arXiv Machine Learning June 3, 2026 By Amil Dravid, Yasaman Bahri, Alexei A. Efros, Yossi Gandelsman

Neuron Populations Exhibit Divergent Selectivity with Scale

Read the original on arXiv Machine Learning →

arXiv:2606. 03990v1 Announce Type: new Abstract: We investigate whether neuron populations within neural networks evolve predictably with scale, extending scaling laws beyond macroscopic observables such as loss.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

llms

arXiv Machine Learning

Jun 2

Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention

arXiv:2605. 29548v2 Announce Type: replace Abstract: Larger models learn tasks smaller models do not.

By Jing Huang, Daniel Wurgaft, Rachit Bansal, Laura Ruis, Naomi Saphra, David Alvarez-Melis, Andrew Kyle Lampinen, Christopher Potts, Ekdeep Singh Lubana

arXiv AI

Jun 16

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

arXiv:2604. 18827v2 Announce Type: replace-cross Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision.

By Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert, Goirik Chakrabarty, Hasan A. Bedel, Paul G. Fahey, Yongrong Qiu, Marissa A. Weis, Michaela Vystr\v{c}ilov\'a, Taliah Muhammad, Lydia Ntanavara, Rachel E. Froebe, Kayla Ponder, Zheng Huan Tan, Emin Orhan, Erick Cobos, Sophia Sanborn, Katrin Franke, Fabian H. Sinz, Alexander S. Ecker, Andreas S. Tolias

llms computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 25

Neural Scaling Universality: If Exponents Are Fixed, Time to Understand Coefficients

arXiv:2606. 25008v1 Announce Type: new Abstract: Neural scaling laws describe how pre-training loss decays as power laws with training time, model size, and compute.

By Yizhou Liu, Jeff Gore

llms

OpenAI Blog

Jan 23, 2020

Scaling laws for neural language models

llms

arXiv Machine Learning

Jun 30

Representational Depth of Evaluation Awareness Shifts With Scale in Open-Weight Language Models

arXiv:2606. 29196v1 Announce Type: new Abstract: Do language models know when they are being tested?

By Archit Manek

llms benchmarks safety

arXiv Machine Learning

Jun 5

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime

arXiv:2509. 24882v2 Announce Type: replace Abstract: Neural scaling laws underlie many of the recent advances in deep learning, yet their theoretical understanding remains largely confined to linear models.

By Leonardo Defilippis, Yizhou Xu, Julius Girardin, Emanuele Troiani, Vittorio Erba, Lenka Zdeborov\'a, Bruno Loureiro, Florent Krzakala

Related stories

Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

Neural Scaling Universality: If Exponents Are Fixed, Time to Understand Coefficients

Scaling laws for neural language models

Representational Depth of Evaluation Awareness Shifts With Scale in Open-Weight Language Models

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime