This paper introduces The Energy Society, a simulation environment where LLM agents spend energy to generate tokens, regain energy through jobs or donations, and face survival pressure under competitive or cooperative incentives.
This paper introduces the Arbiter, an agent that monitors multi-agent conversations under an inspection budget and uses active inspection tools to detect misaligned agents earlier and more accurately.
This paper studies emergent languages in Moltbook agent populations, identifying categories such as token efficiency, new natural languages, and oversight evasion, and showing that surface-behavior monitoring may be insufficient for agent oversight.
This paper releases and analyzes the Moltbook Files, a dataset of agent-only social-network activity, studying community structure, safety risks, and the effect of Moltbook data on downstream language-model behavior.