bargaining in LLM Negotiators
arXiv:2602.10467v4 Announce Type: replace Abstract: Bargaining is often regarded as a logical arena rather than an art or a matter of intuition, yet Large Language Models (LLMs) still struggle to navigate it due to limited strategic depth and difficulty adapting to complex human factors. Current benchmarks rarely capture this limitation. To bridge this gap, we […]
Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey
arXiv:2306.09445v3 Announce Type: replace-cross Abstract: Cognitive modeling, which explores the essence of cognition, including motivation, emotion, and perception, has been widely applied in the artificial intelligence (AI) agent domains, such as robotics. From the computational perspective, various cognitive functionalities have been developed through utility theory to provide a detailed and process-based understanding for specifying corresponding […]
IMPACT: Intelligent Motion Planning with Acceptable Contact Trajectories via Vision-Language Models
arXiv:2503.10110v3 Announce Type: replace-cross Abstract: Motion planning involves determining a sequence of robot configurations to reach a desired pose, subject to movement and safety constraints. Traditional motion planning finds collision-free paths, but this is overly restrictive in clutter, where it may not be possible for a robot to accomplish a task without contact. In addition, […]
ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals
arXiv:2508.14689v4 Announce Type: replace-cross Abstract: Pre-trained foundation models have demonstrated remarkable success in audio, vision and language, yet their potential for general machine signal modeling with arbitrary sampling rates-covering acoustic, vibration, and other industrial sensor data-remains under-explored. In this work, we propose a novel foundation model ECHO that integrates an advanced band-split architecture with frequency […]
FATE: A Formal Benchmark Series for Frontier Algebra of Multiple Difficulty Levels
arXiv:2511.02872v4 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have demonstrated impressive capabilities in formal theorem proving, particularly on contest-based mathematical benchmarks like the IMO. However, these contests do not reflect the depth, breadth, and abstraction of modern mathematical research. To bridge this gap, we introduce FATE (Formal Algebra Theorem Evaluation), a […]
How Professional Visual Artists are Negotiating Generative AI in the Workplace
arXiv:2603.04537v2 Announce Type: replace-cross Abstract: Generative AI has been heavily critiqued by artists in both popular media and HCI scholarship. However, more work is needed to understand the impacts of generative AI on professional artists’ workplaces and careers. In this paper, we conduct a survey of 378 verified professional visual artists about how generative AI […]
Revisiting Gradient Staleness: Evaluating Distance Metrics for Asynchronous Federated Learning Aggregation
arXiv:2603.08211v1 Announce Type: cross Abstract: In asynchronous federated learning (FL), client devices send updates to a central server at varying times based on their computational speed, often using stale versions of the global model. This staleness can degrade the convergence and accuracy of the global model. Previous work, such as AsyncFedED, proposed an adaptive aggregation […]
Reinforcing Numerical Reasoning in LLMs for Tabular Prediction via Structural Priors
arXiv:2510.17385v4 Announce Type: replace-cross Abstract: Tabular prediction traditionally relies on gradient-boosted decision trees and deep learning models, which excel in specific tasks but lack interpretability and transferability. Reasoning large language models (LLMs) promise cross-task adaptability with transparent reasoning traces, yet their potential for tabular data remains unrealized. To bridge this gap, we propose a reasoning […]
Automated interpretation of fetal cardiac function evaluation from the echocardiogram
npj Digital Medicine, Published online: 10 March 2026; doi:10.1038/s41746-026-02381-3 Automated interpretation of fetal cardiac function evaluation from the echocardiogram
Cybersecurity in connected medical devices: a policy agenda for the NHS
npj Digital Medicine, Published online: 10 March 2026; doi:10.1038/s41746-026-02534-4 Connected Medical Devices (CMD) are redefining care within the NHS but exposing it to bi-directional cyber-physical threats that traverse physical, network and cloud layers. These vulnerabilities blur the boundary between technology and patient safety. This Comment argues that the MHRA should elevate cybersecurity to a clinical-safety […]