MemArena: An Ego-Centric Benchmark for On-Device Agentic Personal Memory Assistants at Scale
Edge-deployed personal memory assistants must handle private interpersonal conversations on-device with open-weight models. Yet, existing memory benchmarks often under-test the combination of activity-dense interaction, ego-centric perspective, and coherent multi-session worlds. MemArena fills these gaps with a single-world conversational benchmark built with its MASim agent simulator, for 50 agents over 15 days (10.3M dialog-text tokens, 24.1K text-only ego-observed tokens/agent/day). With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions. We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle ...
| Authors | Jiadong Zhang, Xiaosong Ma |
|---|---|
| Primary category | cs.CL |
| Published | 20 May 2026 |
| Last updated at source | 2 October 2026 |
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