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

Abstract: Hierarchical orchestration, in which a Manager agent reviews worker output and can send it back for revision, is the default coordination pattern in production multi-agent LLM frameworks. Classical organizational theory predicts that the authority link speeds convergence on decisive output; work on sycophancy and Degeneration-of-Thought predicts that authoritative critique makes LLM output worse. Prior comparisons vary whole frameworks on tasks with checkable answers, leaving the authority link untested on open-ended work. We present a paired experiment that holds five LLM agents, their roles, prompts, tools, models, and data fixed and varies one link: whether the Manager may reject a worker's output and oblige a revision. Across 43 paired products and 86 runs of a business-intelligence reporting task, a five-model judge panel and a deterministic specification check score every report. The flat organization scores higher on Utility (d = 0.42, p = 0.009) and on Writing Clarity (d = 0.34, p = 0.030); the classical prediction fails. The reports are the same length, but hierarchical reports hedge 53% more, each revision loop is associated with a 0.14-point drop in Writing Clarity, and the hierarchical Writer's first draft is indistinguishable from the flat report: the gap opens inside the revision loop. Specification accuracy is at ceiling in both organizations, and the supervisory tier costs 51.5% more tokens for no quality gain. A supervisor pays for itself when it can verify and becomes a liability when it can only opine.

• The paper isolates and tests the effect of loop-back authority (the Manager's ability to reject and require revision) in hierarchical vs. flat coordination of LLM-based multi-agent systems, finding that loop-back authority degrades strategic depth in language generation
• Hierarchical coordination with loop-back authority results in reduced Utility and Writing Clarity, increased hedging, and higher costs compared to flat coordination, calls for a deeper analysis the relationship between managerial oversight and low-cost language generation tasks.
• Loop-back authority’s detrimental effects are primarily due to its influence on the Writer’s epistemic stance, increasing hedging and reducing decisiveness, rather than a loss of facts.

Research question and contribution

The paper isolates a specific organizational primitive in LLM-based multi-agent systems: whether a Manager can reject a worker’s output and require revision. This design choice is common in hierarchical frameworks, but its causal effect is difficult to identify because prior comparisons typically change several variables simultaneously, including prompts, role definitions, communication topology, model assignments, and tools. “Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordination” addresses this confounding problem through a paired comparison in which the authority link is the principal manipulated variable (2609.14767).

The study contrasts two forms of the same five-agent system. Both contain a Researcher, Analyst, Writer, Critic, and Manager; use the same prompts, tools, model pool, data, and shared blackboard; and execute the same business-intelligence reporting task. In the flat condition, the Manager routes work and may comment but cannot reject an output or compel revision. In the hierarchical condition, the Manager reviews the accumulated blackboard, issues binding directives, and can send the Researcher, Analyst, or Writer back for up to two revisions per run. The experiment therefore operationalizes hierarchy not as a wholesale change in topology, but as a change in the deontic status of one communication link.

This manipulation tests competing theoretical predictions. Classical organizational theory suggests that authoritative coordination should accelerate convergence and improve decisive, coherent output. In contrast, research on sycophancy and Degeneration-of-Thought predicts that authoritative criticism may induce compliance, hedging, and reduced exploratory reasoning. The paper’s central claim is that these predictions diverge specifically because LLM agents do not treat authority as a neutral control channel: authoritative feedback also conditions the language-generation process.

Experimental design

The task involves generating reports on 43 Amazon US gaming laptops using product specifications and customer reviews. The dataset was restricted to laptops with an RTX 4060 GPU, and products were included only when they had at least five reviews. The final paired sample contains 86 runs: one flat and one hierarchical run for each product. Random assignments of five frontier models to roles were held constant within each product pair. The models were GPT-5.4, Gemini-3.1-Pro, Qwen-3.5-122B, GLM-5, and Mistral-Large-3; the Manager used a four-model subset because Qwen’s thinking-mode output was incompatible with the routing schema.

