[2603.22367] Reasoner-Executor-Synthesizer: Scalable Agentic Architecture with Static O(1) Context Window

Computer Science > Information Retrieval arXiv:2603.22367 (cs) [Submitted on 23 Mar 2026] Title:Reasoner-Executor-Synthesizer: Scalable Agentic Architecture with Static O(1) Context Window Authors:Ivan Dobrovolskyi View a PDF of the paper titled Reasoner-Executor-Synthesizer: Scalable Agentic Architecture with Static O(1) Context Window, by Ivan Dobrovolskyi View PDF Abstract: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 respect to dataset size, and validate this on ScholarSearch, a scholarly research assistant backed by the Crossref API (130M+ articles). Across 100 benchmark runs, RES achieves a mean token cost of 1,574 tokens regardless of whether the dataset contains 42,000 or 16.3 million articles. The architecture eliminates data hallucination by construction: the LLM never sees raw records. KEYWORDS LLM agents; agentic architecture...