Toward a Theory of Hierarchical Memory for Language Agents
Many recent long-context and agentic systems address context-length limitations by adding hierarchical memory: they extract atomic units from raw data, build multi-level representatives by grouping and compression, and traverse this structure to retrieve content under a token budget. Despite recurring implementations, there is no shared formalism for comparing design choices. We propose a unifying theory in terms of three operators. Extraction ($α$) maps raw data to atomic information units; coarsening ($C = (π, ρ)$) partitions units and assigns a representative to each group; and traversal ($τ$) selects which units to include in context given a query and budget. We identify a self-suffic...
| Authors | Yashar Talebirad, Ali Parsaee, Csongor Y. Szepesvari et al. |
|---|---|
| Primary category | cs.IR |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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