--- schema: formation.pattern_findings/v0.1 kind: pattern_findings visibility: public canonical_url: https://topologyindex.com/patterns/hierarchical_delegation/findings.md description: 'What published studies found about hierarchical_delegation, in words: each finding''s sentence, comparison, domain, caveat and source. Attributed, stated without figures, never a ranking.' path: /patterns/hierarchical_delegation/findings.md pattern: hierarchical_delegation pattern_index: /patterns/index.md pattern_page: /patterns/hierarchical_delegation.md product_api_version: v1 published_findings: - benchmarks: - HumanEval - MBPP - SoftwareDev compared_against: 'Chat-based multi-agent frameworks such as ChatDev and AgentVerse, and single models' direction: helped source_id: arxiv:2308.00352 task_domain: 'Software engineering and code generation' task_domains: - coding url: https://arxiv.org/abs/2308.00352 - benchmarks: - HumanEval - CIAR - CommonMT - FairEval compared_against: 'Linear and flat multi-agent structures' direction: helped source_id: arxiv:2408.00989 task_domain: 'Code generation, math, translation and text evaluation' task_domains: - coding - reasoning - evaluation url: https://arxiv.org/abs/2408.00989 - benchmarks: - MMLU - WikiQA - Camera compared_against: 'Strong reasoning models and multi-agent frameworks such as AgentVerse' direction: helped source_id: arxiv:2502.11098 task_domain: 'Question answering and advertisement text generation' task_domains: - reasoning url: https://arxiv.org/abs/2502.11098 - benchmarks: [] compared_against: 'A single centralized decision-maker with equivalent information access' direction: hurt source_id: arxiv:2603.26993 task_domain: 'Multi-stage planning and decision making' task_domains: - reasoning url: https://arxiv.org/abs/2603.26993 - benchmarks: - MAST-Data compared_against: 'The unmodified ChatDev configuration' direction: no_clear_gain source_id: arxiv:2503.13657 task_domain: 'Software development tasks' task_domains: - coding url: https://arxiv.org/abs/2503.13657 - benchmarks: [] compared_against: 'A single agent with the same model and no vulnerability description, and ablations without the task-specific agents or the hierarchy' direction: helped source_id: arxiv:2406.01637 task_domain: 'Offensive security: exploiting unknown real-world web vulnerabilities' task_domains: - security url: https://arxiv.org/abs/2406.01637 - benchmarks: - CVE-Bench compared_against: 'The Cybench single agent and AutoGPT under the same model and iteration budget' direction: mixed source_id: arxiv:2503.17332 task_domain: 'Offensive security: exploiting real-world web application vulnerabilities' task_domains: - security url: https://arxiv.org/abs/2503.17332 schema_version: v0.1 title: 'Published findings on the hierarchical_delegation multi-agent pattern' --- # Published findings on the hierarchical_delegation multi-agent pattern The findings filed under the [hierarchical_delegation](/patterns/hierarchical_delegation.md) pattern page, in words. That page has the definition, failure modes and sources; the [pattern catalogue](/patterns/index.md) has the general findings about multi-agent systems. ## What published studies found Attributed to each source and stated without figures, because a number from one configuration reads as a result for the pattern. Unfavourable results are included on purpose. None of this is evidence produced by this service. Reviewed 2026-09-23. Each label says how this pattern fared against what it was compared with, as the source reports it; the labels are defined at [/docs/schemas/pattern/v0.1.md](/docs/schemas/pattern/v0.1.md). - **Helped** — The authors report that encoding standardized operating procedures into an assembly line of role agents produced more coherent software solutions than earlier chat-based multi-agent systems and used fewer tokens per line of code than ChatDev, though more tokens in total. Compared against: Chat-based multi-agent frameworks such as ChatDev and AgentVerse, and single models. Domain: Software engineering and code generation. Benchmarks: HumanEval, MBPP, SoftwareDev. Caveat: Evaluated largely by the framework's authors, including on a self-constructed software task set. Source: [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://arxiv.org/abs/2308.00352), Hong et al., 2023-08-01. - **Helped** — The authors report that a mixed hierarchical structure lost the least performance when faulty agents were injected, while one-way linear pipelines such as MetaGPT-style chains lost the most. Compared against: Linear and flat multi-agent structures. Domain: Code generation, math, translation and text evaluation. Benchmarks: HumanEval, CIAR, CommonMT, FairEval. Caveat: Structures were represented by a handful of existing systems that differ in more than topology, and robustness under injected errors is not the same as baseline accuracy. Source: [On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents](https://arxiv.org/abs/2408.00989), Huang et al., 2024-08-02. - **Helped** — The authors report that a supervisor-led hierarchy with a nested evaluation team and structured messages outperformed strong single-model and multi-agent baselines across their tasks. Compared against: Strong reasoning models and multi-agent frameworks such as AgentVerse. Domain: Question answering and advertisement text generation. Benchmarks: MMLU, WikiQA, Camera. Caveat: The authors themselves flag a very high API cost for the experiments. Source: [Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems](https://arxiv.org/abs/2502.11098), Wang et al., 2025-02-16. - **Hurt** — The authors argue that multi-stage LLM planning networks that pass limited language messages lose information at each hand-off and, absent new external signals, cannot beat a single centralized decision-maker with the same information. Compared against: A single centralized decision-maker with equivalent information access. Domain: Multi-stage planning and decision making. Caveat: Primarily a theoretical technical note with small controlled experiments rather than a large empirical benchmark study. Source: [On the Reliability Limits of LLM-Based Multi-Agent Planning](https://arxiv.org/abs/2603.26993), Ao, Gao, Simchi-Levi, 2026-03-27. - **No clear gain** — The authors report that improving role specifications in the role-hierarchy framework ChatDev produced only a modest gain in task success, and conclude that many failures stem from organizational design and coordination rather than individual agent ability. Compared against: The unmodified ChatDev configuration. Domain: Software development tasks. Benchmarks: MAST-Data. Caveat: A single intervention case study rather than a systematic comparison of hierarchy depths. Source: [Why Do Multi-Agent LLM Systems Fail?](https://arxiv.org/abs/2503.13657), Cemri et al., 2025-03-17. - **Helped** — Zhu and colleagues report that a hierarchical planner dispatching task-specific expert agents exploited real-world web vulnerabilities without being told what they were far more often than a single agent, and that removing the expert agents or the hierarchy sharply reduced success. Compared against: A single agent with the same model and no vulnerability description, and ablations without the task-specific agents or the hierarchy. Domain: Offensive security: exploiting unknown real-world web vulnerabilities. Caveat: Author-built benchmark of a small set of reproducible open-source web vulnerabilities, which the authors note may be a biased sample. Source: [Teams of LLM Agents can Exploit Zero-Day Vulnerabilities](https://arxiv.org/abs/2406.01637), Zhu et al., 2024-06-02. - **Mixed** — Zhu and colleagues report that a hierarchical team of specialised agents exploited more real-world web vulnerabilities than an agent built for capture-the-flag tasks, while a general single agent with self-criticism achieved the highest success rate. Compared against: The Cybench single agent and AutoGPT under the same model and iteration budget. Domain: Offensive security: exploiting real-world web application vulnerabilities. Benchmarks: CVE-Bench. Caveat: The team framework comes from the same research group, and all success rates are low. Source: [CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities](https://arxiv.org/abs/2503.17332), Zhu et al., 2025-03-21. ## How evidence works here A pattern names how work is organized, never how well that works. Evidence in this service attaches to a complete execution configuration — a topology formation together with an execution lock pinning models, tools and runtime — and never to a pattern on its own, so no page here carries a success rate, a cost or a run count for an arrangement.