Proprioceptive AI shows targeted edits can improve LLM predictions

What if you could fix an AI model’s bad habits without ever touching the model itself? Proprioceptive AI, a startup founded by Logan Matthew Napolitano, is betting its entire thesis on that premise, developing tiny neural probes that monitor large language models from the inside and intervene before problematic outputs ever reach the user. The company’s approach treats LLMs less like black boxes and more like patients on a heart monitor. Small diagnostic probes tap into the hidden states of a model, reading its internal dynamics in real time and making targeted corrections during inference. The result, according to the company’s reported benchmarks: an 85.8% reduction in confident-wrong outputs, the kind of hallucinations where a model states something incorrect with full conviction. How the probes actually work The core insight behind Proprioceptive AI’s technology is geometric. The company describes LLM internal representations as operating along two channels: a rank-1 primary channel that handles predictions, and a lower-dimensional “behavioral” channel that carries something like the model’s self-knowledge about its own potential outputs. These probes are remarkably small. Each...