# Sarbloh

ARC-AGI-3 asks agents to clear uninstructed interactive games with as few actions as possible, and handing a reasoning model the board as a raw integer matrix makes it collide with walls repeatedly. Sarbloh computes geometric facts such as reachability, enclosure, midpoint tethers and dihedral symmetry in host code with no learned parameters, keeps role labels as guesses each carrying a one-action test, and lets the agent commit multi-step paths from inside its Python kernel via await play(). On the official hidden set its score rose from an initial baseline of 0.27 to 16.53; the source describes that gain as a 13.7 efficiency improvement over the first Milestone winner, while the same table gives that winner a score of 1.21.

ARC-AGI-3 asks agents to clear uninstructed interactive games with as few actions as possible, and handing a reasoning model the board as a raw integer matrix makes it collide with walls repeatedly. Sarbloh computes geometric facts such as reachability, enclosure, midpoint tethers and dihedral symmetry in host code with no learned parameters, keeps role labels as guesses each carrying a one-action test, and lets the agent commit multi-step paths from inside its Python kernel via await play(). On the official hidden set its score rose from an initial baseline of 0.27 to 16.53; the source describes that gain as a 13.7 efficiency improvement over the first Milestone winner, while the same table gives that winner a score of 1.21.