# Code as Model

Memento 3 introduces Code as Model: a frozen LLM agent writes its hypotheses about an environment into a natural-language rulebook, compiles that rulebook into executable code for prediction and planning, and revises both through a loop of observation, reflection, rule revision, compilation, and verification, accepting an update only when the LLM judges the code faithful to the rulebook and cell-exact replay reproduces every recorded transition. On ARC-AGI-3 the single-model agent clears every level of all public games, reaches the mean RHAE ceiling, and uses 44% of the human action count; in an Atari Pong case study a learned feedback controller wins all three evaluated episodes with different openings without further LLM calls.

Memento 3 introduces Code as Model: a frozen LLM agent writes its hypotheses about an environment into a natural-language rulebook, compiles that rulebook into executable code for prediction and planning, and revises both through a loop of observation, reflection, rule revision, compilation, and verification, accepting an update only when the LLM judges the code faithful to the rulebook and cell-exact replay reproduces every recorded transition. On ARC-AGI-3 the single-model agent clears every level of all public games, reaches the mean RHAE ceiling, and uses 44% of the human action count; in an Atari Pong case study a learned feedback controller wins all three evaluated episodes with different openings without further LLM calls.