pith. sign in

arxiv: 2606.22488 · v1 · pith:EZBDCLNInew · submitted 2026-06-21 · 💻 cs.AI

SCOPE: Evolving Symbolic World for Planning in Open-Ended Environments

Yundaichuan Zhan , Minghe Gao , Zhongqi Yue , Wendong Bu , Wenqiao Zhang , Guoming Wang , Jisheng Dang , Juncheng Li
show 2 more authors
Siliang Tang Yueting Zhuang
This is my paper

Pith reviewed 2026-06-26 10:55 UTC · model grok-4.3

classification 💻 cs.AI
keywords symbolic planningopen-ended environmentsembodied AIplan refinementsymbolic world evolutionvision-language modelsself-adaptive memory
0
0 comments X

The pith

SCOPE evolves incomplete symbolic environment models using execution feedback to enable more reliable long-horizon planning.

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

The paper presents SCOPE as a framework that refines action plans and updates symbolic representations of open-ended environments when initial perceptions leave gaps. It pairs a simulator that checks plans both symbolically and through real execution with a memory component that turns the resulting signals into updated knowledge for future use. A sympathetic reader would care because incomplete symbols currently cause planners paired with vision-language models to fail on long tasks when surroundings shift. If the method works, agents could maintain usable world models without hand-crafted fixes for each new setting or task.

Core claim

SCOPE is a self-adaptive symbolic planning framework consisting of a Symbolic Execution Simulator that validates and executes plans to refine them and evolve the symbolic world, and a Self-Adaptive Symbolic Memory that distills feedback into evolving symbolic knowledge for enhanced long-horizon planning.

What carries the argument

The Symbolic Execution Simulator (SESim) for validation and real-execution feedback paired with the Self-Adaptive Symbolic Memory (SASMem) that converts that feedback into updated symbolic knowledge.

If this is right

  • The symbolic world grows more complete as planning cycles accumulate.
  • Plan success rates rise when the environment is perturbed after initial modeling.
  • Grounding and adaptability improve across different embodied tasks without extra tuning.

Where Pith is reading between the lines

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

  • Agents could begin with sparse initial symbols and build usable models through repeated interaction rather than requiring exhaustive upfront perception.
  • The same feedback-driven update loop could be tested in non-embodied planning settings that also rely on incomplete symbolic descriptions.
  • If distilled knowledge begins to conflict with new observations, an explicit consistency check would be needed beyond what is described.

Load-bearing premise

Feedback from symbolic validation and real execution can be reliably distilled into evolving symbolic knowledge that improves future planning without introducing new inconsistencies or requiring domain-specific tuning.

What would settle it

Measure the completeness of the symbolic world and plan success rates under perturbations after repeated cycles of planning, validation, and memory updates; if neither quantity increases, the central claim does not hold.

Figures

Figures reproduced from arXiv: 2606.22488 by Guoming Wang, Jisheng Dang, Juncheng Li, Minghe Gao, Siliang Tang, Wendong Bu, Wenqiao Zhang, Yueting Zhuang, Yundaichuan Zhan, Zhongqi Yue.

