LLMTopic: User-Tuned, Continuously Updating Bodies of Knowledge — A Secretary Suite Project

DOI: To Be Assigned

John Swygert

July 25, 2026

Abstract

Traditional internet search returns links, while conventional large language model systems usually return temporary answers. Neither approach fully satisfies a user who wants an entire topic researched, organized, preserved, refined to a chosen level of complexity, and continuously updated as new information appears.

LLMTopic is proposed as a public-facing knowledge platform through which a person enters any topic and receives a structured, persistent, and updateable body of work assembled from available internet sources. Rather than forcing every user to accept the same vocabulary, depth, length, and technical level, the system would provide simple controls that allow each person to tune the resulting material to their own needs. A beginner could request plain language and a concise explanation, while an advanced researcher could request technical terminology, methodological detail, formal evidence classification, extensive citations, and doctoral-level analysis.

The system would not merely answer a question. It would search, collect, classify, compare, organize, preserve, and monitor a topic over time. Each completed topic could become a private, public, collaborative, or unlisted Topic Room that the user could bookmark and revisit. The system could then continue monitoring relevant sources and notify the user when meaningful new evidence, publications, contradictions, regulatory changes, or developments appear.

This paper defines LLMTopic as an adaptive knowledge architecture combining internet research, user-directed guardrails, adjustable communication levels, persistent topic organization, provenance tracking, and continuous update monitoring.

1. Introduction

The internet contains enormous quantities of useful information, but it remains structurally difficult for most people to convert that information into a coherent body of knowledge.

A search engine generally provides a ranked list of links. The user must then open the links, determine which sources are reliable, compare overlapping claims, distinguish old information from current information, recognize contradictions, identify missing evidence, and manually organize the findings.

A large language model improves this process by synthesizing information into a direct answer. However, an answer is not necessarily a durable research structure. It may be limited to one conversation, one prompt, one level of explanation, or one moment in time.

LLMTopic would address this gap.

A person would visit a domain such as LLMTopic.com, enter any subject, and instruct the system to organize the available information into a coherent, cited, persistent body of work.

The central promise would be:

Enter a topic. Choose how you want to understand it. Build a body of knowledge that can continue growing.

The system would be designed for everyone, not only technical specialists. Its initial interface should therefore use basic, direct language. Greater complexity would remain available, but it would be introduced only when the user requests it.

2. From Search Results to a Body of Work

A conventional search engine asks:

What pages might contain information related to these words?

LLMTopic would ask:

What body of knowledge exists around this subject, how should it be organized, what does this user want to accomplish, and what rules should govern the result?

The system would gather and organize relevant material into structures such as:

• a plain-language overview;
• a detailed explanation;
• major topic branches;
• historical development;
• evidence tables;
• competing theories;
• areas of agreement;
• unresolved disagreements;
• key people and institutions;
• important terminology;
• timelines;
• source libraries;
• claim-to-source relationships;
• unanswered questions;
• proposed next research steps;
• and a continuing update record.

The result would not merely be a response. It would become a navigable research object.

3. The Topic Room

Every organized subject could become its own persistent Topic Room.

A Topic Room would contain the accumulated body of work, the user’s preferences, source records, prior updates, unanswered questions, and the current organizational structure.

A Topic Room might be:

• Private, accessible only to its creator;
• Unlisted, accessible through a private link;
• Public, searchable or viewable by others;
• Collaborative, allowing invited participants to contribute;
• or Published, presented as a stable reference work.

The user could bookmark the Topic Room and return to it without repeating the original research process.

A sample address might appear as:

llmtopic.com/topic/gut-microbiome-and-atrial-fibrillation

The room could retain its own research history while allowing the user to request new perspectives or reorganizations without destroying the original version.

4. A Simple Interface for Every User

The public interface should begin with the simplest possible instruction:

What topic would you like organized?

After the user enters a topic, the system could ask several short questions:

What are you trying to accomplish?

How advanced should the explanation be?

How much detail do you want?

Should the system include emerging or speculative ideas?

Do you want this topic monitored for new information?

The interface should avoid presenting a complicated technical control panel before the user understands the product. Advanced controls could appear gradually or remain available under a section labeled:

Fine-tune your results

This allows the product to attract people who want simplicity without limiting users who want precise control.

5. The Knowledge-Level Dial

One of the most important features would be an adjustable knowledge-level control.

The system could present three immediately understandable choices:

• Beginner
• Intermediate
• Expert

It could also offer a more precise dial from 1 to 10.

