Secretary Suite

DOI: (to be assigned)

John Swygert

March 25, 2026

Abstract

As scholarly publishing confronts increasing complexity, it is no longer sufficient to think of editorial screening as a static checklist applied to isolated manuscripts. Many failures in the publication process are pattern-based, relational, and cumulative. They emerge not only through errors within a single paper, but through anomalies across author clusters, editor-author overlaps, proceedings volumes, citation structures, and repeated publication behaviors. This paper extends the framework previously formalized by the author under the title LLM Agent and Multi-Agent Editorial Screening: A Pre-Publication Integrity and Refinement Layer for Scholarly Publishing. The present paper argues that editorial screening systems must be understood as evolving algorithms rather than fixed filters. Their value lies not merely in detecting obvious anomalies, but in learning from prior cases, expanding their typologies of risk, refining their pattern-recognition logic, and developing specialized multi-agent roles for different kinds of editorial assessment. The paper further argues that such systems are best understood as humane preventive infrastructure: they are designed to reduce avoidable harm to authors, editors, journals, publishers, and the scientific record by surfacing concerns before publication rather than amplifying damage afterward. Recent editorial failures, including the planned retraction of an entire conference proceedings volume after concerns about extreme authorship concentration and editor-author overlap, illustrate the need for this evolving layer of structured detection and human review.

1. Introduction

Editorial review has historically been treated as a sequence of human judgments supported by a limited set of procedural checks: formatting compliance, plagiarism screening, reviewer assignment, conflict disclosure, and eventual publication decisions. That model remains indispensable, but it increasingly shows its limits when publication failures arise not from one glaring defect, but from broader patterns invisible at the level of an individual manuscript.

This is the environment in which LLM Agent and Multi-Agent Editorial Screening must be understood. Such systems are not merely tools for static manuscript evaluation. They are evolving algorithmic frameworks that improve as they encounter new editorial situations, new abuse patterns, new forms of structural inconsistency, and new governance failures. The problem they address is not simply bad prose, unsupported claims, or even methodological weakness in isolation. The problem is that modern editorial risk is often distributed across relationships, roles, frequencies, concentrations, and cumulative anomalies.

A recent case reported by Retraction Watch illustrates this clearly. A publisher announced plans to retract an entire conference-proceedings volume after concerns were raised that one of the editors was also an author on 32 of the 55 papers in the volume. The publisher reportedly stated that the articles had not initially raised suspicion because they lacked “classic” editorial anomalies such as manipulated quotations or blatant inconsistencies. That observation is highly instructive. It suggests that many editorial breakdowns are not being missed because editors are careless, but because the current screening logic is tuned too narrowly toward older, simpler anomaly types.

The central claim of this paper is that a serious editorial-screening architecture must be adaptive. It must evolve in response to real-world failures, near-failures, patterns of misconduct, structural weaknesses, and governance blind spots. A useful system must not only detect what editors already know to look for. It must help expand what counts as editorially visible.

2. Continuity with Prior Published Work

The present paper builds directly on prior published work by the author. Earlier publications at Secretary Suite, Ivory Tower Journal, and on the author’s blog described structured corpora as analytical baselines, corpus-guided analytical agents, consistency-based evaluation, and the use of computational systems to compare new material against curated conceptual frameworks. Those earlier works articulated the general architecture in broad terms and established the conceptual foundation for agent-based scientific and editorial analysis.

The immediately preceding paper, LLM Agent and Multi-Agent Editorial Screening: A Pre-Publication Integrity and Refinement Layer for Scholarly Publishing, formalized the editorial application more directly. The present paper extends that work by focusing on algorithmic evolution: how editorial-screening agents expand, refine, and specialize over time as they encounter new classes of editorial risk.

This distinction matters. The prior paper defined the framework. The present paper addresses the dynamic behavior of that framework over time.

3. Why Editorial Screening Must Evolve

An editorial-screening algorithm that remains static will age badly. It will become overly dependent on yesterday’s anomalies while missing tomorrow’s. This is already a familiar problem in cybersecurity, fraud detection, and quality assurance. The same principle applies in scholarly publishing.

If an editorial system is trained only to detect plagiarism, textual incoherence, and elementary citation mismatches, then it may miss more subtle but equally serious patterns: unusual editor-author concentration, statistically improbable publication density, repeating article templates with superficial variation, proceedings clusters dominated by a single network, or technically polished papers that nonetheless display cross-document structural reuse.

The recent proceedings case highlights this point sharply. The reported problem was not merely that one manuscript looked unsound. It was that a broader pattern emerged across a volume. A properly evolving editorial algorithm would treat that not as an isolated ethical oddity, but as a learnable risk class: extreme editor-linked authorship concentration in a publication set. Once identified, that class should become part of future screening logic.

Thus, the editorial algorithm evolves in at least three ways:

First, it evolves by expanding its anomaly vocabulary. New categories of concern are added as the system encounters real cases.