Figure 1
Figure 1. Figure 1: Comparison of existing method and SCOPE. In the middle illustration, black arrows denote the pipeline used by existing methods; blue arrows highlight SCOPE extensions. as task-specific traces and is not often distilled into symbolic￾friendly knowledge that is aligned with symbolic planning. As a consequence, the stored information has limited density and transferability, providing weaker cross-task and cro… view at source ↗
Figure 2
Figure 2. Figure 2: The overview of our framework. (a) Evolving Symbolic World: The agent actively explores the environment to refine the symbolic world, represented in PDDL problem file. Based on this symbolic world, the VLM generates a symbolic action plan for the embodied task. (b) Symbolic Execution Simulator: The generated plan is validated within the symbolic world using the PDDL validator and executed in the environmen… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of SASMem, illustrating its structure and symbolic knowledge components. world entries, and aggregates them as FSASMem to guide localized plan/world updates: P ′ A, W′ symbol = VLM(PA, Fsymbol, Freal, FSASMem) SESim iterates this process, until validation succeeds or a budget is reached, thereby increasing robustness in the open￾ended environment. By leveraging complementary symbolic validation an… view at source ↗
Figure 4
Figure 4. Figure 4: Symbolic world evolution in open-ended settings. Environment settings. Static settings keep the environ￾ment state unchanged unless affected by the agent’s ac￾tions. Dynamic settings introduce state changes during an episode, requiring the agent to re-ground and re-plan under non-stationary conditions. Open-ended settings stream a sequence of steps in which new task-required affordances or state dependenci… view at source ↗
Figure 5
Figure 5. Figure 5: Symbolic world evolution example. SCOPE’s Plan More Grounded 4: [walk] agent sink 6: [putback] agent barsoap counter ... ... Symbolic Execution Simulator Invalid: sink is not a valid object for wash 5: [wash] agent sink Task: Wash hands Action plan 5.1: [turnOn] agent faucet 5.2: [grab] agent barsoap 5.3: [wash] agent barsoap Update symbolic world: (switchable faucet) (grabbable barsoap) ... Refined Action… view at source ↗
Figure 6
Figure 6. Figure 6: Action plan refinement example. 4.4. In-Depth Analysis and Generalization Qualitative Analysis. We qualitatively examine how SCOPE enhances symbolic modeling and planning under environment perturbations, shown in [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example of how the Symbolic Execution Simulator (SESim) jointly uses symbolic validation and real execution feedback to refine both the action plan and the symbolic world. For VirtualHome, we align PDDL actions with the built-in program-based API (e.g., Walk, Grab, SwitchOn), and execute them sequentially in the environment. The simu￾lator reports whether an action succeeds, fails because the target object… view at source ↗
read the original abstract

Recent works have explored integrating Vision-Language Models (VLMs) with classical planners that rely on symbolic representations of planning problems to generate long-horizon plans for complex embodied tasks. However, in open-ended environments, these symbolic representations obtained from perception are often incomplete, leading to suboptimal performance. To address this, we introduce SCOPE, a self-adaptive symbolic planning framework that supports refining action plans and evolving the symbolic world, i.e., the symbolic representations of open-ended environments. SCOPE comprises two synergistic modules: a Symbolic Execution Simulator (SESim) that conducts symbolic validation and real execution of action plans, leveraging the feedback to refine the plans and evolve the symbolic world; and a Self-Adaptive Symbolic Memory (SASMem) that further distills feedback into evolving symbolic knowledge to enhance long-horizon planning and modeling of the symbolic world. Experiments in open-ended environments show that SCOPE significantly improves the completeness of the symbolic world, the success rate of plans under environment perturbations, and cross-task grounding and adaptability across diverse embodied 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

0 major / 2 minor

Summary. The manuscript introduces SCOPE, a self-adaptive symbolic planning framework for open-ended embodied environments. It consists of two modules: Symbolic Execution Simulator (SESim), which performs symbolic validation and real execution of plans while using feedback to refine plans and evolve the symbolic world, and Self-Adaptive Symbolic Memory (SASMem), which distills execution feedback into evolving symbolic knowledge. The central empirical claim is that SCOPE improves completeness of the symbolic world, success rates of plans under perturbations, and cross-task grounding/adaptability compared to prior VLM+classical planner approaches.

Significance. If the reported gains hold under rigorous controls, the framework offers a concrete mechanism for maintaining and updating symbolic state representations from mixed symbolic/real feedback. This addresses a recognized bottleneck in long-horizon planning for dynamic environments and could be adopted in embodied AI pipelines that already combine VLMs with PDDL-style planners.

minor comments (2)
  1. The abstract describes SESim and SASMem at a high level but does not specify the representation language, consistency invariants maintained during evolution, or the exact distillation procedure; these details are needed to evaluate whether the evolving symbolic world remains sound.
  2. No information is provided on the choice of baselines, number of environments, statistical tests, or ablation isolating the contribution of feedback distillation versus plan refinement.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their summary of our manuscript on SCOPE and for noting its potential relevance to maintaining symbolic representations in dynamic embodied environments. The recommendation is marked 'uncertain,' but the report contains no specific major comments. We therefore provide no point-by-point responses and stand ready to address any concrete concerns the referee may wish to raise.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents a high-level framework description of SCOPE with SESim and SASMem modules for evolving symbolic representations in planning tasks. No equations, derivations, fitted parameters, or mathematical claims are present in the abstract or provided text. The central claims rest on experimental improvements in completeness and success rates rather than any self-referential definitions, predictions derived from inputs by construction, or load-bearing self-citations. The framework is described conceptually without reducing any result to its own inputs, making the derivation chain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the framework implicitly assumes that symbolic feedback loops can be maintained without external domain knowledge.

