scalable

mimalloc is an open-source, high-performance, scalable memory allocator that serves as a drop-in replacement for malloc and free. Designed for modern highly concurrent applications and large memory scales, it is used in major services like Bing and integrated into projects such as NoGIL CPython and Unreal Engine.

Browser Use launches a new browser infrastructure service featuring subsecond cold starts, lower cost at $0.02/h, and unlimited scaling, now live for developers.

This paper presents a distributed approach for constrained multi-agent reinforcement learning that uses state-augmented policy learning and neighbor-to-neighbor consensus over dual variables to satisfy global resource constraints while scaling linearly with the number of agents. Experiments on smart grid demand response demonstrate that consensus coordination is essential for feasibility, scaling to thousands of agents unlike centralized training approaches.

This paper introduces the concept of Access Sets to budget expert reads, enabling scalable weight-space model merging.

This paper proposes AgentDoG 1.5, a lightweight and scalable alignment framework for AI agent safety, using taxonomy-guided training with minimal samples to achieve performance comparable to leading closed-source models.

This paper presents a scalable heterogeneous graph neural network workflow for data-driven optimal power flow surrogate modeling, using distributed training on supercomputers and demonstrating improvements via fine-tuning pretrained models.

Equilibrium Reasoners (EqR) introduce a novel framework for scalable reasoning by learning task-conditioned attractors in latent dynamical systems, achieving over 99% accuracy on Sudoku-Extreme by unrolling up to 40,000 layers.

TideGS introduces an out-of-core training framework that enables 3D Gaussian Splatting with over one billion primitives on a single GPU by managing parameters across SSD-CPU-GPU hierarchy via block-virtualization, asynchronous pipeline, and differential streaming techniques.

M1 by Montage is an agentic UI platform that scales on demand.

PACER is a new scalable framework for causal discovery from large-scale interventional data that guarantees acyclicity by design, achieving up to two orders of magnitude speedups over penalty-based methods on benchmarks with thousands of variables.