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This work addresses the problem of long-horizon task planning with the LargeLanguage Model (LLM) in an open-world household environment. Existing worksfail to explicitly track key objects and attributes, leading to erroneousdecisions in long-horizon tasks, or rely on highly engineered state featuresand feedback, which is not generalizable. We propose an open staterepresentation that provides continuous expansion and updating of objectattributes from the LLM's inherent capabilities for context understanding andhistorical action reasoning. Our proposed representation maintains acomprehensive record of an object's attributes and changes, enabling robustretrospective summary of the sequence of actions leading to the current state.This allows continuously updating world model to enhance context understandingfor decision-making in task planning. We validate our model through experimentsacross simulated and real-world task planning scenarios, demonstratingsignificant improvements over baseline methods in a variety of tasks requiringlong-horizon state tracking and reasoning. (Video\footnote{Video demonstration:\url{https://youtu.be/QkN-8pxV3Mo}.})

LLM-State: Open World State Representation for Long-horizon Task Planning with Large Language Model