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Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The introduction of the Generative Semantic Workspace (GSW) marks a significant advancement in the capabilities of Large Language Models (LLMs) for long-context reasoning. Traditional retrieval-augmented generation (RAG) methods have struggled with the limitations of finite context windows and performance degradation with longer texts. GSW overcomes these challenges by providing a neuro-inspired generative memory framework that builds structured, interpretable representations of evolving situations. This allows LLMs to effectively track entities through episodic events, enhancing their reasoning capabilities. Evidence shows that GSW outperforms existing RAG-based baselines by up to 20% and reduces query-time context tokens by 51%. These improvements are crucial for applications that require complex narrative tracking, making GSW a pivotal development in the field of artificial intelligence.
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