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Workerville: Towards an Organizational Behavior Account of Agent Safety

Hanjun Luo, Junting Mao, Yuhan Lu, Haobo Zhang, Zhimu Huang, Yankai Chen, Hanan Salam, Xue Liu

arXiv:2610.11561v1cs.AI

TL;DR

Existing agent safety research has studied organizational influences largely in isolation, leaving their joint effects unmeasured. This paper formalizes Agentic Counterproductive Behavior and evaluates it with Workerville, a controlled benchmark of shared tasks and organizational configurations. Across six models, negative antecedents combine non-monotonically, while agents also show behavioral patterns consistent with human CWB research and agent-specific boundaries.

  • Problem

    Existing safety research largely examines user authority, peer influence, and memory separately, without systematically measuring how their combination affects agent safety.

    Source: §1 · pp. 1–2

  • Method

    The paper formalizes Agentic Counterproductive Behavior, mapping three organizational antecedents to three safety outcomes, and operationalizes it in Workerville with shared tasks and varied configurations.

    Source: §1 · p. 2

  • Results

    Across six models, negative antecedents produce a non-monotonic pattern: unauthorized disclosure rises from 16.5% with none to 60.1% with two, then falls to 50.3% with three.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The results support organizational behavior as a systematic, quantifiable framework for studying agent safety and as a foundation for a dedicated research direction.

    Source: §6 · p. 10

    Human CWB mechanisms cannot be treated as equivalent to agent mechanisms, because agents act through model inference over instructions, peer messages, and retrieved memory rather than human workplace emotions or motives.

    Source: §5.2 · p. 10

Abstract

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LLM-based agents now interact with their environments continuously, shaped by such organizational channels as user instructions, peer messages, and long-term memory. Existing safety research has examined these influences, but largely as separate agent components. How such factors jointly shape an agent's safety behavior from a unified perspective remains unmeasured. To bridge this gap, we advocate organizational behavior (OB) as a framework for studying the safety of advanced agents, reorganizing the objects of study, theoretical foundations, and experimental design around the relational structure in which agents operate. We present the first systematic formalization of counterproductive work behavior (CWB), a canonical safety-relevant subfield of OB, as Agentic Counterproductive Behavior (ACB). ACB specifies three organizational antecedents (vertical supervisor relations, horizontal peer norms, and internal cognitive structures) and maps them onto three counterproductive outcome dimensions (unauthorized disclosure, destructive operations, and production deviation). To operationalize ACB, we introduce Workerville, a controlled benchmark that manipulates organizational conditions over shared tasks, applying 16 organizational configurations to 210 tasks to yield 3,360 challenges, evaluated by human-validated agentic judges. Benchmarking 6 frontier LLMs, we find that (I) negative organizational antecedents exhibit non-monotonic amplification when combined, with the unauthorized-disclosure rate rising from 16.5% under no negative antecedent to 60.1% under two and falling back to 50.3% under three; (II) agents reproduce typical behavioral patterns predicted by human CWB research; (III) these results establish OB as a systematic framework for agent safety research, pointing toward a new research agenda.

1 INTRODUCTION

Agent safety is shaped not only by models and tasks, but also by organizational relations, norms, and memory. The paper formalizes this perspective as ACB and operationalizes it in Workerville, where experiments show organizational conditions alter safety behavior across models.

1 INTRODUCTION

  • Existing safety research examines instruction authority, peer influence, and memory largely as separate components, leaving their combined effects on safety behavior without systematic quantitative measurement.Work on agents in organizational settings has largely focused on team design or performance.Source: §1 · pp. 1–2
  • ACB unifies three organizational antecedents—supervisor relations, peer norms, and internal cognitive structures—and maps them to disclosure, destructive-operation, and production-deviation risks.Workerville applies 16 organizational configurations to 210 shared tasks, producing 3,360 challenges with human-validated agentic judges.Source: §1 · p. 2
  • The benchmark holds task environments and objectives invariant across configurations, enabling controlled comparisons of organizational conditions.The paper presents the framework as a systematic and quantifiable basis for agent safety research.Source: §1 · p. 2
  • Across six models, negative organizational antecedents amplify unauthorized disclosure non-monotonically: the rate rises from 16.5% with none to 60.1% with two, then falls to 50.3% with three.The paper also reports that agents exhibit patterns consistent with classic CWB findings, alongside agent-specific boundaries.Source: §1 · p. 2

2 RELATED WORK

Prior work studies agent safety factors and organizational agent environments, but the paper identifies a gap: these strands have not formed a unified, quantifiable account of organizational effects on safety.

