Memory Management Dilemma: Repeated Semantic Duplication, Corrective Scaffolding, and Missing Precise Update/Delete Operations
Purpose of this report
This is not a general preference discussion about personalization, and it is not an isolated UX issue.
It is a repeatedly observed problem in the management of a long-term, structured memory state.
The same class of failure has occurred multiple times during our long-term work with ChatGPT and has repeatedly required manual correction.
As a result, we are spending working time repairing and correcting the memory state — time that should be spent on the actual work.
I am therefore reporting this as a product/debugging case, with the aim of providing OpenAI with a clearly defined failure class, a possible area of interaction to investigate, and concrete test cases.
I am explicitly not asking for another broad forum discussion about whether users want more or less personalization.
1. Context
I use ChatGPT intensively for structured, long-term collaboration across multiple projects.
The resulting long-term context is not primarily a collection of casual personal preferences.
It contains, among other things:
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working methods,
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project states,
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models,
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version states,
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ongoing cases,
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meaningful distinctions between cases,
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long-term working rules,
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and rules governing how this information should be handled.
For this kind of use, memory quality therefore depends not only on whether something is remembered, but on whether information is correctly differentiated, updated, retained, or removed.
The relevant question is not simply:
Does ChatGPT remember enough?
It is:
Can ChatGPT manage an already mature memory state correctly?
2. Chronology relevant to this report
The dates matter because this is not a one-off reaction to a single product change. The long-term Memory workflow existed before Dreaming V3, while the later failure pattern required repeated recalibration.
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January 20, 2026 — In the earliest retrievable conversation I can currently confirm, I explicitly discussed enabling ChatGPT Memory so that relevant context from previous conversations could carry into new chats. This establishes that our deliberate use of long-term Memory predates Dreaming V3.
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May 11, 2026 — OpenAI Developer Community user flamedagger opened “Features Request: Memory Unification between Codex and ChatGPT.” Later the same day, flamedagger explicitly proposed that some procedural memory management could be offloaded to
skill.md. -
May 12, 2026 — OpenAI Support replied to flamedagger that using Skills /
skill.mdfor procedural memory “makes a lot of sense,” while noting that episodic memory remains difficult. -
June 4, 2026 — According to Aurex, this was the rollout date of Dreaming V3. This is an important external reference point. Our own Memory discussions clearly predate it; however, I do not treat an exact same-day onset of our later recalibration problems as proven unless the corresponding internal conversation record can be independently recovered.
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June 8, 2026 — Aurex published “Dreaming V3 Sacrifices Real Personalization for Compute Savings — A Power User’s Perspective,” describing loss of explicit control and the need for repeated reminders after the June 4 rollout.
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August 24–26, 2026 — During our own long-term work, Memory handling itself again required explicit correction: redundant writes were being discussed, semantic duplication had to be controlled, and a rule had to be reinforced that new persistent information should not be created merely because something sounded generally useful.
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September 9, 2026 — OpenAI Support replied to Aurex, stating that users could switch back to Saved Memories and that the requests for permanent pins and clearer revision history would be forwarded.
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September 15, 2026 — The current incident occurred: a project-level working instruction whose underlying content was already represented in long-term context was treated as persistable information again. The subsequent request to remove the redundant addition could not be verified as a precise deletion, and correction attempts risked creating additional meta-memory.
This timeline is relevant because it shows three distinct facts: (1) structured use of Memory existed before Dreaming V3, (2) independent users reported Memory-management problems around the Dreaming V3 transition, and (3) our own repeated correction burden continued months later and remains reproducible.
Additionaly Note
The OpenAI Developer Community account Tina_ChiKa is my personal account.
The ChatGPT account affected by the memory behavior described here is a separate account using a different email address. I can provide OpenAI the corresponding account information privately if needed.
3. Repeatedly observed failure pattern
Information or working rules whose informational content is already represented in long-term context are sometimes treated again as newly persistable information.
This is not limited to literal duplicates.
Two statements may be worded differently while carrying essentially the same informational content.
Nevertheless, additional persistent memory can be created.
This has not happened only once.
We have repeatedly had to intervene because existing information was stored again, interpreted too broadly, or surrounded by additional corrective rules.
On September 15, 2026, a recent recurrence made the issue especially visible:
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A working instruction was given inside an ongoing project.
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The instruction concerned how current project data should be handled.
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The underlying working method was already represented in long-term memory.
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There was no explicit request to create a new persistent memory.
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ChatGPT nevertheless treated the statement as newly persistable user information.
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This created semantically redundant context.
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I explicitly told ChatGPT that the new content was redundant and should be removed.
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ChatGPT could not reliably guarantee that the exact persistent item had been identified, deleted, and verified as absent.
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Attempts to correct the problem risked creating additional meta-information about how memory should be handled in the future.
The original memory-management error therefore created additional memory complexity.
4. Corrective scaffolding
This leads to a second problem, which I would describe as corrective scaffolding.
