FlowBank: Query-Adaptive Agentic Workflows Optimization through Precompute-and-Reuse
📰 ArXiv cs.AI
Optimize LLM-based multi-agent systems with FlowBank, a query-adaptive approach that precomputes and reuses workflows to reduce inference costs and improve performance.
Action Steps
- Analyze existing workflow optimization paradigms to identify trade-offs and limitations.
- Implement FlowBank's precompute-and-reuse approach to optimize workflows for LLM-based multi-agent systems.
- Evaluate the performance of FlowBank using metrics such as inference cost and workflow efficiency.
- Apply FlowBank to real-world applications, such as query-answering systems or decision-support systems.
- Compare the results of FlowBank with traditional task-level and query-level methods to demonstrate its effectiveness.
Who Needs to Know This
Machine learning engineers and researchers working on LLM-based multi-agent systems can benefit from FlowBank to optimize their workflows and improve overall system performance. This approach can be particularly useful for teams dealing with large-scale, complex workflows.
Key Insight
💡 FlowBank's precompute-and-reuse approach can significantly reduce inference costs and improve workflow efficiency in LLM-based multi-agent systems.
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🚀 Optimize LLM-based multi-agent systems with FlowBank! 🤖
Full Article
Title: FlowBank: Query-Adaptive Agentic Workflows Optimization through Precompute-and-Reuse
Abstract:
arXiv:2606.11290v1 Announce Type: cross Abstract: Large Language Model (LLM)-based multi-agent systems are increasingly powerful, but current agentic workflow optimization paradigms make an unsatisfying trade-off. Task-level methods spend substantial offline compute yet deploy only a single workflow, leaving complementary candidates unused, while query-level methods synthesize a new workflow per query at substantial inference cost. Our motivating analysis shows these paradigms are more complemen
Abstract:
arXiv:2606.11290v1 Announce Type: cross Abstract: Large Language Model (LLM)-based multi-agent systems are increasingly powerful, but current agentic workflow optimization paradigms make an unsatisfying trade-off. Task-level methods spend substantial offline compute yet deploy only a single workflow, leaving complementary candidates unused, while query-level methods synthesize a new workflow per query at substantial inference cost. Our motivating analysis shows these paradigms are more complemen
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