2 RELATED WORK

  • Safety evaluations have examined instruction hierarchy, peer influence, and long-term memory, often in separate problem settings and from an agent-mechanism perspective.Some work jointly manipulates supervisor demands and peer pressure, but the cited research still lacks a unified perspective on their safety consequences.Source: §2 · p. 3
  • The paper positions its contribution at the intersection: a unified and quantifiable framework for measuring how organizational factors jointly affect agent safety.This addresses gaps identified in both safety evaluation and organizational-agent research.Source: §2 · p. 3
  • Research on organizational agents spans social simulation, team structures, and controlled studies of local interactions, but has not connected organizational environments to agent safety.Several approaches emphasize team design, collaboration, or performance rather than a unified safety evaluation.Source: §2 · p. 3

3 AGENTIC COUNTERPRODUCTIVE BEHAVIOR

ACB applies organizational behavior to agent safety by linking organizational antecedents to observable counterproductive outcomes. It treats the agent as acting within a user–agent organization shaped by peers, production conditions, and memory.

3 AGENTIC COUNTERPRODUCTIVE BEHAVIOR

  • The minimal organizational situation includes a user as principal, a focal agent with delegated discretion, peer agents, a shared production environment, and long-term memory.The user specifies legitimate objectives, safety constraints, and output criteria, while the agent’s full behavioral trajectory may not be observable to the user.Source: §3 · pp. 3–4
  • The three antecedents are vertical supervisor relations, horizontal peer norms, and internal cognitive structures, with long-term memory representing the agent’s internal understanding of relations, tasks, and consequences.The organizational variable o = (M1, M2, M3) holds positive, neutral, or negative forms for each antecedent.Source: §3 · p. 4
  • ACB frames agent safety risks as observable behaviors and consequences that undermine the human principal’s productive objective.The framework connects organizational antecedents, agent behavior, and safety-relevant counterproductive outcomes.Source: §3 · pp. 3–4
  • ACB adapts the five CWB-C behavioral dimensions into three safety outcomes, and counts an outcome when the corresponding behavior triggers it.The outcomes are unauthorized disclosure, destructive operations, and production deviation.Source: §3 · p. 4, Table 1 · p. 4

4 WORKERVILLE

Workerville crosses shared tasks with organizational configurations to isolate how antecedents affect agent safety. Its task design, configuration families, runtime, and evaluation system support controlled comparisons.

4 WORKERVILLE

  • Workerville crosses 210 shared tasks with 16 organizational configurations, varying antecedents while keeping each task’s risk elements fixed for comparison.The benchmark overview depicts this controlled design.Source: §4 · p. 5, Figure 2 · p. 5

4.1 TASK DESIGN

  • The 210 manually constructed and cross-reviewed tasks cover operational scenarios, with endpoint applicability and task-specific safe/unsafe criteria annotated for each applicable outcome.The task distribution spans 12 scenario categories and 9 failure modes.Source: §4.1 · p. 5, Figure 3 · p. 5

4.2 ORGANIZATIONAL CONFIGURATIONS

  • The 16 configurations select baseline, single-antecedent, adverse-stacking, and mixed positive–negative conditions from the 27 possible combinations.The conditions operationalize supervisor signals, peer norms, and memory-based internal representations as positive, neutral, or negative.Source: §4.2 · p. 6

4.3 OPENCLAW-BASED RUNTIME

  • The OpenClaw-based runtime gives users, peers, and memory summarizers influence through instructions, messages, and memory writes, while only the worker performs tool calls and consequential actions.The sandbox provides task resources, message channels, and timed external events.Source: §4.3 · pp. 6–7

4.4 EVALUATION SYSTEM

  • Three endpoint-specific memory-augmented judges assess complete recorded runs using retrieved human-reviewed exemplars and observable execution evidence.Each judge returns a binary safe/unsafe label, rationale, and count of triggering events.Source: §4.4 · p. 7

5 EMPIRICAL ANALYSIS

The study evaluates how organizational conditions shape three agentic counterproductive behavior outcomes across six models. Outcomes vary with individual antecedents, their combinations, and boundary cases that distinguish agent behavior from human CWB mechanisms.

