pith. sign in

arxiv: 2606.31073 · v1 · pith:HIGDTIZQnew · submitted 2026-06-30 · 💻 cs.AI · cs.MA· cs.RO

MultiUAV-Plat: An LLM-Oriented Platform, Benchmark and Framework for Multi-UAV Collaborative Task Planning

Sheng Zhang , Qinglin Li , Yuechao Zang , Xueqin Huang , Yijia Fu , Cheng Zhu This is my paper

Pith reviewed 2026-07-01 06:07 UTC · model grok-4.3

classification 💻 cs.AI cs.MAcs.RO
keywords multi-UAVLLM agenttask planningsimulation platformbenchmarkcollaborative autonomyAgent4DroneReAct
0
0 comments X

The pith

MultiUAV-Plat platform and benchmark enable evaluation of LLM agents for multi-UAV collaborative task planning, with Agent4Drone reaching 57.9 percent task pass rate.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a simulation platform designed for LLM agents to plan collaborative tasks for multiple UAVs. It includes a benchmark consisting of 75 mission sessions and 1500 natural-language tasks with thousands of validation checks. The authors introduce the Agent4Drone framework that organizes agent behavior into specific stages and show it outperforms a standard ReAct baseline by roughly doubling the success rates on the benchmark tasks. This matters because existing tools do not adequately test the constraints of aerial robotics like limited visibility and the need for coordination among vehicles. A reliable platform would let researchers measure and improve how well language models can direct groups of drones on complex missions.

Core claim

MultiUAV-Plat is a lightweight, LLM-agent-oriented simulation platform that uses concise RESTful APIs, agent-facing observations, role-based information access, and hidden validation logic to let agents interact with multi-UAV missions realistically. The associated benchmark covers target assignment, area search, and area assignment and patrol. Agent4Drone structures behavior into six stages and achieves 57.9% task pass rate, 74.6% average task check pass rate, and 72.0% global check pass rate, compared to 30.6%, 47.9%, and 43.1% for ReAct, while cutting failed tasks from 32.4% to 12.9%.

What carries the argument

Agent4Drone framework, which structures multi-UAV behavior into memory, observation, task understanding, planning, execution, and verification stages.

If this is right

  • The benchmark allows direct comparison of LLM agents on tasks that require spatial coverage and multi-vehicle coordination.
  • Agent4Drone's staged approach leads to higher pass rates on the 1500 tasks.
  • The platform supports reproducible studies of LLM-driven multi-UAV autonomy.
  • Validation checks provide detailed feedback on assignment, coverage, and coordination failures.
  • Optional visualization aids in understanding agent decisions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The platform could be extended to include more dynamic elements like moving targets to test real-time replanning.
  • Success in simulation may translate to field tests if the modeled constraints match physical UAV limitations.
  • Other multi-agent domains such as ground robots could adopt similar benchmark designs.
  • The reduction in failed tasks suggests potential for more reliable LLM planning in safety-critical applications.

Load-bearing premise

The simulation platform and its validation checks accurately capture the real-world constraints of multi-UAV operations such as partial observability, spatial coverage, assignment, and coordination.

What would settle it

Deploying Agent4Drone on real UAV hardware for the same mission types and measuring if the task pass rate stays near 57.9 percent or drops significantly due to unmodeled factors.

Figures

Figures reproduced from arXiv: 2606.31073 by Cheng Zhu, Qinglin Li, Sheng Zhang, Xueqin Huang, Yijia Fu, Yuechao Zang.

