The paper investigates whether scaling artificial general intelligence (AGI) can be achieved more efficiently by co‑designing a lightweight, semantically aware predictor (System‑1) with a resource‑intensive executive (System‑2). It introduces a conditional interaction loop—predict, gate, execute, update—where System‑1 estimates the utility of candidate actions and only allows System‑2 to execute those that meet an adaptive threshold. The authors show that, under ideal conditions, this approach can reduce execution compute by a factor related to the density of utility‑relevant interactions, and they argue that this density (ρu) is a measurable coordinate of scaling efficiency, making informative compute a better metric than total compute.
By Qi Wang, Yun Qu, Lizhou Cai, Heming Zou, Haoang Chi, Yiqin Lv, Xiang Li, Haiyan Feng, Mengyu Ge, Yize Geng, Yuhang Jiang, Yixiu Mao, Xiangyang Ji
arXiv:2609.14500v1 Announce Type: new
Abstract: AI scaling studies increasingly evaluate systems that combine a pretrained model with retrieval, search, verification, tools, and interaction. Yet a hi...
By Seyed Morteza Emadi
arXiv:2607. 06764v1 Announce Type: new Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary search, exhaustive sampling, extended chain-of-thought), or benchmark-specific training in which small models are fine-tuned on ARC data, often with task-specialized architectures.
By Kabir Moghe, Peter Chin
arXiv:2607. 14408v1 Announce Type: new Abstract: A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality signal.
By Minghao Liu, Yu Wang, Jiayun Wang, Wei Wei
arXiv:2606. 01667v1 Announce Type: new Abstract: Test-time scaling has become a major way to improve large language model reasoning, but its orchestration has remained designer-engineered: a fixed sample budget, a fixed refinement loop, a fixed scoring rule, or a fixed search policy decides how compute is spent, leaving the model in charge of solving but not of orchestration.
By Peijia Qin, Qi Cao, Pengtao Xie
The paper investigates how data should be scaled in agentic reinforcement learning, where data is now a structured design space of tasks, environments, and trajectories. By fitting per‑axis scaling laws across various large language models and RL algorithms, the authors find that environment diversity has the strongest impact, followed by task and trajectory diversity. Using these laws, they price synthesis methods, identify critical synthetic‑to‑real ratios, and formulate a compute‑optimal budget allocation that can improve efficiency by up to 1.7× compared to the common practice of scaling trajectories alone.
By Bowei He, Yankai Chen, Xiaokun Zhang, Changjiang Han, Ye Yuan, Chong Li, Xue Liu