Level 1

Extremely simple language, short explanations, minimal terminology, and familiar examples.

Level 3

General layperson language with essential terms explained as they appear.

Level 5

Informed adult or undergraduate-level treatment with moderate detail and standard terminology.

Level 7

Advanced professional or graduate-level discussion with greater methodological and technical precision.

Level 10

Expert, specialist, or doctoral-level treatment using field-specific vocabulary, extensive evidence distinctions, methodological criticism, formal notation where appropriate, and direct engagement with the primary literature.

The important principle is that the subject itself would not change. The representation of the subject would change.

A user could begin at Level 2, develop familiarity, and then move gradually to Levels 4, 6, or 8. The Topic Room could therefore become an educational pathway rather than a one-time answer.

The user should be able to move at the speed they desire.

6. Multiple Tuning Controls

Knowledge level should not be the only adjustable variable. Different users may want different combinations of simplicity, detail, breadth, evidence standards, and output length.

LLMTopic could provide several independent dials.

6.1 Language Complexity

Controls the vocabulary and sentence structure.

• Plain language
• General audience
• Professional
• Technical
• Specialist

6.2 Depth

Controls how far the system develops each subject.

• Essential points only
• Standard explanation
• Detailed treatment
• Comprehensive review
• Exhaustive research architecture

6.3 Breadth

Controls how many related branches the system includes.

• Narrow question
• Core topic
• Topic plus major connections
• Broad interdisciplinary treatment
• Maximum relevant context

6.4 Evidence Strictness

Controls the standard for including claims.

• General informational sources allowed
• Reputable secondary sources preferred
• Institutional and scholarly sources prioritized
• Peer-reviewed evidence strongly prioritized
• Primary evidence and methodological scrutiny required

6.5 Speculation Tolerance

Controls whether emerging or uncertain ideas are included.

• Established findings only
• Strongly supported interpretations
• Preliminary research included
• Emerging hypotheses included
• Broad speculative exploration, clearly labeled

6.6 Output Length

Controls the amount of material presented.

• Brief
• Standard
• Detailed
• Long-form
• Full body of work

6.7 Citation Density

Controls how frequently the system shows source support.

• Key sources only
• Citations for major claims
• Detailed citations
• Claim-level citations
• Full provenance and source traceability

These settings should remain independent. A person might request plain language but still want exhaustive evidence. Another person might want expert terminology but only a one-page summary.

The user would not be forced into a predetermined package.

7. Adaptive Rewriting Without Repeating the Research

Once a Topic Room has been created, the user should be able to change the dials without requiring the entire internet research process to begin again.

For example, a completed technical review could be transformed into:

• a child-accessible explanation;
• a high-school lesson;
• a college-level study guide;
• an executive briefing;
• a doctoral research review;
• a public policy summary;
• a patient-oriented explanation;
• a presentation outline;
• or a publication-ready paper.

The underlying source architecture would remain stable while the presentation layer changed.

This separates two functions that are often mistakenly combined:

1. What the evidence says
2. How the evidence should be explained to this user

The evidence should not be weakened merely because the language is simplified. Plain language should preserve accuracy rather than replacing accuracy.

8. Outcome-Directed Organization

The same topic may need to be organized differently depending upon the user’s purpose.

A user researching artificial intelligence might be preparing:

• a general education document;
• a technical paper;
• an investment analysis;
• a policy proposal;
• a business plan;
• a classroom lesson;
• a legal argument;
• a book;
• or a personal study program.

The system should therefore ask:

What do you want this body of work to help you accomplish?

The user’s desired outcome would guide the structure.

A book project might require chapters, narrative order, terminology consistency, and a missing-section analysis.

A scientific review might require methods, evidence quality, replication status, competing models, and research gaps.

A business analysis might require market structure, competitors, customer needs, risks, operating costs, and development opportunities.

The topic defines the subject. The outcome defines the architecture.

9. System-Inferred and User-Defined Guardrails

Before producing a major body of work, LLMTopic should create provisional rules based on the material it finds.

For example, the system might infer that:

• peer-reviewed studies should outrank unsourced commentary;
• published documents should not be silently altered;
• disputed claims should be labeled;
• old versions should be preserved;
• medical information requires stronger evidence standards;
• legal information must be separated by jurisdiction;
• and speculation must not be presented as established fact.

The user would then provide personal or project-specific rules.

