Tootfinder

Jerry Jones says Cowboys not planning a video tribute for Micah Parsons' return to Dallas in Week 4 https://www.nfl.com/news/jerry-jones-says-cowboys-not-planning-a-video-tribute-for-micah-parsons-return-to-dallas-in-w…

Improving Zero-shot Sentence Decontextualisation with Content Selection and Planning
Zhenyun Deng, Yulong Chen, Andreas Vlachos
https://arxiv.org/abs/2509.17921 https://

Improving Zero-shot Sentence Decontextualisation with Content Selection and Planning
Extracting individual sentences from a document as evidence or reasoning steps is commonly done in many NLP tasks. However, extracted sentences often lack context necessary to make them understood, e.g., coreference and background information. To this end, we propose a content selection and planning framework for zero-shot decontextualisation, which determines what content should be mentioned and in what order for a sentence to be understood out of context. Specifically, given a potentially amb…

A Multimodal Stochastic Planning Approach for Navigation and Multi-Robot Coordination
Mark Gonzales, Ethan Oh, Joseph Moore
https://arxiv.org/abs/2509.19168 https://

A Multimodal Stochastic Planning Approach for Navigation and Multi-Robot Coordination
In this paper, we present a receding-horizon, sampling-based planner capable of reasoning over multimodal policy distributions. By using the cross-entropy method to optimize a multimodal policy under a common cost function, our approach increases robustness against local minima and promotes effective exploration of the solution space. We show that our approach naturally extends to multi-robot collision-free planning, enables agents to share diverse candidate policies to avoid deadlocks, and all…

The Architecture and Planning of Fascist New Towns in Sardinia
https://sah.org/2013/03/14/the-architecture-and-planning-of-fascist-new-towns-in-sardinia/

The Architecture and Planning of Fascist New Towns in Sardinia - Society of Architectural Historians
Ruth Lo documented three towns built between 1928 and 1938 on the island of Sardinia.

Planning with Minimal Disruption
Alberto Pozanco, Marianela Morales, Daniel Borrajo, Manuela Veloso
https://arxiv.org/abs/2508.15358 https://arxiv.org/pdf/…

Planning with Minimal Disruption
In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to this concept as plan disruption. In this paper, we formally introduce it, and define various planning-based compilations that aim to jointly optimize both the sum of action costs and plan disruption. Experimental results in different benchmarks show that the reformulated task can be effectively solved in practice to generate plans that balance both obj…

If you are planning anything in Denmark that is more long term than about 5 years, then you should definitely be using the Climate Atlas to check your climate adaptation impacts...
I wrote about it last year:
http://sternaparadisaea.net/2024/09/06/a-climate-atlas-is-discovered/
#NCKF25

Sources: WaPo is planning newsroom cuts to the audio, sports, and metro teams, and Opinion Editor Adam O'Neal said WaPo is in "severe financial distress" (Natalie Korach/Status)
https://www.status.news/p/washington-post-layoffs-cuts-morale

Pain at The Post
As morale sinks and top talent exits, staffers at The Washington Post are bracing for more pain ahead of the holidays—raising questions about Will Lewis’ leadership, nearly two years into his tenure as chief executive.

Officials have been planning for weeks to send National Guard to Chicago as Trump seeks to expand crime crackdown (CNN)
https://cnn.com/2025/08/23/politics/national-guard-chicago-plans-trump
http://www.memeorandum.com/250824/p21#a250824p21

Ministers tell Environment Agency to wave planning applications through | Environment Agency | The Guardian #UKPol

"Nations’ plans to ramp up coal, gas and oil extraction ‘will put climate goals beyond reach’"
#FossilFuels #Energy #Climate

Perception in Plan: Coupled Perception and Planning for End-to-End Autonomous Driving
Bozhou Zhang, Jingyu Li, Nan Song, Li Zhang
https://arxiv.org/abs/2508.11488 https://

Perception in Plan: Coupled Perception and Planning for End-to-End Autonomous Driving
End-to-end autonomous driving has achieved remarkable advancements in recent years. Existing methods primarily follow a perception-planning paradigm, where perception and planning are executed sequentially within a fully differentiable framework for planning-oriented optimization. We further advance this paradigm through a perception-in-plan framework design, which integrates perception into the planning process. This design facilitates targeted perception guided by evolving planning objectives…

I wondered what the Ceauşescus were up to these days. https://sfba.social/@skykiss/115420725296644745

♾️🇺🇦 Vote Midterms (@skykiss@sfba.social)
Attached: 1 image Demolition of East Wing—traditionally the First Lady’s domain—has already begun, despite this lack of approval. The illegal ballroom expects to exceed $250 MILLION. The obscene gold ballroom that dwarfs the core of the white house itself. The re-making of a nation's identity. The controversy illegal construction lacks formal approval from the National Capital Planning Commission, which oversees federal construction projects in Washington, DC Look at how ugly this is. …

80 people came to the Bartlett
seminar by Joe Penny last night, with 30 more online from as far away as Mexico. Very strong talk and discussion. Read the (free) book by Joe and Amy Horton. Next London Planning seminar 6 November. uclpress.co.uk/book/disrupt...

I am kinda planning the upcoming months and the next year.
Any conferences you think I should put on my radar? Would love to speak more about decomputing/active luddism and tech.

RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation
Jane Luo, Xin Zhang, Steven Liu, Jie Wu, Yiming Huang, Yangyu Huang, Chengyu Yin, Ying Xin, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Qi Chen, Scarlett Li, Mao Yang
https://arxiv.org/abs/2509.16198

RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation
Large language models excel at function- and file-level code generation, yet generating complete repositories from scratch remains a fundamental challenge. This process demands coherent and reliable planning across proposal- and implementation-level stages, while natural language, due to its ambiguity and verbosity, is ill-suited for faithfully representing complex software structures. To address this, we introduce the Repository Planning Graph (RPG), a persistent representation that unifies pr…

Neural Network-Driven Direct CBCT-Based Dose Calculation for Head-and-Neck Proton Treatment Planning
Muheng Li, Evangelia Choulilitsa, Lisa Fankhauser, Francesca Albertini, Antony Lomax, Ye Zhang
https://arxiv.org/abs/2509.18378

Neural Network-Driven Direct CBCT-Based Dose Calculation for Head-and-Neck Proton Treatment Planning
Accurate dose calculation on cone beam computed tomography (CBCT) images is essential for modern proton treatment planning workflows, particularly when accounting for inter-fractional anatomical changes in adaptive treatment scenarios. Traditional CBCT-based dose calculation suffers from image quality limitations, requiring complex correction workflows. This study develops and validates a deep learning approach for direct proton dose calculation from CBCT images using extended Long Short-Term M…

Nvidia and OpenAI announce a partnership to deploy at least 10 GW of Nvidia systems for OpenAI's infrastructure, with Nvidia planning to invest $100B in OpenAI (OpenAI)
https://openai.com/index/openai-nvidia-systems-partnership/

