The paper "DynaSaur: Large Language Agents Beyond Predefined Actions" introduces a novel large language model (LLM) agent framework that dynamically generates and executes actions using a general-purpose programming language, overcoming limitations of existing systems restricted to predefined action sets. This approach enhances the LLM agent's flexibility and planning capabilities, significantly improving performance as demonstrated by its top ranking on the GAIA benchmark. The framework allows for action reuse and recovery from unexpected situations. The authors provide code and a preprint of their research.

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