These might include:

• use only sources published after a certain date;
• include minority scientific positions;
• exclude social media;
• preserve all historical versions;
• prioritize primary sources;
• use layman’s terms;
• include doctoral-level terminology;
• separate fact from interpretation;
• or organize the final result as a book.

The system would compare its inferred guardrails with the user’s requested guardrails and produce a visible Topic Contract.

10. The Topic Contract

The Topic Contract would define the assignment before the system completed the major organization.

A Topic Contract might state:

Topic: Gut-microbiome contributions to atrial fibrillation

User objective: Understand proposed biological mechanisms and the current strength of evidence

Knowledge level: 4 of 10

Language: Plain but medically accurate

Depth: Detailed

Evidence standard: Peer-reviewed and major medical institutions prioritized

Speculation: Preliminary findings allowed when clearly labeled

Date emphasis: Most recent five years, with earlier foundational research included

Output: Overview, mechanism map, evidence table, major uncertainties, terminology guide, and source list

Monitoring: Weekly review for meaningful new research

The user could approve or modify this contract before the system generated the full Topic Room.

This would prevent the model from silently making critical organizational decisions.

11. Continuous Topic Monitoring

A Topic Room should remain capable of growth.

After the first body of work has been created, the user could enable monitoring.

The system would periodically search for meaningful changes, including:

• newly published studies;
• corrections or retractions;
• new government rules;
• court decisions;
• updated technical standards;
• product releases;
• newly available datasets;
• major criticisms;
• replication failures;
• changes in expert consensus;
• and evidence that contradicts an existing conclusion.

The system should not notify the user merely because another webpage repeated old information. Notifications should be reserved for changes that materially affect the organized body of work.

A notification might state:

New evidence found: A newly published study challenges one of the mechanisms currently described in Section 4.

Or:

Important update: A source relied upon in this Topic Room has been corrected or retracted.

Or:

Topic expansion available: Three new studies support adding a section on an emerging mechanism.

The user could then choose:

• Review the new information;
• Add it to the Topic Room;
• Compare it with existing conclusions;
• Save it without changing the current work;
• or reject it as irrelevant.

12. Update Frequency and Notification Controls

Different topics change at different speeds. The system should therefore permit the user to select an update schedule.

Possible choices could include:

• Only when I request an update
• Notify me of major developments
• Weekly
• Monthly
• Quarterly
• Before a specified deadline
• When a named source publishes something new
• When evidence crosses a defined threshold

The user might also choose the sensitivity of the monitoring system:

Low sensitivity

Notify only when a major development changes the overall understanding of the topic.

Moderate sensitivity

Notify when credible new evidence adds, weakens, or revises an important section.

High sensitivity

Notify when any relevant reputable source contributes meaningful new material.

The user should be able to receive a simple notice or a complete update report.

13. Versioning and Update Integrity

A continuously updating body of work must retain a clear history.

The system should never silently rewrite the Topic Room in a way that hides what previously appeared.

Each major update should record:

• what changed;
• why it changed;
• which sources caused the change;
• which sections were affected;
• what conclusion existed previously;
• and what conclusion is supported now.

The user should be able to view:

• the current version;
• earlier versions;
• a comparison between versions;
• and a timeline of major developments.

This creates a transparent intellectual history of the topic.

14. Evidence Classification

LLMTopic should distinguish between different strengths and types of information.

A practical evidence-labeling system might include:

• Established
• Strongly supported
• Supported but incomplete
• Preliminary
• Disputed
• Speculative
• Contradicted
• Outdated
• Retracted
• Unknown

These labels could remain visible regardless of the selected language level.

A beginner might see:

Preliminary: Scientists have found an interesting connection, but more research is needed.

An expert might see:

Preliminary evidence: The association is supported by limited observational and mechanistic data but lacks prospective validation and independent replication.

The phrasing changes. The evidence classification does not.

15. Source Provenance

Every significant claim should remain connected to its sources.

The system should record:

• page title;
• author;
• publisher or institution;
• publication date;
• retrieval date;
• source type;
• relevant section;
• reliability assessment;
• and which claims depend upon that source.

This would permit the user to move from a simple explanation to the underlying evidence.

A person reading at Level 2 could select:

Why does the system say this?

The interface could then reveal the source trail without forcing all citations into the initial explanation.

This creates simplicity without sacrificing transparency.

16. Public and Private Knowledge

Users should control whether their Topic Rooms are private or shared.