Improving After-sales Service: Deep Reinforcement Learning for Dynamic Time Slot Assignment with Commitments and Customer Preferences
Xiao Mao, Albert H. Schrotenboer, Guohua Wu, Willem van Jaarsveld
https://arxiv.org/abs/2509.17870

Improving After-sales Service: Deep Reinforcement Learning for Dynamic Time Slot Assignment with Commitments and Customer Preferences
Problem definition: For original equipment manufacturers (OEMs), high-tech maintenance is a strategic component in after-sales services, involving close coordination between customers and service engineers. Each customer suggests several time slots for their maintenance task, from which the OEM must select one. This decision needs to be made promptly to support customers' planning. At the end of each day, routes for service engineers are planned to fulfill the tasks scheduled for the following …

Task and Motion Planning for Humanoid Loco-manipulation
Michal Ciebielski, Victor Dh\'edin, Majid Khadiv
https://arxiv.org/abs/2508.14099 https://arxiv…

Task and Motion Planning for Humanoid Loco-manipulation
This work presents an optimization-based task and motion planning (TAMP) framework that unifies planning for locomotion and manipulation through a shared representation of contact modes. We define symbolic actions as contact mode changes, grounding high-level planning in low-level motion. This enables a unified search that spans task, contact, and motion planning while incorporating whole-body dynamics, as well as all constraints between the robot, the manipulated object, and the environment. R…

RETAIL: Towards Real-world Travel Planning for Large Language Models
Bin Deng, Yizhe Feng, Zeming Liu, Qing Wei, Xiangrong Zhu, Shuai Chen, Yuanfang Guo, Yunhong Wang
https://arxiv.org/abs/2508.15335

RETAIL: Towards Real-world Travel Planning for Large Language Models
Although large language models have enhanced automated travel planning abilities, current systems remain misaligned with real-world scenarios. First, they assume users provide explicit queries, while in reality requirements are often implicit. Second, existing solutions ignore diverse environmental factors and user preferences, limiting the feasibility of plans. Third, systems can only generate plans with basic POI arrangements, failing to provide all-in-one plans with rich details. To mitigate…

Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI
Karin A. Olthof, Matteo Fusagli, Bianca G\"uttner, Tiziano Natali, Bram Westerink, Stefanie Speidel, Theo J. M. Ruers, Koert F. D. Kuhlmann, Andrey Zhylka
https://arxiv.org/abs/2508.14133

Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI
Background: The aim of this study was to develop and evaluate a deep learning-based automated segmentation method for hepatic anatomy (i.e., parenchyma, tumors, portal vein, hepatic vein and biliary tree) from the hepatobiliary phase of gadoxetic acid-enhanced MRI. This method should ease the clinical workflow of preoperative planning. Methods: Manual segmentation was performed on hepatobiliary phase MRI scans from 90 consecutive patients who underwent liver surgery between January 2020 and O…

Raiders Get Strong Words Amid Maxx Crosby Trade Rumors https://heavy.com/sports/nfl/las-vegas-raiders/raiders-get-strong-words-amid-maxx-crosby-trade-rumors/?adt_ei=[email]

"Top Fossil Fuel Producing Nations Plan to Blow Past Climate Targets"
#FossilFuels #Climate #ClimateChange

Top Fossil Fuel Producing Nations Plan to Blow Past Climate Targets - Inside Climate News
A report finds that the top producers of coal, gas and oil are planning to mine and drill even more of the fuels in 2030 than they were two years ago.

Crosslisted article(s) found for cs.FL. https://arxiv.org/list/cs.FL/new
[1/1]:
- AD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods ...
Yunhao Yang, Junyuan Hong, Gabriel Jacob Perin, Zhiwen Fan, Li Yin, Zhangyang Wang, Ufuk Topcu

Transforming Next-generation Network Planning assisted by Data Acquisition of Top Three Spanish MNOs
M. Umar Khan
https://arxiv.org/abs/2508.14445 https://…

Transforming Next-generation Network Planning assisted by Data Acquisition of Top Three Spanish MNOs
In this paper, we address the necessity of data related to mobile traffic of the legacy infrastructure to extract useful information and perform network dimensioning for 5G. These data can help us achieve a more efficient network planning design, especially in terms of topology and cost. To that end, a real open database of top three Spanish mobile network operators (MNOs) is used to estimate the traffic and to identify the area of highest user density for the deployment of new services. We pro…

I'm on the "Don't Be Caught Dead" podcast hosted by Catherine Ashton - we talk found family, compassion at end of life, and - as always - why good advance planning eases grief for everyone.
Listen: https://criticalinfo.com.au/podcast/fo

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

AERO-MPPI: Anchor-Guided Ensemble Trajectory Optimization for Agile Mapless Drone Navigation
Xin Chen, Rui Huang, Longbin Tang, Lin Zhao
https://arxiv.org/abs/2509.17340 https:/…

AERO-MPPI: Anchor-Guided Ensemble Trajectory Optimization for Agile Mapless Drone Navigation
Agile mapless navigation in cluttered 3D environments poses significant challenges for autonomous drones. Conventional mapping-planning-control pipelines incur high computational cost and propagate estimation errors. We present AERO-MPPI, a fully GPU-accelerated framework that unifies perception and planning through an anchor-guided ensemble of Model Predictive Path Integral (MPPI) optimizers. Specifically, we design a multi-resolution LiDAR point-cloud representation that rapidly extracts spat…

Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning
Guoxin Wang, Jun Zhao, Xinyi Liu, Yanbo Liu, Xuyang Cao, Chao Li, Zhuoyun Liu, Qintian Sun, Fangru Zhou, Haoqiang Xing, Zhenhong Yang
https://arxiv.org/abs/2509.19090

Sources: Giants to bench QB Wilson, start Dart https://www.espn.com/nfl/story/_/id/46356208/giants-bench-qb-russell-wilson-start-jaxson-dart

Coarse-to-Fine Grounded Memory for LLM Agent Planning
Wei Yang, Jinwei Xiao, Hongming Zhang, Qingyang Zhang, Yanna Wang, Bo Xu
https://arxiv.org/abs/2508.15305 https://

Coarse-to-Fine Grounded Memory for LLM Agent Planning
Recent advancements in Large Language Models (LLMs) have driven growing interest in LLM-based agents for complex planning tasks. To avoid costly agent training, many studies adopted memory mechanism that enhances LLM with offline experiences or online trajectory analysis. However, existing works focus on single-granularity memory derived from dynamic environmental interactions, which are inherently constrained by the quality of the collected experiences. This limitation, in turn, constrain the …

Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach
Minfeng Qi, Tianqing Zhu, Lefeng Zhang, Ningran Li, Wanlei Zhou
https://arxiv.org/abs/2509.16736

Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach
Large Language Models (LLMs) have enabled the emergence of autonomous agents capable of complex reasoning, planning, and interaction. However, coordinating such agents at scale remains a fundamental challenge, particularly in decentralized environments where communication lacks transparency and agent behavior cannot be shaped through centralized incentives. We propose a blockchain-based framework that enables transparent agent registration, verifiable task allocation, and dynamic reputation tra…

SF has so many parking lots and other soft sites especially on the westside, we should be building housing there instead of demolishing rent-controlled homes. But #SB79 offers few protections & none for rent-controlled duplexes. Sign this petition to amend the bill to fix this!
#SFPol

Amend SB 79 to Protect Tenants and Communities
SB 79 puts thousands of rent-controlled homes and small businesses in San Francisco at risk by allowing demolition without strong affordability requirements or local oversight. It overrides years of community planning and opens the door to displacement in working-class neighborhoods across San Francisco. Sign the petition to demand smart, equitable amendments that protect tenants, preserve local plans, and ensure transit-oriented development works for everyone.

