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WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment.
Toward Trustworthy Neural Program Synthesis
We develop an approach to estimate the probability that a program sampled from a large language model is correct.
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