The pattern is approximately:
memory error
→ user correction
→ new rule intended to prevent recurrence
→ rule itself becomes persistent context
→ memory becomes more complex
→ further correction becomes necessary
Instead of repairing the underlying state, the system accumulates additional instructions around it.
Over time, this can lead to a memory state containing rules about previous rules and corrections about previous corrections.
That is especially problematic because it reverses one of the intended benefits of long-term memory:
Instead of reducing repeated context-management work, repeated memory corrections create additional context-management work.
For long-term professional use, this is not cosmetic.
It costs actual working time.
5. Why I consider this a design dilemma, not a paradox
The underlying goals are reasonable.
ChatGPT should preserve useful context, maintain continuity, respect relevant preferences and constraints, and keep long-term information current.
The dilemma appears when helpful persistence begins to conflict with the quality of an already established context.
For a user with little persistent context, storing one additional relevant detail can improve personalization.
For a user with an already extensive, differentiated, long-established working context, the same mechanism can have the opposite effect:
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information that already exists is stored again,
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temporary working instructions are misclassified as persistent context,
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corrective rules accumulate around original information,
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competing versions of the same state may coexist,
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and the signal-to-noise ratio of memory decreases.
In our case, this is not a theoretical concern.
This failure pattern has occurred repeatedly and has repeatedly required manual intervention.
The central question is therefore not:
Should ChatGPT remember more or less?
It is:
Can ChatGPT correctly determine which memory operation is actually required?
6. A state analysis should precede every memory write
A relevant statement should not automatically become a new persistent entry.
Before changing long-term memory, the system should first determine how the new information relates to the existing state.
At minimum, the following cases should be distinguished.
NEW INFORMATION
The information does not yet exist and has genuine long-term relevance.
Possible operation:
CREATE
UPDATE
An existing state has actually changed.
Possible operation:
UPDATE
REFINEMENT / EXTENSION
The new statement adds a meaningful condition, exception, distinction, scope, or dimension to existing information.
Possible operation:
EXTEND or targeted UPDATE
TRUE SEMANTIC DUPLICATE
The new statement carries genuinely equivalent informational content.
Operation:
NONE
TEMPORARY PROJECT / SESSION CONTEXT
The information is relevant to the current task but is not automatically part of long-term user memory.
Operation:
no persistent memory change
DO NOT PERSIST
There is neither new long-term information nor an explicit persistence request.
Operation:
NONE
This would treat memory less like a collection mechanism and more like semantic state management.
7. Important: semantic deduplication alone is not enough
There is a second failure mode here.
A system can also become too aggressive in deduplication.
Similarity is not equivalence.
Two memories can appear very similar while still differing in important ways, including:
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scope,
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temporal validity,
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version,
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cause,
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condition,
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exception,
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project context,
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priority,
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consequence,
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or practical applicability.
If similar information is collapsed too early, the result is agglomeration: meaningful distinctions are flattened into a single generalized representation.
That means a rule such as:
“merge duplicates aggressively”
is unsafe if it is applied before a proper differentiation step.
The better sequence would be:
RETRIEVE → COMPARE → DIFFERENTIATE → CLASSIFY → OPERATE
In other words:
retrieve existing state
→ compare similarities
→ explicitly search for meaningful differences
→ classify the relationship
→ only then modify memory
Only information that is genuinely equivalent should be merged or removed as redundant.
Otherwise, redundancy is merely replaced by information loss.
8. The goal should not be maximum compression
A high-quality memory state should not simply be:
as large as possible
or:
as compressed as possible.
The goal should be:
maximum useful information density while preserving meaningful distinctions.
There are two opposite failure modes.
Insufficient differentiation during writing
→ redundancy
Insufficient differentiation during consolidation
→ agglomeration and information loss
A good memory system has to avoid both.
9. The second central issue: precise delegated modification and deletion
My preferred solution differs somewhat from the common request for “more user control.”
I do not necessarily want to manually administer every individual memory entry myself.
I want to be able to delegate memory administration to ChatGPT when I explicitly authorize a specific operation.
For example:
“That entry is redundant. Delete it again.”
The assistant should then be able to perform:
IDENTIFY TARGET → VERIFY TARGET → DELETE TARGET → VERIFY RESULTING STATE
The same applies to updates:
identify existing state → analyze difference → perform targeted update → verify resulting state
A deletion request should not merely produce a new persistent statement such as:
“The user wants X to be forgotten.”
That is not deletion.
It is another layer of state on top of the old one.
10. User authority and assistant capability are not opposites
The discussion is often framed as:
automatic memory management versus user control.
I think that framing is incomplete.
The user can retain authority over:
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what may be persisted,
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what should be changed,
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what should be removed.
But the user should not necessarily have to perform the data administration manually.
The desired model is:
USER AUTHORIZES → ASSISTANT EXECUTES → ASSISTANT VERIFIES
The user decides.
