mlsanityData to 14 Oct 2026

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

arXiv 2606.22488 ↗First posted 21 Jun 2026cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 36 of its 45 references that are in the map. First posted on arXiv 21 Jun 2026.

Builds on

36 of 45 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs2 Jul
  2. 2024VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought20 Jun
  3. 2024SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models3 Jun
  4. 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities22 Jan
  5. 2023Grounding Classical Task Planners via Vision-Language Models17 Apr

Other references · 31

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  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2024GPT-4o System Cardcited by 4,588
  4. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  5. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,348
  6. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,210
  7. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,081
  8. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,035
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  11. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  12. 2018VirtualHome: Simulating Household Activities via Programscited by 415
19 more
  1. 2019The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervisioncited by 372
  2. 2016Visual Relationship Detection with Language Priorscited by 334
  3. 2022A Survey of Vision-Language Pre-Trained Modelscited by 177
  4. 2023Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planningcited by 161
  5. 2019Language as an Abstraction for Hierarchical Deep Reinforcement Learningcited by 138
  6. 2019Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generationcited by 83
  7. 2020Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphscited by 83
  8. 2023ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planningcited by 77
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  16. 2021Grounding Predicates through Actionscited by 25
  17. 2025NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks In Open Domainscited by 15
  18. 2025EMAC+: Embodied Multimodal Agent for Collaborative Planning with VLM+LLMcited by 5
  19. 2025Vote-Tree-Planner: Optimizing Execution Order in LLM-based Task Planning Pipeline via Votingcited by 1

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  1. 2025SCOPE: Language Models as One-Time Teacher for Hierarchical Planning in Text Environments
  2. 2024Can-Do! A Dataset and Neuro-Symbolic Grounded Framework for Embodied Planning with Large Multimodal Models
  3. 2026Plan-Bench: Can Existing VLMs Go Well Beyond Perception to Reason and Plan in Embodied Worlds?
  4. 2025Expand Your SCOPE: Semantic Cognition over Potential-Based Exploration for Embodied Visual Navigation
  5. 2026Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation
  6. 2025World-aware Planning Narratives Enhance Large Vision-Language Model Planner
  7. 2025Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
  8. 2024Learning for Long-Horizon Planning via Neuro-Symbolic Abductive Imitation