PerspectiveGap: A New Benchmark Reveals LLMs Struggle with Multi-Agent Orchestration Prompting
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[Submitted on 7 Jun 2026]
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Summary
The article introduces PerspectiveGap, a benchmark designed to evaluate LLMs' ability to compose orchestration prompts for multi-agent systems. It contains 110 scenarios across 10 topologies, tested via role-fragment assignment and free-form prompt writing. Experiments with 27 commercial models show GPT-5.5 significantly outperforms competitors, but overall performance remains low (14.9% average pass rate), indicating multi-agent orchestration prompting is a distinct and under-evaluated capability.
Key quotes
· 4 pulledReal-world LLM applications are moving beyond single-agent workflows toward orchestrated multi-agent systems, yet current models still struggle to determine what each sub-agent needs to know.
PerspectiveGap contains 110 scenarios, each evaluated through two distractor-mixed task formats: role-fragment assignment and free-form prompt writing.
the evaluated models achieve an average combined pass rate of only 14.9% (GPT-5.5 62.0%) and an average overall leakage rate of 246.5%
These findings suggest that multi-agent orchestration prompting is a distinct and under-evaluated capability, and PerspectiveGap provides a foundation for measuring and improving it systematically.
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