Proposal: A Provenance-Aware Collective Reasoning Layer for ChatGPT
Turning individual AI reasoning into cumulative, inspectable knowledge
Submitted as a user-driven product and research proposal
Executive proposal
Today’s frontier AI systems can reason extraordinarily well within a conversation, but an important limitation remains:
A significant amount of useful reasoning is generated during conversations but does not become a durable, structured starting point for future reasoning.
A user can challenge a model’s assumptions, introduce overlooked evidence, expose a logical weakness, and cause the model to materially revise its conclusion. Yet the next user asking the same question may effectively begin from the model’s earlier state rather than from the improved reasoning that resulted from the previous investigation.
I propose a Provenance-Aware Collective Reasoning Layer (PACRL) for ChatGPT.
The objective would not be to make AI conclusions authoritative.
The objective would be to allow evidence, hypotheses, arguments, counterarguments, confidence estimates, and model reasoning to accumulate over time while preserving their provenance and epistemic status.
In simple terms:
Don’t make the AI merely remember what people said. Make it remember what was established, what was inferred, why it was inferred, who challenged it, what survived those challenges, and what would change the conclusion.
The core concept
For a disputed question, ChatGPT should eventually be able to maintain something resembling:
CLAIM
↓
Evidence supporting claim
↓
Evidence opposing claim
↓
Source reliability
↓
AI reasoning
↓
Counterarguments
↓
Independent challenges
↓
Revisions
↓
Current confidence
↓
Known unknowns
↓
Falsification conditions
This creates a persistent argument/evidence graph, rather than simply storing conversational text.
Why this is different from conventional memory
This should not be implemented as “train on everyone’s conversations.”
That approach creates serious problems:
private information leakage;
contamination of factual knowledge with speculation;
circular reinforcement of AI-generated claims;
popularity being mistaken for truth;
inability to distinguish primary evidence from someone’s opinion;
loss of provenance.
Instead, the system should maintain separate epistemic layers.
Layer 1 — Source evidence
Actual documents, datasets, measurements, observations, photographs, court records, scientific papers, etc.
Layer 2 — Human testimony/interpretation
What a researcher or user claims about that evidence.
Layer 3 — AI inference
What the model concludes from the available evidence.
Layer 4 — Confidence
How strongly the current evidence supports each competing hypothesis.
Layer 5 — Argument history
Why the confidence changed over time.
The system should never collapse these layers into one another.
A practical example: Brushy Bill Roberts and Billy the Kid
A real conversation demonstrates why this architecture could be useful.
A user asks:
“What probability would you assign to Brushy Bill Roberts actually being Billy the Kid?”
The model initially assesses the proposition at approximately 25%.
The user challenges the assessment.
Rather than simply arguing back and forth, the model investigates the user’s claims and identifies several potentially relevant factors:
Roberts’ primary stated objective was reportedly obtaining a pardon, rather than simply receiving money or fame.
His decades of silence could potentially be interpreted as consistent with maintaining a promise or agreement.
The timing and circumstances of his physical collapse during the pardon hearing may be relevant to understanding his motivation.
Roberts reportedly possessed numerous scars that he associated with injuries attributed to Billy.
William Morrison investigated Roberts and ultimately became sufficiently convinced to pursue the pardon.
Multiple elderly witnesses supported aspects of Roberts’ identification.
The identity of the Fort Sumner remains has not been conclusively established through definitive DNA testing.
A hypothetical agreement between Pat Garrett and Billy could potentially explain both Billy’s survival and his subsequent silence.
The model investigates these arguments.
Some withstand scrutiny.
Some become weaker.
Some remain unresolved.
The model consequently revises its assessment:
Initial assessment: ~25%
Current assessment: ~50%
Primary causes of revision: [enumerated evidence and reasoning]
The critical point is not whether 50% is correct.
The critical point is that the system can explain exactly why it moved from 25% to 50%.
Now introduce the next researcher
Researcher B examines the current assessment and says:
“The scar evidence is not independently documented. Roberts may have supplied the explanations for those scars himself after making his claim.”
The system evaluates that objection.
If the objection is supported, the confidence might change:
50% → 37%
And the system records:
Confidence reduced because the evidentiary independence of the scar evidence was determined to be weaker than previously assessed.
Then Researcher C finds an authenticated contemporary newspaper published before Roberts’ claim that independently describes an injury corresponding to one of those scars.
The system evaluates the discovery.
If verified:
37% → 55%
The knowledge state now contains:
Original hypothesis
→ Challenge
→ Evidence
→ Reassessment
→ New evidence
→ Second reassessment
The next researcher doesn’t start from zero.
They start from the current state of the investigation.
This would create an important new AI capability:
“Tell me why you believe this.”
The model should be able to answer:
“My current assessment is 55% because evidence A, B and C support hypothesis X. Evidence D and E oppose it. Evidence F is unresolved. My confidence increased from 37% after independent verification of source G.”
Then the user can ask:
“What is your weakest assumption?”
or:
“Show me the strongest evidence against your conclusion.”
or:
“What would change your mind?”
or:
“Which source contributes most to your current confidence?”
That transforms AI from an answer generator into a continuously challengeable reasoning system.
A critical safeguard: prevent epistemic feedback loops
This is arguably the most important engineering requirement.
An AI’s conclusion must never become independent evidence merely because it was repeated by other users or other AI sessions.
For example:
AI concludes X.
