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 a
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Does Scaling Toward AGI Depend on What System-1 Lets System-2 Execute? was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 14 checks on 2 different days. Its best position was #3 of 30, reached on Nov 2, 13:28 UTC. It dropped off the list after Nov 3, 05:28 UTC.
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It was first seen on Nov 2, 2026 and has been on the list on 2 days (14 checks) since.