Reasoner-Executor-Synthesizer: Scalable Agentic Architecture with Static O(1) Context Window
Large Language Models (LLMs) deployed as autonomous agents commonly use Retrieval-Augmented Generation (RAG), feeding retrieved documents into the context window, which creates two problems: the risk of hallucination grows with context length, and token cost scales linearly with dataset size. We propose the Reasoner-Executor-Synthesizer (RES) architecture, a three-layer design that strictly separates intent parsing (Reasoner), deterministic data retrieval and aggregation (Executor), and narrative generation (Synthesizer). The Executor uses zero LLM tokens and passes only fixed-size statistical summaries to the Synthesizer. We formally prove that RES achieves O(1) token complexity with res...
| Authors | Ivan Dobrovolskyi |
|---|---|
| Primary category | cs.IR |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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