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

Researchers introduce SCOPE, a self-adaptive framework that enhances Vision-Language Models' planning capabilities by refining symbolic representations of open-ended environments through iterative execution feedback. The system combines symbolic validation with adaptive memory mechanisms to improve long-horizon planning success rates and cross-task generalization in complex embodied AI scenarios.

SCOPE addresses a fundamental challenge in embodied AI: translating visual perception into actionable symbolic representations that classical planners can leverage. Vision-Language Models excel at understanding visual scenes, but their symbolic abstractions often remain incomplete in dynamic, open-ended environments—a critical bottleneck for robots and autonomous systems operating beyond controlled laboratory settings. The framework's innovation lies in treating symbolic world models as evolving artifacts rather than static constructs, using real execution feedback to iteratively refine both action plans and environmental representations.

This work builds on the growing convergence of neuro-symbolic AI, where deep learning perception meets classical planning logic. Recent years have seen increased research exploring VLM-planner integration, yet most approaches struggle when environments deviate from training distributions or contain unforeseen obstacles. SCOPE's two-module architecture—the Symbolic Execution Simulator for validation and refinement, plus the Self-Adaptive Symbolic Memory for knowledge distillation—creates a feedback loop that gradually builds more complete world models.

For the AI and robotics industries, this represents meaningful progress toward deployable autonomous systems in unpredictable real-world settings. Companies developing warehouse automation, household robots, or industrial manipulation systems face exactly these challenges: incomplete environment models lead to plan failures and costly trial-and-error cycles. By improving robustness to perturbations and cross-task adaptability, SCOPE reduces deployment friction.

Developers should monitor whether SCOPE's approach—continuous symbolic world refinement through execution—becomes a standard pattern in embodied AI architectures. Integration with commercial robotic platforms and open-source frameworks will determine practical impact.

• →SCOPE improves symbolic representation completeness in open-ended environments through iterative execution-based feedback loops.
• →The framework combines symbolic execution simulation with adaptive memory mechanisms to enhance long-horizon planning robustness.
• →Performance gains include increased plan success rates under environmental perturbations and improved cross-task generalization.
• →This work advances neuro-symbolic AI by treating world models as evolving constructs rather than static representations.
• →The approach has direct applications for robotics and autonomous systems requiring real-world deployment beyond controlled settings.