LongMemEval-S

The 'LongMemEval-S' dataset/benchmark is used to evaluate the performance of long-context LLM agents in managing persistent state across interactions.

Papers using LongMemEval-S (10)

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning2025 · 98 citesEpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments2025Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory2026MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents2026MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing2026DimMem: Dimensional Structuring for Efficient Long-Term Agent Memory2026MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents2026Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents2026LightMem: Lightweight and Efficient Memory-Augmented Generation2025In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents2025