The paper investigates whether scaling toward artificial general intelligence (AGI) can be achieved by co‑designing a lightweight, semantically aware predictor (System‑1) with a resource‑intensive executive (System‑2). It introduces a conditional interaction loop where System‑1 estimates the utility of candidate actions, System‑2 executes those that pass an adaptive threshold, and the outcomes update System‑1. The study shows that, under ideal conditions, this approach can reduce execution compute by a factor related to the density of utility‑relevant interactions, and it proposes that this density is a key metric for scaling efficiency.
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
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
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
arXiv:2608. 09629v1 Announce Type: new Abstract: Self-evolving agents are usually built around prescribed optimization pipelines: the framework decides how to gather evidence, revise a persistent artifact, select candidates, and stop.
By Hui Xue, Fan Yang
The paper investigates how to choose the size of a language model for AI agents to balance reliability, energy, cost, and time. By treating model selection as a multi‑criteria decision problem, the authors derive task‑length thresholds—crossover points—where a smaller model becomes more efficient than a larger one, based on per‑step reliability and resource use. Experiments with 15 open‑weight models on synthetic tasks confirm that these predicted crossovers match observed data for most cases, showing that smaller models are greener only up to a predictable task length.
By Sajid Siraj, Maria Barbati
RSI-Opt introduces Recursive Self-Improvement Optimizer Agents that evolve their own training policies by accumulating knowledge from previous runs, including prompts, playbooks, optimizer code, and decision cadences. A meta‑agent rewrites and archives these policies, enabling reusable procedures and code that adapt to new tasks. Across multiple generations and tasks, RSI‑Opt improves optimizer performance, achieving higher z‑scores on evolution suites, better cross‑model transfer, and significant error reductions on PDE benchmarks compared to fixed‑prompt baselines.
By Binghang Lu, Yige Sun, Guang Lin
The paper introduces Guidance‑TTT, a method that separates strategic planning from execution in test‑time training for large language models. A small guidance model is trained at test time to propose high‑level changes, while a frozen, larger execution model implements these changes, reducing the cost of maintaining gradients and optimizer states. Guidance‑TTT achieves strong results across four domains—combinatorial optimization, heuristic programming, machine learning, and GPU kernel optimization—outperforming prior work and matching state‑of‑the‑art leaderboard scores.
MLE-World is a world model fine‑tuned from Qwen3.5‑4B that predicts the terminal output of a machine learning program—such as error traces, training logs, and validation scores—directly from its code and data description, without executing the program. It also provides a confidence score indicating the likelihood that the prediction is correct. Using these predictions, the authors design five test‑time‑scaling policies that compare multiple candidate programs per step, executing only those with low confidence, and demonstrate that MLE‑World matches real outputs on 77.9% of held‑out programs and improves agent performance by up to 22.1% with fewer real executions.
By Brian Ko, Han Wang, Hejia Geng, Li Erran Li, Sherry Yang
EvalResearchBench (ERB) is a benchmark that tests whether AI agents can autonomously design evaluation tasks for recursive self‑improvement. In ERB, a researcher agent selects or synthesizes tasks, builds graders, and refines them through pilot tests before producing a final evaluator that runs within fixed time and API budgets. Experiments with nine researchers and thirteen candidate models show that the best automated evaluators achieve about 75% concordance with target benchmarks, below the 91% ceiling set by target disagreements, and that human‑designed task samples still outperform many automated approaches.
By Yaolun Zhang, Tianyi Xu, Yujie Zhao, Jishen Zhao, Qingyun Wu, Huazheng Wang