H$^{2}$MT: Semantic Hierarchy-Aware Hierarchical Memory Transformer
Transformer-based LLMs achieve strong results on many language tasks; however, long inputs remain challenging because context windows are finite, and prefill latency and memory grow rapidly with prompt length. Flat token-stream processing and chunk-based retrieval can therefore spend substantial computation and context budget on text unrelated to the query. Offline-indexed RAG additionally introduces external storage and index management overhead, and typically appends retrieved evidence as raw text, increasing prefill cost and latency. H^{2}MT makes long-context inference structure-aware: it builds a semantic hierarchy offline, computes a memory embedding for each node via bottom-up post...
| Authors | Maryam Haghifam, Zifan He, Jason Cong et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 May 2026 |
| Last updated at source | 18 August 2026 |
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