Tootfinder

While Trump described his call for the U.S. military to reestablish a position in Afghanistan as “breaking news,” he has previously raised the idea.
The White House did not immediately respond to questions about whether it or the Pentagon has done any planning around returning to the sprawling [Bagram] air base, which was central to America’s longest war.

Trump says US working to take back Bagram Air Base from Taliban
President Donald Trump has suggested he's working to reestablish a U.S. presence at Bagram Air Base in Afghanistan.

Central Ohio city invests **$50K** to join the Midwest passenger rail push, capping a decade of planning and signaling strong local support for connecting Columbus to the national network. 🚆 #OhioRail #PassengerRail

Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning
Sahil Bansal, Sai Shruthi Sistla, Aarti Arikatala, Sebastian Schreiber
https://arxiv.org/abs/2508.05888

Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning
Effective tool retrieval is essential for AI agents to select from a vast array of tools when identifying and planning actions in the context of complex user queries. Despite its central role in planning, this aspect remains underexplored in the literature. Traditional approaches rely primarily on similarities between user queries and tool descriptions, which significantly limits retrieval accuracy, specifically when handling multi-step user requests. To address these limitations, we propose a …

Learning Discrete Abstractions for Visual Rearrangement Tasks Using Vision-Guided Graph Coloring
Abhiroop Ajith, Constantinos Chamzas
https://arxiv.org/abs/2509.14460 https://…

Learning Discrete Abstractions for Visual Rearrangement Tasks Using Vision-Guided Graph Coloring
Learning abstractions directly from data is a core challenge in robotics. Humans naturally operate at an abstract level, reasoning over high-level subgoals while delegating execution to low-level motor skills -- an ability that enables efficient problem solving in complex environments. In robotics, abstractions and hierarchical reasoning have long been central to planning, yet they are typically hand-engineered, demanding significant human effort and limiting scalability. Automating the discove…

Optimal CO2 storage management considering safety constraints in multi-stakeholder multi-site CCS projects: a game theoretic perspective
Jungang Chen, Seyyed A. Hosseini
https://arxiv.org/abs/2508.11618

Optimal CO2 storage management considering safety constraints in multi-stakeholder multi-site CCS projects: a game theoretic perspective
Carbon capture and storage (CCS) projects typically involve a diverse array of stakeholders or players from public, private, and regulatory sectors, each with different objectives and responsibilities. Given the complexity, scale, and long-term nature of CCS operations, determining whether individual stakeholders can independently maximize their interests or whether collaborative coalition agreements are needed remains a central question for effective CCS project planning and management. CCS pr…

Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
Wenkai Yu, Jianhang Tang, Yang Zhang, Shanjiang Tang, Kebing Jin, Hankz Hankui Zhuo
https://arxiv.org/abs/2508.11524

Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
Addressing large-scale planning problems has become one of the central challenges in the planning community, deriving from the state-space explosion caused by growing objects and actions. Recently, researchers have explored the effectiveness of leveraging Large Language Models (LLMs) to generate helpful actions and states to prune the search space. However, prior works have largely overlooked integrating LLMs with domain-specific knowledge to ensure valid plans. In this paper, we propose a nove…

Not everything Bill Bonner writes about economics is particularly orthodox, but this clear, simple description of why this tariff policy is idiotic makes this short piece well worth reading.
https://www.bonnerprivateresearch.com/p/fire-and-ice-e21

Last week, October sunshine in Freiburg at the anual #NFDIMatWerk planning & strategy meeting at Freiburg Innovation Center FRIZ. Usually, I don't complain about the food, but ... hey guys, there is room for improvement ;-)
Overall outcome: The MSE KnowledgeGraph will be THE central hub for all NFDIMatWerk resources

Energy Efficient Multi Robot Package Delivery under Capacity-Constraints via Voronoi-Constrained Networks
Alkesh K. Srivastava, Jared Michael Levin, Philip Dames
https://arxiv.org/abs/2509.14127