IDfRA: Self-Verification for Iterative Design in Robotic Assembly
Nishka Khendry, Christos Margadji, Sebastian W. Pattinson
https://arxiv.org/abs/2509.16998 https://

IDfRA: Self-Verification for Iterative Design in Robotic Assembly
As robots proliferate in manufacturing, Design for Robotic Assembly (DfRA), which is designing products for efficient automated assembly, is increasingly important. Traditional approaches to DfRA rely on manual planning, which is time-consuming, expensive and potentially impractical for complex objects. Large language models (LLM) have exhibited proficiency in semantic interpretation and robotic task planning, stimulating interest in their application to the automation of DfRA. But existing met…

SmaRT: Style-Modulated Robust Test-Time Adaptation for Cross-Domain Brain Tumor Segmentation in MRI
Yuanhan Wang, Yifei Chen, Shuo Jiang, Wenjing Yu, Mingxuan Liu, Beining Wu, Jinying Zong, Feiwei Qin, Changmiao Wang, Qiyuan Tian
https://arxiv.org/abs/2509.17925

SmaRT: Style-Modulated Robust Test-Time Adaptation for Cross-Domain Brain Tumor Segmentation in MRI
Reliable brain tumor segmentation in MRI is indispensable for treatment planning and outcome monitoring, yet models trained on curated benchmarks often fail under domain shifts arising from scanner and protocol variability as well as population heterogeneity. Such gaps are especially severe in low-resource and pediatric cohorts, where conventional test-time or source-free adaptation strategies often suffer from instability and structural inconsistency. We propose SmaRT, a style-modulated robust…

Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators
Yongliang Wang, Hamidreza Kasaei
https://arxiv.org/abs/2509.17381 https://…

Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators
Generating obstacle-free trajectories for robotic manipulators in unstructured and cluttered environments remains a significant challenge. Existing motion planning methods often require additional computational effort to generate the final trajectory by solving kinematic or dynamic equations. This paper highlights the strong potential of model-free reinforcement learning methods over model-based approaches for obstacle-free trajectory planning in joint space. We propose a fast trajectory planni…

Code Driven Planning with Domain-Adaptive Critic
Zikang Tian, Shaohui Peng, Du Huang, Jiaming Guo, Ruizhi Chen, Rui Zhang, Xishan Zhang, Yuxuan Guo, Zidong Du, Qi Guo, Ling Li, Yewen Pu, Xing Hu, Yunji Chen
https://arxiv.org/abs/2509.19077

"Rachel Reeves, boasted to corporate executives that she has “unblocked” a large housing development in Sussex being held up by “some snails … a protected species or something … microscopic snails that you cannot even see”. .. The very rare little whirlpool ramshorn snail, by no means microscopic, is an indicator of fresh water not affected by sewage pollution"

Analyzing Information Sharing and Coordination in Multi-Agent Planning
Tianyue Ou, Saujas Vaduguru, Daniel Fried
https://arxiv.org/abs/2508.12981 https://a…

Analyzing Information Sharing and Coordination in Multi-Agent Planning
Multi-agent systems (MASs) have pushed the boundaries of large language model (LLM) agents in domains such as web research and software engineering. However, long-horizon, multi-constraint planning tasks involve conditioning on detailed information and satisfying complex interdependent constraints, which can pose a challenge for these systems. In this study, we construct an LLM-based MAS for a travel planning task which is representative of these challenges. We evaluate the impact of a notebook…

AI-Driven Multi-Agent Vehicular Planning for Battery Efficiency and QoS in 6G Smart Cities
Rohin Gillgallon, Giacomo Bergami, Reham Almutairi, Graham Morgan
https://arxiv.org/abs/2509.14877

AI-Driven Multi-Agent Vehicular Planning for Battery Efficiency and QoS in 6G Smart Cities
While simulators exist for vehicular IoT nodes communicating with the Cloud through Edge nodes in a fully-simulated osmotic architecture, they often lack support for dynamic agent planning and optimisation to minimise vehicular battery consumption while ensuring fair communication times. Addressing these challenges requires extending current simulator architectures with AI algorithms for both traffic prediction and dynamic agent planning. This paper presents an extension of SimulatorOrchestrato…

Real-Time Planning and Control with a Vortex Particle Model for Fixed-Wing UAVs in Unsteady Flows
Ashwin Gupta, Kevin Wolfe, Gino Perrotta, Joseph Moore
https://arxiv.org/abs/2509.16079

Real-Time Planning and Control with a Vortex Particle Model for Fixed-Wing UAVs in Unsteady Flows
Unsteady aerodynamic effects can have a profound impact on aerial vehicle flight performance, especially during agile maneuvers and in complex aerodynamic environments. In this paper, we present a real-time planning and control approach capable of reasoning about unsteady aerodynamics. Our approach relies on a lightweight vortex particle model, parallelized to allow GPU acceleration, and a sampling-based policy optimization strategy capable of leveraging the vortex particle model for predictive…

LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
Ronit Virwani, Ruchika Suryawanshi
https://arxiv.org/abs/2508.13371 https://

LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
Planning is one of the most critical tasks in autonomous systems, where even a small error can lead to major failures or million-dollar losses. Current state-of-the-art neural planning approaches struggle with complex domains, producing plans with missing preconditions, inconsistent goals, and hallucinations. While classical planners provide logical guarantees, they lack the flexibility and natural language understanding capabilities needed for modern autonomous systems. Existing neuro-symbolic…

LLM-Driven Self-Refinement for Embodied Drone Task Planning
Deyu Zhang, Xicheng Zhang, Jiahao Li, Tingting Long, Xunhua Dai, Yongjian Fu, Jinrui Zhang, Ju Ren, Yaoxue Zhang
https://arxiv.org/abs/2508.15501

LLM-Driven Self-Refinement for Embodied Drone Task Planning
We introduce SRDrone, a novel system designed for self-refinement task planning in industrial-grade embodied drones. SRDrone incorporates two key technical contributions: First, it employs a continuous state evaluation methodology to robustly and accurately determine task outcomes and provide explanatory feedback. This approach supersedes conventional reliance on single-frame final-state assessment for continuous, dynamic drone operations. Second, SRDrone implements a hierarchical Behavior Tree…

Improved Generalized Planning with LLMs through Strategy Refinement and Reflection
Katharina Stein, Nils Hodel, Daniel Fi\v{s}er, J\"org Hoffmann, Michael Katz, Alexander Koller
https://arxiv.org/abs/2508.13876