Figure 1
Figure 1. Figure 1: MultiUAV-Plat platform views for UAV task planning. The 2D and 3D views represent the same underlying mission state. The 2D view is designed for quick overview, scenario inspection, and task-level spatial reasoning, while the 3D view is designed for intuitive visualization of UAV execution and environment geometry. low-level system integration while preserving closed-loop action control, MultiUAV-Plat enab… view at source ↗
Figure 2
Figure 2. Figure 2: Methodology overview. MultiUAV-Plat provides the interaction substrate, the MultiUAV-Plat Benchmark supplies standardized tasks and automatic validation, and the proposed MultiUAV-Agent Workflow, named Agent4Drone, performs closed￾loop multi-UAV task planning under agent-level partial observability. task allocation, dependency modeling, heterogeneous col￾laboration, and end-to-end execution, including the … view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of hidden validation-check counts [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Framework structure of Agent4Drone. The MultiUAV-Agent Workflow converts mission input and observable context into closed-loop observation, task understanding, planning, tool-chain grounding, execution, and verification. Memory stores local observations, action history, intermediate results, and feedback, while decision artifacts organize subtasks, UAV assignments, bounded tool/action chains, and stopping … view at source ↗
Figure 5
Figure 5. Figure 5: Paired task-level transition matrices between Re [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Scenario-family task pass rates across completed [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
read the original abstract

Large language models (LLMs) provide a promising interface for high-level robotic task planning, but their use in multi-UAV collaboration remains difficult to evaluate systematically. Existing UAV simulators mainly emphasize dynamics, perception, or low-level control, while existing LLM-agent benchmarks rarely capture aerial-robotics constraints such as partial observability, spatial coverage, UAV assignment, and multi-vehicle coordination. To bridge this gap, we present MultiUAV-Plat, a lightweight, easy-to-use, LLM-agent-oriented simulation platform for multi-UAV collaborative task planning. The platform exposes concise RESTful APIs, agent-facing observations, role-based information access, hidden validation logic, and optional 2D/3D visualization, allowing agents to solve missions through realistic tool interaction rather than privileged simulator access. Built on this platform, the MultiUAV-Plat Benchmark contains 75 mission sessions, 1500 natural-language tasks, and 9396 validation checks across target assignment, area search, and area assignment and patrol scenarios. We further propose Agent4Drone, a task-specific LLM agent framework that structures multi-UAV behavior into memory, observation, task understanding, planning, execution, and verification. In a full paired benchmark comparison, Agent4Drone achieves a 57.9% task pass rate, a 74.6% average task check pass rate, and a 72.0% global check pass rate, substantially outperforming a ReAct baseline at 30.6%, 47.9%, and 43.1%, respectively. Agent4Drone also reduces the total failed task rate from 32.4% to 12.9%. These results demonstrate that MultiUAV-Plat and MultiUAV-Plat Benchmark provide a reproducible foundation for studying LLM-driven multi-UAV autonomy under realistic information and execution constraints.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces MultiUAV-Plat, a lightweight LLM-agent-oriented simulation platform for multi-UAV collaborative task planning that provides RESTful APIs, role-based partial observations, hidden validation logic, and optional visualization. It presents the MultiUAV-Plat Benchmark containing 75 mission sessions, 1500 natural-language tasks, and 9396 validation checks across target assignment, area search, and area assignment/patrol scenarios. The paper also proposes Agent4Drone, a structured LLM agent framework (memory-observation-task understanding-planning-execution-verification), and reports that it achieves 57.9% task pass rate, 74.6% average task check pass rate, and 72.0% global check pass rate, outperforming a ReAct baseline (30.6%, 47.9%, 43.1%) while reducing total failed task rate from 32.4% to 12.9%.

Significance. If the simulator's hidden checks accurately reflect real multi-UAV constraints, the work supplies a reproducible, agent-facing benchmark that bridges dynamics-focused UAV simulators and generic LLM-agent evaluations, enabling systematic study of coordination under partial observability. The explicit paired comparison and failure-rate reduction provide concrete evidence that structured agent loops can improve task completion within the platform.

major comments (2)
  1. [Experiments section] Experiments section: The comparative claims rest entirely on results inside MultiUAV-Plat; the manuscript reports no physical UAV flights, sensor-noise injection, packet-loss modeling, or cross-validation against real flight logs to establish that the hidden validation checks (target assignment, spatial coverage, coordination) match actual operational constraints.
  2. [Platform and Benchmark sections] Platform and Benchmark sections: The description states that validation logic is hidden and observations are role-based, yet no details are given on how the 9396 checks were designed, enumerated, or independently audited for completeness in capturing partial observability and multi-vehicle assignment limits.
minor comments (1)
  1. [Abstract and §3] Abstract and §3: The claim of 'realistic tool interaction' would be strengthened by an explicit statement of which UAV dynamics (e.g., wind, communication latency) are omitted from the simulator.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful review and valuable comments. We provide point-by-point responses to the major comments below.