Energy Efficient Multi Robot Package Delivery under Capacity-Constraints via Voronoi-Constrained Networks
We consider the problem of delivering multiple packages from a single pickup depot to distinct goal locations using a homogeneous fleet of robots with limited carrying capacity. We propose VCST-RCP, a Voronoi-Constrained Steiner Tree Relay Coordination Planning framework that constructs sparse relay trunks using Steiner tree optimization and then synthesizes robot-level pickup, relay, and delivery schedules. This framework reframes relays from incidental byproducts into central elements of coor…

Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
Yijia Xiao, Edward Sun, Tong Chen, Fang Wu, Di Luo, Wei Wang
https://arxiv.org/abs/2509.11420 https:/…

Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
Developing professional, structured reasoning on par with human financial analysts and traders remains a central challenge in AI for finance, where markets demand interpretability and trust. Traditional time-series models lack explainability, while LLMs face challenges in turning natural-language analysis into disciplined, executable trades. Although reasoning LLMs have advanced in step-by-step planning and verification, their application to risk-sensitive financial decisions is underexplored. …

Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks
Simen Storesund, Kristian Valset Aars, Robin Dietrich, Nicolai Waniek
https://arxiv.org/abs/2509.10077

Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks
Efficient planning and sequence selection are central to intelligence, yet current approaches remain largely incompatible with biological computation. Classical graph algorithms like Dijkstra's or A* require global state and biologically implausible operations such as backtracing, while reinforcement learning methods rely on slow gradient-based policy updates that appear inconsistent with rapid behavioral adaptation observed in natural systems. We propose a biologically plausible algorithm fo…

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Morteza Kimiaei, Vyacheslav Kungurtsev, Brian Olimba
https://arxiv.org/abs/2508.06906

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Integer and mixed-integer nonlinear programming (INLP, MINLP) are central to logistics, energy, and scheduling, but remain computationally challenging. This survey examines how machine learning and reinforcement learning can enhance exact optimization methods - particularly branch-and-bound (BB), without compromising global optimality. We cover discrete, continuous, and mixed-integer formulations, and highlight applications such as crew scheduling, vehicle routing, and hydropower planning. We i…

Prime Implicant Explanations for Reaction Feasibility Prediction
Klaus Weinbauer, Tieu-Long Phan, Peter F. Stadler, Thomas G\"artner, Sagar Malhotra
https://arxiv.org/abs/2510.09226

Prime Implicant Explanations for Reaction Feasibility Prediction
Machine learning models that predict the feasibility of chemical reactions have become central to automated synthesis planning. Despite their predictive success, these models often lack transparency and interpretability. We introduce a novel formulation of prime implicant explanations--also known as minimally sufficient reasons--tailored to this domain, and propose an algorithm for computing such explanations in small-scale reaction prediction tasks. Preliminary experiments demonstrate that our…

Deep Generative Model for Human Mobility Behavior
Ye Hong, Yatao Zhang, Konrad Schindler, Martin Raubal
https://arxiv.org/abs/2510.06473 https://arxiv.org/…

Deep Generative Model for Human Mobility Behavior
Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite decades of effort, simulating individual mobility remains challenging because of its complex, context-dependent, and exploratory nature. Here, we present MobilityGen, a deep generative model that produces realistic mobility trajectories spanning days to weeks at large spatial scales. By linking behavioral attributes with environmental context, MobilityGe…

Scalable Asynchronous Federated Modeling for Spatial Data
Jianwei Shi, Sameh Abdulah, Ying Sun, Marc G. Genton
https://arxiv.org/abs/2510.01771 https://arx…

Scalable Asynchronous Federated Modeling for Spatial Data
Spatial data are central to applications such as environmental monitoring and urban planning, but are often distributed across devices where privacy and communication constraints limit direct sharing. Federated modeling offers a practical solution that preserves data privacy while enabling global modeling across distributed data sources. For instance, environmental sensor networks are privacy- and bandwidth-constrained, motivating federated spatial modeling that shares only privacy-preserving s…

