A cell-based simulation calculates how cells move and respond to their neighbours. Repeating it under many settings is expensive, and random variation means the same settings can produce different final patterns.

Comlekoglu et al. trained a class-conditional diffusion model on images from a simulation of vessel-like network formation. The input is one of the tested simulation settings plus noise; the output is a possible final pattern, rather than the full sequence of cell movements.

They reported 20.6 seconds per generated pattern on four V100 GPUs, versus 447.6 seconds for CompuCell3D on an AMD EPYC CPU node. These are different hardware setups, and the comparison excludes training on 16 V100 GPUs.

The model covered 25 discrete parameter settings. Classifier checks and network-shape measurements helped evaluate outputs, but some patterns remained imperfect. Generating valid samples between those settings remains unresolved. The result concerns reproducing simulation outputs, not proving that the underlying biology is correct.

Paper: https://arxiv.org/abs/2505.09630

Explanation: https://rewirebio.io/blog/diffusion-surrogates-for-agent-based-biological-simulations/

Disclosure: the explanation is from my site, rewirebio.io. This summary and schematic were prepared with AI assistance. I have not run or independently reproduced the model.

Researchers tracked more than 2,600 AI agent attempts targeting Alibaba's Amap from Tencent-linked cloud infrastructure. None of the agents had authorization to access that service.

These were not human operators pivoting through tooling. They were autonomous agents running coordinated, repeated reconnaissance against a live production system at machine speed. Human-paced incident response cannot match that cadence.

The exposure here is not a misconfigured firewall or a leaked credential. The agents operated against a production service with no verified identity, no declared scope, and no gate between their initialization and their first outbound request. The target service had no way to distinguish a legitimate automated caller from an adversarial one before the probe landed.

For those running production APIs or services that autonomous agents — not humans, not static service accounts — might reach: what does your authorization boundary actually look like at the moment of first contact? Not at login, not in a post-incident log — at the exact moment an unknown agent makes its first request.

I've been working on Mixture of experts (MoEs) and sparsity for a few years now, so I thought I'd make a short video explaining how MoE routing works.

For the video I trained a tiny character model with a 32-expert layer three times, on English, Python and arithmetic text.

Run 1, token choice with no balancing loss: a few experts get most of the tokens. Early in training one expert gets 57% of them while 17 of the 32 are barely used.

Run 2, token choice with a load-balancing loss: tokens spread out roughly evenly and every expert gets work.

Run 3, expert choice: each expert picks its own tokens, so the load is perfectly even, but some tokens get picked by no expert and others by several. It also ended with the lowest eval loss of the three.

For those who are familiar with the books by Sebastian Raschka (i.e. "Build a LLM/Reasoning Model from Scratch"), do you know of any recent similar books with a focus on computer vision models (e.g. CNNs, ViTs, Swin etc)? I would like to develop a more in depth understanding by building these models from scratch and I think the approach used by Raschka does exactly that for language models. Any recommendations are welcome!