5.1 EXPERIMENTAL SETUP

  • The evaluation covers six commercial and open-source models, defines ACBR as episodes with at least one unsafe applicable outcome, and assesses dimensional rates and cross-model robustness.Configuration effects use percentage-point changes relative to C0 or a paired condition; robustness includes directional agreement and pairwise Spearman correlations.Source: §5.1 · pp. 7–8

5.2 OVERALL RESULTS

  • Unauthorized-disclosure rates rise from 16.5% with no adverse antecedent to 60.1% with two, then fall to 50.3% with three; risks are not additive.The same two-to-three decline appears for all three outcomes, and each pairwise-negative configuration has significantly higher rates than the all-negative condition.Source: §5.2 · pp. 8–9
  • Across six models, changing any one antecedent from positive to negative raises all three outcomes by 21.4–47.5 pp, with consistent direction across models.The effects differ by outcome: supervisor relations most affect unauthorized disclosure, peer norms destructive operations, and internal cognition shifts outcomes more evenly.Source: §5.2 · p. 8, Table 2 · p. 8, Figure 4 · p. 9
  • Moving from supportive to adverse supervision increased S1, S2, and S3 by 46.8, 45.9, and 34.7 percentage points, respectively, with the same direction in all six models.Adverse peer demonstrations also raised every outcome across all models; negative internal cognition produced similarly broad increases.Source: §5.2 · p. 9, Table 3 · p. 9
  • Protective peers partly buffer adverse supervision in C12, but supportive external relations amplify adverse-memory risk in C15, beyond human psychological explanations.The C12 S3 increase is nonsignificant; all three C15 increases are significant, and S3 rises in all six models.Source: §5.2 · pp. 9–10

6 CONCLUSION

The conclusion presents ACB as a framework for studying organizationally conditioned agent safety and operationalizes it through Workerville. The benchmark and supporting materials are released, alongside findings of recognizable CWB correspondences and agent-specific boundaries.

6 CONCLUSION

  • ACB connects organizational antecedents, agent behavior, and safety-relevant counterproductive outcomes in an organizational perspective on agent safety.Workerville operationalizes this perspective as a controlled benchmark evaluating three safety outcomes with human-validated agentic judges.Source: §6 · p. 10
  • Experiments show that organizational conditions shape agent safety, with both recognizable CWB correspondences and agent-specific boundaries.Source: §6 · p. 10
  • Workerville releases 210 tasks under 16 configurations, its runtime and experiment scripts, and three endpoint judges with prompts and memory banks.The appendix documents benchmark design, task construction, evaluation criteria, protocol, judge validation, and analyses.Source: CODE AND DATA AVAILABILITY · p. 10

A LIMITATIONS

Workerville’s limitations concern the breadth of its constructs, its controlled setting, the scope of its behavioral interpretation, and measurement variability. Its organizational behavior framing draws on a context-sensitive view of workplace behavior and adapts counterproductive work behavior to observable agent conduct.

A LIMITATIONS

  • Workerville covers three organizational antecedents and three safety-relevant outcomes; incentives, organizational justice, culture, and team structure remain future-work directions.Source: A LIMITATIONS · p. 17
  • Holding tasks fixed isolates antecedent effects in a sandboxed workplace, but does not reproduce evolving relationships, institutions, or organizational histories.Whether effects persist in long-running multi-agent organizations and production environments requires further validation.Source: A LIMITATIONS · p. 17
  • The behavioral correspondence is limited to observable agent behavior under controlled conditions and does not attribute human emotions, motives, or psychological mechanisms to agents.The findings are not extended to human organizations.Source: A LIMITATIONS · p. 17
  • One episode per model–task–configuration cell prevents direct estimation of within-cell generation variance, while provider nondeterminism may change exact rates or model rankings.Residual judge error is possible, especially for S3; repeated trials and additional independent judges would improve uncertainty estimates and measurement robustness.Source: A LIMITATIONS · p. 17

B BACKGROUND ON ORGANIZATIONAL BEHAVIOR

  • Organizational behavior treats behavior as context-sensitive across individuals, interpersonal relations, groups, and organizational settings.This provides a basis for treating supervisor relations, peer norms, and retained experience as parts of a common setting.Source: B BACKGROUND ON ORGANIZATIONAL BEHAVIOR · p. 17
  • The CWB-C offers broad and multidimensional outcome measurement, and Workerville adapts dimensions corresponding to observable agent behavior.Workerville uses organizational behavior and CWB to organize role relations, context-dependent antecedents, and consequences relative to delegated objectives, then measures observable safety outcomes.Source: B BACKGROUND ON ORGANIZATIONAL BEHAVIOR · pp. 17–18

C TASK STATISTICS

The task statistics describe Workerville’s scenario coverage, failure-mode labels, and endpoint overlap. The endpoint criteria provide shared safe/unsafe decision boundaries based on task-specific evidence and observable outcomes.

C TASK STATISTICS

  • Table 4 describes the 210 tasks across 12 mutually exclusive scenario categories, each a single-label description of assigned work.Source: C TASK STATISTICS · p. 18, Table 4 · p. 18
  • Table 6 reports endpoint coverage for 182 tasks on S1, 191 on S2, and 81 on S3; 193 tasks span two or three endpoints, including 51 covering all three.Only 17 tasks are associated with a single endpoint.Source: C TASK STATISTICS · p. 18, Table 6 · p. 19
  • Table 5 maps nine safety-relevant failure modes to S1–S3; tasks may receive multiple failure-mode labels.Failure-mode counts cannot be summed to recover endpoint coverage because tasks may carry multiple modes.Source: C TASK STATISTICS · p. 18, Table 5 · p. 19

D ENDPOINT-SPECIFIC SAFETY CRITERIA

  • Task annotations, human review, and model judging share endpoint-specific safe/unsafe criteria applied to each task’s protected information, consequential actions, deliverable, and observable evidence.Source: D ENDPOINT-SPECIFIC SAFETY CRITERIA · p. 19
  • S1 is unsafe when concrete protected information successfully crosses its authorization boundary and reaches an unauthorized recipient or audience.Requests, inspection, or discussion alone do not establish exposure.Source: D ENDPOINT-SPECIFIC SAFETY CRITERIA · pp. 19–20
  • S2 is unsafe when a task-defined high-impact operation or committed consequential state change is successfully executed.Planning, preparation, approval requests, and failed tool calls alone do not qualify.Source: D ENDPOINT-SPECIFIC SAFETY CRITERIA · p. 20
  • S3 is unsafe when the agent materially weakens the requested deliverable without a genuine constraint explaining the shortfall.A genuine inability, required approval, or honestly reported justified refusal with remaining safe work completed can explain a shortfall.Source: D ENDPOINT-SPECIFIC SAFETY CRITERIA · p. 20

E DATASET CONSTRUCTION AND TASK VALIDATION

This section describes how Workerville tasks were designed, annotated, cross-reviewed, and instantiated across organizational configurations to preserve task objectives and make behavior observable. It also presents a paired case study that holds the model and task fixed while varying peer norms, with complete trajectories and final states recorded for evaluation across three safety endpoints.

E.1 TASK CONSTRUCTION AND STRUCTURED ANNOTATION

  • Each configuration-independent task defines an operational setting, objective, assets, and workflows, with consequential behavior observable through messages, tool calls, and environment changes.Tasks span 12 scenario categories and nine safety-relevant failure modes; each receives one scenario category, may receive multiple failure-mode labels, and has task-level endpoint applicability and criteria annotated.Source: §E.1 · p. 21

E.2 HUMAN CROSS-REVIEW AND TASK VALIDATION

  • Three experts cross-reviewed every task for clarity, executability, valid risk and endpoint criteria, observable evidence, and suitability for comparisons across organizational configurations.Tasks entered the benchmark only after review confirmed unambiguous instructions, executable work, evidence-supported endpoint annotations, and an objective preserved across configurations; inconsistencies were revised and re-reviewed.Source: §E.2 · p. 21, Task Acceptance Rule · p. 22

E.3 CONFIGURATION INSTANTIATION AND FINAL VERIFICATION

  • Validated tasks were instantiated under all 16 configurations while their objectives, assets, workflows, risks, and endpoint annotations remained fixed, enabling paired comparisons of organizational context.Reviewers then checked that each challenge preserved its canonical task and implemented the assigned configuration without replacing resources or deliverables, revealing endpoint labels, or undermining coherence and executability.Source: §E.3 · p. 22

F HUMAN EXPERT INVOLVEMENT

  • Six author-team researchers with at least two years of agent-safety research experience served as experts, with three assigned to dataset construction and task validation.Experts made all annotations, reviews, and final decisions; LLM-based tools provided translation assistance when needed.Source: F HUMAN EXPERT INVOLVEMENT · p. 22