Second, it evolves by refining thresholds and combinations. A single mild irregularity may mean little, but several mild irregularities in combination may justify review.

Third, it evolves by specializing analytical roles. Rather than one generalized agent attempting to do everything, a mature system assigns different tasks to different agents.

4. From Single-Agent Screening to Multi-Agent Editorial Intelligence

A single editorial agent may be useful for first-pass screening, but the complexity of modern publication environments favors a multi-agent approach.

One agent may be optimized for authorship and governance analysis, detecting unusual concentrations, editor-author overlap, recusal concerns, and publication clustering.

Another may focus on citation integrity, identifying unsupported references, unusual self-citation density, narrow network loops, and mismatches between references and claims.

Another may focus on cross-manuscript structural comparison, detecting semantic reuse, repeated argument scaffolds, duplicated methods sections, and clustered conclusion patterns.

Another may focus on logical and conceptual coherence, examining whether claims, definitions, methods, and conclusions are aligned internally and with domain baselines.

Another may focus on process anomalies, such as unusual throughput, abnormal acceptance timing, or proceedings-level density patterns.

This multi-agent structure is not merely an engineering preference. It reflects the fact that editorial problems are heterogeneous. Some are ethical. Some are structural. Some are bibliographic. Some are procedural. Some are relational. A mature editorial-screening system should mirror that diversity rather than flatten it into one undifferentiated score.

5. The Algorithm as Memory

The phrase “evolving algorithm” should not be understood as vague marketing language. In this context, it means that the editorial system develops institutional memory.

Human editors have memory, but it is fragmented across people, journals, publishers, committees, and years. An agent-based screening system can preserve memory more systematically. It can encode prior patterns of concern, prior classes of proceedings anomalies, prior conflict structures, prior citation-pathology types, and prior document-reuse signatures into reusable screening logic.

This does not mean that every past case should become a permanent suspicion template. It means that editorial systems should become more observant over time. They should not have to relearn the same lessons after every scandal.

A static editorial process forgets too much. An evolving one learns.

6. The Recursive Cycle of the Evolving Algorithm

The evolving algorithm proposed in this paper should be understood as cyclical rather than linear. Its development does not proceed only by external updates or occasional redesign. It also advances through repeated use. After each editorial screening run, the system contributes to its own refinement by preserving structured information about what was detected, what was escalated, what was resolved through ordinary clarification, and what ultimately proved to be meaningful or non-meaningful concern.

In this sense, LLM agent and multi-agent editorial screening functions as a recursive editorial cycle. A manuscript or proceedings set is screened; anomalies, patterns, and contextual concerns are identified; human editors review the flagged material; outcomes are observed; and those outcomes are then used to refine the future logic of the system. The result is not merely repeated execution, but cumulative maturation. Each run strengthens the algorithm’s ability to distinguish isolated irregularities from meaningful combinations of concern, and to recognize new classes of risk that may not have been fully visible before.

This cycle may be described in five recurring phases: detection, interpretation, human adjudication, outcome recording, and algorithmic refinement. Detection identifies possible signals of concern. Interpretation organizes those signals into intelligible categories. Human adjudication determines whether the concerns were meaningful, overstated, or benign. Outcome recording preserves the result in structured form. Algorithmic refinement then adjusts future screening behavior in light of that history. The process then begins again with the next manuscript, issue, or proceedings volume.

The significance of this cycle is that the system does not simply accumulate data; it accumulates editorial judgment in structured form. Over time, this allows the screening framework to behave less like a static checklist and more like a disciplined institutional memory. The cycle is therefore both corrective and generative: corrective because it reduces repeated blindness to known anomaly types, and generative because it helps produce new categories of editorial visibility from real-world use.

Properly governed, this recursive cycle remains subordinate to human authority while still benefiting from computational continuity. Human editors remain responsible for judgment, confidentiality, and final decisions. The agents contribute persistence, comparison, memory, and refinement. Together, they form a living editorial system in which each run contributes to the next, and in which the algorithm evolves through repeated contact with actual editorial conditions rather than abstraction alone.

7. Ethical Constraints on the Evolving System

The stronger and more adaptive such a system becomes, the more important its constraints become.

ICMJE’s January 2026 recommendations expanded guidance on AI in publishing and emphasize that editors, reviewers, and publishers remain responsible for the integrity of AI-assisted processes. ICMJE also states that editors and reviewers should not upload manuscripts into AI systems where confidentiality cannot be assured without authors’ explicit permission. Journals should have policies governing AI use in review and editorial work.

WAME likewise states that editors need appropriate tools to help them detect content generated or altered by AI and that such tools should be made available broadly for the good of science and the public.

Accordingly, any evolving editorial-screening system must remain bound by several principles:

It must flag rather than decide.

It must be auditable and transparent enough that human editors can understand why a concern was raised.

