AI 变强，可能不再靠堆算力，而是先学会挑活
大模型变强一直靠同一个配方：更大的模型、更多的数据、更长的推理。这篇论文提出一个反直觉的转向——下一步可能不是继续堆，而是让 AI 先学会「挑活」。具体做法是加一个轻量的预测器(System-1)，在每次调用重推理(System-2)之前先估一下「这事值不值得做」，不值得的直接跳过，值得的才放行。作者在训练、推理、决策、调用工具四个环节都验证了这个「先筛后做」的循环，并给出一个关键数字：如果筛选足够准，执行成本能降到原来的 1/ρ_u,ρ_u 是「值得做的交互」在所有交互里的密度。换句话说，AI 的效率瓶颈可能不在算力总量，而在它把算力花在了多少没用的事上。这不是你明天能用的技术，但它指向一个更本质的转变：衡量 AI 进步，可能要从「算得多不多」变成「算得值不值」。
📄 原文摘要(英文)
Progress toward artificial general intelligence (AGI) has largely relied on scaling a resource-intensive executive, System-2, through larger models, more data and compute, longer reasoning, and more agents. We ask whether the next stage may come instead from co-designing it with System-1, a lightweight, semantically aware predictor that evaluates candidate interactions before execution, so that System-2 acts only on those worth their cost. Our answer is a qualified yes. We study conditional interaction, a predict--gate--execute--update loop: System-1 estimates each candidate's utility, System-2 executes those admitted by an adaptive threshold, and the outcomes update System-1. The loop spans training, test-time reasoning, adaptive decision-making, and agentic tool use, with utility and cost defined by the application. Under ideal screening and equal execution costs, execution compute falls by a factor approaching 1/ρ_u, where ρ_u is the density of utility-relevant interactions (cost-weighted when costs differ); prediction errors, exploration, and distribution shift reduce the saving. We bound selection regret by predictor error and the utility margin. These results motivate our central hypothesis: ρ_u is a measurable coordinate of scaling efficiency and makes informative compute a better description of scaling than total compute.