HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning
Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories. Standard retrieval-augmented generation reduces this problem to top-$K$ chunk retrieval, but such passive access can discard relevant evidence before reasoning begins, especially when relevance depends on broader context. We propose HMARS, a hierarchical multi-agent memory system that treats long contexts as managed memory rather than a flat retrieval corpus. Sub-agents maintain grounded access to bounded memory regions, mid-agents manage regional context and provide query-specific coordination, and a frontier model performs final...
| Authors | Zeju Li, Ziyang Zheng, Yizhou Zhou et al. |
|---|---|
| Primary category | cs.IR |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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