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[2509.11079] Difficulty-Aware Agentic Orchestration for Query-Specific Multi-Agent Workflows
Summary
The paper presents Difficulty-Aware Agentic Orchestration (DAAO), a novel framework for optimizing multi-agent workflows based on query difficulty, enhancing efficiency and accuracy in LLM-based systems.
Why It Matters
As AI systems increasingly utilize multi-agent frameworks, optimizing performance based on query complexity is crucial. DAAO addresses inefficiencies in existing systems, potentially improving user experience and resource allocation in AI applications.
Key Takeaways
- DAAO dynamically adjusts workflows based on predicted query difficulty.
- The framework includes a variational autoencoder for difficulty estimation.
- Experiments show DAAO outperforms existing multi-agent systems in accuracy and efficiency.
Computer Science > Artificial Intelligence arXiv:2509.11079 (cs) [Submitted on 14 Sep 2025 (v1), last revised 13 Feb 2026 (this version, v5)] Title:Difficulty-Aware Agentic Orchestration for Query-Specific Multi-Agent Workflows Authors:Jinwei Su, Qizhen Lan, Yinghui Xia, Lifan Sun, Weiyou Tian, Tianyu Shi, Xinyuan Song, Lewei He, Yang Jingsong View a PDF of the paper titled Difficulty-Aware Agentic Orchestration for Query-Specific Multi-Agent Workflows, by Jinwei Su and 8 other authors View PDF HTML (experimental) Abstract:Large Language Model (LLM)-based agentic systems have shown strong capabilities across various tasks. However, existing multi-agent frameworks often rely on static or task-level workflows, which either over-process simple queries or underperform on complex ones, while also neglecting the efficiency-performance trade-offs across heterogeneous LLMs. To address these limitations, we propose Difficulty-Aware Agentic Orchestration (DAAO), which can dynamically generate query-specific multi-agent workflows guided by predicted query difficulty. DAAO comprises three interdependent modules: a variational autoencoder (VAE) for difficulty estimation, a modular operator allocator, and a cost- and performance-aware LLM router. A self-adjusting policy updates difficulty estimates based on workflow success, enabling simpler workflows for easy queries and more complex strategies for harder ones. Experiments on six benchmarks demonstrate that DAAO surpasses prior multi-a...
