Network World Models as Environments for Algorithm Design on Complex Systems

The paper introduces an action‑conditioned Network World Model that learns how a network’s diffusion dynamics evolve under interventions over time. This model can quickly predict the outcomes of actions, enabling a coding agent to design and refine algorithms that select actions to maximize expected performance on complex network tasks. Experiments on eight network tasks and five diffusion models show that the resulting algorithms match or surpass the best existing baselines in 138 of 141 settings while achieving up to 14.5× faster rollouts than traditional Monte Carlo simulation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• agents
• diffusion
• robotics