Reasoner-Executor-Synthesizer: Scalable Agentic Architecture with Static O(1) Context Window
📰 ArXiv cs.AI
RES architecture proposes a scalable agentic design for Large Language Models, separating intent parsing, deterministic data processing, and response synthesis
Action Steps
- Separate intent parsing from deterministic data processing using the Reasoner layer
- Use the Executor layer for deterministic data processing and knowledge retrieval
- Synthesize responses using the Synthesizer layer, reducing the risk of hallucination and token cost
Who Needs to Know This
AI engineers and researchers working on autonomous agents and LLMs can benefit from this architecture, as it addresses scalability and hallucination issues in Retrieval-Augmented Generation
Key Insight
💡 Strict separation of layers in the RES architecture can improve scalability and reduce hallucination in LLMs
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💡 RES architecture for scalable LLMs: separate intent parsing, data processing, and response synthesis
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