developer tools
Six Game Ladders Fell. Six Still Belonged to Humans.
AAArena lets coding agents revise executable game policies from replays under fixed match and evaluation budgets.
Summary
AAArena lets coding agents revise executable game policies from replays under fixed match and evaluation budgets.
AAArena turns 12 adversarial games and 1,920 archived human programs into a long-running test of heuristic learning. Agents interpret rules, select opponents, inspect replays and revise executable policies while model weights remain fixed. In the reported evaluation, one model-and-harness configuration earned gold on six ladders, but no tested configuration topped the other six. More complex rule specifications generally produced weaker results, while opponent selection and dense feedback helped. The benchmark indicates that agents can learn from both their own matches and other players' replays; it also preserves a clear gap in game understanding and long-horizon strategy.
Why it matters
AAArena lets coding agents revise executable game policies from replays under fixed match and evaluation budgets.
Limits and context
- Agents interpret rules, select opponents, inspect replays and revise executable policies while model weights remain fixed.
Key claims
AAArena lets coding agents revise executable game policies from replays under fixed match and evaluation budgets.
Qualification: Agents interpret rules, select opponents, inspect replays and revise executable policies while model weights remain fixed.
Evidence: source-2026-10-11-014
Sources
- arXiv preprint 2610.12341, original v1arXiv / submitting research authors · primary research
Corrections
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