Tootfinder

Open-Universe Assistance Games
Rachel Ma, Jingyi Qu, Andreea Bobu, Dylan Hadfield-Menell
https://arxiv.org/abs/2508.15119 https://arxiv.org/pdf/2508.15119

Open-Universe Assistance Games
Embodied AI agents must infer and act in an interpretable way on diverse human goals and preferences that are not predefined. To formalize this setting, we introduce Open-Universe Assistance Games (OU-AGs), a framework where the agent must reason over an unbounded and evolving space of possible goals. In this context, we introduce GOOD (GOals from Open-ended Dialogue), a data-efficient, online method that extracts goals in the form of natural language during an interaction with a human, and inf…

A Lightweight Incentive-Based Privacy-Preserving Smart Metering Protocol for Value-Added Services
Farid Zaredar, Morteza Amini
https://arxiv.org/abs/2508.14703 https://

A Lightweight Incentive-Based Privacy-Preserving Smart Metering Protocol for Value-Added Services
The emergence of smart grids and advanced metering infrastructure (AMI) has revolutionized energy management. Unlike traditional power grids, smart grids benefit from two-way communication through AMI, which surpasses earlier automated meter reading (AMR). AMI enables diverse demand- and supply-side utilities such as accurate billing, outage detection, real-time grid control, load forecasting, and value-added services. Smart meters play a key role by delivering consumption values at predefined …

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DiffPF: Differentiable Particle Filtering with Generative Sampling via Conditional Diffusion Models
Ziyu Wan, Lin Zhao
https://arxiv.org/abs/2507.15716 htt…

DiffPF: Differentiable Particle Filtering with Generative Sampling via Conditional Diffusion Models
This paper proposes DiffPF, a differentiable particle filter that leverages diffusion models for state estimation in dynamic systems. Unlike conventional differentiable particle filters, which require importance weighting and typically rely on predefined or low-capacity proposal distributions. DiffPF learns a flexible posterior sampler by conditioning a diffusion model on predicted particles and the current observation. This enables accurate, equally-weighted sampling from complex, high-dimensi…

The Cost Advantage of Virtual Machine Migrations: Empirical Insights into Amazon's EC2 Marketspace
Benedikt Pittl, Werner Mach, Erich Schikuta
https://arxiv.org/abs/2508.14883

The Cost Advantage of Virtual Machine Migrations: Empirical Insights into Amazon's EC2 Marketspace
In recent years, cloud providers have introduced novel approaches for trading virtual machines. For example, Virtustream introduced so-called muVMs to charge cloud computing resources while other providers such as Google, Microsoft, or Amazon re-invented their marketspaces. Today, the market leader Amazon runs six marketspaces for trading virtual machines. Consumers can purchase bundles of virtual machines, which are called cloud-portfolios, from multiple marketspaces and providers. An industry…

A deep Search for Ethylene Glycol and Glycolonitrile in the V883 Ori Protoplanetary Disk
Abubakar M. A. Fadul, Kamber R. Schwarz, Tushar Suhasaria, Jenny K. Calahan, Jane Huang, Merel L. R. van 't Hoff
https://arxiv.org/abs/2507.14905

A deep Search for Ethylene Glycol and Glycolonitrile in the V883 Ori Protoplanetary Disk
Ethylene glycol ($\mathrm{(CH_2OH)_2}$, hereafter EG) and Glycolonitrile ($\mathrm{HOCH_2CN}$, hereafter GN) are considered molecular precursors of nucleic acids. EG is a sugar alcohol and the reduced form of Glycolaldehyde ($\mathrm{CH_2(OH)CHO}$, hereafter GA). GN is considered a key precursor of adenine formation (nucleotide) and can be a precursor of glycine (amino acid). Detections of such prebiotic molecules in the interstellar medium are increasingly common. How much of this complexity e…

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…

Terrestrial Planet Formation from Two Source Reservoirs
David Nesvorny, Alessandro Morbidelli, William F. Bottke, Rogerio Deienno, Max Goldberg
https://arxiv.org/abs/2507.14814

Terrestrial Planet Formation from Two Source Reservoirs
This work describes new dynamical simulations of terrestrial planet formation. The simulations started at the protoplanetary disk stage, when planetesimals formed and accreted into protoplanets, and continued past the late stage of giant impacts. We explored the effect of different parameters, such as the initial radial distribution of planetesimals and Type-I migration of protoplanets, on the final results. In each case, a thousand simulations were completed to characterize the stochastic natu…

TexGS-VolVis: Expressive Scene Editing for Volume Visualization via Textured Gaussian Splatting
Kaiyuan Tang, Kuangshi Ai, Jun Han, Chaoli Wang
https://arxiv.org/abs/2507.13586

TexGS-VolVis: Expressive Scene Editing for Volume Visualization via Textured Gaussian Splatting
Advancements in volume visualization (VolVis) focus on extracting insights from 3D volumetric data by generating visually compelling renderings that reveal complex internal structures. Existing VolVis approaches have explored non-photorealistic rendering techniques to enhance the clarity, expressiveness, and informativeness of visual communication. While effective, these methods often rely on complex predefined rules and are limited to transferring a single style, restricting their flexibility.…

Variable selection for minimum-variance portfolios
Guilherme V. Moura, Andr\'e P. Santos, Hudson S. Torrent
https://arxiv.org/abs/2508.14986 https://ar…

Variable selection for minimum-variance portfolios
Machine learning (ML) methods have been successfully employed in identifying variables that can predict the equity premium of individual stocks. In this paper, we investigate if ML can also be helpful in selecting variables relevant for optimal portfolio choice. To address this question, we parameterize minimum-variance portfolio weights as a function of a large pool of firm-level characteristics as well as their second-order and cross-product transformations, yielding a total of 4,610 predicto…