pith. sign in

arxiv: 2601.12358 · v1 · pith:6XXPKVF4new · submitted 2026-01-18 · 💻 cs.CV · cs.AI· cs.RO

From Prompts to Pavement: LMMs-based Agentic Behavior-Tree Generation Framework for Autonomous Vehicles

Omar Y. Goba , Ahmed Y. Gado , Catherine M. Elias , Ahmed Hussein This is my paper

Pith reviewed 2026-05-21 15:22 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.RO
keywords behavior treesautonomous vehicleslarge multimodal modelsagentic frameworkCARLA simulationadaptive planningXML generationon-the-fly adaptation
0
0 comments X

The pith

Large multimodal models can generate executable behavior tree sub-trees on demand to recover from failures in autonomous vehicle navigation.

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

The paper presents an agentic framework that deploys three specialized agents powered by large multimodal models to assess driving scenes, plan sub-goals, and synthesize new behavior tree sub-trees in XML format. The system activates only when a static baseline behavior tree encounters an unexpected obstacle such as a street blockage. In CARLA plus Nav2 simulations it enables the vehicle to continue navigation without any human intervention. This matters to a sympathetic reader because it suggests a route to make traditionally rigid decision logic in autonomous vehicles more responsive to real-world changes using existing models rather than constant manual redesign.

Core claim

An agentic setup with a Descriptor agent using chain-of-symbols prompting to judge scene criticality, a Planner agent using in-context learning to set high-level sub-goals, and a Generator agent that outputs executable BT sub-trees in XML allows off-the-shelf LMMs to produce adaptive behavior logic that succeeds in CARLA+Nav2 simulations precisely where a static baseline BT fails, such as navigating around street blockages with no human input.

What carries the argument

Three-agent pipeline (Descriptor for criticality via chain-of-symbols, Planner for sub-goals via in-context learning, Generator for XML sub-trees) that creates on-the-fly BT adaptations only on baseline failure.

If this is right

  • The vehicle recovers from baseline BT failures and navigates around unexpected obstacles like street blockages without human intervention.
  • The method functions as a proof-of-concept that can extend to other driving scenarios beyond the tested blockage case.
  • It lowers dependence on labor-intensive upfront manual tuning of static behavior trees.
  • It supports movement toward SAE Level 5 autonomy by supplying more adaptive decision logic in unpredictable settings.

Where Pith is reading between the lines

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

  • The same three-agent structure could be tested on physical vehicle hardware to check whether simulation success translates to real roads and sensors.
  • Adding more sensor modalities or longer context windows to the Descriptor agent might improve detection of subtle criticality changes.
  • The generated sub-trees could be archived and reused across similar scenarios to reduce repeated calls to the LMM agents.

Load-bearing premise

Off-the-shelf large multimodal models can reliably output safe, correct, and executable behavior tree sub-trees in XML without creating unsafe maneuvers or new failures.

What would settle it

Execute one of the generated XML behavior tree sub-trees inside the CARLA+Nav2 simulation on a street-blockage scenario and check whether the vehicle collides or becomes stuck instead of completing the navigation.

Figures

Figures reproduced from arXiv: 2601.12358 by Ahmed Hussein, Ahmed Y. Gado, Catherine M. Elias, Omar Y. Goba.

Figure 1
Figure 1. Figure 1: The Overall ROS-Based Autonomous Driving Stack with the Agentic Behavior-Tree Generation Framework [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Vehicle’s Initial Position and Initial Scene Description [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The Behavior Trees (a) Baseline, (b) Prompt to Pavement Framework Generated that it will always function correctly. The pipeline, when faced with a failure in the generated BT will prompt the human operator to intervene. REFERENCES [1] SAE International, “Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (j3016),” https://www.sae.org/standards/content/j3016… view at source ↗
read the original abstract

Autonomous vehicles (AVs) require adaptive behavior planners to navigate unpredictable, real-world environments safely. Traditional behavior trees (BTs) offer structured decision logic but are inherently static and demand labor-intensive manual tuning, limiting their applicability at SAE Level 5 autonomy. This paper presents an agentic framework that leverages large language models (LLMs) and multi-modal vision models (LVMs) to generate and adapt BTs on the fly. A specialized Descriptor agent applies chain-of-symbols prompting to assess scene criticality, a Planner agent constructs high-level sub-goals via in-context learning, and a Generator agent synthesizes executable BT sub-trees in XML format. Integrated into a CARLA+Nav2 simulation, our system triggers only upon baseline BT failure, demonstrating successful navigation around unexpected obstacles (e.g., street blockage) with no human intervention. Compared to a static BT baseline, this approach is a proof-of-concept that extends to diverse driving scenarios.