Autonomous Inorganic Materials Discovery via Multi-Agent Physics-Aware Scientific Reasoning
Alireza Ghafarollahi, Markus J. Buehler
https://arxiv.org/abs/2508.02956 https://

Autonomous Inorganic Materials Discovery via Multi-Agent Physics-Aware Scientific Reasoning
Conventional machine learning approaches accelerate inorganic materials design via accurate property prediction and targeted material generation, yet they operate as single-shot models limited by the latent knowledge baked into their training data. A central challenge lies in creating an intelligent system capable of autonomously executing the full inorganic materials discovery cycle, from ideation and planning to experimentation and iterative refinement. We introduce SparksMatter, a multi-agen…

MolmoAct: Action Reasoning Models that can Reason in Space
Jason Lee, Jiafei Duan, Haoquan Fang, Yuquan Deng, Shuo Liu, Boyang Li, Bohan Fang, Jieyu Zhang, Yi Ru Wang, Sangho Lee, Winson Han, Wilbert Pumacay, Angelica Wu, Rose Hendrix, Karen Farley, Eli VanderBilt, Ali Farhadi, Dieter Fox, Ranjay Krishna
https://arxiv.org/abs/2508.07917

MolmoAct: Action Reasoning Models that can Reason in Space
Reasoning is central to purposeful action, yet most robotic foundation models map perception and instructions directly to control, which limits adaptability, generalization, and semantic grounding. We introduce Action Reasoning Models (ARMs), a class of vision-language-action models that integrate perception, planning, and control through a structured three-stage pipeline. Our model, MolmoAct, encodes observations and instructions into depth-aware perception tokens, generates mid-level spatial …

MultiSurv: A Multimodal Deep Survival Framework for Prostrate and Bladder Cancer
Noorul Wahab, Ethar Alzaid, Jiaqi Lv, Adam Shephard, Shan E Ahmed Raza
https://arxiv.org/abs/2509.05037

MultiSurv: A Multimodal Deep Survival Framework for Prostrate and Bladder Cancer
Accurate prediction of time-to-event outcomes is a central challenge in oncology, with significant implications for treatment planning and patient management. In this work, we present MultiSurv, a multimodal deep survival model utilising DeepHit with a projection layer and inter-modality cross-attention, which integrates heterogeneous patient data, including clinical, MRI, RNA-seq and whole-slide pathology features. The model is designed to capture complementary prognostic signals across modali…

A Multidimensional AI-powered Framework for Analyzing Tourist Perception in Historic Urban Quarters: A Case Study in Shanghai
Kaizhen Tan, Yufan Wu, Yuxuan Liu, Haoran Zeng
https://arxiv.org/abs/2509.03830

A Multidimensional AI-powered Framework for Analyzing Tourist Perception in Historic Urban Quarters: A Case Study in Shanghai
Historic urban quarters play a vital role in preserving cultural heritage while serving as vibrant spaces for tourism and everyday life. Understanding how tourists perceive these environments is essential for sustainable, human-centered urban planning. This study proposes a multidimensional AI-powered framework for analyzing tourist perception in historic urban quarters using multimodal data from social media. Applied to twelve historic quarters in central Shanghai, the framework integrates foc…

Crop Spirals: Re-thinking the field layout for future robotic agriculture
Lakshan Lavan, Lanojithan Thiyagarasa, Udara Muthugala, Rajitha de Silva
https://arxiv.org/abs/2509.25091

Crop Spirals: Re-thinking the field layout for future robotic agriculture
Conventional linear crop layouts, optimised for tractors, hinder robotic navigation with tight turns, long travel distances, and perceptual aliasing. We propose a robot-centric square spiral layout with a central tramline, enabling simpler motion and more efficient coverage. To exploit this geometry, we develop a navigation stack combining DH-ResNet18 waypoint regression, pixel-to-odometry mapping, A* planning, and model predictive control (MPC). In simulations, the spiral layout yields up to 2…