🔄 Updated Arxiv Paper

Title: Deep Reinforcement Learning Optimization for Uncertain Nonlinear Systems via Event-Triggered Robust Adaptive Dynamic Programming
Authors: Ningwei Bai, Chi Pui Chan, Qichen Yin, Tengyang Gong, Yunda Yan, Zezhi Tang

Read more: https://arxiv.org/abs/2512.15735

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HOLOGRAPH: Active Causal Discovery via Sheaf-Theoretic Alignment of Large Language Model Priors (Kim) Causal discovery from observational data remains fundamentally limited by identifiability constraints. Recent work has explored leveraging Large Language Models (LLMs) as sources of prior

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Functional Network Autoregressive Models for Panel Data (Ando, Hoshino) This study proposes a novel functional vector autoregressive framework for analyzing network interactions of functional outcomes in panel data settings. In this framework, an individual's outcome function is influence

🌬️🤖 How much wind does a quadrotor generate when hovering? This study introduces a lightweight model predicting airflow beneath drones using turbulent jet theory. Validated by 16+ hours of flight data, it aids safe operations, sensor placement, and multi-agent control. 🚁🌱 #Robotics #AeroTech

NOW: Larry Summers & Bob Zoellick discuss the implications of a new US foreign economic policy & how the incoming US administration should approach international economic relations. #EconSky
Watch: www.youtube.com/watch?v=FC_B...

Optimizing robot efficiency, one actuator at a time! 🤖💡 CAPO scales actuator placement and control, reducing actuator use by up to 27.5% while solving complex, large-scale problems faster than existing methods. From soft robots to spacecraft, CAPO boosts performance and design. 🚀
#Robotics #AI