The paper proposes Retrieval Augmented Forecasting (RAF) that augments time-series foundation models with retrieved similar series to improve forecasting accuracy across domains.
Retrieval-augmented generation for knowledge-intensive nlp tasks
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CoRoVA compresses repository context into compact vectors for code LLMs, reducing TTFT 20-38% versus uncompressed RAG with only a small projector module.
citing papers explorer
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Retrieval Augmented Time Series Forecasting
The paper proposes Retrieval Augmented Forecasting (RAF) that augments time-series foundation models with retrieved similar series to improve forecasting accuracy across domains.
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CoRoVA: Compressed Representations for Vector-Augmented Code Completion
CoRoVA compresses repository context into compact vectors for code LLMs, reducing TTFT 20-38% versus uncompressed RAG with only a small projector module.