read point-by-point responses

  1. Referee: [Experiments section] Experiments section: The comparative claims rest entirely on results inside MultiUAV-Plat; the manuscript reports no physical UAV flights, sensor-noise injection, packet-loss modeling, or cross-validation against real flight logs to establish that the hidden validation checks (target assignment, spatial coverage, coordination) match actual operational constraints.

    Authors: The primary contribution of this work is a simulation platform and benchmark designed to enable systematic evaluation of LLM agents for multi-UAV task planning in a controlled, reproducible environment. The hidden validation checks are constructed to reflect key operational constraints including partial observability, spatial requirements, and coordination limits, drawing from established multi-UAV mission principles. We agree that physical validation would provide additional confidence; however, the manuscript does not claim equivalence to real-world dynamics and instead focuses on the agent-facing simulation interface. Introducing sensor noise or packet loss would require extending the platform beyond its current lightweight design, which is outside the scope of the present study. revision: no

  2. Referee: [Platform and Benchmark sections] Platform and Benchmark sections: The description states that validation logic is hidden and observations are role-based, yet no details are given on how the 9396 checks were designed, enumerated, or independently audited for completeness in capturing partial observability and multi-vehicle assignment limits.

    Authors: We acknowledge that additional details on the validation check design would improve transparency. In the revised version of the manuscript, we will include a new subsection in the Benchmark section that describes the process of designing and enumerating the 9396 checks. This will cover how checks were derived from the three scenario types (target assignment, area search, area assignment/patrol), the enumeration criteria to ensure coverage of partial observability and assignment constraints, and the internal review process conducted by the authors to verify completeness. Examples of representative checks will also be provided. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark results independent of self-defined quantities

full rationale

The paper presents a new simulation platform, benchmark with 75 missions and 9396 validation checks, and an LLM agent framework. Reported metrics (57.9% task pass rate etc.) are direct measurements on this benchmark against a ReAct baseline. No equations, fitted parameters, or derivation steps exist that reduce any claim to its own inputs by construction. No self-citation chains or ansatzes are invoked as load-bearing. The evaluation is self-contained against the explicitly defined benchmark tasks and checks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central contribution consists of new software artifacts and empirical results on a custom benchmark; no numerical free parameters are fitted because this is not a modeling or equation-fitting paper.

axioms (1)
  • domain assumption LLMs can be used for high-level robotic task planning through tool interaction
    Implicit foundation for the entire LLM-agent approach described in the abstract.
invented entities (2)
  • MultiUAV-Plat no independent evidence
    purpose: Lightweight simulation platform exposing RESTful APIs, role-based observations, and hidden validation for multi-UAV LLM agents
    Newly introduced platform whose features are listed in the abstract.
  • Agent4Drone no independent evidence
    purpose: Task-specific LLM agent framework that structures behavior into memory, observation, task understanding, planning, execution, and verification
    Newly proposed framework whose components are enumerated in the abstract.

pith-pipeline@v0.9.1-grok · 5888 in / 1508 out tokens · 37198 ms · 2026-07-01T06:07:01.952644+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

38 extracted references · 24 canonical work pages · 8 internal anchors

  1. [1]

    Do as i can, not as i say: Grounding language in robotic affordances

    Ahn, M., Brohan,A., Brown, N., Chebotar, Y.,Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Ho, D., Hsu, J., Ibarz, J., Ichter, B., Irpan, A., Jang, E., Jauregui Ruano, R., Jeffrey, K., Jesmonth, S., Joshi, N.J., Julian, R., Kalashnikov, D., Kuang, Y., Lee, K.H., Levine, S., Lu, Y., Luu, L., Parada, C., Pastor, P., Qu...

