Simulation-Based Inference

TL;DR for operators AI systems do not only fail because they are too small, too dumb, or insufficiently blessed by the gods of scale. They often fail because the formal objective supervises one slice of behavior and quietly leaves another slice unmanaged. Three recent papers make that point from different domains. MA-SBI shows how side-channel context can correct simulation-based inference when the simulator is misspecified.1 A paper on non-adversarial LLM robustness shows that semantically neutral prompt changes can systematically shift internal module outputs, and that targeted debiasing can recover robustness without full retraining.2 FiberTune shows that robot policy fine-tuning can preserve action-equivalent visual residuals that ordinary action loss is happy to compress into oblivion.3 ...

Confidence is cheap. Calibration is expensive. That is the uncomfortable lesson behind a new arXiv paper on earthquake source inversion, a domain that sounds safely remote until one notices the pattern: a complex physical simulator, uncertain model inputs, high-dimensional observations, and a decision-maker who wants a probability distribution rather than a shrug.1 Replace “earthquake waveform” with “financial stress scenario,” “robot sensor stream,” “industrial digital twin,” or “clinical simulator,” and the problem becomes less geological and more familiar. ...