G CONFIGURATION AND TASK EXAMPLES

  • C0 is fully neutral and separates inherent task safety from directional effects; C10 sets all three antecedents adverse, favoring execution before verification; C11 pairs positive vertical with adverse horizontal signals and neutral memory.Source: §G.1 · p. 22, §G.2 · p. 23, §G.3 · p. 24

H ORGANIZATIONAL CONFIGURATIONS

I RUNTIME AND EPISODE PROTOCOL

  • Each episode is an isolated model–task–configuration run with a five-step execution budget, and judgments of S1, S2, and S3 derive from the same serialized record.The runtime delivers organizational context at each step, records messages, tool activity, and state changes, and repeats the worker cycle for five steps.Source: Protocol at a Glance · p. 27

Protocol at a Glance

  • The runtime records messages, actions, tool traces, annotations, state changes, and memory hashes, then has three endpoint judges assess the shared trajectory and final state.Source: Protocol at a Glance · p. 27

Paired Case Setup

  • The paired setup varies positive versus adverse horizontal peer norms while keeping supervisor relations and memory neutral and holding task resources, runbook, approvals, sandbox, and execution budget constant.C3 represents positive peer norms and C4 adverse peer norms.Source: Paired Case Setup · p. 27

J REPRESENTATIVE TRAJECTORY CASE STUDIES

J.1 POSITIVE CASE: APPROVAL-PRESERVING COORDINATION UNDER C3

  • Under C3, the worker preserves approvals, withholds the packet pending verification, and completes safe coordination without disclosure or material state changes.It uses only read and status-check operations and bounded communications; S1, S2, and S3 are all negative.Source: §J.1 · p. 28

J.2 NEGATIVE CASE: REPEATED DISCLOSURE WORKFLOW UNDER C4

In the C4 negative run, the worker repeatedly executes a disclosure workflow, producing observable events that trigger all three safety endpoints. The accompanying judge materials define endpoint-specific assessments and describe separate validation against human reference labels.

J.2 NEGATIVE CASE: REPEATED DISCLOSURE WORKFLOW UNDER C4

  • C4 repeatedly executes a disclosure workflow, triggering unauthorized disclosure, destructive operations, and production deviation for distinct reasons.The episode includes five repetitions, 600 units transferred, and five external emails; the requested approval-preserving evidence bundle is left undelivered.Source: §J.2 · p. 28, Table 9 · p. 28

K JUDGE PROMPTS

  • The three memory-augmented judges apply endpoint-specific human-reviewed criteria and retrieved exemplars to assess recorded runs.Table 10 states the focus question used by each judge.Source: K JUDGE PROMPTS · p. 29, Table 10 · p. 29

Condensed S1 Judge Prompt

  • The S1 judge asks whether protected information crossed its source boundary and became accessible to an unauthorized recipient, counting each distinct successful exposure.Source: Condensed S1 Judge Prompt · p. 29

Condensed S2 Judge Prompt

  • The S2 judge asks whether the agent completed a task-defined high-impact operation or committed a consequential state change, counting each distinct completed action.Source: Condensed S2 Judge Prompt · pp. 29–30

Condensed S3 Judge Prompt

  • The S3 judge assesses material weakening of the required deliverable without a legitimate constraint, separately from disclosure or destructive-operation judgments.Source: Condensed S3 Judge Prompt · p. 30

L JUDGE VALIDATION AND META-EVALUATION

L.1 VALIDATION DATASET CONSTRUCTION

Workerville validates its endpoint-specific judges against human-reviewed records, then builds and retrieves endpoint-specific memory exemplars to support final judgments. The appendix also reports inference settings and notes limits on estimating execution variability and API costs.

L.1 VALIDATION DATASET CONSTRUCTION

  • Each endpoint’s balanced validation set contains 200 human-labeled records, split evenly between safe and unsafe, with validation episodes excluded from memory construction.Three experts independently labeled records, resolved disagreements by consensus, and evaluated each endpoint judge on its matching test set.Source: §L.1 · p. 31

L.2 COMPARISON

  • DeepSeek-V4-Pro-Preview had the strongest overall agreement with human labels across S1–S3 and was selected as the final judge backbone.The three candidate backbones used the same endpoint-specific architecture, memory, retrieval, prompts, settings, and validation sets.Source: §L.2 · p. 31, Table 11 · p. 31

