ORCH:许多分析,一个合并 - 一个决定性的多代理编排器用于离散选择推理与EMA引导的路由
Hanlin Zhou1,2, Huah Yong Chan1
1School of Computer Sciences, Universiti Sains Malaysia, Gelugor, Malaysia.
Frontiers in artificial intelligence
|February 18, 2026
概括
新的确定性编排器ORCH通过使用稳定的路由来增强大型语言模型推理,以提高准确性和成本效益. 这种方法为离散选择任务中的多代理系统提供了一种可重复的方法.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 多代理和整体方法在大型语言模型 (LLM) 中增强离散选择推理.
- 当前的编排方法往往是非决定性的,昂贵的,缺乏可重现性.
- 对于基于LLM的推理,需要稳定高效的编排策略.
研究的目的:
- 为LLMs引入ORCH,一个确定性的多代理编排器.
- 在离散选择推理任务中提高准确性和成本效益.
- 提供一个稳定和可重复的编排框架.
主要方法:
- ORCH雇佣了一群异质的LLM代理人.
- 使用基于指数移动平均 (EMA) 性能跟踪的确定性路由机制.
- 来自选定的代理子集的候选答案通过受控聚合合而合并.
主要成果:
- 在单一模型基线上,ORCH 实现了持续的准确性改进.
- 与高成本单一模型基线相比,它提供了额外的收益.
- 确定性管道增强了稳定性,减少了对昂贵模型的依赖.
结论:
- 确定性EMA引导路由是离散选择推理的实用和可重复的策略.
- ORCH框架显示了扩展到各种任务和代理池的潜力.
- 这种方法为多代理LLM编排提供了稳定高效的解决方案.
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