Tootfinder

Investigating the Sulfur Mystery in Protoplanetary Disks Through Chemical Modeling
Becky J. Williams, L. Ilsedore Cleeves, Rachel E. Gross, Jackson Baker
https://arxiv.org/abs/2510.02176

Investigating the Sulfur Mystery in Protoplanetary Disks Through Chemical Modeling
Sulfur is a critical element to life on Earth, and with detections of sulfur-bearing molecules in exoplanets and comets, questions arise as to how sulfur is incorporated into planets in the first place. In order to understand sulfur's journey from molecular clouds to planets, we need to understand the molecular forms that sulfur takes in protoplanetary disks, where the rotational emission from sulfur-bearing molecules in the gas phase indicates a very low abundance. To address this question, we…

Do Mutual Funds Make Active and Skilled Liquidity Choices in Portfolio Management? Evidence from India
Pankaj K Agarwal, H K Pradhan, Konark Saxena
https://arxiv.org/abs/2510.02741

Do Mutual Funds Make Active and Skilled Liquidity Choices in Portfolio Management? Evidence from India
This study examines active liquidity management by Indian open-ended equity mutual funds. We find that fund managers respond to inflows by increasing cash holdings, which are later used to purchase less-liquid stocks at favourable valuations. Funds with less liquid portfolios tend to maintain larger cash reserves to manage flows. Funds that make active liquidity choices yield statistically and economically significant gross and net returns. The performance differences between funds with varying…

Agentic Aerial Cinematography: From Dialogue Cues to Cinematic Trajectories
Yifan Lin, Sophie Ziyu Liu, Ran Qi, George Z. Xue, Xinping Song, Chao Qin, Hugh H. -T. Liu
https://arxiv.org/abs/2509.16176

Agentic Aerial Cinematography: From Dialogue Cues to Cinematic Trajectories
We present Agentic Aerial Cinematography: From Dialogue Cues to Cinematic Trajectories (ACDC), an autonomous drone cinematography system driven by natural language communication between human directors and drones. The main limitation of previous drone cinematography workflows is that they require manual selection of waypoints and view angles based on predefined human intent, which is labor-intensive and yields inconsistent performance. In this paper, we propose employing large language models (…

Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control
Max Studt, Georg Schildbach
https://arxiv.org/abs/2509.15799 https://arxiv.o…

Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control
Achieving safe and coordinated behavior in dynamic, constraint-rich environments remains a major challenge for learning-based control. Pure end-to-end learning often suffers from poor sample efficiency and limited reliability, while model-based methods depend on predefined references and struggle to generalize. We propose a hierarchical framework that combines tactical decision-making via reinforcement learning (RL) with low-level execution through Model Predictive Control (MPC). For the case o…