The platform operator should not assume unrestricted ownership of private research simply because the material is stored on the platform.

The system should clearly distinguish:

• user-owned private Topic Rooms;
• public Topic Rooms;
• shared collaborative Topic Rooms;
• platform-curated reference rooms;
• and anonymized aggregate usage information.

Administrative access should be limited, logged, disclosed, and governed by clear privacy rules.

A user could voluntarily contribute a completed Topic Room to a growing public library. Multiple public Topic Rooms could eventually be compared, merged, or branched while preserving authorship and source histories.

17. Platform Architecture

LLMTopic could initially operate through a combination of:

• a public website;
• user accounts;
• search services;
• controlled webpage retrieval;
• a structured source database;
• an LLM reasoning and synthesis system;
• persistent Topic Room storage;
• notification services;
• and a monitoring scheduler.

The process could be represented as:

\[ \text{Topic Entry} \rightarrow \text{Preliminary Search} \rightarrow \text{Scope Questions} \rightarrow \text{User Tuning} \rightarrow \text{Topic Contract} \rightarrow \text{Source Collection} \rightarrow \text{Evidence Classification} \rightarrow \text{Organization} \rightarrow \text{Persistent Topic Room} \rightarrow \text{Continuous Monitoring} \]

In normal language:

The user enters a subject, tells the system how the result should be shaped, approves the rules, receives an organized body of work, and allows that body of work to grow as the available knowledge changes.

18. Use of ChatGPT and Future LLM Systems

ChatGPT or an OpenAI model could provide much of the reasoning required for LLMTopic, including:

• planning searches;
• generating clarification questions;
• identifying subtopics;
• comparing sources;
• detecting contradictions;
• adjusting language level;
• restructuring the same evidence for different audiences;
• generating Topic Contracts;
• maintaining update summaries;
• and communicating with users conversationally.

However, the website would require more than a single ordinary ChatGPT conversation.

The persistent product would also need:

• a topic database;
• user authentication;
• source storage or source indexing;
• privacy controls;
• scheduled monitoring;
• notification delivery;
• version histories;
• usage limits;
• and administrative systems.

ChatGPT could operate as the primary intelligence layer while LLMTopic provides the persistent structure around it.

As model capabilities improve, the system could become increasingly autonomous. It could conduct deeper research, recognize more subtle disagreements, follow citation chains, detect methodological weaknesses, and propose new organizational structures.

The product would therefore become more capable without requiring its basic purpose to change.

19. Accessibility as an Intellectual Principle

The adjustable interface is not merely a convenience feature. It represents an important principle:

A person should not be excluded from knowledge because the available explanation is written at the wrong level.

Many subjects are presented either too simply to be useful or too technically to be accessible.

LLMTopic would allow the same body of evidence to be approached from multiple levels without dividing users into separate systems.

A beginner could enter through simple language.

An intermediate user could increase terminology and depth.

An advanced user could move toward primary literature, methodological analysis, equations, formal definitions, and unresolved technical disputes.

The system would permit continuous intellectual advancement rather than assuming a fixed audience.

20. Example User Experience

A person enters:

Quantum computing

The system responds:

What would you like to accomplish?

The user selects:

Understand the subject from the beginning.

The system asks:

How technical should the first version be?

The user chooses:

2 out of 10.

The system creates a simple overview with analogies, a terminology guide, major concepts, and a short history.

After reading, the user moves the dial to Level 4.

The Topic Room introduces superposition, measurement, interference, qubits, and error correction with greater precision.

The user later moves to Level 7.

The system adds mathematical notation, physical implementations, decoherence models, fault-tolerance thresholds, and technical comparisons.

The underlying Topic Room grows with the user.

Several months later, the system sends a notification:

A major peer-reviewed result has changed the current assessment of one error-correction approach. Review the update?

The user opens the same bookmarked Topic Room and sees exactly what changed.

21. Distinction from Existing Systems

LLMTopic would differ from a conventional search engine because it produces organized knowledge rather than a list of destinations.

It would differ from an encyclopedia because each Topic Room could be shaped toward a specific user, purpose, knowledge level, and evidence standard.

It would differ from a standard chatbot because the result would persist as a structured, versioned, monitorable body of work.

It would differ from a citation manager because it would interpret and organize the relationships among the sources.

It would differ from a news alert because it would determine whether new information materially changes the existing knowledge structure.

