MemoryBench aims to provide a standardized and extensible benchmark for evaluating memory and continual learning in LLM systems — encouraging future work toward more adaptive, feedback-driven, and efficient LLM systems.
Paper Link:
https://arxiv.org/abs/2510.17281
Github:
https://github.com/THUIR/MemoryBench
📢 May 26, 2026 Updated: This work has been accepted at ICML 2026 and selected for a SpotLight Paper!
📢 Dec. 8, 2025 Updated: We released an extended version… See the full description on the dataset page:
https://huggingface.co/datasets/THUIR/MemoryBench.