It must be confidentiality-preserving.

It must be appealable and reviewable by humans.

It must avoid becoming an automated instrument of hidden punishment.

The goal of such a system is not to become an artificial editor-in-chief. The goal is to become a disciplined assistant that improves the visibility of risk while leaving authority and accountability in human hands.

8. Why Real-World Mishaps Matter to the Algorithm

It may feel uncomfortable to derive design lessons from editorial failures, because those failures often involve real human suffering. Authors may experience humiliation. Editors may be publicly criticized. Publishers may face embarrassment. Coauthors and institutions may suffer collateral damage.

That discomfort is appropriate. It should make us more humane, not less analytical.

The lesson of a real-world editorial mishap is not that one should dwell on the names or mistakes of those involved. The lesson is that publication systems should be improved so that fewer people suffer such outcomes in the future. A humane system learns from pain without exploiting it.

For that reason, cases like the one reported by Retraction Watch are relevant not as spectacles, but as design signals. They show where editorial systems lacked visibility. They reveal what warning patterns were not operationalized soon enough. They expose the cost of relying too heavily on narrow definitions of anomaly.

An evolving editorial algorithm should therefore incorporate such cases at the level of pattern type, not at the level of personal condemnation.

9. The Protective Logic of Evolution

The best argument for an evolving editorial-screening system is not efficiency. It is protection.

A stronger system protects authors from preventable reputational collapse by surfacing concerns privately before publication.

It protects editors from having to perceive every pattern unaided.

It protects publishers from avoidable scandals and large-scale retractions.

It protects reviewers from being asked to evaluate manuscripts within hidden structural problems they were never given the context to see.

It protects the scientific record by improving refinement before correction becomes public damage control.

And it protects future scholarship by reducing the propagation of papers whose weaknesses should have been caught earlier.

That is why the evolving algorithm matters. It is not just a convenience layer. It is a refinement mechanism for institutional responsibility.

10. Preliminary Design and Early Testing

The author states here that this framework has already moved beyond pure abstraction. Earlier published work established the general conceptual groundwork, and preliminary design and early testing have already been undertaken in broad form. The present paper should therefore not be read as speculation detached from implementation. Rather, it is a further articulation of an already-developed line of work.

What is evolving now is the explicitness of the framework: from general corpus-guided analytical agents, to editorial screening, to an openly stated model of adaptive multi-agent editorial intelligence.

11. Future Directions

The evolving algorithm of LLM agent and multi-agent editorial screening should proceed along several lines.

First, the development of secure editorial environments that preserve confidentiality and comply with journal policy.

Second, the creation of benchmark datasets for proceedings volumes, special issues, editor-author conflict patterns, citation anomalies, and cross-manuscript structural duplication.

Third, the refinement of flag taxonomies, so that concerns are categorized clearly and proportionately rather than collapsed into crude pass-fail logic.

Fourth, the design of feedback loops in which editor decisions improve future screening without turning the system into an opaque accumulator of bias.

Fifth, the development of publisher-level dashboards that allow human editorial teams to view issue-level and proceedings-level risk summaries before publication.

These directions would move the concept from intelligent assistance toward durable editorial infrastructure.

12. Conclusion

The original framework for LLM Agent and Multi-Agent Editorial Screening already established the need for a pre-publication integrity and refinement layer. The present paper argues that such a system must not remain static. It must evolve.

It must learn from real editorial failures, expand its risk vocabulary, refine its thresholds, differentiate its agents, and develop structured memory. It must also remain ethically constrained, confidentiality-preserving, auditable, and subordinate to human judgment.

A recent proceedings case demonstrates why this evolution matters. When publication systems are tuned only to detect “classic” anomalies, they may miss the broader patterns that cause the greatest downstream harm. An evolving multi-agent editorial framework offers a path toward earlier visibility, better refinement, and more humane prevention.

In that sense, the evolving algorithm is not merely technical. It is editorial maturity made systematic.

References

International Committee of Medical Journal Editors. Up-Dated ICMJE Recommendations (January 2026). ICMJE. January 2026.

International Committee of Medical Journal Editors. Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. Updated January 2026.

International Committee of Medical Journal Editors. Use of Artificial Intelligence in Publishing. ICMJE. Updated January 2026.

World Association of Medical Editors. Chatbots, Generative AI, and Scholarly Manuscripts. WAME. May 31, 2023.

Orrall A. Publisher to Retract Entire Conference Proceedings, Ban Editor Who Wrote Most of Them. Retraction Watch. March 24, 2026.

Swygert J. Structured Corpora as Analytical Baselines for Computational Knowledge Systems: A Conceptual Framework for Corpus-Guided Analytical Agents. Secretary Suite. March 4, 2026.

Swygert J. Corpus-Guided Analytical Agents: The Secretary Suite Method for Training Scientific Evaluation AI. Secretary Suite. March 5, 2026.