pith-pipeline@v0.9.1-grok · 5738 in / 1054 out tokens · 15318 ms · 2026-06-26T10:55:24.249204+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

23 extracted references · 21 canonical work pages · 9 internal anchors

  1. [1]

    GPT-4 Technical Report

    Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. Gpt-4 technical report.arXiv preprint arXiv:2303.08774,

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    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., et al. Do as i can, not as i say: Grounding language in robotic affordances.arXiv preprint arXiv:2204.01691,

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    Grounding language to autonomously- acquired skills via goal generation.arXiv preprint arXiv:2006.07185,

    Akakzia, A., Colas, C., Oudeyer, P.-Y ., Chetouani, M., and Sigaud, O. Grounding language to autonomously- acquired skills via goal generation.arXiv preprint arXiv:2006.07185,

    work page arXiv 2006

  4. [4]

    D., and Khan, S

    Ao, S., Salim, F. D., and Khan, S. Emac+: Embodied multimodal agent for collaborative planning with vlm+ llm.arXiv preprint arXiv:2505.19905,

    work page arXiv

  5. [5]

    Nesyc: A neuro-symbolic continual learner for complex embodied tasks in open domains.arXiv preprint arXiv:2503.00870,

    Choi, W., Park, J., Ahn, S., Lee, D., and Woo, H. Nesyc: A neuro-symbolic continual learner for complex embodied tasks in open domains.arXiv preprint arXiv:2503.00870,

    work page arXiv

  6. [6]

    PaLM-E: An Embodied Multimodal Language Model

    URLhttps://arxiv.org/abs/2303.03378. Du, Y ., Liu, Z., Li, J., and Zhao, W. X. A survey of vision-language pre-trained models.arXiv preprint arXiv:2202.10936,

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [7]

    Embodied ai agents: Modeling the world.arXiv preprint arXiv:2506.22355,

    Fung, P., Bachrach, Y ., Celikyilmaz, A., Chaudhuri, K., Chen, D., Chung, W., Dupoux, E., J´egou, H., Lazaric, A., Majumdar, A., et al. Embodied ai agents: Modeling the world.arXiv preprint arXiv:2506.22355,

    work page arXiv

  8. [8]

    Text2world: Bench- marking large language models for symbolic world model generation.arXiv preprint arXiv:2502.13092,

    Hu, M., Chen, T., Zou, Y ., Lei, Y ., Chen, Q., Li, M., Mu, Y ., Zhang, H., Shao, W., and Luo, P. Text2world: Bench- marking large language models for symbolic world model generation.arXiv preprint arXiv:2502.13092,

    work page arXiv

  9. [9]

    Look before you leap: Unveiling the power of gpt-4v in robotic vision- language planning.arXiv preprint arXiv:2311.17842,

    Hu, Y ., Lin, F., Zhang, T., Yi, L., and Gao, Y . Look before you leap: Unveiling the power of gpt-4v in robotic vision- language planning.arXiv preprint arXiv:2311.17842,

    work page arXiv

  10. [10]

    Inner Monologue: Embodied Reasoning through Planning with Language Models

    Huang, W., Abbeel, P., Pathak, D., and Mordatch, I. Lan- guage models as zero-shot planners: Extracting action- able knowledge for embodied agents. InInternational conference on machine learning, pp. 9118–9147. PMLR, 2022a. Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y ., et al. Inner mon...

    work page internal anchor Pith review Pith/arXiv arXiv

  11. [11]

    Code as Policies: Language Model Programs for Embodied Control

    Jiang, Y ., Gu, S. S., Murphy, K. P., and Finn, C. Language as an abstraction for hierarchical deep reinforcement learn- ing.Advances in neural information processing systems, 32, 2019a. Jiang, Y .-q., Zhang, S.-q., Khandelwal, P., and Stone, P. Task planning in robotics: an empirical comparison of pddl-and asp-based systems.Frontiers of Information Techn...

