Does Scaling Toward AGI Depend on What System-1 Lets System-2 Execute?
Does Scaling Toward AGI Depend on What System-1 Lets System-2 Execute?
Research proposes that by allowing the lightweight predictor System-1 to evaluate candidate interactions before execution, the execution cost of the resource-intensive executor System-2 can be reduced, ideally reducing the computational workload to 1/ρ_u times (where ρ_u is the utility-related interaction density), and this collaborative design method is applicable to scenarios such as training, inference, decision-making, and agent tool use.
Published on Oct 29
·
Submitted by
on Nov 2
Authors:
,
,
,
,
,
,
,
,
,
,
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.
View arXiv page View PDF Project page Add to collection
Get this paper in your agent:
hf papers read 2610.39497
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash
Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/2610.39497 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2610.39497 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2610.39497 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.
Source: Hugging Face Daily Papers · huggingface.co