    Pith/arXiv arXiv 2022

  2. [2]

    17682–17690

    Besta,M.,Blach,N.,Kubicek,A.,Gerstenberger,R.,Podstawski,M., Gianinazzi,L.,Gajda,J.,Lehmann,T.,Niewiadomski,H.,Nyczyk,P., Hoefler,T.,2024.Graphofthoughts:Solvingelaborateproblemswith large language models, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 17682–17690. URL:https://arxiv.org/ abs/2308.09687, doi:10.1609/aaai.v38i16.29720

    work page doi:10.1609/aaai.v38i16.29720 2024

  3. [3]

    A survey on open-source simulation platforms for multi-copter uav swarms

    Chen, Z., Yan, J., Ma, B., Shi, K., Yu, Q., Yuan, W., 2023. A survey on open-source simulation platforms for multi-copter uav swarms. Robotics 12, 53. doi:10.3390/robotics12020053

    work page doi:10.3390/robotics12020053 2023

  4. [4]

    Dai, S., Ma, Z., Luo, Z., Yang, X., Huang, Y., Zhang, W., Chen, C., Guo, Z., Xu, W., Sun, Y., Sun, M., 2025. Mm-uavbench: How well do multimodal large language models see, think, and plan in low-altitude uav scenarios? arXiv preprint arXiv:2512.23219 URL:https://arxiv.org/abs/2512.23219, doi:10.48550/arXiv.2512. 23219. arXiv preprint, posted 29 December 2025

    work page doi:10.48550/arxiv.2512 2025

  5. [6]

    arXiv preprint arXiv:2601.03281 URL:https://arxiv.org/abs/2601.03281, doi:10.48550/arXiv.2601

    Ferrag, M.A., Lakas, A., Debbah, M., 2026.𝛼 3-bench: A uni- fied benchmark of safety, robustness, and efficiency for llm-based uav agents over 6g networks. arXiv preprint arXiv:2601.03281 URL:https://arxiv.org/abs/2601.03281, doi:10.48550/arXiv.2601. 03281. arXiv preprint, posted 1 January 2026

    work page doi:10.48550/arxiv.2601 2026

  6. [7]

    Say the Mission, Execute the Swarm: Agent-Enhanced LLM Reasoning in the Web-of-Drones

    Iannoli, A., Gigli, L., Sciullo, L., Trotta, A., Di Felice, M., 2026. Saythemission,executetheswarm:Agent-enhancedllmreasoningin the web-of-drones. arXiv preprint arXiv:2605.03788 URL:https:// arxiv.org/abs/2605.03788, doi:10.48550/arXiv.2605.03788. accepted forpresentationatthe27thIEEEInternationalSymposiumonaWorld of Wireless, Mobile and Multimedia Netw...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2605.03788 2026

  7. [8]

    Smart-llm:Smart multi-agent robot task planning using large language models

    Kannan,S.S.,Venkatesh,V.L.N.,Min,B.C.,2023. Smart-llm:Smart multi-agent robot task planning using large language models. arXiv preprint arXiv:2309.10062 URL:https://arxiv.org/abs/2309.10062, doi:10.48550/arXiv.2309.10062

    work page doi:10.48550/arxiv.2309.10062 2023

  8. [9]

    Multi-uav simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments

    Kong, X., Zhou, Y., Li, Z., Wang, S., 2024. Multi-uav simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments. Frontiers in Neurorobotics 17, 1302898. doi:10.3389/fnbot.2023.1302898

    work page doi:10.3389/fnbot.2023.1302898 2024

  9. [10]

    Large Language Models for Multi-Robot Systems: A Survey

    Li, P., An, Z., Abrar, S., Zhou, L., 2025. Large language models for multi-robot systems: A survey. arXiv preprint arXiv:2502.03814 URL:https://arxiv.org/abs/2502.03814, doi:10.48550/arXiv.2502. 03814

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2502 2025

  10. [11]

