Benchmarking Multi-Agent LLM Architectures for Financial Document Processing: A Comparative Study of Orchestration Patterns, Cost-Accuracy Tradeoffs and Production Scaling Strategies
📰 ArXiv cs.AI
Benchmarking study compares multi-agent LLM architectures for financial document processing, evaluating orchestration patterns, cost-accuracy tradeoffs, and production scaling strategies
Action Steps
- Identify the requirements for financial document processing, including accuracy and cost constraints
- Evaluate the four multi-agent orchestration architectures: sequential pipeline, parallel fan-out with merge, hierarchical supervisor-worker, and reflexive self-correcting loop
- Compare the cost-accuracy tradeoffs of each architecture and consider production scaling strategies
- Select the most suitable architecture based on the specific use case and deploy it in a production environment
Who Needs to Know This
AI engineers, data scientists, and software engineers on a team can benefit from this study to inform their architectural decisions for production deployments of LLMs for financial document processing
Key Insight
💡 The choice of multi-agent orchestration architecture significantly impacts the cost-accuracy tradeoff and production scaling of LLMs for financial document processing
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💡 Benchmarking multi-agent LLM architectures for financial document processing reveals key insights for production deployments
Key Takeaways
Benchmarking study compares multi-agent LLM architectures for financial document processing, evaluating orchestration patterns, cost-accuracy tradeoffs, and production scaling strategies
Full Article
Title: Benchmarking Multi-Agent LLM Architectures for Financial Document Processing: A Comparative Study of Orchestration Patterns, Cost-Accuracy Tradeoffs and Production Scaling Strategies
Abstract:
arXiv:2603.22651v1 Announce Type: new Abstract: The adoption of large language models (LLMs) for structured information extraction from financial documents has accelerated rapidly, yet production deployments face fundamental architectural decisions with limited empirical guidance. We present a systematic benchmark comparing four multi-agent orchestration architectures: sequential pipeline, parallel fan-out with merge, hierarchical supervisor-worker and reflexive self-correcting loop. These are e
Abstract:
arXiv:2603.22651v1 Announce Type: new Abstract: The adoption of large language models (LLMs) for structured information extraction from financial documents has accelerated rapidly, yet production deployments face fundamental architectural decisions with limited empirical guidance. We present a systematic benchmark comparing four multi-agent orchestration architectures: sequential pipeline, parallel fan-out with merge, hierarchical supervisor-worker and reflexive self-correcting loop. These are e
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