ORCH:多くの分析,一つのマージ - EMAのガイドされたルーティングによる離散選択推論のための決定的マルチエージェントオーケストレーター
Hanlin Zhou1,2, Huah Yong Chan1
1School of Computer Sciences, Universiti Sains Malaysia, Gelugor, Malaysia.
Frontiers in artificial intelligence
|February 18, 2026
まとめ
ORCHは,新しい決定的オーケストレーターで,安定したルーティングを使用して,より高い精度とコストパフォーマンスのために,大規模な言語モデル推論を強化します. このアプローチは,離散選択タスクにおけるマルチエージェントシステムの再現可能な方法を提供します.
科学分野:
- 人工知能 (AI) とは,人工知能 (AI) のことです.
- 自然言語処理 (Natural Language Processing) とは,自然言語処理で処理される言語のことです.
- 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) と呼ばれるものです.
背景:
- マルチエージェントおよびアンサンブル・メソッドは,大型言語モデル (LLM) の離散選択推論を強化します.
- 現在のオーケストレーション方法は,しばしば非決定的であり,コストが高く,再現性が欠けている.
- LLMベースの推論には,安定的かつ効率的なオーケストレーション戦略が必要である.
研究 の 目的:
- LLMsのための決定的マルチエージェントオーケストレーターのORCHを導入します.
- 離散選択推論のタスクの正確性とコストパフォーマンスを向上させるため.
- 安定し,再現可能なオーケストレーションの枠組みを提供すること.
主な方法:
- ORCHは,異質なLLMエージェントのプールを雇用しています.
- 指数関数移動平均 (EMA) のパフォーマンストラッキングに基づいた決定的ルーティングメカニズムが利用されます.
- 選択されたエージェントのサブセットからの候補回答は,制御された集積によって合併されます.
主要な成果:
- ORCHは,単一モデルベースラインよりも一貫した精度改善を達成しています.
- 高コストの単一モデルのベースラインと比較して,追加の利益を提供します.
- 決定的パイプラインは安定性を高め,高価なモデルへの依存を軽減します.
結論:
- 決定的EMA誘導ルーティングは,離散選択推論のための実用的で再現可能な戦略です.
- ORCHのフレームワークは,多様なタスクとエージェントプールに拡張する可能性を示しています.
- このアプローチは,マルチエージェントのLLMオーケストレーションのための安定的かつ効率的なソリューションを提供します.
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