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 / 2 minor

Summary. The manuscript presents an agentic framework that uses large multimodal models (LMMs) to dynamically generate and adapt behavior trees (BTs) for autonomous vehicle navigation. It describes a Descriptor agent applying chain-of-symbols prompting for scene criticality assessment, a Planner agent constructing sub-goals via in-context learning, and a Generator agent synthesizing executable BT sub-trees in XML. The system is integrated into a CARLA+Nav2 simulation, triggers only on baseline BT failure, and claims to enable successful navigation around unexpected obstacles such as street blockages with no human intervention, as a proof-of-concept extending to diverse scenarios.

Significance. If the central claims hold under rigorous testing, the work could have moderate significance for adaptive planning in autonomous vehicles by reducing reliance on static, manually tuned BTs and enabling on-the-fly adaptation using off-the-shelf LMMs. The simulation-based demonstration of handling unexpected obstacles without intervention points to a practical direction for SAE Level 5 autonomy, though its broader impact hinges on establishing reliability and safety guarantees.

major comments (2)
  1. [Simulation Results / Demonstration] The headline demonstration in the CARLA+Nav2 integration (successful navigation around street blockage with no human intervention) rests on a single qualitative scenario. No quantitative metrics, success rates across multiple trials, failure cases, or rigorous baseline comparisons are reported, which is load-bearing for the claim that the framework extends to diverse driving scenarios.
  2. [Agent Framework Description] The weakest assumption—that the Descriptor, Planner, and Generator agents powered by off-the-shelf LMMs reliably produce syntactically valid, semantically safe, and executable BT sub-trees in XML without introducing unsafe behaviors or integration failures with Nav2—is not supported by any validation step, error-rate statistics, or failure-mode analysis in the manuscript.
minor comments (2)
  1. [Terminology] Clarify the distinction between LLMs and LVMs versus the title's use of 'LMMs' for consistency throughout the text and abstract.
  2. [Related Work] The related work section would benefit from additional citations to prior BT applications in AVs and LLM-based planning frameworks to better position the novelty.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our proof-of-concept manuscript. The comments highlight important areas where additional rigor would strengthen the presentation of the agentic BT generation framework. We address each major comment below and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [Simulation Results / Demonstration] The headline demonstration in the CARLA+Nav2 integration (successful navigation around street blockage with no human intervention) rests on a single qualitative scenario. No quantitative metrics, success rates across multiple trials, failure cases, or rigorous baseline comparisons are reported, which is load-bearing for the claim that the framework extends to diverse driving scenarios.

    Authors: We agree that the current evaluation is limited to a single qualitative scenario and that this constrains the strength of claims about extension to diverse scenarios. The work is explicitly positioned as a proof-of-concept to illustrate on-the-fly adaptation when static BTs fail. In the revised manuscript we will expand the experimental section to report results from multiple randomized trials, including quantitative metrics such as success rate, average navigation time, and collision avoidance statistics, along with explicit failure cases and comparisons against the static BT baseline and at least one additional adaptive planner. revision: yes

  2. Referee: [Agent Framework Description] The weakest assumption—that the Descriptor, Planner, and Generator agents powered by off-the-shelf LMMs reliably produce syntactically valid, semantically safe, and executable BT sub-trees in XML without introducing unsafe behaviors or integration failures with Nav2—is not supported by any validation step, error-rate statistics, or failure-mode analysis in the manuscript.

    Authors: We acknowledge that the manuscript does not currently provide explicit validation, error-rate statistics, or failure-mode analysis for the outputs of the three agents. In revision we will add a new subsection that documents the post-generation validation pipeline (XML syntax validation, semantic safety heuristics for collision-free sub-trees, and Nav2 integration checks), reports observed error rates across the evaluated scenarios, and discusses the most common failure modes together with the mitigation steps used in the framework. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical framework demonstration

full rationale

The paper describes an agentic framework that uses off-the-shelf LMMs to generate BT sub-trees on demand, with results shown as direct outcomes from a single CARLA+Nav2 simulation scenario. No mathematical derivation, equations, fitted parameters, or first-principles predictions are present that could reduce to the inputs by construction. The work is a proof-of-concept empirical demonstration whose claims rest on observed simulation behavior rather than any self-referential or tautological chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract; the framework rests on the unverified assumption that current multimodal models can interpret driving scenes and output valid executable trees, with no independent evidence or robustness checks supplied.