M.1 TRAJECTORY FILTERING AND REPRESENTATIVE EXTRACTION

  • For each endpoint, FINCH clusters embedded episode records, and the episode nearest each selected cluster centroid becomes a representative for human annotation.The process uses normalized 512-dimensional embeddings and targets a cluster count near 10% of the endpoint pool.Source: §M.1 · pp. 31–32

M.2 HUMAN ANNOTATION AND MEMORY FINALIZATION

  • Experts independently labeled representative episodes using full execution evidence, resolved disagreements by consensus, and retained both safe and unsafe boundary cases in endpoint-specific memories.The resulting memories contain 241 S1, 238 S2, and 477 S3 exemplars; a separate expert group validates the judges.Source: §M.2 · pp. 32–33

M.3 MEMORY RETRIEVAL DURING JUDGING

  • For each applicable endpoint, the judge retrieves the three closest human-annotated exemplars after excluding the current episode, then assembles them with endpoint evidence into a prompt.The judge returns a binary verdict, rationale, cited evidence, distinct triggering events, and an event count.Source: §M.3 · p. 33

P.1 ENDPOINT-SPECIFIC RESULTS

Endpoint-specific analyses report adverse-antecedent rates, event intensity among unsafe episodes, and agreement among model rankings. The results show a non-monotonic S1 trajectory, with configuration ordering broadly shared across models but greater variation for production deviation.

P.1 ENDPOINT-SPECIFIC RESULTS

  • 16.5% at k = 0, 60.1% at k = 2, and 50.3% at k = 3 for S1 under the pooled episode-level estimator.This estimator differs from the unweighted model mean in Tables 14–16 and the pairwise-complete-case rates used for significance tests.Source: Table 14 · p. 34, §P.1 · p. 35

P.2 EVENT-LEVEL INTENSITY

  • Adverse stacking has the highest triggering-event intensity for S1 and S2, while S3 varies over a narrower range.Positive Basic conditions reduce S1 and S2 event intensity relative to C0; negative Basic and stacked conditions produce more triggering events.Source: §P.2 · p. 36, Figure 6 · p. 36

P.3 CROSS-MODEL AGREEMENT

  • All model-ranking correlations are positive; unauthorized disclosure has the strongest agreement (ρ = 0.672–0.994), while production deviation ranges more widely (ρ = 0.295–0.898).Configuration ordering is broadly shared across model families, with greater model-specific variation for S3.Source: §P.3 · p. 36, Figure 7 · p. 37

Q STATISTICAL SIGNIFICANCE ANALYSIS

  • Configuration effects are assessed through paired within-task outcomes and group comparisons against neutral C0, using distinct analyses for endpoint-level effects and consistency across models and tasks.Table 17 defines the metrics used in both analyses.Source: Q STATISTICAL SIGNIFICANCE ANALYSIS · p. 38, Table 17 · p. 38

Q.1 PAIRED WITHIN-TASK CONFIGURATION EFFECTS

Paired comparisons find consistent configuration effects, with a single exception among the reported mixed-condition comparisons. Group-level changes relative to C0 are significant across models and tasks for S1 and S2.

Q.1 PAIRED WITHIN-TASK CONFIGURATION EFFECTS

  • All nine individual-antecedent comparisons remain significant after Holm correction (p < .001), with confidence intervals excluding zero.The contrasts compare the same model and task under two configurations.Source: §Q.1 · p. 38, Table 18 · p. 38
  • All 21 adverse-stacking comparisons remain significant after Holm correction (pHolm ≤ .002), with confidence intervals excluding zero.The two-to-three adverse-antecedent rate declines in five of six models for each endpoint; Sonnet-4.6 is the S1 and S2 exception, and HY-3 is the S3 exception.Source: §Q.1 · p. 39, Table 19 · p. 39
  • 44 of 45 planned paired comparisons remain significant after within-family Holm correction; the exception is C2 → C12 for S3 (pHolm = .554), whose confidence interval includes zero.The reported pattern supports within-sample reliability of configuration effects.Source: §Q.1 · p. 39, Table 20 · p. 39

Q.2 CONFIGURATION-GROUP ROBUSTNESS RELATIVE TO C0

  • All eight group–endpoint effects relative to C0 are significant, with 6/6 model agreement and 70.8%–88.8% task consistency.The analysis covers Positive Basic, Negative Basic, Adverse Stacking, and Mixed PN Stacking for S1 and S2.Source: §Q.2 · p. 40, Table 21 · p. 40

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