Researchers have introduced MEME, a novel evaluation framework for assessing the capabilities of large language models (LLMs) in multi-entity and evolving environments. This framework defines six tasks that span the full spectrum of multi-entity and evolving axes, including dependency reasoning and deletion. Notably, three of these tasks - Cascade, Absence, and Deletion - are not scored by prior benchmarks, highlighting a significant gap in existing evaluation methodologies. The MEME framework is particularly relevant in the context of LLM-based agents operating in persistent environments, where they must store, update, and reason over information across multiple sessions1. As LLM developments continue to advance, driven in part by innovations in decentralized finance (DeFi), the security implications of these advancements will become increasingly important to consider. The MEME framework provides a critical tool for evaluating the capabilities and limitations of LLMs in complex, dynamic environments, making it essential for practitioners to understand and address the associated security risks.