Profile-Graph Memory for LLM Agents: Implicit Cross-Entity Traversal through Narrative Profiles

Researchers introduce MemHop, a multi-hop memory benchmark of 1,000 questions at hop depths 1-5 across 10 social-network scenarios, and ProGraph, a two-layer memory architecture combining profile expansion and compression residuals, achieving 80.1% on MemHop and 78.4% on LoCoMo, outperforming Mem0, A-Mem, HippoRAG, and RAG.

arXiv:2607.19359v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents that interact across sessions, yet current memory benchmarks primarily evaluate single-hop recall, leaving multi-hop association largely unmeasured. We make three contributions. First, we introduce MemHop, a multi-hop memory benchmark of 1,000 questions at hop depths 1-5 across 10 social-network scenarios, with per-hop evidence annotations. Second, we present Profile-Graph Memory ProGraph , a two-layer memory architecture combining i profile expansion -- substring-matched traversal of entity names that naturally appear in LLM-written profile narratives, a minimal alternative to explicit knowledge-graph construction -- and ii compression residuals -- exact dates, quantities, and named items co-extracted with each profile update at zero extra API cost. Third, a full-grid ablation shows cross-benchmark mechanism specialization: profile expansion drives multi-hop reasoning -22.6pp on MemHop when removed while compression residuals drive precision recall -8.6pp on LoCoMo when not co-extracted , with cross-effects under 3pp within a single architecture. ProGraph averages 80.1% on MemHop matching the FullContext reference and 78.4% on LoCoMo exceeding FullContext by 11.3pp , outperforming Mem0, A-Mem, HippoRAG, and RAG on both. We release MemHop, ProGraph, and baseline implementations.