Preference Chains of Command: Making LLM Agents Pick Like People
TL;DR for operators Cities rarely wait for perfect data. A new district still needs a transit plan, a campus still needs a shuttle model, and a developer still wants to know whether people will walk, drive, or quietly defeat the entire urban-design deck by ordering a car. The paper behind this article introduces Preference Chain, a method that uses a small sample of behavioural mobility data to guide an LLM agent’s transport choices.1 The important bit is not that it “adds Graph RAG” to an LLM. That phrase now covers everything from serious retrieval systems to someone throwing a Neo4j logo onto a slide. The real mechanism is narrower and more useful: Preference Chain turns sparse human travel records into structured priors over likely choices, then lets the LLM adjust those priors for context. ...