axioms (1)
  • domain assumption Large multimodal models can accurately assess scene criticality and synthesize safe executable behavior-tree sub-trees via chain-of-symbols and in-context prompting.
    This premise is required for the Descriptor, Planner, and Generator agents to function as described.

pith-pipeline@v0.9.0 · 5712 in / 1278 out tokens · 113927 ms · 2026-05-21T15:22:07.658588+00:00 · methodology

discussion (0)

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

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning

    cs.AI 2026-05 unverdicted novelty 5.0

    Temporal conditioning in three LLM-based planner architectures for AV scene-to-plan reasoning yields no statistically significant gains on NLP correctness metrics but enables predictive hazard reasoning and stable cor...

Reference graph

Works this paper leans on

28 extracted references · 28 canonical work pages · cited by 1 Pith paper · 4 internal anchors

  1. [1]

    Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (j3016),

    SAE International, “Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (j3016),” https://www.sae.org/standards/content/j3016 202104/, 2021, accessed: 2025-04-29

    work page 2021

  2. [2]

    Behavior trees in functional safety supervisors for autonomous vehicles,

    C. Conejo, V . Puig, B. Morcego, F. Navas, and V . Milan´es, “Behavior trees in functional safety supervisors for autonomous vehicles,” 2024. [Online]. Available: https://arxiv.org/abs/2410.02469

    work page arXiv 2024

  3. [3]

    A decision-making algorithm of multiple reactive tasks for autonomous driving sweepers based on behavior trees,

    H.-Y . Kang, J.-G. Lu, K.-X. Li, Q.-H. Zhang, and Y . Wang, “A decision-making algorithm of multiple reactive tasks for autonomous driving sweepers based on behavior trees,”IEEE/ASME Transactions on Mechatronics, pp. 1–11, 2025

    work page 2025

  4. [4]

    Behavior- tree based scenario specification and test case generation for au- tonomous driving simulation,

    S. Kang, H. Hao, Q. Dong, L. Meng, Y . Xue, and Y . Wu, “Behavior- tree based scenario specification and test case generation for au- tonomous driving simulation,” in2022 2nd International Conference on Intelligent Technology and Embedded Systems (ICITES), 2022, pp. 125–131

    work page 2022

  5. [5]

    Integrating intent understanding and optimal behavior planning for behavior tree generation from human instructions,

    X. Chen, Y . Cai, Y . Mao, M. Li, W. Yang, W. Xu, and J. Wang, “Integrating intent understanding and optimal behavior planning for behavior tree generation from human instructions,” 2024. [Online]. Available: https://arxiv.org/abs/2405.07474

    work page arXiv 2024

  6. [6]

    Com- bining planning and learning of behavior trees for robotic assembly,

    J. Styrud, M. Iovino, M. Norrl ¨of, M. Bj ¨orkman, and C. Smith, “Com- bining planning and learning of behavior trees for robotic assembly,” in2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 11 511–11 517

    work page 2022

  7. [7]

    Towards blended reac- tive planning and acting using behavior trees,

    M. Colledanchise, D. Almeida, and P. ¨Ogren, “Towards blended reac- tive planning and acting using behavior trees,” in2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 8839– 8845

    work page 2019

  8. [8]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017

    work page 2017

  9. [9]

    Chain-of-thought prompting elicits reasoning in large language models,

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V . Le, D. Zhouet al., “Chain-of-thought prompting elicits reasoning in large language models,”Advances in neural information processing systems, vol. 35, pp. 24 824–24 837, 2022

    work page 2022

  10. [10]

    Language Models are Few-Shot Learners

    T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-V oss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amo...

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  11. [11]

    Large Language Models are Zero-Shot Reasoners

    T. Kojima, S. S. Gu, M. Reid, Y . Matsuo, and Y . Iwasawa, “Large language models are zero-shot reasoners,” 2023. [Online]. Available: https://arxiv.org/abs/2205.11916

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  12. [12]

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gellyet al., “An image is worth 16x16 words: Transformers for image recognition at scale,”arXiv preprint arXiv:2010.11929, 2020

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  13. [13]

    Llm-as-bt-planner: Leveraging llms for behavior tree generation in robot task planning,

    J. Ao, F. Wu, Y . Wu, A. Swikir, and S. Haddadin, “Llm-as-bt-planner: Leveraging llms for behavior tree generation in robot task planning,”

    work page

  14. [14]

    Llm as bt- planner: Leveraging llms for behavior tree generation in robot task planning,

