ABM

TL;DR Shachi is a modular methodology for building LLM-driven agent-based models (ABMs) that replaces ad‑hoc prompt spaghetti with four standardized cognitive components—Configs, Memory, Tools, and an LLM reasoning core. The result: agents you can port across environments, benchmark rigorously, and use to study nontrivial dynamics like tariff shocks with externally valid outcomes. For enterprises, Shachi is the missing method for turning agent demos into decision simulators. Why this paper matters to operators (not just researchers) Most enterprise “agent” pilots die in the gap between a clever demo and a reliable simulator that leaders can trust for planning. Shachi closes that gap by: ...

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. ...