LLMTopic would combine:

\[ \text{Search} + \text{Research} + \text{Organization} + \text{Education} + \text{Personalization} + \text{Monitoring} + \text{Persistent Memory} \]

22. Development Path

The first version would not need to crawl and store the entire internet.

A practical initial system could:

1. Accept a topic.
2. Ask the user several preference questions.
3. Search through approved search services.
4. Retrieve selected accessible sources.
5. Build a cited organized report.
6. Save it as a Topic Room.
7. Allow the user to change presentation settings.
8. Rerun searches periodically.
9. Notify the user of meaningful changes.

Later versions could add:

• larger and more specialized source collections;
• academic database integration;
• collaborative editing;
• public topic libraries;
• expert review;
• multilingual Topic Rooms;
• multimedia explanations;
• visual knowledge maps;
• automated contradiction detection;
• and paid specialist research modes.

23. Commercial Model

The platform could offer several levels of service.

A free user might receive limited Topic Rooms, moderate search depth, and manual updates.

A paid user might receive:

• deeper research;
• more sources;
• continuous monitoring;
• advanced tuning controls;
• private rooms;
• exports;
• collaborative access;
• and longer version histories.

Professional or institutional users might receive:

• team workspaces;
• private source connections;
• domain-specific evidence rules;
• organizational knowledge libraries;
• access controls;
• audit trails;
• and customized monitoring.

Public Topic Rooms could also create a growing discovery library that attracts future users through ordinary web search.

24. Risks and Necessary Safeguards

The platform would need to address several important risks.

Source quality

The system must not treat every internet source as equally reliable.

Hallucination

Claims must remain connected to retrievable sources, and uncertainty must be stated directly.

Copyright

The platform should summarize and cite protected material rather than reproducing complete works without permission.

Privacy

Private Topic Rooms must not be treated as public platform property.

Overconfidence

Expert-level language must not create the false appearance of stronger evidence.

Manipulation

A user’s preferred guardrails should not permit unsupported claims to be misrepresented as established facts.

Monitoring overload

Notifications should identify meaningful changes rather than flooding users with repetitive information.

Medical, legal, and financial topics

High-stakes topics require stronger sourcing, jurisdictional or clinical context, and clear boundaries between information and professional advice.

25. The Larger Significance

LLMTopic would convert the internet from a collection of pages into a source environment from which personalized bodies of knowledge could be constructed.

The significance lies not only in collecting information, but in allowing users to control how that information reaches them.

People differ in:

• education;
• vocabulary;
• available time;
• professional background;
• cognitive style;
• purpose;
• and desired pace of learning.

A fixed answer cannot satisfy every person.

An adjustable knowledge architecture can.

The user would not be required to understand the system’s technical structure. The interface could remain as simple as:

What do you want to understand?

How would you like it explained?

How deeply should we research it?

Should we keep watching for new information?

Behind that simple interface, the system would perform a sophisticated process of retrieval, evaluation, organization, adaptation, preservation, and monitoring.

Conclusion

LLMTopic is a proposed platform for turning any internet-researchable subject into a persistent, organized, user-tuned, and continuously updateable body of work.

Its defining innovation is not merely that an LLM searches the internet. Its defining innovation is that the resulting knowledge can be shaped according to the user’s desired language level, depth, breadth, evidence standard, purpose, and pace of learning.

A beginner could receive a simple explanation. An expert could receive doctoral-level analysis. Either user could adjust the same Topic Room over time without abandoning the underlying research.

The completed Topic Room could be bookmarked, revisited, shared, refined, and monitored. When meaningful new information appeared, the system could notify the user, explain what changed, and request permission before altering the established body of work.

The result would be more than search, more than a chatbot response, and more than a static encyclopedia entry.

It would be a living knowledge structure built around the topic, the evidence, the desired outcome, and the person seeking to understand it.

References

OpenAI. ChatGPT and Large Language Model Systems. OpenAI platform and product documentation.

Secretary Suite. User-Directed Artificial Intelligence, Persistent Workspaces, and Life-Administration Architecture. Ongoing conceptual development.

Swygert, John. Secretary Suite Papers on User-Controlled AI Systems, Knowledge Organization, Boundary Rules, and Persistent Digital Workspaces. Ivory Tower Publishing and Secretary Suite.

World Wide Web Consortium. Web Standards, Linked Data, Accessibility, and Information Architecture Resources.

Relevant technical, legal, privacy, search, retrieval, monitoring, and language-model sources will be incorporated during formal development and DOI publication.