mlsanityData to 14 Oct 2026

Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordination

arXiv 2609.14767 ↗First posted 13 Sep 2026cs.MA cs.AI cs.CLcited by 1

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Does authority in AI teams improve the outcome? Organizational theory asserts that authority facilitates decision making, improving quality. Meanwhile, some nascent AI research suggests that revision under authority makes LLM output worse. Multi-agent LLM frameworks default to giving a Manager agent the authority to send a worker's output back for revision. Prior comparisons test the effect of authority using verifiable tasks. We conduct an experiment on an open-ended task, business-intelligence reporting, using a sample of 43 paired laptop products and 86 runs. Each report is written once by a hierarchical team and once by a flat team. We find that flat teams produce higher-quality reports, scoring higher on Utility (d = 0.42, p = 0.009) and Writing Clarity (d = 0.34, p = 0.030). The reports are the same length, but hierarchical team reports use 53% more hedging words such as "may" and "could", and each revision is associated with a 0.14-point drop in Writing Clarity on a 1 to 5 scale. Before any revision, the hierarchical team's first draft is indistinguishable from the flat team's report. In other words, the quality gap can be traced to revision. Authority improves quality when the Manager can verify the work, else when it can only provide feedback it has a negative effect on quality.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 23 of its 60 references that are in the map. First posted on arXiv 13 Sep 2026.

Builds on

23 of 60 references.

Closest ancestors · 6

Same direction, newest first

  1. 2026OrgAgent: Organize Your Multi-Agent System like a Company1 Apr
  2. 2025Reliable agent engineering should integrate machine-compatible organizational principles8 Dec
  3. 2025Can Lessons From Human Teams Be Applied to Multi-Agent Systems? The Role of Structure, Diversity, and Interaction Dynamics8 Oct
  4. 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMs10 Jan
  5. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challenges21 Jan
  6. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug

Other references · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  5. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  6. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  7. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  8. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  9. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  10. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  11. 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
  12. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
5 more
  1. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
  2. 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
  3. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 222
  4. 2023Simple synthetic data reduces sycophancy in large language modelscited by 187
  5. 2025Stay Focused: Problem Drift in Multi-Agent Debatecited by 23

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  5. 2026How Fast Do Agents Rot? An Empirical Study of Long-Horizon Degradation in LLM Agents for Production Decision-Making
  6. 2026Can LLM Agents Sustain Long-Horizon Organizational Dynamics?
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