  Social norms play a crucial role in guiding agents towards understanding and
adhering to standards of behavior, thus reducing social conflicts within
multi-agent systems (MASs). However, current LLM-based (or generative) MASs
lack the capability to be normative. In this paper, we propose a novel
architecture, named CRSEC, to empower the emergence of social norms within
generative MASs. Our architecture consists of four modules: Creation &
Representation, Spreading, Evaluation, and Compliance. This addresses several
important aspects of the emergent processes all in one: (i) where social norms
come from, (ii) how they are formally represented, (iii) how they spread
through agents' communications and observations, (iv) how they are examined
with a sanity check and synthesized in the long term, and (v) how they are
incorporated into agents' planning and actions. Our experiments deployed in the
Smallville sandbox game environment demonstrate the capability of our
architecture to establish social norms and reduce social conflicts within
generative MASs. The positive outcomes of our human evaluation, conducted with
30 evaluators, further affirm the effectiveness of our approach. Our project
can be accessed via the following link: https://github.com/sxswz213/CRSEC.
