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    FlowBank proposes reusable workflows for AI-agent teams

    FlowBank’s author reports an average score of 73.40 across five benchmarks, versus 70.40 for the strongest automated baseline they evaluated.

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    TLDR

    FlowBank’s author says it builds a compact bank of complementary AI-agent workflows and selects one for each query, rather than keeping a single overall winner. They report an average score of 73.40 across five benchmarks, versus 70.40 for the strongest automated baseline they evaluated, at a lower reported average inference cost. They say all agentic workflows used the same GPT-4o mini executor.

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    10 Sources, first seen 3h ago

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    10 Sources

    @furonghOpenAI reported ~10,000 agents working on Navier–Stokes. That raises a question for teams of any size: how should agents work together, and which ways of working are worth reusing? Our insight: a workflow that loses on average can solve what the winner misses. FlowBank learns which workflows to keep and when to use each. Accepted at #NeurIPS2026 #AgenticAI A 🧵3h
    @lingzhi_yuan🙌 Thanks Furong for sharing this work! I will be at Atlanta for this year’s NeurIPS and hope to chat anything about Agent Harness and Multi-agent System design!2h

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    10 Sources

    @furonghOpenAI reported ~10,000 agents working on Navier–Stokes. That raises a question for teams of any size: how should agents work together, and which ways of working are worth reusing? Our insight: a workflow that loses on average can solve what the winner misses. FlowBank learns which workflows to keep and when to use each. Accepted at #NeurIPS2026 #AgenticAI A 🧵3h
    @lingzhi_yuan🙌 Thanks Furong for sharing this work! I will be at Atlanta for this year’s NeurIPS and hope to chat anything about Agent Harness and Multi-agent System design!2h