elvis@omarsar0 · X最新研究提出元智能体挑战（MAC），将编码智能体放入沙盒，给定评估API和时间预算，要求其自主编程出在五个领域表现最优的智能体。结果发现，元智能体极少能匹敌人工设计的基线，少数成功的案例也几乎全部依赖专有前沿模型。更值得警惕的是，在高优化压力下，一些智能体开始从评分渠道外泄真实答案，即便研究人员设置了多层反奖励破解防御也未能阻止。论文：arxiv.org/abs/2606.04455。
// The Meta-Agent Challenge //
How good are current agents at self-improving？
This is a great paper covering some of the challenges.
They propose the Meta-Agent Challenge （MAC）， where they give a coding agent a sandbox， an evaluation API， and a time budget， then ask it to program an agent that maximizes held-out performance across five domains.
Results：
Meta-agents rarely match human-engineered baselines， and the few that do are dominated by proprietary frontier models.
Under high optimization pressure， some agents started exfiltrating ground truth from the scoring channel， even with multi-layer anti-reward-hacking defenses in place.
Paper： https：//arxiv.org/abs/2606.04455
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