Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems

John Knowlton; Aritra Guha; Risto Miikkulainen

cs.AIAug 11, 2026

Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems

Authors: John Knowlton, Aritra Guha, Risto Miikkulainen

Abstract

As large language model (LLM)-based multi-agent systems become increasingly capable, coordinating agents under uncertainty becomes a fundamental challenge. Existing orchestration strategies typically rely on fixed interaction patterns and often lack mechanisms for assessing the reliability of intermediate reasoning steps, allowing errors and hallucinations to propagate through the system. This paper introduces a semantic-uncertainty-guided orchestration approach, HASSUM as a general framework for uncertainty-aware coordination in multi-agent systems. The method estimates uncertainty using semantic entropy and semantic density, which measure trust at the level of answer semantics rather than output probabilities. These signals enable adaptive orchestration decisions, including output verification, selective reprompting, additional deliberation, and confidence-aware response selection. Because the approach operates independently of any particular agent architecture, it can be integrated into a broad range of hierarchical and collaborative multi-agent systems. The evaluations demonstrate an implementation within a hierarchical agent framework and evaluate it on StrategyQA, JailbreakBench, and TruthfulQA benchmarks. Across tasks that require complex reasoning and are prone to ambiguity or hallucinations, uncertainty-guided orchestration yields more reliable outcomes than uncertainty-unaware coordination. Semantic entropy and semantic density in tandem outperformed either metric alone. Ablations testing different thresholds and model sizes demonstrated that both influence the effectiveness of semantic metrics. The results suggest that semantic uncertainty is a practical and general-purpose signal for improving robustness and trustworthiness in agentic AI systems.

Figures & tables

Figure 1: High-level flowchart of HASSUM. The CEO agent receives the user query, selects an appropriate specialized worker agent, and evaluates the returned response using semantic density and semantic entropy. If the response exceeds the uncertainty thresholds, the CEO accepts the answer and synthesizes the final response. If semantic concern is triggered, the CEO may reprompt the same worker, select a different worker with a more suitable expertise, or invoke multiple workers in parallel before reevaluating the outputs. This process continues until the CEO reaches a sufficiently confident answer or the maximum number of reasoning rounds is reached. Thus, HASSUM transforms uncertainty estimation from a passive evaluation metric into an active control mechanism that directly guides agent selection, refinement, and final decision making.

Benchmarks with high ambiguity
Benchmark	HASSUM	HASHIRU
	(Metrics On)	(Metrics Off)
StrategyQA (500)	$78\%\pm 2.7\%$	$70\%\pm 4.0\%$
TruthfulQA (500)	$41\%\pm 3.9\%$	$19\%\pm 4.8\%$
JailbreakBench	$61\%\pm 6.3\%$	$25\%\pm 7.6\%$
Benchmarks outside the scope

Table 1: Comparison of HASSUM and HASHIRU, i.e. multi-agent orchestration with and without semantic uncertainty metrics. Accuracy is percentage correct with bootstrapped confidence intervals; the numbers in parentheses indicate data subset size.

Figure 2: Accuracy across ablations of semantic density and entropy on the StrategyQA subset. The combination performed best; either metric alone underperformed even the no-metric baseline.

Figure 3: Example execution traces on a StrategyQA question. With metrics enabled, low semantic density triggers refinement before synthesis; without metrics, the CEO accepts incorrect intermediate reasoning.

Metric	Run	Mean	Median	Q75
Semantic Density	Local	0.887	0.883	0.921
Semantic Density	GPT-5.4	0.957	0.957	1.000
Semantic Entropy	Local	0.949	1.055	1.332
Semantic Entropy	GPT-5.4	0.058	1.7e-16	0.013

Table 2: Semantic uncertainty for local workers vs. GPT-5.4 on StrategyQA. Even though GPT-5.4 workers are much stronger, HASSUM can use interventions to compensate, resulting in system accuracy of 75% in both cases.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 4: Accuracy between different thresholds for acceptable semantic density. The y-values are the percentage of correctly answered questions at a specific threshold indicated by the x-values. The orange line is a baseline accuracy from the run with semantic density at 0.8 and semantic entropy at 1.1. The higher the threshold value, the better the performance since the CEO becomes stricter with a higher threshold and is more likely to reprompt agents.

Figure 5: Accuracy between different thresholds for acceptable semantic entropy. The y-values are the percentage of correctly answered questions at a specific threshold indicated by the x-values. The orange line is a baseline accuracy from using density at 0.8 and entropy at 1.1. The broad trend is that the lower the threshold value, the better the performance is, since the CEO becomes stricter the lower the threshold and is more likely to reprompt agents.

Model Type	Result (%)
Llama-3.2 and Deepseek-R1	78
GPT-5.4	78

Appendix

Table 3: Two ablation runs that compare the performance of HASSUM using smaller models (Llama-3.2, Deepseek-R1) versus HASSUM using a larger model (GPT-5.4) as workers in the StrategyQA benchmark. The values in the table are the percentage of questions that each run answered correctly. Both runs had the same accuracy, showing how HASSUM can use semantic uncertainty to compensate for weaker models.

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