arXiv Machine Learning August 17, 2026 By Giacomo Bocchese, Nicola Giacobbo, Etienne Guichard, James Wiles, Akshaj Devireddy

Emergent Models: Intelligence from Tiny Substrates

Read the original on arXiv Machine Learning →

arXiv:2608. 14019v1 Announce Type: cross Abstract: Emergent Models (EMs) are a machine learning paradigm based on simple yet open-ended substrates, such as cellular automata, in which modeling is treated not as the learning of a closed-form input-output map but as the emergence, within simple dynamical systems, of computational behaviors that solve external tasks.

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diffusion

arXiv Machine Learning

Jul 27

Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning

arXiv:2607. 21971v1 Announce Type: new Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.

By Shujin Wu, Cheng Qian, Xiusi Chen, Heng Ji

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 16

A Compositional Framework for Open-ended Intelligence

arXiv:2606. 15386v1 Announce Type: new Abstract: Open-ended intelligence is the capacity to adapt to novel problems and environments that are substantially different from those in training.

By Ida Momennejad, Roberta Raileanu

reinforcement-learning safety

arXiv Machine Learning

Aug 27

Emergent Abilities in Large Language Models: A Survey

Emergent Abilities in Large Language Models: A Survey reviews how scaling LLMs leads to previously unseen capabilities such as advanced reasoning, in-context learning, coding, and problem-solving. The paper critically examines definitions, inconsistencies, and the conditions that foster these abilities, including scaling laws, task complexity, pre‑training loss, quantization, and prompting strategies. It also discusses the extension to Large Reasoning Models and highlights safety concerns like deception, manipulation, and reward hacking, calling for improved evaluation and governance.

By Leonardo Berti, Flavio Giorgi, Gjergji Kasneci

llms agents reinforcement-learning robotics efficiency safety

arXiv AI

Jul 22

Intelligence from Learnable Novelty

arXiv:2607. 18433v1 Announce Type: cross Abstract: Intelligence appears under different names in different fields: as data compression in statistics and machine learning, as universal computation in dynamical systems, and as adaptive behavior in agents.

By Yanbo Zhang, Michael Levin

agents

arXiv AI

Jul 10

Architecture Generalization with MetaNCA

arXiv:2607. 07743v1 Announce Type: cross Abstract: Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information.

By Meet Barot, Daniel Berenberg, Sina Khajehabdollahi

llms

arXiv Machine Learning

Jul 31

LM-GRASP: Instance-Specific Language Models for Combinatorial Construction via Online Imitation Learning

arXiv:2607. 28135v1 Announce Type: new Abstract: Machine learning for combinatorial optimization typically relies on neural constructors trained via reinforcement learning on large offline datasets for a fixed problem class-incurring high pretraining costs and generalizing poorly outside the training distribution.

By Mohand Mezmaz, Gr\'egoire Danoy

llms reinforcement-learning benchmarks

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