Multi-Agent LLM Architectures: Benchmarking Precision in Financial Processing

Orchestrating multiple LLM agents for structured document extraction is the new frontier in AI reliability. This study benchmarks four core architectures—Sequential, Parallel, Hierarchical, and Reflective—across 10,000 SEC filings to find the ultimate cost-precision balance.Results show that Reflective architectures lead in accuracy (0.943 F1), while Hierarchical models define the Pareto frontier for cost-effectiveness. With optimization strategies like semantic caching, we can now recover performance gains with minimal overhead.Key Takeaways:Global benchmark on 10,000+ financial reports.Reflective vs. Hierarchical performance trade-offs.Proven strategies for high-throughput AI deployment.#GenerativeAI #MultiAgentSystems #FinTech #LLM #DocumentProcessing #SEC #AIStrategy #learnbydoingwithstevenAll my links: https://linktr.ee/learnbydoingwithstevenPaper: https://arxiv.org/abs/2603.11888