Improved Generalized Planning with LLMs through Strategy Refinement and Reflection
LLMs have recently been used to generate Python programs representing generalized plans in PDDL planning, i.e., plans that generalize across the tasks of a given PDDL domain. Previous work proposed a framework consisting of three steps: the LLM first generates a summary and then a strategy for the domain, both in natural language, and then implements that strategy as a Python program, that gets debugged on example planning tasks. In that work, only one strategy is generated and passed directly …

Tom Brady joins Raiders coaches in booth on 'MNF' as involvement in offensive game planning comes to light

https://www.cbssports.com/nfl/news/tom-br…

Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation
Nikita Kachaev, Andrei Spiridonov, Andrey Gorodetsky, Kirill Muravyev, Nikita Oskolkov, Aditya Narendra, Vlad Shakhuro, Dmitry Makarov, Aleksandr I. Panov, Polina Fedotova, Alexey K. Kovalev
https://arxiv.org/abs/2…

Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation
Benchmarks are crucial for evaluating progress in robotics and embodied AI. However, a significant gap exists between benchmarks designed for high-level language instruction following, which often assume perfect low-level execution, and those for low-level robot control, which rely on simple, one-step commands. This disconnect prevents a comprehensive evaluation of integrated systems where both task planning and physical execution are critical. To address this, we propose Kitchen-R, a novel ben…

Automatic Network Planning with Digital Radio Twin
Xiaomeng Li, Yuru Zhang, Qiang Liu, Mehmet Can Vuran, Nathan Huynh, Li Zhao, Mizan Rahman, Eren Erman Ozguven
https://arxiv.org/abs/2509.12441

Automatic Network Planning with Digital Radio Twin
Network planning seeks to determine base station parameters that maximize coverage and capacity in cellular networks. However, achieving optimal planning remains challenging due to the diversity of deployment scenarios and the significant simulation-to-reality discrepancy. In this paper, we propose \emph{AutoPlan}, a new automatic network planning framework by leveraging digital radio twin (DRT) techniques. We derive the DRT by finetuning the parameters of building materials to reduce the sim-t…

Reasoning Core: A Scalable RL Environment for LLM Symbolic Reasoning
Valentin Lacombe, Valentin Quesnel, Damien Sileo
https://arxiv.org/abs/2509.18083 https://

Reasoning Core: A Scalable RL Environment for LLM Symbolic Reasoning
We introduce Reasoning Core, a new scalable environment for Reinforcement Learning with Verifiable Rewards (RLVR), designed to advance foundational symbolic reasoning in Large Language Models (LLMs). Unlike existing benchmarks that focus on games or isolated puzzles, Reasoning Core procedurally generates problems across core formal domains, including PDDL planning, first-order logic, context-free grammar parsing, causal reasoning, and system equation solving. The environment is built on key des…

CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps
Franz Hanke, Antonia Bieringer, Olaf Wysocki, Boris Jutzi
https://arxiv.org/abs/2508.15672 https://

CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps
Detailed 3D building models are crucial for urban planning, digital twins, and disaster management applications. While Level of Detail 1 (LoD)1 and LoD2 building models are widely available, they lack detailed facade elements essential for advanced urban analysis. In contrast, LoD3 models address this limitation by incorporating facade elements such as windows, doors, and underpasses. However, their generation has traditionally required manual modeling, making large-scale adoption challenging. …

Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
Jo\~ao Vitor de Carvalho Silva, Douglas G. Macharet
https://arxiv.org/abs/2508.14635

Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
The ability to coordinate actions across multiple agents is critical for solving complex, real-world problems. Large Language Models (LLMs) have shown strong capabilities in communication, planning, and reasoning, raising the question of whether they can also support effective collaboration in multi-agent settings. In this work, we investigate the use of LLM agents to solve a structured victim rescue task that requires division of labor, prioritization, and cooperative planning. Agents operate …

Toward PDDL Planning Copilot
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg, Roni Stern
https://arxiv.org/abs/2509.12987 https://arxiv.org/pdf/2509.1…

Toward PDDL Planning Copilot
Large Language Models (LLMs) are increasingly being used as autonomous agents capable of performing complicated tasks. However, they lack the ability to perform reliable long-horizon planning on their own. This paper bridges this gap by introducing the Planning Copilot, a chatbot that integrates multiple planning tools and allows users to invoke them through instructions in natural language. The Planning Copilot leverages the Model Context Protocol (MCP), a recently developed standard for conne…

LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion
Chengqi Dong, Fenghe Tang, Rongge Mao, Xinpei Gao, S. Kevin Zhou
https://arxiv.org/abs/2508.15476

LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion
Medical image segmentation plays a pivotal role in disease diagnosis and treatment planning, particularly in resource-constrained clinical settings where lightweight and generalizable models are urgently needed. However, existing lightweight models often compromise performance for efficiency and rarely adopt computationally expensive attention mechanisms, severely restricting their global contextual perception capabilities. Additionally, current architectures neglect the channel redundancy issu…

Imitation-Guided Bimanual Planning for Stable Manipulation under Changing External Forces
Kuanqi Cai, Chunfeng Wang, Zeqi Li, Haowen Yao, Weinan Chen, Luis Figueredo, Aude Billard, Arash Ajoudani
https://arxiv.org/abs/2509.19261

Scaling Multi-Agent Epistemic Planning through GNN-Derived Heuristics
Giovanni Briglia, Francesco Fabiano, Stefano Mariani
https://arxiv.org/abs/2508.12840 https://

Scaling Multi-Agent Epistemic Planning through GNN-Derived Heuristics
Multi-agent Epistemic Planning (MEP) is an autonomous planning framework for reasoning about both the physical world and the beliefs of agents, with applications in domains where information flow and awareness among agents are critical. The richness of MEP requires states to be represented as Kripke structures, i.e., directed labeled graphs. This representation limits the applicability of existing heuristics, hindering the scalability of epistemic solvers, which must explore an exponential sear…

Distributionally Robust Safe Motion Planning with Contextual Information
Kaizer Rahaman, Simran Kumari, Ashish R. Hota
https://arxiv.org/abs/2509.18666 https://

GTool: Graph Enhanced Tool Planning with Large Language Model
Wenjie Chen, Wenbin Li, Di Yao, Xuying Meng, Chang Gong, Jingping Bi
https://arxiv.org/abs/2508.12725 https://

GTool: Graph Enhanced Tool Planning with Large Language Model
Tool planning with large language models (LLMs), referring to selecting, organizing, and preparing the tools necessary to complete a user request, bridges the gap between natural language understanding and task execution. However, current works treat different tools as isolated components and fail to leverage the inherent dependencies of tools, leading to invalid planning results. Since tool dependencies are often incomplete, it becomes challenging for LLMs to accurately identify the appropriat…

Multi-perspective monitoring of wildlife and human activities from camera traps and drones with deep learning models
Hao Chen, Fang Qiu, Li An, Douglas Stow, Eve Bohnett, Haitao Lyu, Shuang Tian
https://arxiv.org/abs/2508.15629