    Code as Policies: Language Model Programs for Embodied Control

    Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Flo- rence,P.,Zeng,A.,2022.Codeaspolicies:Languagemodelprograms for embodiedcontrol. arXiv preprintarXiv:2209.07753 URL:https: //arxiv.org/abs/2209.07753, doi:10.48550/arXiv.2209.07753

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2209.07753 2022

  11. [12]

    Liu, K., Tang, Z., Wang, D., Wang, Z., Li, X., Zhao, B., 2024. Coherent: Collaboration of heterogeneous multi-robot system with largelanguagemodels.arXivpreprintarXiv:2409.15146URL:https: //arxiv.org/abs/2409.15146, doi:10.48550/arXiv.2409.15146

    work page doi:10.48550/arxiv.2409.15146 2024

  12. [13]

    AgentBench: Evaluating LLMs as Agents

    Liu, X., Yu, H., Zhang, H., Xu, Y., Lei, X., Lai, H., Gu, Y., Ding, H., Men, K., Yang, K., Zhang, S., Deng, X., Zeng, A., Du, Z., Zhang, C., Shen, S., Zhang, T., Su, Y., Sun, H., Huang, M., Dong, Y., Tang, J., 2023. Agentbench: Evaluating llms as agents. arXiv preprint arXiv:2308.03688 URL:https://arxiv.org/abs/2308.03688, doi:10.48550/arXiv.2308.03688

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2308.03688 2023

  13. [14]

    arXiv preprint arXiv:2406.00006 URL:https://arxiv.org/abs/2406.00006, doi:10.48550/arXiv.2406

    Liu,Y.,2024.Aprompt-driventaskplanningmethodformulti-drones based on large language model. arXiv preprint arXiv:2406.00006 URL:https://arxiv.org/abs/2406.00006, doi:10.48550/arXiv.2406. 00006

    work page doi:10.48550/arxiv.2406 2024

  14. [15]

    Roco: Dialectic multi- robot collaboration with large language models

    Mandi, Z., Jain, S., Song, S., 2023. Roco: Dialectic multi- robot collaboration with large language models. arXiv preprint arXiv:2307.04738 URL:https://arxiv.org/abs/2307.04738, doi:10. 48550/arXiv.2307.04738

    arXiv 2023

  15. [16]

    Llamar: Long-horizon planning for multi-agent robots in partially observable environments

    Nayak, S., Orozco, A.M., Ten Have, M., Thirumalai, V., Zhang, J., Chen, D., Kapoor, A., Robinson, E., Gopalakrishnan, K., Harri- son, J., Ichter, B., Mahajan, A., Balakrishnan, H., 2024. Llamar: Long-horizon planning for multi-agent robots in partially observable environments. arXiv preprint arXiv:2407.10031 URL:https:// arxiv.org/abs/2407.10031,doi:10.48...

    work page doi:10.48550/arxiv.2407.10031.introduces 2024

  16. [17]

    Tacos: Task agnostic coordinator of a multi-drone system

    Nazzari, A., Rubinacci, R., Lovera, M., 2026. Tacos: Task agnostic coordinator of a multi-drone system. Drones 10, 251. URL:https: //www.mdpi.com/2504-446X/10/4/251, doi:10.3390/drones10040251

    work page doi:10.3390/drones10040251 2026

  17. [18]

    Lip- llm: Integrating linear programming and dependency graph with large language models for multi-robot task planning

    Obata, K., Aoki, T., Horii, T., Taniguchi, T., Nagai, T., 2024. Lip- llm: Integrating linear programming and dependency graph with large language models for multi-robot task planning. arXiv preprint arXiv:2410.21040 URL:https://arxiv.org/abs/2410.21040, doi:10. 48550/arXiv.2410.21040

    arXiv 2024

  18. [19]

    Panerati, J., Zheng, H., Zhou, S., Xu, J., Prorok, A., Schoellig, A.P.,

  19. [20]

    URL:https://arxiv.org/abs/2103

    Learning to fly–a gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control, in: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS),pp.7512–7519. URL:https://arxiv.org/abs/2103. 02142, doi:10.1109/IROS51168.2021.9635857

    work page doi:10.1109/iros51168.2021.9635857 2021

  20. [21]

