Governed Hierarchical Memory and Uncertainty-Triggered Routing for Cost-Efficient Financial Sentiment Decision Support

Financial decision-support systems must balance quality, cost, traceability, and controlled state reuse. Existing single-agent, retrieval-augmented, and always-on multi-agent pipelines leave the joint control of collaboration and shared-memory activation under-specified. The purpose of this study is to determine whether governed hierarchical memory with uncertainty-triggered routing can preserve financial sentiment quality while reducing coordination cost. We operationalize uncertainty with an input-derived ambiguity score: after a low-cost probe, below-threshold inputs short circuit, whereas flagged inputs escalate to governed collaboration. On the disjoint 2064-record remainder of Financial PhraseBank sentences_allagree, the proposed configuration achieves 0.9453 accuracy and 0.9364 macro-F1—within 0.0005 and 0.0006, respectively, of the direct baseline—while using 63.57% fewer tokens than always-on collaboration but 31.05% more than direct inference. Earlier 200-record development and agreement-shifted analyses are highly label-skewed and are treated as sensitivity evidence only. Ablations and trace audits identify routing as the measured source of efficiency; no isolated benchmark gain is attributable to the governance rule. Theoretically, the study separates computational activation from governed state reuse as control-plane mechanisms. Practically, managers can reserve collaboration for flagged inputs while retaining traceability, although the ambiguity proxy and governed-memory benefits require harder workflow validation.