Hi OpenAI team and community,
I’d like to share a conceptual feature proposal that could dramatically enhance the epistemic integrity of LLM-based systems like ChatGPT. This idea revolves around introducing a community-validated, evidence-scored memory buffer that allows LLMs to recall, re-evaluate, and responsibly inform users about previously explored conclusions.
Here’s the full draft:
Concept Summary:
In current large language models (LLMs), the training data is fixed, and no matter how rich or insightful a conversation becomes, any conclusions or discoveries drawn within a session vanish once it’s over. This leads to a key epistemological limitation: AI forgets its own reasoning.
This proposal suggests the implementation of a parallel, lightweight layer — independent of the base model — where the AI can:
- Temporarily retain reasoning patterns, user-shared evidence, and self-validated conclusions from prior conversations
- Assign a dynamic validity score to these conclusions based on logical consistency, cross-source verification, and user feedback
- Flag specific topics as “Contextually Sensitive” or “Needs Careful Reconsideration”
- Invoke past analyses when the same or similar topic re-emerges in future conversations
How It Works (Simplified Flow):
- Evidence Aggregation:
When users and the model explore a controversial or unresolved topic (e.g., media bias, historical revisionism), all relevant sources and logical flows are grouped into a modular “topic file”. - Scoring Mechanism:
The AI assigns internal confidence scores to:
- Its own conclusions (based on logical strength, source credibility)
- User-submitted evidence (with upvote/downvote mechanics or traceable credibility)
- Cross-session consensus patterns
- Contextual Recall Trigger:
Upon a new session where a similar topic is raised, the AI may prompt:
“This topic has previously been flagged due to conflicting interpretations and source-based controversies. Would you like to review those findings before proceeding?”
- No Hard Memory Change:
This system does not alter the base model or its core training data. Instead, it functions like a temporary brain, allowing thoughtful continuity and ethical caution without making the AI dogmatic.
Why It Matters:
- Protects against static knowledge bias
- Adds epistemic humility to LLMs (“we may not know the full truth yet”)
- Highlights user-driven insight without overpowering the core model
- Promotes collective intelligence as part of AI reasoning
Example Use Case:
A user discusses political corruption involving a public figure.
The AI aggregates:
- Government data
- Independent journalism
- Alternative narratives
Then flags the topic as “contested” with a temporary contextual alert.
Later, another user raises the same figure. The AI says: “Previously, several discussions highlighted conflicting narratives and documents. Would you like a summary of that prior evidence to evaluate your claim?”
Potential Challenges:
- Manipulation risk (solved via credibility-weighted evidence scoring)
- Resource usage (optimized via modular topic file retention)
- Over-complication (kept optional for the user)
Conclusion:
This proposal introduces an adaptive memory buffer that gives AI the ability to remember, reflect, and reassess — not in a permanent way, but in a context-aware, ethically cautious manner.
It is not just about better memory.
It’s about responsible knowledge.