HEMA : A Hippocampus-Inspired Extended Memory Architecture for Long-Context AI Conversations

Large - language models (LLMs) maintain coherence over a few thousand tokens but degrade sharply in multi - hundred - turn conversations. We present a hippocampus - inspired dual - memory architecture that separates dialogue context into (1) Compact Memory, a continuously updated one - sentence summary that preserves the global narrative, and (2) Vector Memory, an episodic store of chunk embeddings queried via cosine similarity. Integrated with an onull - the - shelf 6 B - parameter transformer, the system sustains > 300 - turn dialogues while keeping the prompt under 3.5 K tokens. On long - form QA and story - continuation benchmarks, Compact + Vector Memory elevates factual - recall accuracy from 41 % to 87 % and human - rated coherence from 2.7 to 4.3. Precision - recall analysis shows that, with 10 K indexed chunks, Vector Memory achieves P@5 0.80 and R@50 0.74, doubling the area under the PR curve relative to a summarisation - only baseline. Ablation experiments reveal that (i) semantic forgetting -- age - weighted pruning of low - salience chunks -- cuts retrieval latency by 34 % with < 2 pp reca ll loss, and (ii) a two - level summary - of - summaries eliminates cascade errors that otherwise emerge after 1,000 turns. By reconciling verbatim recall with semantic continuity, our architecture offers a practical path toward scalable, privacy - aware conversational AI capable of engaging in months - long dialogue without retraining the underlying model.