Large attention models are great for offline reasoning, but their cost grows with context and their behavior is hard to bound. For systems that must react under tight latency and safety constraints - robots, simulators, industrial and chemical processes - that compute model is a poor fit.

We explore an Elastic State Model (ESM): a streaming state-space backbone with a small geometric correction block that only “wakes up” when the dynamics get fragile. At each step, a fast SSM predicts the next state, estimates how sensitive it is to small perturbations, and - when needed - takes a few extra preconditioned steps in latent space to correct the trajectory. Compute stays cheap on easy stretches and increases only around junctions, shocks, and high-stakes events, keeping latency and compute per step tightly bounded and enabling online adaptation at inference time, without retraining.

We illustrate this with two contrasting demos: a maze exploration agent that automatically spends more compute at new junctions and tight passages, and a protein “repair” scenario where extra effort is focused only on damaged or unstable regions of a molecule. Together they show how the same ESM block can power responsive, budget-aware decision-making in both robotics-style navigation and molecular simulation.