Multi-perspective monitoring of wildlife and human activities from camera traps and drones with deep learning models
Wildlife and human activities are key components of landscape systems. Understanding their spatial distribution is essential for evaluating human wildlife interactions and informing effective conservation planning. Multiperspective monitoring of wildlife and human activities by combining camera traps and drone imagery. Capturing the spatial patterns of their distributions, which allows the identification of the overlap of their activity zones and the assessment of the degree of human wildlife c…

FiReFly: Fair Distributed Receding Horizon Planning for Multiple UAVs
Nicole Fronda, Bardh Hoxha, Houssam Abbas
https://arxiv.org/abs/2508.14381 https://ar…

FiReFly: Fair Distributed Receding Horizon Planning for Multiple UAVs
We propose injecting notions of fairness into multi-robot motion planning. When robots have competing interests, it is important to optimize for some kind of fairness in their usage of resources. In this work, we explore how the robots' energy expenditures might be fairly distributed among them, while maintaining mission success. We formulate a distributed fair motion planner and integrate it with safe controllers in a algorithm called FiReFly. For simulated reach-avoid missions, FiReFly produc…

Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
Pulkit Verma, Ngoc La, Anthony Favier, Swaroop Mishra, Julie A. Shah
https://arxiv.org/abs/2509.13351

Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
Large language models (LLMs) have demonstrated impressive capabilities across diverse tasks, yet their ability to perform structured symbolic planning remains limited, particularly in domains requiring formal representations like the Planning Domain Definition Language (PDDL). In this paper, we present a novel instruction tuning framework, PDDL-Instruct, designed to enhance LLMs' symbolic planning capabilities through logical chain-of-thought reasoning. Our approach focuses on teaching models t…

EAROL: Environmental Augmented Perception-Aware Planning and Robust Odometry via Downward-Mounted Tilted LiDAR
Xinkai Liang, Yigu Ge, Yangxi Shi, Haoyu Yang, Xu Cao, Hao Fang
https://arxiv.org/abs/2508.14554

EAROL: Environmental Augmented Perception-Aware Planning and Robust Odometry via Downward-Mounted Tilted LiDAR
To address the challenges of localization drift and perception-planning coupling in unmanned aerial vehicles (UAVs) operating in open-top scenarios (e.g., collapsed buildings, roofless mazes), this paper proposes EAROL, a novel framework with a downward-mounted tilted LiDAR configuration (20° inclination), integrating a LiDAR-Inertial Odometry (LIO) system and a hierarchical trajectory-yaw optimization algorithm. The hardware innovation enables constraint enhancement via dense ground point clo…

The Maximum Coverage Model and Recommendation System for UAV Vertiports Location Planning
Chunliang Hua, Xiao Hu, Jiayang Sun, Zeyuan Yang
https://arxiv.org/abs/2508.12651 https…

The Maximum Coverage Model and Recommendation System for UAV Vertiports Location Planning
As urban aerial mobility (UAM) infrastructure development accelerates globally, cities like Shenzhen are planning large-scale vertiport networks (e.g., 1,200+ facilities by 2026). Existing planning frameworks remain inadequate for this complexity due to historical limitations in data granularity and real-world applicability. This paper addresses these gaps by first proposing the Capacitated Dynamic Maximum Covering Location Problem (CDMCLP), a novel optimization framework that simultaneously mo…

Self-Supervised Sparse Sensor Fusion for Long Range Perception
Edoardo Palladin, Samuel Brucker, Filippo Ghilotti, Praveen Narayanan, Mario Bijelic, Felix Heide
https://arxiv.org/abs/2508.13995

Self-Supervised Sparse Sensor Fusion for Long Range Perception
Outside of urban hubs, autonomous cars and trucks have to master driving on intercity highways. Safe, long-distance highway travel at speeds exceeding 100 km/h demands perception distances of at least 250 m, which is about five times the 50-100m typically addressed in city driving, to allow sufficient planning and braking margins. Increasing the perception ranges also allows to extend autonomy from light two-ton passenger vehicles to large-scale forty-ton trucks, which need a longer planning ho…

Dynamic Objects Relocalization in Changing Environments with Flow Matching
Francesco Argenziano, Miguel Saavedra-Ruiz, Sacha Morin, Daniele Nardi, Liam Paull
https://arxiv.org/abs/2509.16398

Dynamic Objects Relocalization in Changing Environments with Flow Matching
Task and motion planning are long-standing challenges in robotics, especially when robots have to deal with dynamic environments exhibiting long-term dynamics, such as households or warehouses. In these environments, long-term dynamics mostly stem from human activities, since previously detected objects can be moved or removed from the scene. This adds the necessity to find such objects again before completing the designed task, increasing the risk of failure due to missed relocalizations. Howe…

HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds
Petr Anokhin, Roman Khalikov, Stefan Rebrikov, Viktor Volkov, Artyom Sorokin, Vincent Bissonnette
https://arxiv.org/abs/2508.12782

HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds
Large language models (LLMs) have shown remarkable capabilities in isolated step-by-step reasoning tasks such as mathematics and programming, but their proficiency in long-horizon planning, where solutions require extended, structured sequences of interdependent actions, remains underexplored. Existing benchmarks typically assess LLMs through abstract or low-dimensional algorithmic tasks, failing to capture the complexity of realistic planning environments. We introduce HeroBench, a novel bench…

Accelerating Signal-Temporal-Logic-Based Task and Motion Planning of Bipedal Navigation using Benders Decomposition
Jiming Ren, Xuan Lin, Roman Mineyev, Karen M. Feigh, Samuel Coogan, Ye Zhao
https://arxiv.org/abs/2508.13407

Accelerating Signal-Temporal-Logic-Based Task and Motion Planning of Bipedal Navigation using Benders Decomposition
Task and motion planning under Signal Temporal Logic constraints is known to be NP-hard. A common class of approaches formulates these hybrid problems, which involve discrete task scheduling and continuous motion planning, as mixed-integer programs (MIP). However, in applications for bipedal locomotion, introduction of non-convex constraints such as kinematic reachability and footstep rotation exacerbates the computational complexity of MIPs. In this work, we present a method based on Benders D…

Artificial Intelligence in Breast Cancer Care: Transforming Preoperative Planning and Patient Education with 3D Reconstruction
Mustafa Khanbhai, Giulia Di Nardo, Jun Ma, Vivienne Freitas, Caterina Masino, Ali Dolatabadi, Zhaoxun "Lorenz" Liu, Wey Leong, Wagner H. Souza, Amin Madani
https://arxiv.org/abs/2509.12242

Artificial Intelligence in Breast Cancer Care: Transforming Preoperative Planning and Patient Education with 3D Reconstruction
Effective preoperative planning requires accurate algorithms for segmenting anatomical structures across diverse datasets, but traditional models struggle with generalization. This study presents a novel machine learning methodology to improve algorithm generalization for 3D anatomical reconstruction beyond breast cancer applications. We processed 120 retrospective breast MRIs (January 2018-June 2023) through three phases: anonymization and manual segmentation of T1-weighted and dynamic contras…

CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving
Ruiguo Zhong, Ruoyu Yao, Pei Liu, Xiaolong Chen, Rui Yang, Jun Ma
https://arxiv.org/abs/2509.17080

CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving
Accurate trajectory prediction and motion planning are crucial for autonomous driving systems to navigate safely in complex, interactive environments characterized by multimodal uncertainties. However, current generation-then-evaluation frameworks typically construct multiple plausible trajectory hypotheses but ultimately adopt a single most likely outcome, leading to overconfident decisions and a lack of fallback strategies that are vital for safety in rare but critical scenarios. Moreover, th…

Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation
Sangwoo Jeon, Juchul Shin, YeonJe Cho, Gyeong-Tae Kim, Seongwoo Kim
https://arxiv.org/abs/2508.11890

Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation
Modern autonomous drone missions increasingly require software frameworks capable of seamlessly integrating structured symbolic planning with adaptive reinforcement learning (RL). Although traditional rule-based architectures offer robust structured reasoning for drone autonomy, their capabilities fall short in dynamically complex operational environments that require adaptive symbolic planning. Symbolic RL (SRL), using the Planning Domain Definition Language (PDDL), explicitly integrates domai…

Learn to optimize for automatic proton PBS treatment planning for H&N cancers
Qingqing Wang, Liqiang Xiao, Chang Chang
https://arxiv.org/abs/2508.11085 https://

Learn to optimize for automatic proton PBS treatment planning for H&N cancers
Proton PBS treatment planning for H&N cancers involves numerous conflicting objectives, requiring significant effort from human planners to balance and satisfy multiple clinical goals during planning. To achieve this, experience-demanding objective parameter adjustment and computationally expensive inverse optimization are performed iteratively. Extensive efforts have been made to automatically adjust objective parameters, but the most time-consuming component, i.e., inverse optimization, still…

MAP: End-to-End Autonomous Driving with Map-Assisted Planning
Huilin Yin, Yiming Kan, Daniel Watzenig
https://arxiv.org/abs/2509.13926 https://arxiv.org/pd…

MAP: End-to-End Autonomous Driving with Map-Assisted Planning
In recent years, end-to-end autonomous driving has attracted increasing attention for its ability to jointly model perception, prediction, and planning within a unified framework. However, most existing approaches underutilize the online mapping module, leaving its potential to enhance trajectory planning largely untapped. This paper proposes MAP (Map-Assisted Planning), a novel map-assisted end-to-end trajectory planning framework. MAP explicitly integrates segmentation-based map features and …

Unified Crew Planning and Replanning Optimization in Multi-Line Metro Systems Considering Workforce Heterogeneity
Qihang Chen
https://arxiv.org/abs/2509.14251 https://

Unified Crew Planning and Replanning Optimization in Multi-Line Metro Systems Considering Workforce Heterogeneity
Metro crew planning is a key component of smart city development as it directly impacts the operational efficiency and service reliability of public transportation. With the rapid expansion of metro networks, effective multi-line scheduling and emergency management have become essential for large-scale seamless operations. However, current research focuses primarily on individual metro lines,with insufficient attention on cross-line coordination and rapid replanning during disruptions. Here, a …

GPS Denied IBVS-Based Navigation and Collision Avoidance of UAV Using a Low-Cost RGB Camera
Xiaoyu Wang, Yan Rui Tan, William Leong, Sunan Huang, Rodney Teo, Cheng Xiang
https://arxiv.org/abs/2509.17435

GPS Denied IBVS-Based Navigation and Collision Avoidance of UAV Using a Low-Cost RGB Camera
This paper proposes an image-based visual servoing (IBVS) framework for UAV navigation and collision avoidance using only an RGB camera. While UAV navigation has been extensively studied, it remains challenging to apply IBVS in missions involving multiple visual targets and collision avoidance. The proposed method achieves navigation without explicit path planning, and collision avoidance is realized through AI-based monocular depth estimation from RGB images. Unlike approaches that rely on ste…

V2V-GoT: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multimodal Large Language Models and Graph-of-Thoughts
Hsu-kuang Chiu, Ryo Hachiuma, Chien-Yi Wang, Yu-Chiang Frank Wang, Min-Hung Chen, Stephen F. Smith
https://arxiv.org/abs/2509.18053

V2V-GoT: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multimodal Large Language Models and Graph-of-Thoughts
Current state-of-the-art autonomous vehicles could face safety-critical situations when their local sensors are occluded by large nearby objects on the road. Vehicle-to-vehicle (V2V) cooperative autonomous driving has been proposed as a means of addressing this problem, and one recently introduced framework for cooperative autonomous driving has further adopted an approach that incorporates a Multimodal Large Language Model (MLLM) to integrate cooperative perception and planning processes. Howe…

Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning
Sangwoo Jeon, Juchul Shin, Gyeong-Tae Kim, YeonJe Cho, Seongwoo Kim
https://arxiv.org/abs/2508.10747

Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning
Generalized planning using deep reinforcement learning (RL) combined with graph neural networks (GNNs) has shown promising results in various symbolic planning domains described by PDDL. However, existing approaches typically represent planning states as fully connected graphs, leading to a combinatorial explosion in edge information and substantial sparsity as problem scales grow, especially evident in large grid-based environments. This dense representation results in diluted node-level infor…

BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
Sourav Raxit, Abdullah Al Redwan Newaz, Paulo Padrao, Jose Fuentes, Leonardo Bobadilla
https://arxiv.org/abs/2508.13052

BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
This paper introduces the BOW Planner, a scalable motion planning algorithm designed to navigate robots through complex environments using constrained Bayesian optimization (CBO). Unlike traditional methods, which often struggle with kinodynamic constraints such as velocity and acceleration limits, the BOW Planner excels by concentrating on a planning window of reachable velocities and employing CBO to sample control inputs efficiently. This approach enables the planner to manage high-dimension…

Landmark-Assisted Monte Carlo Planning
David H. Chan, Mark Roberts, Dana S. Nau
https://arxiv.org/abs/2508.11493 https://arxiv.org/pdf/2508.11493

Landmark-Assisted Monte Carlo Planning
Landmarks$\unicode{x2013}$conditions that must be satisfied at some point in every solution plan$\unicode{x2013}$have contributed to major advancements in classical planning, but they have seldom been used in stochastic domains. We formalize probabilistic landmarks and adapt the UCT algorithm to leverage them as subgoals to decompose MDPs; core to the adaptation is balancing between greedy landmark achievement and final goal achievement. Our results in benchmark domains show that well-chosen la…

Bridging Perception and Planning: Towards End-to-End Planning for Signal Temporal Logic Tasks
Bowen Ye, Junyue Huang, Yang Liu, Xiaozhen Qiao, Xiang Yin
https://arxiv.org/abs/2509.12813