The Researcher used product-specification retrieval and sentiment-filtered review search. The Analyst synthesized patterns and root causes, the Writer generated the report, and the Critic used a verify_claim tool to assess technical assertions against the product data. This tool-grounded critique was present in both conditions, which is important: the experiment does not compare critique against no critique. It compares non-binding managerial commentary against binding managerial loop-back authority.

The evaluation combines subjective and deterministic components. A five-model judge panel scores Structure, Coherence, Conciseness, Actionability, Root Cause Analysis, and Strategic Depth on a five-point scale. These dimensions form the Writing Clarity and Utility composites. Specification accuracy is evaluated separately using automated extraction and verification. A repaired version of this metric scores both precision and recall against all validated product specifications rather than only the specifications asserted in the report.

The primary statistical analysis uses paired-sample tests across products, supplemented by mixed-effects models with product as a random intercept and controls for writer model, review count, and mean rating. The authors also analyze the number of Manager-issued loops, successive Writer drafts, hedging density, lexical diversity, report length, and token cost. This combination is methodologically valuable because it separates the overall treatment effect from the proposed mechanism and from operational efficiency.

Main quality results

The flat condition outperformed the hierarchical condition on the two principal open-ended quality measures. Utility showed the strongest result: the flat form achieved a mean of 4.715 compared with 4.621 for the hierarchical form, corresponding to a paired effect size of $d = 0.42$ and $p = 0.009$. The Writing Clarity difference was smaller but still statistically significant under the primary paired test: 4.454 versus 4.360, $d = 0.34$, $p = 0.030$.

The mixed-effects analysis yields nearly identical treatment estimates. Hierarchy is associated with a $0.094$-point reduction in both Writing Clarity and Utility, with $p = 0.025$ and $p = 0.006$, respectively. The flat form also wins on Final Score in 28 of the 43 product pairs, whereas the hierarchical form wins 14 and one pair ties. These results contradict the classical prediction that authoritative loop-back should improve clarity. They support the LLM-specific prediction that forced revision can degrade open-ended synthesis.

The Utility result is more robust than the Writing Clarity result. All five judges favor the flat condition for Utility, and leave-one-judge-out estimates remain significant, with effect sizes between $d = 0.38$ and $d = 0.43$. Strategic Depth is likewise robust to judge omission. Writing Clarity is directionally stable but judge-sensitive: removing certain judges causes the paired result to become nonsignificant, and the Wilcoxon signed-rank test yields $p = 0.051$. The paper appropriately treats Utility and Strategic Depth as the stronger findings and the Writing Clarity composite as a less stable result.

The decomposition of Writing Clarity identifies Conciseness as the relevant dimension. Flat reports score 4.18 on Conciseness, compared with 3.98 for hierarchical reports, with $p = 0.009$0 and $p = 0.009$1. Structure and Coherence do not differ significantly. Thus, the hierarchical reports are not materially more disorganized; they are more padded. This distinction matters because it rules out a simple interpretation in which hierarchy causes broad textual deterioration.

Figure 1: Per-dimension mean judge scores show that the principal quality differences are concentrated in Conciseness and Strategic Depth.

The Utility effect is concentrated in Strategic Depth: the flat condition scores 4.70 versus 4.56, with $p = 0.009$2 and $p = 0.009$3. Root Cause Analysis trends in the same direction but is not significant, while Actionability is effectively at ceiling in both conditions. The implication is specific: loop-back authority appears to reduce the depth of strategic interpretation rather than the basic usability or formal organization of the report.

Specification accuracy and the role of verification

The two organizational forms do not differ in specification accuracy. On the original metric, the flat condition averages 4.520 and the hierarchical condition 4.543, with $p = 0.009$4 and $p = 0.009$5. A repaired precision-and-recall metric produces near-perfect results in both conditions: mean precision and recall are 1.000 for flat reports and 0.997 for hierarchical reports, with 85 of 86 reports correctly restating every validated listing specification.

