Hi OpenAI team and community,
I’d like to propose a feature that could enhance the way ChatGPT handles long-term conversations, complex projects, and structured knowledge management.
Summary
A Hierarchical Chat System with Thematic Navigation**, based on structured identifiers like:
UserID.ChatID.TopicID.MessageID
This system would allow users and the AI to:
Organize conversations by topic across sessions.
Perform global searches and thematic filtering.
Compare ideas across time and track their evolution.
Improve memory and context modeling.
Examples
Search:* Show all TopicID = T7 (quantum physics) across all chats.
Compare:** Compare U123.C45.T7.M2 vs U123.C88.T7.M1
Export:** Pull all entries under T7 into PDF or Notion.
Implementation Ideas
Use a graph DB (Neo4j) or document store (MongoDB).
Add a tree-style UI in the sidebar for navigation.
Offer a minimal version using hashtags (e.g., #T7).
Why It Matters
No competing models (Gemini, Claude) offer this kind of structured workspace.
Power users (researchers, writers, devs) would gain a huge productivity boost.
Potential premium feature for Pro/Enterprise plans.
Attachment
I’ve described the idea in more detail in this PDF:
Feature_Request_Hierarchical_ChatGPT.pdf
Would love your thoughts, support, and suggestions!
Best,
Denis Lyukshin
Based on further development and feedback, I have prepared a full technical specification and a visual presentation for easier evaluation of this feature request.
New Materials:
Corporate-style Specification (PDF) Links omitted due to forum restrictions
Visual Presentation (PDF) Links omitted due to forum restrictions
Key Improvements:
Clear API structure and data models
Detailed user scenarios and examples
Architecture aligned with ChatGPT’s scalability and privacy standards
Focus on professional users and enterprise use cases
Potential for premium feature monetization
If anyone is interested in reviewing the detailed documents, feel free to message me directly!
I would be happy to share the specification and presentation files.
Thanks again for the feedback and attention!
— Denis Lyukshin
(luksindenis@gmail.com | @DenisLyukshin)
Synaptic Chat Network — Enhanced Cross-Chat Semantic Interaction
Hello OpenAI Team and Developer Community,
Following my previous proposal on Hierarchical Chat Systems with Thematic Navigation, I am excited to share a major evolution of the idea based on further development and research:
What’s New?
Synaptic Chat Network (SCN) — an architecture where:
- Chats act as dynamic neurons.
- Topics become weighted synaptic connections between chats.
- Semantic strength determines link formation, reinforcement, or pruning.
In short: Chats can now “talk” to each other across sessions based on shared topics and semantic similarity.
Technical Highlights
- Cross-Chat Semantic Reinforcement
Topics dynamically link and strengthen across chats when semantic overlap is detected.
- Neural Link Management
- Pruning weak/inactive connections automatically.
- Dynamic recalibration of link relevance.
- Archiving old/merged topic links historically.
- AI Components
- SentenceBERT for semantic embeddings.
- PyTorch Geometric (GNN) to evolve the topic-graph structure intelligently.
- Data Infrastructure
- Neo4j (Graph Database) or PostgreSQL + Graph Extensions.
- AES-256 encryption for message content.
- SHA-256 anonymization of UserIDs for privacy.
- Metadata-Driven Evolution
- Activity scoring, user tagging, dynamic threshold adjustment for topics.
- Auto-merging or manual merge suggestions based on semantic affinity.
- Interfaces
- Cross-topic navigation filters.
- Graph-based visualizations using D3.js or Vis.js.
- Smart alerts: “Topic X is strongly related to Topic Y — Merge?”
Expanded Use Cases
- Persistent knowledge networks across long-term projects.
- Multi-session thematic research and analysis.
- Enterprise team collaboration on evolving knowledge graphs.
- AI-assisted topic merging and rediscovery of latent knowledge.
Materials Available
Updated Whitepaper (Technical Specification v2.0) — [Message me to receive]
Visual Presentation (Architecture Diagrams, UI Concepts) — [Message me to receive]
Why It Matters
“Structured memory and semantic knowledge evolution are essential for AI systems that aim to assist users in complex, long-term tasks.”
The Synaptic Chat Network approach could position ChatGPT as not just a conversational tool but as a scalable, intelligent, cross-session knowledge engine.
No major competing platforms currently offer such an architecture — this could be a real strategic advantage for OpenAI Pro/Enterprise offerings.
Contact
If you’re interested in discussing further or reviewing the full documents, feel free to reach out!
Denis Lyukshin
(luksindenis@gmail.com | @DenisLyukshin)
Synaptic Chat Network v4.0 — Topic Evolution + Collective Voting
Hi OpenAI team and developer community,
Following the original idea of Hierarchical Chat Systems and subsequent evolution into Synaptic Chat Networks (SCN) and SCN-E, I’d like to share version 4.0 of the concept, now featuring collective governance over topic evolution.
What’s New in SCN-EV?
SCN-EV (Evolution + Voting) introduces democratic control over how topics evolve across chats. Instead of fully automatic transformations, users can now participate in shaping the knowledge structure through voting-based mechanisms.
Key Mechanism: Collective Topic Evolution
When does it trigger?
- Automatic detection: If the system predicts a topic shift (e.g., Physics → Philosophy, with >75% confidence).
- Manual proposal: Any user can propose merging, renaming, or reclassifying a topic.
How does voting work?
| Type |
Context |
Example |
| Auto-vote |
Solo or private |
“Rename ‘Physics’ to ‘Quantum Philosophy’?” |
| Group vote |
Shared chats |
“3 of 5 users voted ‘Yes’ → topic updated” |
Technical Details:
json
{
"topic_id": "T7",
"proposed_evolution": {
"new_name": "Quantum Philosophy",
"trigger": "auto",
"votes": {
"user123": "yes",
"user456": "no"
},
"threshold": 0.51
}
}
python
def process_vote(topic_id, user_id, vote):
topic = get_topic(topic_id)
topic.proposed_evolution.votes[user_id] = vote
if voting_complete(topic):
if votes_approved(topic):
merge_topics(topic_id, topic.proposed_evolution.new_name)
Example Use Case
Research Group Chat
- The system detects that Topic T7 (Physics) is now heavily focused on consciousness.
- Proposal auto-launched: Rename to “Quantum Philosophy”?
- Vote: 4 out of 7 users agree → topic renamed.
- History logged:
vbnet
2024-07-28: T7 renamed to "Quantum Philosophy" (vote: 4/7).
Why It Matters
Democracy in topic management
Increased accuracy via social feedback loops
Transparency through metadata and vote logs- Scalability for research, teams, and enterprise workspaces
Rating the Upgrade
| System |
Evolution |
Intelligence |
Control |
Final Score |
| SCN |
Static |
7/10 |
5/10 |
8.0 |
| SCN-E |
Bayesian |
9/10 |
6/10 |
9.5 |
| SCN-EV |
+Voting |
10/10 |
10/10 |
10.0 |
Next Steps
- Build voting interface in sidebar (
Yes / 
No)
- A/B test impact of voting vs. prediction-only
- Optional: integrate Slack-style slash commands:
/propose_merge T7 T8
If you’re interested in reviewing the full technical whitepaper or visual presentation (PDF), just DM me — I’d be happy to share!
Thanks again for the support, feedback, and vision for ChatGPT as a knowledge development environment — not just a chatbot.
—
Denis Lyukshin
luksindenis@gmail.com
@DenisLyukshin