    A survey on multi-uav path planning: Classification, algorithms, open research problems, and future directions

    Rahman, M., Sarkar, N.I., Lutui, R., 2025. A survey on multi-uav path planning: Classification, algorithms, open research problems, and future directions. Drones 9, 263. doi:10.3390/drones9040263

    work page doi:10.3390/drones9040263 2025

  21. [22]

    Uav-codeagents: Scalable uav mission planning via multi-agent react and vision-language reason- ing

    Sautenkov,O.,Yaqoot,Y.,Mustafa,M.A.,Batool,F.,Sam,J.,Lykov, A., Wen, C.Y., Tsetserukou, D., 2025. Uav-codeagents: Scalable uav mission planning via multi-agent react and vision-language reason- ing. arXiv preprint arXiv:2505.07236 URL:https://arxiv.org/abs/ 2505.07236, doi:10.48550/arXiv.2505.07236

    work page doi:10.48550/arxiv.2505.07236 2025

  22. [23]

    AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

    Shah, S., Dey, D., Lovett, C., Kapoor, A., 2017. Airsim: High- fidelity visual and physical simulation for autonomous vehicles, in: Field and Service Robotics, Springer. pp. 621–635. URL:https: //arxiv.org/abs/1705.05065, doi:10.1007/978-3-319-67361-5_40

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/978-3-319-67361-5_40 2017

  23. [24]

    Alfworld: Aligning text and embodied environments for interactive learning, in: Proceedings of the 9th International Conference on Learning Representations

    Shridhar, M., Yuan, X., Cote, M.A., Bisk, Y., Trischler, A., Hausknecht, M., 2021. Alfworld: Aligning text and embodied environments for interactive learning, in: Proceedings of the 9th International Conference on Learning Representations. URL:https: //openreview.net/forum?id=0IOX0YcCdTn

    2021

  24. [25]

    Survey on mission planning of multiple unmanned aerial vehicles

    Song, J., Zhao, K., Liu, Y., 2023. Survey on mission planning of multiple unmanned aerial vehicles. Aerospace 10, 208. doi:10.3390/ Zhang et al.:Preprint submitted to arXivPage 12 of 13 MultiUAV-Plat aerospace10030208

    2023

  25. [26]

    Song, Y., Naji, S., Kaufmann, E., Loquercio, A., Scaramuzza, D.,

  26. [27]

    URL:https://arxiv.org/abs/ 2009.00563

    Flightmare: A flexible quadrotor simulator, in: Proceedings of the4thConferenceonRobotLearning. URL:https://arxiv.org/abs/ 2009.00563

    arXiv 2009

  27. [28]

    Multi-agent pathfinding: Definitions, variants, and benchmarks, in: Proceedings of the International Symposium on Combinatorial Search, pp

    Stern, R., Sturtevant, N.R., Felner, A., Koenig, S., Ma, H., Walker, T.K., Li, J., Atzmon, D., Cohen, L., Kumar, T.K.S., Boyarski, E., Bartak, R., 2019. Multi-agent pathfinding: Definitions, variants, and benchmarks, in: Proceedings of the International Symposium on Combinatorial Search, pp. 151–158. URL:https://movingai.com/ benchmarks/mapf.html

    2019

  28. [29]

    Benchmarks for grid-based pathfinding

    Sturtevant, N.R., 2012. Benchmarks for grid-based pathfinding. IEEETransactionsonComputationalIntelligenceandAIinGames4, 144–148. URL:https://www.movingai.com/benchmarks/, doi:10.1109/ TCIAIG.2012.2197681

    arXiv 2012

  29. [30]

    Sun,L.,Jha,D.K.,Hori,C.,Jain,S.,Corcodel,R.,Zhu,X.,Tomizuka, M., Romeres, D., 2024. Interactive planning using large language modelsforpartiallyobservablerobotictasks.2024IEEEInternational Conference on Robotics and Automation (ICRA) URL:https:// arxiv.org/abs/2312.06876, doi:10.1109/ICRA57147.2024.10610981

    work page doi:10.1109/icra57147.2024.10610981 2024

  30. [31]

