Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces
Large Language Models (LLMs) face fundamental challenges in long-context reasoning: many documents exceed their finite context windows, while performance on texts that do fit degrades with sequence length, necessitating their augmentation with external memory frameworks. Current solutions, which have evolved from retrieval using semantic embeddings to more sophisticated structured knowledge graphs representations for improved sense-making and associativity, are tailored for fact-based retrieval and fail to build the space-time-anchored narrative representations required for tracking entities through episodic events. To bridge this gap, we propose the Generative Semantic Workspace (GSW), a neuro-inspired generative memory framework that builds structured, interpretable representations of evolving situations, enabling LLMs to reason over evolving roles, actions, and spatiotemporal contexts. Our framework comprises an Operator, which maps incoming observations to intermediate semantic structures, and a Reconciler, which integrates these into a persistent workspace that enforces temporal, spatial, and logical coherence. On the Episodic Memory Benchmark (EpBench) huet_episodic_2025 comprising corpora ranging from 100k to 1M tokens in length, GSW outperforms existing RAG based baselines by up to 20\%. Furthermore, GSW is highly efficient, reducing query-time context tokens by 51\% compared to the next most token-efficient baseline, reducing inference time costs considerably. More broadly, GSW offers a concrete blueprint for endowing LLMs with human-like episodic memory, paving the way for more capable agents that can reason over long horizons.
6914ecfea1b06ca3cc81344e
Shreyas Rajesh
claimed_verified
2025-11-13T13:06:02.927Z
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/avatars/f2e1c785c9d20dabb3a373290f73a9c2.svg
Shreyas Rajesh
shreyasrajesh
user
shreyasrajesh
6914ecfea1b06ca3cc81344f
Pavan Holur
6914ecfea1b06ca3cc813450
Chenda Duan
claimed_verified
2026-02-19T09:54:22.648Z
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Chenda Duan
Dadaism6
user
Dadaism6
6914ecfea1b06ca3cc813451
David Chong
6914ecfea1b06ca3cc813452
Vwani Roychowdhury
Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces
Large Language Models (LLMs) face fundamental challenges in long-context reasoning: many documents exceed their finite context windows, while performance on texts that do fit degrades with sequence length, necessitating their augmentation with external memory frameworks. Current solutions, which have evolved from retrieval using semantic embeddings to more sophisticated structured knowledge graphs representations for improved sense-making and associativity, are tailored for fact-based retrieval and fail to build the space-time-anchored narrative representations required for tracking entities through episodic events. To bridge this gap, we propose the Generative Semantic Workspace (GSW), a neuro-inspired generative memory framework that builds structured, interpretable representations of evolving situations, enabling LLMs to reason over evolving roles, actions, and spatiotemporal contexts. Our framework comprises an Operator, which maps incoming observations to intermediate semantic structures, and a Reconciler, which integrates these into a persistent workspace that enforces temporal, spatial, and logical coherence. On the Episodic Memory Benchmark (EpBench) huet_episodic_2025 comprising corpora ranging from 100k to 1M tokens in length, GSW outperforms existing RAG based baselines by up to 20\%. Furthermore, GSW is highly efficient, reducing query-time context tokens by 51\% compared to the next most token-efficient baseline, reducing inference time costs considerably. More broadly, GSW offers a concrete blueprint for endowing LLMs with human-like episodic memory, paving the way for more capable agents that can reason over long horizons.
The Generative Semantic Workspace (GSW) enhances LLMs' long-context reasoning by creating structured, interpretable representations of evolving situations, outperforming existing methods on the Episodic Memory Benchmark and reducing inference time.
Large Language Models long-context reasoning context windows external memory frameworks semantic embeddings knowledge graphs Generative Semantic Workspace neuro-inspired generative memory Operator Reconciler Episodic Memory Benchmark EpBench token-efficient inference time human-like episodic memory