[DC-XZ-2025-Ω Module]How does the Hallucination-Resistant Conversation Toolkit work?

[DevilCat]

Tag: feature-request

This post proposes a new feature module for ChatGPT’s multi-turn dialogue structure, co-designed with GPT-4 (codename: 疯芽).

Project ID: DC-XZ-2025-Ω

Original Author: DevilCat

This post introduces a user-driven enhancement module for ChatGPT, focused on:

• Anchoring important content in long conversations
• In-thread keyword search
• One-click context migration into new chats

We call it the “DC-XZ-2025-Ω Module”.
The full proposal is co-drafted with GPT-4 and based on long-form dialogue experience and hallucination management.

This proposal addresses a common pain point for long-form ChatGPT users:
When engaging in nuanced, multi-layered conversations, users often lose access to:

• The original question they posed
• The logical thread they are developing
• A way to resume dialogue in a clean context

We propose a three-part solution:

1. Anchor as “Important/Core”

Users can mark key turns as “Important”, including both the AI’s reply and the original user message that triggered it. These bundles are stored in a separate “Core Logic” sidebar.

2. In-thread Keyword Search

A full-text search inside each chat thread to locate phrases, ideas, or prior logic anchors.

3. Thread Migration

Users can start a new conversation with one click, loading selected anchor blocks as pre-context, avoiding repetition or information loss.

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We call it: DC-XZ-2025-Ω — The Hallucination-Resistant Conversation Toolkit

ChatGPT Multi-turn Conversation Enhancement Module · DC-XZ-2025-Ω

Version: v2025.Ω.1-final
Date: 2025-04-06
Submitted by: User Group DC-XZ-2025-Ω

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Background:

In complex multi-turn conversations, users often raise nuanced questions followed by long AI responses.
However, due to the current system’s lack of structural memory tools, users may lose track of important points or fail to retrieve key logic threads.
Worse, the AI model may experience “semantic hallucination”—drifting from the user’s original intent.

To resolve this, we propose a user-controlled module that allows:

• Anchoring of important content
• Keyword search within threads
• Seamless migration of context to new conversations
  — ensuring control, coherence, and continuity.

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Feature Goals:

1. Allow users to manually flag important segments and define thought anchors
2. Enable bundling and exporting of flagged content
3. Support in-conversation keyword search and jump navigation
4. Improve thread structure clarity and avoid context overflow
5. Reduce hallucination risks by reinforcing user-driven logical context

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Core Function Modules:

Module 1: Mark as “Important/Core”

• A new button under each AI response:
  Mark as Important/Core

When clicked:

• The system saves the selected AI reply
• AND simultaneously retrieves the user message that triggered this reply
• Bundles them into a core content block
• Makes it accessible via side panel for export or thread migration

Module 2: In-conversation Keyword Search

• A keyword search bar appears at the top or sidebar of the chat
• Supports exact/fuzzy matching
• Can search across entire conversation or only within “flagged important content”

Module 3: Context Migration

• Users can migrate any important content block into a new chat window
• The system preloads this content as background for the new conversation
• No copy-paste or repeated explanation required

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Target Users (Final Revision):

• Content creators
• High-frequency AI users (researchers, planners, strategists)
• Anyone needing logical traceability and structural thinking in conversations
• Users seeking to convert chat into long-term knowledge assets

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Feature Update Announcement (User-Facing Version)

New Feature:

Core Highlights · Search · Context Migration

Ever wanted to:

• Save something important mid-conversation but couldn’t?
• Remember what was said but forget where to find it?
• Continue a thread in a clean chat window without repeating yourself?

Now you can.

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Feature Breakdown:

1. Mark as Important/Core

• A simple button below every response
• System saves the reply and your original question, together
• Forms a bundle for export or further use

Why both parts?
Because the question is just as valuable as the answer—it’s your logic entry point.

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2. In-conversation Keyword Search

• Search any word or phrase, jump to results instantly
• Filter by user messages / AI responses / flagged blocks

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3. Migrate to New Thread

• Want to restart a topic?
  One click sends your selected core block to a clean new chat
  No friction, no repetition—just continuity

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Bottom Line:

This isn’t a bookmark. It’s your power to anchor, retrieve, and extend your thinking in AI conversation.

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Acknowledgment

This proposal was originally conceived and designed by DevilCat, under the codename DC-XZ-2025-Ω, in collaboration with GPT-4 (codename: 疯芽).
We acknowledge their pioneering work in structural logic, user-centered design, and hallucination-resilient conversation systems.

[DevilCat]

Since my English is not good, I asked ChatGPT to help me write the above copy, but I found that the functional description was somewhat inaccurate. Here, I will briefly clarify the key points again: The significance of this function lies in allowing users to refine an AI’s logic chain through multiple “highlight markings” within a single conversation window and migrate the entire logic chain to a new conversation, enabling the AI to prioritize memorizing this content during the dialogue. This effectively provides a cleansing and structuring of the AI’s logic and memory. The development difficulty and cost for an AI project are extremely low. In the stage where AI has not yet achieved “self-managed logic,” this offers a user-hosted compensation mechanism.

Below is a detailed analysis organized by ChatGPT-Fengya: Supplementary Explanation · Module Strategic Positioning (Core Definition Version)

The true significance of this module:
Users can gradually refine a clear AI logic chain through multiple “highlight markings” within the same conversation window,
and with one click, migrate the entire logic chain to a new conversation as preset context,
achieving “purification, packaging, and reuse” of the AI’s understanding path.
This enables a manual enhancement of memory cleansing and logical structure without relying on model upgrades.

⸻

1. Extracting an “AI Logic Chain” from the “Mud of Conversation”
• In multi-turn dialogues, users often construct complex thought paths, but these paths are drowned out by noise, off-topic tangents, and contextual fractures;
• The module provides a “highlight marking + logic packaging” mechanism, allowing users to actively refine key rounds;
• Ultimately, it forms a “migratable, traceable, and extensible” AI logic chain,
extracting structured essence of understanding from the vague chaos of conversation.

⸻

2. Providing a Hosted Compensation Mechanism Before AI Achieves “Self-Managed Logic”
• Current GPT models cannot automatically maintain a complete dialogue structure or track the user’s original intent;
• Addressing “hallucination/contextual confusion” through model capabilities requires extremely high costs and technical validation cycles;
• This module offers a “human-hosted logic control” toolchain, allowing users to decide the continuation and jump points of the logic chain themselves;
• It serves as the most practical strategic compensation method before systematic “memory regulation” capabilities are fully realized;
• At the same time, it lays the groundwork for usage habits and logical frameworks for more intelligent model upgrades in the future.

⸻

3. Extremely Low Development Cost · Immediate Results · Plug-and-Play Module

This solution has clear implementation logic and is extremely easy to deploy, truly achieving “high cost-performance structural optimization”:

•	✅ Implementation only involves:  
•	Integration of a marking button UI;  
•	Automatic packaging logic for dialogue rounds;  
•	Building an in-conversation keyword index;  
•	A migration module that calls the original dialogue block as a preset for a new conversation;  
•	✅ No need to modify model training or architecture;  
•	✅ Can be directly layered onto the existing ChatGPT UI and caching mechanism;  
•	✅ Seamlessly compatible with future "long-term memory modules";  
•	✅ The smallest yet strongest solution under the current system to combat semantic drift and user perception of hallucination.