研究表明迈向通用人工智能的系统一与系统二协同设计可降低执行成本
Does Scaling Toward AGI Depend on What System-1 Lets System-2 Execute?
研究提出通过让轻量级预测器 System-1 在执行前评估候选交互，可降低资源密集型执行器 System-2 的执行成本，理想情况下执行计算量可减少至 1/ρ_u 倍（ρ_u 为效用相关交互密度），该协同设计方法适用于训练、推理、决策和智能体工具使用等场景。
Published on Oct 29
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Abstract
Co-designing a resource-heavy reasoning system with a lightweight predictive evaluator can reduce execution costs by filtering interactions, with efficiency governed by the density of useful interactions.
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.
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