Episodic-Semantic Memory Architecture for Long-Horizon Scientific Agents
The paper presents a new memory architecture designed for Large Language Models (LLMs) to enhance their performance in scientific workflows. This Dual Process Memory Architecture separates immediate episodic needs from long-term knowledge, addressing issues of context saturation and cognitive degradation. The findings indicate that this architecture can maintain high accuracy and efficiency even with extensive data inputs, outperforming traditional models in specific tasks.
- ▪The proposed architecture maintains 70-85% accuracy with reduced token usage compared to full-context models.
- ▪Cross-model validation shows that the Dual Process excels in numeric and temporal queries, while RAG is better for historical retrieval.
- ▪The architecture successfully manages over 14,000 scientific facts, demonstrating its capability to operate beyond full-context limits.
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Computer Science > Artificial Intelligence arXiv:2605.17625 (cs) [Submitted on 17 May 2026] Title:Episodic-Semantic Memory Architecture for Long-Horizon Scientific Agents Authors:Nikola Milosevic View a PDF of the paper titled Episodic-Semantic Memory Architecture for Long-Horizon Scientific Agents, by Nikola Milosevic View PDF HTML (experimental) Abstract:As Large Language Models (LLMs) evolve into persistent scientific collaborators, context window saturation has emerged as a critical bottleneck. Scientific workflows involving iterative data analysis and hypothesis refinement rapidly saturate even extended contexts with dense technical content, while monolithic approaches suffer from quadratic cost scaling and cognitive degradation.
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