PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting

In a multi-agent LLM system, the main agent must write each sub-agent's prompt, yet current models struggle to determine what each sub-agent needs to know. Existing benchmarks score belief tracking or downstream task success, not the prompts themselves. We introduce PerspectiveGap, a benchmark for evaluating LLMs' ability to compose orchestration prompts for multi-agent systems. PerspectiveGap contains 110 scenarios, each evaluated through two distractor-mixed task formats: role-fragment assignment and verbatim prompt assembly, in which the model assembles each role's prompt from the given fragments. These scenarios are organized into 10 loop-centered topologies distilled from the authors' real-world engineering practice. Across 35 commercial models from 10 companies, the average combined pass rate is only 17.6%, and in role-fragment assignment, models give roles an average of 2.1 out-of-role fragments per scenario. GPT-5.5 leads by a wide margin (62.0% pass rate). These results show that current models often fail to give each sub-agent exactly the context it needs, an ability that existing benchmarks do not measure.