This result is not merely a null finding. It identifies a boundary condition for managerial oversight. When correctness is directly verifiable, both architectures perform at ceiling, leaving little room for hierarchy to improve the output. The Manager’s additional authority provides no measurable advantage because the Critic already has access to a verification tool and the task’s factual component is easy to validate.

The result also clarifies why hierarchical systems may perform better on some benchmarks than on this task. If the target is a short answer with a readily checkable ground truth, a supervisory tier can detect and correct factual errors. Here, however, the factual substrate is already saturated, while the performance differences arise in strategic depth, concision, and epistemic stance. The supervisor can verify specifications but cannot objectively verify the quality of strategic interpretation. The paper’s proposed rule—that supervision pays when the supervisor can verify and becomes harmful when it can only opine—is therefore grounded in the contrast between the deterministic and open-ended metrics.

Revision loops as the mechanism

The central mechanistic evidence indicates that the authority link is not inherently harmful. It becomes harmful when exercised. Of the 43 hierarchical runs, 11 finish without a loop, 12 contain one loop, 17 contain two loops, and three receive a third revision request that is blocked by the cap. Writing Clarity declines monotonically with the number of loops, with Spearman’s $p = 0.009$6 and $p = 0.009$7. A controlled within-hierarchical regression estimates a reduction of 0.142 Writing Clarity points per loop, with $p = 0.009$8 and $p = 0.009$9.

This estimate is not causal because the Manager chooses which outputs to return. Weak first drafts may be more likely to receive a loop, producing reverse causality or selection bias. The paper acknowledges this limitation and uses draft-level comparisons as the cleaner mechanistic analysis. When the three cap-blocked runs are excluded, the estimated loop penalty decreases to $d = 0.34$0 points but remains significant. Thus, the magnitude is sensitive to specification, while the negative association is not eliminated.

The zero-loop subset provides additional triangulation. In the 11 hierarchical runs without intervention, hierarchical reports are numerically higher than their flat counterparts on Writing Clarity, Utility, and specification accuracy, although none of these differences is significant. Because the subset is selected by the Manager’s own judgment, it cannot establish equivalence or causality. It does, however, support the claim that the mere presence of a hierarchical role is insufficient to explain the observed degradation.

Figure 2: Writing Clarity decreases as the number of Manager-issued revision loops increases, although loop count is observationally selected by the Manager.

The strongest evidence comes from comparing the Writer’s successive drafts. The first hierarchical draft is statistically indistinguishable from the paired flat report on hedging density, lexical diversity, and length. The difference emerges after the Manager requires revision. Among revised runs, the second draft contains 1.95 additional hedges per 1,000 words, with $d = 0.34$1 and $d = 0.34$2, and is 38 words longer. Lexical diversity does not materially change.

The revisions are also conservative in content. The median revised draft retains 88% of its predecessor as a common subsequence and 85% of its content vocabulary. None of the 34 draft transitions qualifies as a substantial rewrite; one draft is returned unchanged. These results contradict an interpretation in which Manager feedback induces substantive analytical reconsideration. Instead, the Writer largely preserves its analysis while weakening its commitments and adding qualifications.

Hierarchical reports contain 5.03 epistemic hedges per 1,000 words, compared with 3.30 in flat reports, a 53% increase with $d = 0.34$3 and $d = 0.34$4. The increase is distributed across terms such as “possible,” “may,” “suggests,” and “could,” rather than being driven by one lexical artifact. Report length is effectively unchanged, and lexical diversity is also unchanged. Consequently, the mechanism is not simply verbosity or lexical impoverishment. It is a change in epistemic stance: revision produces more qualified and less decisive language.

The paper interprets this pattern as stronger evidence for sycophantic compliance than for strong Degeneration-of-Thought. The Writer does not substantially drift in content, collapse its vocabulary, or replace its reasoning. It responds to an authoritative directive by preserving the draft while softening its claims. This is an important refinement of the mechanism: the authority link may reduce commitment without necessarily reducing the underlying analytical content.