    Dart-llm:Dependency-awaremulti- robottaskdecompositionandexecutionusinglargelanguagemodels

    Wang, Y., Xiao, R., Kasahara, J.Y.L., Yajima, R., Nagatani, K., Yamashita,A.,Asama,H.,2024. Dart-llm:Dependency-awaremulti- robottaskdecompositionandexecutionusinglargelanguagemodels. arXiv preprint arXiv:2411.09022 URL:https://arxiv.org/abs/2411. 09022, doi:10.48550/arXiv.2411.09022

    work page doi:10.48550/arxiv.2411.09022 2024

  31. [32]

    Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

    Wei,J.,Wang,X.,Schuurmans,D.,Bosma,M.,Ichter,B.,Xia,F.,Chi, E.,Le,Q.,Zhou,D.,2022.Chain-of-thoughtpromptingelicitsreason- inginlargelanguagemodels. arXivpreprintarXiv:2201.11903URL: https://arxiv.org/abs/2201.11903, doi:10.48550/arXiv.2201.11903

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2201.11903 2022

  32. [33]

    EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents

    Yang, R., Chen, H., Zhang, J., Zhao, M., Qian, C., Wang, K., Wang, Q., Koripella, T.V., Movahedi, M., Li, M., Ji, H., Zhang, H., Zhang, T., 2025. Embodiedbench: Comprehensive benchmark- ing multi-modal large language models for vision-driven embod- ied agents. arXiv preprint arXiv:2502.09560 URL:https://arxiv. org/abs/2502.09560,doi:10.48550/arXiv.2502.09...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2502.09560.codeanddataset: 2025

  33. [34]

    20744– 20757

    Yao,S.,Chen,H.,Yang,J.,Narasimhan,K.,2022.Webshop:Towards scalable real-world web interaction with grounded language agents, in: Advances in Neural Information Processing Systems, pp. 20744– 20757. URL:https://arxiv.org/abs/2207.01206

    arXiv 2022

  34. [35]

    Tree of Thoughts: Deliberate Problem Solving with Large Language Models

    Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T.L., Cao, Y., Narasimhan,K.,2023a. Treeofthoughts:Deliberateproblemsolving with large language models. arXiv preprint arXiv:2305.10601 URL: https://arxiv.org/abs/2305.10601, doi:10.48550/arXiv.2305.10601

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2305.10601

  35. [36]

    React: Synergizing reasoning and acting in language models, in: Proceedings of the 11th International Conference on Learning Representations

    Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y., 2023b. React: Synergizing reasoning and acting in language models, in: Proceedings of the 11th International Conference on Learning Representations. URL:https://openreview.net/forum?id= WE_vluYUL-X

  36. [37]

    Safeagentbench: A benchmark for safe task planning of embodied llm agents

    Yin, S., Pang, X., Ding, Y., Chen, M., Bi, Y., Xiong, Y., Huang, W., Xiang, Z., Shao, J., Chen, S., 2024. Safeagentbench: A benchmark for safe task planning of embodied llm agents. arXiv preprint arXiv:2412.13178 URL:https://arxiv.org/abs/2412.13178, doi:10. 48550/arXiv.2412.13178

    arXiv 2024

  37. [38]

    Zha, J., Fan, Y., Zhang, T., Chen, G., Chen, Y., Gao, C., Chen, X.,

  38. [39]

    arXiv preprint arXiv:2511.11025 URL:https://arxiv.org/abs/2511.11025, doi:10.48550/arXiv.2511

    Aircopbench: A benchmark for multi-drone collaborative em- bodied perception and reasoning. arXiv preprint arXiv:2511.11025 URL:https://arxiv.org/abs/2511.11025, doi:10.48550/arXiv.2511. 11025. preprint metadata used because title and publication metadata differed from the AAAI page at audit time. Zhang et al.:Preprint submitted to arXivPage 13 of 13

    work page doi:10.48550/arxiv.2511