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 ($10.3$M dialog-text tokens, $24.1$K 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 retrieval, Memobase, and MemSearch as memory backends. Three results stand out: (1) Memory-backend choice matters more for content accuracy: At Qwen3-0.6B, Memobase $\to$ MemSearch gains $+32.5/+19.2$ pp, exceeding MemSearch reader scaling ($+10.6/+6.8$ pp). (2) Permission-aware access fails universally: with Oracle leaking heavily and other backends too timid to disclose. (3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed $87$/$7$/$48$ ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations. We release code, the MASim simulator, and the MemArena-L benchmark at https://anonymous.4open.science/r/MemArena-NeurIPS2026-anon-3E9D