Bridging Perception and Planning: Towards End-to-End Planning for Signal Temporal Logic Tasks
We investigate the task and motion planning problem for Signal Temporal Logic (STL) specifications in robotics. Existing STL methods rely on pre-defined maps or mobility representations, which are ineffective in unstructured real-world environments. We propose the \emph{Structured-MoE STL Planner} (\textbf{S-MSP}), a differentiable framework that maps synchronized multi-view camera observations and an STL specification directly to a feasible trajectory. S-MSP integrates STL constraints within a…

ParaCook: On Time-Efficient Planning for Multi-Agent Systems
Shiqi Zhang, Xinbei Ma, Yunqing Xu, Zouying Cao, Pengrui Lu, Haobo Yuan, Tiancheng Shen, Zhuosheng Zhang, Hai Zhao, Ming-Hsuan Yang
https://arxiv.org/abs/2510.11608

ParaCook: On Time-Efficient Planning for Multi-Agent Systems
Large Language Models (LLMs) exhibit strong reasoning abilities for planning long-horizon, real-world tasks, yet existing agent benchmarks focus on task completion while neglecting time efficiency in parallel and asynchronous operations. To address this, we present ParaCook, a benchmark for time-efficient collaborative planning. Inspired by the Overcooked game, ParaCook provides an environment for various challenging interaction planning of multi-agent systems that are instantiated as cooking t…

An Informative Planning Framework for Target Tracking and Active Mapping in Dynamic Environments with ASVs
Sanjeev Ramkumar Sudha, Marija Popovi\'c, Erlend M. Coates
https://arxiv.org/abs/2508.14636

An Informative Planning Framework for Target Tracking and Active Mapping in Dynamic Environments with ASVs
Mobile robot platforms are increasingly being used to automate information gathering tasks such as environmental monitoring. Efficient target tracking in dynamic environments is critical for applications such as search and rescue and pollutant cleanups. In this letter, we study active mapping of floating targets that drift due to environmental disturbances such as wind and currents. This is a challenging problem as it involves predicting both spatial and temporal variations in the map due to ch…

Fair-CoPlan: Negotiated Flight Planning with Fair Deconfliction for Urban Air Mobility
Nicole Fronda, Phil Smith, Bardh Hoxha, Yash Pant, Houssam Abbas
https://arxiv.org/abs/2508.14380

Fair-CoPlan: Negotiated Flight Planning with Fair Deconfliction for Urban Air Mobility
Urban Air Mobility (UAM) is an emerging transportation paradigm in which Uncrewed Aerial Systems (UAS) autonomously transport passengers and goods in cities. The UAS have different operators with different, sometimes competing goals, yet must share the airspace. We propose a negotiated, semi-distributed flight planner that optimizes UAS' flight lengths {\em in a fair manner}. Current flight planners might result in some UAS being given disproportionately shorter flight paths at the expense of o…

R-ConstraintBench: Evaluating LLMs on NP-Complete Scheduling
Raj Jain, Marc Wetter
https://arxiv.org/abs/2508.15204 https://arxiv.org/pdf/2508.15204…

R-ConstraintBench: Evaluating LLMs on NP-Complete Scheduling
Effective scheduling under tight resource, timing, and operational constraints underpins large-scale planning across sectors such as capital projects, manufacturing, logistics, and IT fleet transitions. However, the reliability of large language models (LLMs) when reasoning under high-constraint regimes is insufficiently characterized. To address this gap, we present R-ConstraintBench, a scalable framework that evaluates models on Resource-Constrained Project Scheduling Problems (RCPSP), an NP-…

FragmentRetro: A Quadratic Retrosynthetic Method Based on Fragmentation Algorithms
Yu Shee, Anthony M. Smaldone, Anton Morgunov, Gregory W. Kyro, Victor S. Batista
https://arxiv.org/abs/2509.15409

FragmentRetro: A Quadratic Retrosynthetic Method Based on Fragmentation Algorithms
Retrosynthesis, the process of deconstructing a target molecule into simpler precursors, is crucial for computer-aided synthesis planning (CASP). Widely adopted tree-search methods often suffer from exponential computational complexity. In this work, we introduce FragmentRetro, a novel retrosynthetic method that leverages fragmentation algorithms, specifically BRICS and r-BRICS, combined with stock-aware exploration and pattern fingerprint screening to achieve quadratic complexity. FragmentRetr…

Relative Position Matters: Trajectory Prediction and Planning with Polar Representation
Bozhou Zhang, Nan Song, Bingzhao Gao, Li Zhang
https://arxiv.org/abs/2508.11492 https://

Relative Position Matters: Trajectory Prediction and Planning with Polar Representation
Trajectory prediction and planning in autonomous driving are highly challenging due to the complexity of predicting surrounding agents' movements and planning the ego agent's actions in dynamic environments. Existing methods encode map and agent positions and decode future trajectories in Cartesian coordinates. However, modeling the relationships between the ego vehicle and surrounding traffic elements in Cartesian space can be suboptimal, as it does not naturally capture the varying influence …

From Bits to Boardrooms: A Cutting-Edge Multi-Agent LLM Framework for Business Excellence
Zihao Wang, Junming Zhang
https://arxiv.org/abs/2508.15447 https://

From Bits to Boardrooms: A Cutting-Edge Multi-Agent LLM Framework for Business Excellence
Large Language Models (LLMs) have shown promising potential in business applications, particularly in enterprise decision support and strategic planning, yet current approaches often struggle to reconcile intricate operational analyses with overarching strategic goals across diverse market environments, leading to fragmented workflows and reduced collaboration across organizational levels. This paper introduces BusiAgent, a novel multi-agent framework leveraging LLMs for advanced decision-makin…

Repulsive Trajectory Modification and Conflict Resolution for Efficient Multi-Manipulator Motion Planning
Junhwa Hong, Beomjoon Lee, Woojin Lee, Changjoo Nam
https://arxiv.org/abs/2509.13882

Repulsive Trajectory Modification and Conflict Resolution for Efficient Multi-Manipulator Motion Planning
We propose an efficient motion planning method designed to efficiently find collision-free trajectories for multiple manipulators. While multi-manipulator systems offer significant advantages, coordinating their motions is computationally challenging owing to the high dimensionality of their composite configuration space. Conflict-Based Search (CBS) addresses this by decoupling motion planning, but suffers from subsequent conflicts incurred by resolving existing conflicts, leading to an exponen…

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 …

Hierarchical Planning and Scheduling for Reconfigurable Multi-Robot Disassembly Systems under Structural Constraints
Takuya Kiyokawa, Tomoki Ishikura, Shingo Hamada, Genichiro Matsuda, Kensuke Harada
https://arxiv.org/abs/2509.14564

Hierarchical Planning and Scheduling for Reconfigurable Multi-Robot Disassembly Systems under Structural Constraints
This study presents a system integration approach for planning schedules, sequences, tasks, and motions for reconfigurable robots to automatically disassemble constrained structures in a non-destructive manner. Such systems must adapt their configuration and coordination to the target structure, but the large and complex search space makes them prone to local optima. To address this, we integrate multiple robot arms equipped with different types of tools, together with a rotary stage, into a re…

