Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents
The transition from stateless language model inference to persistent, multi session autonomous agents has revealed memory to be a primary architectural bottleneck in the deployment of production grade agentic systems. Existing methodologies largely depend on hybrid semantic graph architectures, which impose substantial computational overhead during both ingestion and retrieval. These systems typically require large language model mediated entity extraction, explicit graph schema maintenance, and multi query retrieval pipelines. This paper introduces Memanto, a universal memory layer for agentic artificial intelligence that challenges the prevailing assumption that knowledge graph complexi...
| Authors | Seyed Moein Abtahi, Rasa Rahnema, Hetkumar Patel et al. |
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| Primary category | cs.AI |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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