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

    Liu, B., Jiang, Y ., Zhang, X., Liu, Q., Zhang, S., Biswas, J., and Stone, P. Llm+ p: Empowering large language models with optimal planning proficiency.arXiv preprint arXiv:2304.11477,

    work page internal anchor Pith review Pith/arXiv arXiv

  13. [13]

    The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

    Mao, J., Gan, C., Kohli, P., Tenenbaum, J. B., and Wu, J. The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision.arXiv preprint arXiv:1904.12584,

    work page internal anchor Pith review Pith/arXiv arXiv 1904

  14. [14]

    and Finn, C

    Nair, S. and Finn, C. Hierarchical foresight: Self-supervised learning of long-horizon tasks via visual subgoal genera- tion.arXiv preprint arXiv:1909.05829,

    work page arXiv 1909

  15. [15]

    S., Kumar, N., Lozano-P´erez, T., and Kaelbling, L

    Silver, T., Hariprasad, V ., Shuttleworth, R. S., Kumar, N., Lozano-P´erez, T., and Kaelbling, L. P. Pddl planning with pretrained large language models. InNeurIPS 2022 foundation models for decision making workshop,

    2022

  16. [16]

    S., Feng, J., Ko- rneev, N., Tenenbaum, J

    Wong, L., Mao, J., Sharma, P., Siegel, Z. S., Feng, J., Ko- rneev, N., Tenenbaum, J. B., and Andreas, J. Learning adaptive planning representations with natural language guidance.arXiv preprint arXiv:2312.08566,

    work page arXiv

  17. [17]

    Y ., Zhang, Y ., and Chang, S

    Wu, Q., Zhao, H., Saxon, M., Bui, T., Wang, W. Y ., Zhang, Y ., and Chang, S. Vsp: Assessing the dual challenges of perception and reasoning in spatial planning tasks for vlms.arXiv preprint arXiv:2407.01863,

    work page arXiv

  18. [18]

    Symplanner: Delib- erate planning in language models with symbolic repre- sentation.arXiv preprint arXiv:2505.01479,

    Xiong, S., Zhou, J., Liu, Z., and Su, Y . Symplanner: Delib- erate planning in language models with symbolic repre- sentation.arXiv preprint arXiv:2505.01479,

    work page arXiv

  19. [19]

    Qwen3 Technical Report

    Yang, A., Li, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Gao, C., Huang, C., Lv, C., et al. Qwen3 technical report.arXiv preprint arXiv:2505.09388,

    work page internal anchor Pith review Pith/arXiv arXiv

  20. [20]

    Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

    10 SCOPE: Evolving Symbolic World for Planning in Open-Ended Environments Zeng, A., Attarian, M., Ichter, B., Choromanski, K., Wong, A., Welker, S., Tombari, F., Purohit, A., Ryoo, M., Sind- hwani, V ., et al. Socratic models: Composing zero-shot multimodal reasoning with language.arXiv preprint arXiv:2204.00598,

    work page internal anchor Pith review Pith/arXiv arXiv

  21. [21]

    V ote-tree-planner: Optimiz- ing execution order in llm-based task planning pipeline via voting.arXiv preprint arXiv:2502.09749,

    Zhang, C., Li, Z., and Yuan, W. V ote-tree-planner: Optimiz- ing execution order in llm-based task planning pipeline via voting.arXiv preprint arXiv:2502.09749,

    work page arXiv

  22. [22]

    Grounding classical task planners via vision-language models.arXiv preprint arXiv:2304.08587,

    Zhang, X., Ding, Y ., Amiri, S., Yang, H., Kaminski, A., Esselink, C., and Zhang, S. Grounding classical task planners via vision-language models.arXiv preprint arXiv:2304.08587,

    work page arXiv

  23. [23]

    Isr-llm: Iter- ative self-refined large language model for long-horizon sequential task planning

    Zhou, Z., Song, J., Yao, K., Shu, Z., and Ma, L. Isr-llm: Iter- ative self-refined large language model for long-horizon sequential task planning. In2024 IEEE International Con- ference on Robotics and Automation (ICRA), pp. 2081–

    2081