Cost and operational consequences

Hierarchy imposes a substantial operational penalty. Hierarchical runs consume 74,781 tokens per report compared with 49,370 for flat runs, a 51.5% increase. Total cost rises from $d = 0.34$50.606 per report, generation cost rises by 40.5%, and runtime increases from 210 to 282 seconds, or 34.3%. The additional tokens do not produce gains in Writing Clarity, Utility, or specification accuracy.

The cost result strengthens the substantive conclusion because the hierarchical condition does not trade efficiency for quality or quality for cost in a favorable way. Within this experimental regime, it pays more for a supervisory process that does not improve factual correctness and degrades two open-ended quality dimensions. The conclusion is narrower than a general rejection of hierarchy: it applies to a Manager whose revisions are based on qualitative judgment rather than a reliable external verifier.

Relation to organizational and LLM-agent theory

The paper contributes to organizational design research by distinguishing structural authority from the agent’s response to authority. In classical multi-agent systems, authority primarily routes tasks and controls transitions. In LLM systems, the same link also changes the conditioning context under which the worker generates text. The organizational form therefore affects not only coordination and search but also the linguistic behavior of the subordinate model.

The findings support the exploration-side prediction of organizational theory. Flat coordination preserves Strategic Depth, whereas hierarchical coordination appears to converge toward shallower, safer content. This is consistent with an entrapment mechanism in which authoritative feedback prematurely narrows the search space. The exploitation-side prediction fails: hierarchy does not improve Structure or Coherence and instead reduces Conciseness.

The paper also identifies an asymmetry between AI managers coordinating humans and AI managers coordinating LLM workers. The cited work on human teams reports substantial gains from an AI manager, whereas the present experiment finds that managerial loop-back can harm LLM-generated reports. The difference may arise because human workers can interpret critique as information and revise their beliefs, while RLHF-trained models may interpret authoritative criticism primarily as a cue to hedge and comply. The study does not establish this explanation directly, but its draft-level evidence is consistent with it.

Limitations and open questions

The external validity of the experiment is constrained. It studies one business-intelligence task, one product domain, one dataset, one Manager prompt, five model families, and 43 paired products. The result should not be generalized without qualification to coding, long-context reasoning, planning, or tasks with stronger verifiers.

The subjective evaluation also depends on LLM judges whose preferences may overlap with those of the systems being evaluated. Absolute Krippendorff’s alpha is low for Writing Clarity, ranging from 0.08 to 0.17, although directional agreement and the Utility result are substantially stronger. The hedge lexicon cannot determine whether additional uncertainty is appropriately calibrated or merely evasive. The interpretation of hedging as a quality failure therefore depends partly on the judge rubric.

The loop-count analyses remain correlational because the Manager selects when to intervene. A randomized manipulation of revision triggers would be required to estimate the causal effect of revision itself. The paper also does not measure factuality of analytical claims beyond product specifications, so it remains open whether a verifier for strategic claims would reverse the result. More specifically, the study leaves unresolved whether the observed compliance arises from the wording of authority, the absence of sanctions, the Manager’s review of the entire blackboard, the revision cap, or the particular prompt used to request correction.

Conclusion

The experiment finds that granting an LLM Manager authority to force revisions does not improve an open-ended synthesis task. Across 43 paired products, the flat architecture yields higher Utility and, with weaker judge robustness, higher Writing Clarity; hierarchical reports contain 53% more hedging, incur 51.5% more token usage, and obtain no accuracy benefit. The first drafts are equivalent, while the quality gap emerges after authoritative revision requests.

The paper’s most precise conclusion is conditional: hierarchy is not intrinsically damaging, but unverified managerial loop-back is. When supervision can check a concrete property, both forms already perform at ceiling; when supervision can only express an opinion, revision induces conservative compliance rather than substantive improvement. The direct open question is whether verifier-triggered revision, rather than Manager-judged revision, can retain the coordination benefits of hierarchy without producing hedging and reduced strategic depth (2609.14767).