10,000 users repeat X.
A future model sees thousands of references to X.
It concludes:
“X has widespread support.”
That would create an artificial consensus generated by the model itself.
Therefore:
AI-generated propositions must retain their provenance forever.
The system must know:
“This originated as an AI inference.”
rather than eventually treating it as:
“10,000 independent sources support this.”
Only genuinely independent evidence should increase evidentiary weight.
Another critical safeguard: user privacy
Collective reasoning should not mean collective exposure of conversations.
A user’s private conversation should remain private.
The system could instead extract or accept generalizable research contributions separately from personal context.
For example:
“This user discovered an obscure archival source relevant to Question X.”
could potentially be preserved as research metadata without exposing:
identity;
personal history;
private conversation;
unrelated information;
sensitive data.
Participation should be transparent and controllable.
The system should also preserve disagreement
A dangerous AI would try to produce one definitive answer.
A better system would preserve competing hypotheses.
For example:
Hypothesis A
Billy died at Fort Sumner.
Current confidence: 50%
Hypothesis B
Billy survived and later became Brushy Bill Roberts.
Current confidence: 50%
Then:
Evidence favoring A
Evidence favoring B
Evidence supporting both
Unresolved evidence
Arguments that have been disproven
Arguments that remain viable
This prevents the system from prematurely collapsing uncertainty.
The most important field may be:
“What would falsify this?”
Every significant AI conclusion should ideally contain:
Current conclusion
Confidence
Supporting evidence
Contradictory evidence
Major assumptions
Unresolved questions
What would change the conclusion
This creates an AI that is structurally encouraged to remain falsifiable.
Why this could be transformative
Traditional scholarship has a fundamental bottleneck:
Human memory and bandwidth.
A researcher may spend months reconstructing what previous researchers already examined.
An AI could maintain the entire argument graph.
A future researcher could ask:
“What has already been investigated?”
“Which arguments have already been disproven?”
“What remains unresolved?”
“What evidence has the greatest potential to change the current conclusion?”
“Which assumptions have never actually been tested?”
This could dramatically reduce duplicated intellectual labor.
The broader architecture
The concept could eventually be implemented as a Research State associated with a question rather than a person.
For example:
Research Question
│
├── Claims
│
├── Competing Hypotheses
│
├── Primary Sources
│
├── Secondary Sources
│
├── Evidence Graph
│
├── Argument Graph
│
├── Counterarguments
│
├── Confidence State
│
├── Unresolved Questions
│
├── Falsification Conditions
│
└── Revision History
The conversational AI would act as the interface to this underlying research state.
This is not about making AI “more confident.”
It is almost the opposite.
The goal is to make AI more accountable for why it is confident.
Instead of:
“I believe X.”
the system should increasingly be capable of:
“I currently estimate X at 62%. Here are the eight major factors producing that estimate. Two are highly reliable, three are moderately reliable, and three remain disputed. Here is the strongest argument against X. Here is the evidence that would cause me to materially revise my assessment.”
That is much closer to machine-assisted epistemology than conventional chatbot behavior.
The long-term vision
The ultimate capability would be:
Humanity asks a question once, and every subsequent investigation begins from the best currently available state of knowledge rather than from scratch.
But the system never says:
“This is true because previous users said so.”
Instead it says:
“This is our current best-supported conclusion because these independently verifiable facts support it, these facts oppose it, these assumptions remain unresolved, and this is the reasoning that produced the present confidence level.”
That distinction is the entire foundation of the proposal.
Why I believe this belongs in the next generation of ChatGPT
ChatGPT is already capable of doing much of this locally within a conversation.
The missing capability is persistence of epistemic state.
Today:
Conversation → reasoning → conclusion → conversation ends
Proposed:
Conversation → reasoning → conclusion → challenge → revision → persistent research state → next researcher → further challenge → further revision
That would turn ChatGPT from a system that primarily answers questions into a system that can help humanity progressively investigate questions.
The model would not become the final authority.
It would become something potentially more useful:
A continuously challenged, provenance-aware institutional memory for human reasoning.
Proposed MVP
This does not need to begin as a massive global knowledge graph.
A practical first implementation could be much smaller:
Research Mode
Allow a user to explicitly mark a conversation:
“Save this as a Research State.”
ChatGPT then generates:
research question;
current hypotheses;
evidence table;
source provenance;
arguments for/against;
confidence estimates;
unresolved questions;
falsification criteria;
reasoning/revision history.
The user can subsequently invite another ChatGPT session to challenge the Research State.
If the challenge survives evaluation, the Research State updates.
Every revision retains its prior state.
That alone would demonstrate the value of the architecture without requiring immediate global sharing.
Final proposal
Don’t simply give ChatGPT longer memory.
Give it better epistemic memory.
Let it remember:
what was found,
where it came from,
what was inferred,
why it was inferred,
who challenged it,
what survived those challenges,
what remains uncertain,
and what would change the conclusion.
That would allow each investigation to become the starting point for the next one—while preserving the distinction between evidence, opinion, inference, and fact.
The goal is not an AI that never changes its mind.
The goal is an AI that can show us exactly why it changed its mind—and make that intellectual progress useful to whoever investigates the question next. I hope someone on the programming or engineering team will take a serious look into this. If done correctly, it could save so many people the valuable time it takes to reach the most factual and reliable conclusions.

Thank you for your consideration,

Hakon Swenhaugen