MEME: Multi-entity & Evolving Memory Evaluation

LLM-based agents increasingly operate in persistent environments where they must store, update, and reason over information across many sessions. While prior benchmarks evaluate only single-entity updates, MEME defines six tasks spanning the full space defined by the multi-entity and evolving axes, including three not scored by prior work: Cascade and Absence (dependency reasoning) and Dele- tion (post-removal state). Evaluating six memory systems spanning three memory paradigms on 100 controlled episodes, we find that all systems collapse on de- pendency reasoning under the default configuration (Cascade: 3%, Absence: 1% in average accuracy) despite adequate static retrieval performance. Prompt opti- mization, deeper retrieval, reduced filler noise, and most stronger LLMs fail to close this gap. Only a file-based agent paired with Claude Opus 4.7 as its internal LLM partially closes the gap, but at∼70× the baseline cost, indicating closure currently depends on configurations that are not practical at scale. Code is avail- able at https://anonymous.4open.science/r/MEME-0612 and the dataset at https://huggingface.co/datasets/meme-benchmark/MEME.