Preference Chains of Command: Making LLM Agents Pick Like People
The gist Most “LLM agents for cities” sound magical until you ask them a basic planning question—which mode would this person actually take at 8am in Cambridge? This paper’s answer is refreshingly concrete: put a belief–desire–intention (BDI) graph around the agent, retrieve analogous people and contexts (Graph RAG), score paths through that graph to get prior choice probabilities, then let the LLM remodel those priors with current conditions (weather, time, place). The authors call this a Preference Chain. ...
