多智能体协作框架通过验证闭环提升复杂问题回答质量
Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework for Complex Query Resolution
- 按依赖关系并行执行子问题，自动传递上下文
- 验证结果不完整时自动重规划，提升答案完整度至4.2分
- 支持灵活停止条件，兼顾质量与资源消耗
我们提出验证驱动的多智能体编排框架VMAO，通过迭代验证循环协调基于大模型的专用智能体。面对复杂查询，系统将其分解为带依赖关系的有向无环图（DAG）形式的子问题，由领域专用智能体并行执行，利用大模型评估结果完整性，并根据验证反馈自适应重规划以填补空白。关键贡献包括：(1) 基于依赖关系的并行执行与自动上下文传播；(2) 以大模型验证器作为协调信号的验证驱动重规划机制；(3) 可配置的终止条件，在答案质量与资源开销间实现平衡。在25个专家标注的市场调研查询上，相比单智能体基线，答案完整度从3.1提升至4.2，来源质量从2.6提升至4.1（1-5分制），证明编排层级的验证是多智能体质量保障的有效手段。
原文摘要 · Abstract (English)
We present Verified Multi-Agent Orchestration (VMAO), a framework that coordinates specialized LLM-based agents through a verification-driven iterative loop. Given a complex query, our system decomposes it into a directed acyclic graph (DAG) of sub-questions, executes them through domain-specific agents in parallel, verifies result completeness via LLM-based evaluation, and adaptively replans to address gaps. The key contributions are: (1) dependency-aware parallel execution over a DAG of sub-questions with automatic context propagation, (2) verification-driven adaptive replanning that uses an LLM-based verifier as an orchestration-level coordination signal, and (3) configurable stop conditions that balance answer quality against resource usage. On 25 expert-curated market research queries, VMAO improves answer completeness from 3.1 to 4.2 and source quality from 2.6 to 4.1 (1-5 scale) compared to a single-agent baseline, demonstrating that orchestration-level verification is an effective mechanism for multi-agent quality assurance.
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