This article describes a macroeconomic research agent built with Deep Agents, LangSmith, and the You.com Finance Research API. It analyzes GDP data across all 27 EU member states, detects anomalies, and produces a cited briefing in approximately 45 minutes. The report details the anomalous growth in Ireland and contraction in Germany, emphasizing the importance of traceability and auditability.

This paper presents a dual-drone docking platform where two quadrotors operate in a leader-follower formation and dock using a lightweight modular frame with passive magnetic latching. A progress-aware mission supervisor manages phase transitions: approach, alignment, capture, and settle. The platform integrates a complete hardware-software stack (ROS 2 with Crazyflie/PX4 interfaces) and is evaluated in simulation and real-world experiments using quantitative metrics such as formation error, docking success rate, and time-to-dock.

This paper presents a phase-conditioned, force-aware framework for robust deformable object manipulation. Using FiLM-conditioned ACT encoder and multi-modal phase predictor, the system autonomously detects and recovers from contact failures, improving T-shirt hanging success rate from 56% to 87%.

This paper presents a decentralized framework that combines large language models (LLMs) with acoustic mobile robots for contactless object manipulation. Using Whisper speech recognition, LLM semantic parsing, and JSON task scheduling, the system converts spoken commands into coordinated multi-robot actions. Experiments with two TurtleBot3-based acoustic robots achieved success rates of 96% for sequential, 86% for parallel, and 70% for synchronized tasks, showcasing the potential of LLM-driven automation for human-robot interaction.

The Open Motion Planning Library (OMPL), first released in 2008, has become a cornerstone of the motion planning community, providing implementations of a wide range of state-of-the-art sampling-based algorithms. Over almost two decades of continuous development, OMPL 2.0 targets real-time motion planning through hardware acceleration and integrates seamlessly with modern AI research workflows.

MonoDuo proposes a framework for learning bimanual manipulation policies using single-arm robot demonstrations paired with human collaboration. By collecting data through teleoperation with role swapping and generating synthetic demonstrations via hand pose estimation, segmentation, and inpainting, MonoDuo achieves zero-shot success rates up to 70% on five tasks, with few-shot finetuning further boosting performance.

Researchers propose dynamic symmetry, quantified by dynamic isotropy, as a measure of uniformity in a robot's attainable center-of-mass accelerations. Through simulations and physical experiments, high dynamic symmetry improves trajectory tracking, task success, robustness, resilience, and energy efficiency. The Argus family of spherical robots, especially a 20-legged variant with near-extreme dynamic isotropy, demonstrates orientation-invariant locomotion, agile terrain traversal, rapid self-stabilization, and resilience to actuator failures.

This paper proposes a data-driven approach using recurrent neural networks and one-step-ahead predictive control for bead geometry control in Wire Arc Additive Manufacturing (WAAM). By updating the model online to account for changing thermal conditions, it significantly improves bead height and width consistency.

Researchers propose a multi-resolution end-to-end deep neural network to balance latency and safety in autonomous driving. By selecting input resolution at runtime, the network improves safety metrics like lane invasions, red-light infractions, and collisions in CARLA simulations compared to fixed-resolution baselines.

This paper introduces the 'Bionic Swarm,' a human-in-the-loop system that lowers barriers to real-world validation of swarm robotics. It uses a smartphone web-app, Bluetooth sensors, and a centralized server to direct human users. The Score-Biased-Search algorithm for soil mapping demonstrates superlinear map reconstruction in both simulations and outdoor experiments.

This paper proposes a target-informed self-supervised pretraining and model-ensemble strategy that leverages unlabeled target-domain data to improve cross-device generalization of medical imaging AI. Applied to pediatric wrist fracture assessment using point-of-care ultrasound, the method achieves over 6% Dice improvement on the target domain, demonstrating a label-efficient and privacy-preserving approach.

This paper introduces GeRaF 2.0, a unified framework integrating Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) neural geometry reconstruction, leveraging LoS geometry to guide RF propagation for stable and physically consistent 3D reconstruction of hidden scenes, achieving state-of-the-art results.