SakanaAI/ale_agent

ALE-Agent is the algorithm-engineering scaffold introduced with ALE-Bench. It treats every scored program as a search state, expands the most promising unexpanded state, and generates 30 independent refinement branches from it. Each branch gets three implementation turns with fresh evaluator feedback; only the strongest version from those turns returns to the best-first frontier. Expanded parents are tabu and are never selected for expansion again.

The first turn in each branch separates planning from implementation. It samples one of the four domain prompts published in the paper supplement—complexity/speed, simulated-annealing state, simulated-annealing neighborhood, or beam search—asks for a strategy, then asks the model to implement it. Later turns receive the current code, its evaluation feedback, the historically best code and feedback, and the branch strategy.

This is a paper-specification port. The public ALE-Bench repository contains the benchmark and generic self-refinement harness, but not the experimental ALE-Agent source. The implementation here follows the main paper and supplement rather than claiming source-level parity with unreleased code.

Galapagos tasks require an initial genome, so the selected task's bundled baseline is the root state; the paper instead describes a conceptual root whose first children are generated from the problem statement alone. For deterministic sequential execution, the historically best state is frozen when a 30-sibling cohort starts, preventing an earlier scheduled sibling from leaking into a later one's prompt as though the originally concurrent requests had observed each other.