Mobile-Agent-v3: Foundamental Agents for GUI Automation
Jiabo Ye, Xi Zhang, Haiyang Xu, Haowei Liu, Junyang Wang, Zhaoqing Zhu, Ziwei Zheng, Feiyu Gao, Junjie Cao, Zhengxi Lu, Jitong Liao, Qi Zheng, Fei Huang, Jingren Zhou, Ming Yan
https://arxiv.org/abs/2508.15144

Mobile-Agent-v3: Foundamental Agents for GUI Automation
This paper introduces GUI-Owl, a foundational GUI agent model that achieves state-of-the-art performance among open-source end-to-end models on ten GUI benchmarks across desktop and mobile environments, covering grounding, question answering, planning, decision-making, and procedural knowledge. GUI-Owl-7B achieves 66.4 on AndroidWorld and 29.4 on OSWorld. Building on this, we propose Mobile-Agent-v3, a general-purpose GUI agent framework that further improves performance to 73.3 on AndroidWorld…

TRUST-Planner: Topology-guided Robust Trajectory Planner for AAVs with Uncertain Obstacle Spatial-temporal Avoidance
Junzhi Li, Teng Long, Jingliang Sun, Jianxin Zhong
https://arxiv.org/abs/2508.14610 …

TRUST-Planner: Topology-guided Robust Trajectory Planner for AAVs with Uncertain Obstacle Spatial-temporal Avoidance
Despite extensive developments in motion planning of autonomous aerial vehicles (AAVs), existing frameworks faces the challenges of local minima and deadlock in complex dynamic environments, leading to increased collision risks. To address these challenges, we present TRUST-Planner, a topology-guided hierarchical planning framework for robust spatial-temporal obstacle avoidance. In the frontend, a dynamic enhanced visible probabilistic roadmap (DEV-PRM) is proposed to rapidly explore topologica…

Diagnostics of cognitive failures in multi-agent expert systems using dynamic evaluation protocols and subsequent mutation of the processing context
Andrejs Sorstkins, Josh Bailey, Dr Alistair Baron
https://arxiv.org/abs/2509.15366

Diagnostics of cognitive failures in multi-agent expert systems using dynamic evaluation protocols and subsequent mutation of the processing context
The rapid evolution of neural architectures - from multilayer perceptrons to large-scale Transformer-based models - has enabled language models (LLMs) to exhibit emergent agentic behaviours when equipped with memory, planning, and external tool use. However, their inherent stochasticity and multi-step decision processes render classical evaluation methods inadequate for diagnosing agentic performance. This work introduces a diagnostic framework for expert systems that not only evaluates but als…

Simultaneous Contact Sequence and Patch Planning for Dynamic Locomotion
Victor Dh\'edin, Haizhou Zhao, Majid Khadiv
https://arxiv.org/abs/2508.12928 https://

Simultaneous Contact Sequence and Patch Planning for Dynamic Locomotion
Legged robots have the potential to traverse highly constrained environments with agile maneuvers. However, planning such motions requires solving a highly challenging optimization problem with a mixture of continuous and discrete decision variables. In this paper, we present a full pipeline based on Monte-Carlo tree search (MCTS) and whole-body trajectory optimization (TO) to perform simultaneous contact sequence and patch selection on highly challenging environments. Through extensive simulat…

TripScore: Benchmarking and rewarding real-world travel planning with fine-grained evaluation
Yincen Qu, Huan Xiao, Feng Li, Hui Zhou, Xiangying Dai
https://arxiv.org/abs/2510.09011

TripScore: Benchmarking and rewarding real-world travel planning with fine-grained evaluation
Travel planning is a valuable yet complex task that poses significant challenges even for advanced large language models (LLMs). While recent benchmarks have advanced in evaluating LLMs' planning capabilities, they often fall short in evaluating feasibility, reliability, and engagement of travel plans. We introduce a comprehensive benchmark for travel planning that unifies fine-grained criteria into a single reward, enabling direct comparison of plan quality and seamless integration with reinfo…

Research on UAV Applications in Public Administration: Based on an Improved RRT Algorithm
Zhanxi Xie, Baili Lu, Yanzhao Gu, Zikun Li, Junhao Wei, Ngai Cheong
https://arxiv.org/abs/2508.14096

Research on UAV Applications in Public Administration: Based on an Improved RRT Algorithm
This study investigates the application of unmanned aerial vehicles (UAVs) in public management, focusing on optimizing path planning to address challenges such as energy consumption, obstacle avoidance, and airspace constraints. As UAVs transition from 'technical tools' to 'governance infrastructure', driven by advancements in low-altitude economy policies and smart city demands, efficient path planning becomes critical. The research proposes an enhanced Rapidly-exploring Random Tree algorithm…

Multi-CAP: A Multi-Robot Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments
Zongyuan Shen, Burhanuddin Shirose, Prasanna Sriganesh, Bhaskar Vundurthy, Howie Choset, Matthew Travers
https://arxiv.org/abs/2509.14941

Multi-CAP: A Multi-Robot Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments
Efficient coordination of multiple robots for coverage of large, unknown environments is a significant challenge that involves minimizing the total coverage path length while reducing inter-robot conflicts. In this paper, we introduce a Multi-robot Connectivity-Aware Planner (Multi-CAP), a hierarchical coverage path planning algorithm that facilitates multi-robot coordination through a novel connectivity-aware approach. The algorithm constructs and dynamically maintains an adjacency graph that …

DEXTER-LLM: Dynamic and Explainable Coordination of Multi-Robot Systems in Unknown Environments via Large Language Models
Yuxiao Zhu, Junfeng Chen, Xintong Zhang, Meng Guo, Zhongkui Li
https://arxiv.org/abs/2508.14387

DEXTER-LLM: Dynamic and Explainable Coordination of Multi-Robot Systems in Unknown Environments via Large Language Models
Online coordination of multi-robot systems in open and unknown environments faces significant challenges, particularly when semantic features detected during operation dynamically trigger new tasks. Recent large language model (LLMs)-based approaches for scene reasoning and planning primarily focus on one-shot, end-to-end solutions in known environments, lacking both dynamic adaptation capabilities for online operation and explainability in the processes of planning. To address these issues, a …

Automated Behavior Planning for Fruit Tree Pruning via Redundant Robot Manipulators: Addressing the Behavior Planning Challenge
Gaoyuan Liu, Bas Boom, Naftali Slob, Yuri Durodi\'e, Ann Now\'e, Bram Vanderborght
https://arxiv.org/abs/2510.12509

Automated Behavior Planning for Fruit Tree Pruning via Redundant Robot Manipulators: Addressing the Behavior Planning Challenge
Pruning is an essential agricultural practice for orchards. Proper pruning can promote healthier growth and optimize fruit production throughout the orchard's lifespan. Robot manipulators have been developed as an automated solution for this repetitive task, which typically requires seasonal labor with specialized skills. While previous research has primarily focused on the challenges of perception, the complexities of manipulation are often overlooked. These challenges involve planning and con…