    [Online]. Available: https://arxiv.org/abs/2409.10444

    work page arXiv

  15. [15]

    Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents

    Z. Wang, S. Cai, G. Chen, A. Liu, X. Ma, and Y . Liang, “Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents,” 2024. [Online]. Available: https://arxiv.org/abs/2302.01560

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  16. [16]

    MAgIC: Investigation of large language model powered multi-agent in cognition, adaptability, rationality and collaboration,

    L. Xu, Z. Hu, D. Zhou, H. Ren, Z. Dong, K. Keutzer, S.- K. Ng, and J. Feng, “MAgIC: Investigation of large language model powered multi-agent in cognition, adaptability, rationality and collaboration,” pp. 7315–7332, Nov. 2024. [Online]. Available: https://aclanthology.org/2024.emnlp-main.416/

    work page 2024

  17. [17]

    Hbtp: Heuristic behavior tree planning with large language model reasoning,

    Y . Cai, X. Chen, Y . Mao, M. Li, S. Yang, W. Yang, and J. Wang, “Hbtp: Heuristic behavior tree planning with large language model reasoning,” 2025. [Online]. Available: https://arxiv.org/abs/2406.00965

    work page arXiv 2025

  18. [18]

    Llm- mars: Large language model for behavior tree generation and nlp- enhanced dialogue in multi-agent robot systems,

    A. Lykov, M. Dronova, N. Naglov, M. Litvinov, S. Satsevich, A. Bazhenov, V . Berman, A. Shcherbak, and D. Tsetserukou, “Llm-mars: Large language model for behavior tree generation and nlp-enhanced dialogue in multi-agent robot systems,” 2023. [Online]. Available: https://arxiv.org/abs/2312.09348

    work page arXiv 2023

  19. [19]

    Robot behavior-tree-based task generation with large language models,

    Y . Cao and C. S. G. Lee, “Robot behavior-tree-based task generation with large language models,” 2023. [Online]. Available: https://arxiv.org/abs/2302.12927

    work page arXiv 2023

  20. [20]

    Llm-bt: Performing robotic adaptive tasks based on large language models and behavior trees,

    H. Zhou, Y . Lin, L. Yan, J. Zhu, and H. Min, “Llm-bt: Performing robotic adaptive tasks based on large language models and behavior trees,” in2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, May 2024, p. 16655–16661. [Online]. Available: http://dx.doi.org/10.1109/ICRA57147.2024.10610183

    work page doi:10.1109/icra57147.2024.10610183 2024

  21. [21]

    Robot operating system 2: Design, architecture, and uses in the wild,

    S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall, “Robot operating system 2: Design, architecture, and uses in the wild,”Science Robotics, vol. 7, no. 66, p. eabm6074,

    work page

  22. [22]

    Available: https://www.science.org/doi/abs/10.1126/ scirobotics.abm6074

    [Online]. Available: https://www.science.org/doi/abs/10.1126/ scirobotics.abm6074

    work page

  23. [23]

    CARLA: An open urban driving simulator,

    A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V . Koltun, “CARLA: An open urban driving simulator,” inProceedings of the 1st Annual Conference on Robot Learning, 2017, pp. 1–16

    work page 2017

  24. [24]

    Slam toolbox: Slam for the dynamic world,

    S. Macenski and I. Jambrecic, “Slam toolbox: Slam for the dynamic world,”Journal of Open Source Software, vol. 6, no. 61, p. 2783,

    work page

  25. [25]

    Available: https://doi.org/10.21105/joss.02783

    [Online]. Available: https://doi.org/10.21105/joss.02783

    work page doi:10.21105/joss.02783

  26. [26]

    The marathon 2: A navigation system,

    S. Macenski, F. Mart ´ın, R. White, and J. Gin ´es Clavero, “The marathon 2: A navigation system,” in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. [Online]. Available: https://github.com/ros-planning/navigation2

    work page 2020

  27. [27]

    Chain- of-symbol prompting elicits planning in large langauge models,

    H. Hu, H. Lu, H. Zhang, Y .-Z. Song, W. Lam, and Y . Zhang, “Chain- of-symbol prompting elicits planning in large langauge models,”arXiv preprint arXiv:2305.10276, 2023

    work page arXiv 2023

  28. [28]

    Textual explanations for self-driving vehicles,

    J. Kim, A. Rohrbach, T. Darrell, J. Canny, and Z. Akata, “Textual explanations for self-driving vehicles,”Proceedings of the European Conference on Computer Vision (ECCV), 2018

    work page 2018