FairAMO 评估协议揭示智能体时序模型编排收益有限
Rethinking Agentic Model Orchestration for Time Series Forecasting
这篇论文用 FairAMO 协议证明：时序预测的多模型编排收益其实不到 6%，堆模型数量没用，互补性才是关键。
arXiv 论文提出 FairAMO，一个冻结候选预测结果的控制性评估协议，用于拆分候选模型质量与编排策略各自的贡献。在 GIFT-Eval 基准上，编排（AMO）虽能提升时序预测准确率，但最好的编排器相比最佳单一时序基础模型，MASE 仅降低不到 6%。研究还发现收益主要来自候选模型间的互补性而非数量，增加成员收益递减，冗余成员甚至会抵消收益，历史证据的价值也很快饱和。
Rethinking Agentic Model Orchestration for Time Series Forecasting
Time series foundation models (TSFMs) perform unevenly across forecasting tasks. Recent work uses agentic model orchestration (AMO) to select or combine TSFMs, often outperforming individual forecasters. Yet these gains may reflect stronger or more complementary candidates rather than better orchestration. To disentangle these effects, we introduce FairAMO, a controlled evaluation protocol with frozen candidate forecasts. Experiments on GIFT-Eval show that AMO improves accuracy, but its gains are bounded by the candidate TSFMs. The best orchestrators reduce MASE by less than 6% relative to the best single TSFM across all tasks. These gains depend on complementarity (different candidates excel on different tasks) rather than the number of candidates. Adding members yields diminishing returns, and a redundant member can erase these gains. The value of historical evidence also saturates quickly. A few recent windows capture all gains, and further history adds none. These findings separate system-level forecasting gains from the contribution of orchestration itself.