MEME: Multi-entity & Evolving Memory Evaluation
For researchers building persistent LLM agents, MEME exposes a critical failure in current memory systems on dependency reasoning tasks that prior benchmarks missed.
MEME introduces six tasks to evaluate LLM-based agents on multi-entity and evolving memory, revealing that all tested memory systems collapse on dependency reasoning tasks (Cascade: 3%, Absence: 1% accuracy) despite adequate static retrieval, and only an expensive file-based agent with Claude Opus 4.7 partially closes the gap at ~70x cost.
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 Deletion (post-removal state). Evaluating six memory systems spanning three memory paradigms on 100 controlled episodes, we find that all systems collapse on dependency reasoning under the default configuration (Cascade: 3%, Absence: 1% in average accuracy) despite adequate static retrieval performance. Prompt optimization, 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 ~70x the baseline cost, indicating closure currently depends on configurations that are not practical at scale. Code and data are available on the project page: https://seokwonjung-jay.github.io/meme-eval/.