DecisionBench: A Benchmark for Emergent Delegation in Long-Horizon Agentic Workflows
The paper introduces DecisionBench, a benchmark for emergent delegation in long-horizon agentic workflows. It evaluates various models and metrics to assess the performance of delegation strategies. The findings highlight significant unrealized potential for improving orchestration methods in AI workflows.
- ▪DecisionBench fixes a task suite and a peer-model pool for evaluating delegation in workflows.
- ▪The study reveals that mean end-task quality is similar across different awareness conditions.
- ▪A counterfactual ceiling indicates that perfect delegation could significantly outperform current measured performance.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.19099 (cs) [Submitted on 18 May 2026] Title:DecisionBench: A Benchmark for Emergent Delegation in Long-Horizon Agentic Workflows Authors:Yuxuan Gao, Megan Wang, Yi Ling Yu, Zijian Carl Ma, Ao Qu View a PDF of the paper titled DecisionBench: A Benchmark for Emergent Delegation in Long-Horizon Agentic Workflows, by Yuxuan Gao and 4 other authors View PDF HTML (experimental) Abstract:We introduce DecisionBench, a benchmark substrate for emergent delegation in long-horizon agentic workflows.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