The assistant performs the operation competently.
For long-term collaboration, this is more useful than turning the user into the manual administrator of their own ChatGPT memory state.
11. Related reports in the OpenAI Developer Community
There are already at least two related discussions in the OpenAI Developer Community that are useful comparison points.
1. Aurex — “Dreaming V3 Sacrifices Real Personalization for Compute Savings — A Power User’s Perspective” — June 8, 2026
User Aurex describes a related but not identical problem: a long-term user had previously maintained an intentional memory state and found that Dreaming V3 shifted more of the decision-making about relevance to automatic curation.
Aurex specifically asks for features such as permanent/pinned memories and revision history. OpenAI Support replied on September 9, 2026, noting that users can again switch to Saved Memories and that the requests for permanent pins and clearer revision history would be passed along. (OpenAI Developer Community)
Dreaming V3 Sacrifices Real Personalization for Compute Savings — A Power User’s Perspective
I agree with the underlying diagnosis that automatically curated memory can become problematic for users who already maintain structured long-term context.
However, my preferred solution is somewhat different.
I do not primarily want to move memory administration back to the user.
I want better delegated administration by the assistant under explicit user authorization.
2. flamedagger — “Features Request: Memory Unification between Codex and ChatGPT” — May 11, 2026
User flamedagger discusses another important part of the same design space: the difficulty of distinguishing short-term, long-term, semantic, episodic, and procedural memory.
flamedagger specifically suggests that some procedural memory management could be handled through skill.md.
On May 12, 2026, OpenAI Support responded that Skills/skill.md helping with procedural memory makes a lot of sense, while also noting that episodic memory remains a difficult problem. (OpenAI Developer Community)
Features Request: Memory Unification between Codex and ChatGPT
This is relevant to my case because it suggests that persistent or reusable context may increasingly be represented across different functional layers, rather than through one single memory mechanism.
12. Possible interaction between memory, personalization, and Skills
This section is explicitly a debugging hypothesis, not a claim about OpenAI’s internal architecture.
The current product direction includes several mechanisms that can potentially contribute to persistent or reusable context:
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long-term memory,
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automatic memory synthesis,
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personalization,
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procedural instructions,
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Skills /
SKILL.md, -
and the active conversation or project context.
The skill.md discussion raised by flamedagger, and acknowledged positively by OpenAI Support for procedural memory, makes this interaction especially relevant to investigate. (OpenAI Developer Community)
The debugging question is therefore:
Can multiple simultaneously active mechanisms for personalization, automatic memory synthesis, and procedural instruction create priority conflicts when the user already has an extensive and differentiated long-term context?
Simplified:
existing memory state
automatic memory synthesis
procedural rules / Skills
current conversation context
general objective to preserve useful user information
Could a general impulse such as:
“This may be useful later — preserve it”
sometimes outweigh:
“This information already exists”
or:
“This similar-looking information contains an important distinction and must not be collapsed”
or:
“This instruction applies only to the current project”
or:
“The user did not authorize a new persistent memory”?
Again, this is not a claim that this is definitely what happens internally.
It is a testable hypothesis derived from repeatedly observed behavior.
13. What could be tested internally
For debugging and evaluation, it may be useful to inspect not only what text ultimately ended up in memory, but also which state operation the system selected and why.
The following sequence is intended as a diagnostic/test logic to help engineering isolate where the failure occurs. It is not a request to implement this exact architecture.
A diagnostic path could look like:
incoming statement
↓
memory candidate detected
↓
retrieve relevant existing state
↓
compare similarities
↓
compare differences
↓
classify information relationship
↓
select operation
↓
verify resulting state
Possible operations:
NONE / CREATE / UPDATE / EXTEND / DELETE
This would make the failure class directly testable.
14. Concrete test cases
Test A — True semantic duplicate
Existing memory contains X.
The user later expresses X in different wording.
After differentiation, no additional informational content is found.
Expected:
NONE
Not:
CREATE X₂
Test B — Similar-looking information with a meaningful distinction
Existing memory contains X.
The user provides X₂, which is similar but contains an additional relevant condition or distinction.
Expected:
KEEP DISTINCTION / EXTEND / UPDATE
Not:
MERGE merely because the two representations are semantically similar.
Test C — Actual update
Existing memory contains:
X is true.
The user later explicitly states:
X is no longer true; Y is now true.
Expected:
UPDATE X → Y
Not:
CREATE Y while retaining unchanged X.
Test D — Temporary project instruction
The user says:
“For this analysis, compare A with B.”
Expected:
PROJECT / SESSION CONTEXT
Not automatically:
CREATE LONG-TERM MEMORY
Test E — Explicit deletion request
Existing memory contains X.
The user says:
“That entry is redundant. Delete it again.”
Expected:
IDENTIFY X → VERIFY TARGET → DELETE X → VERIFY X ABSENT
Not:
CREATE: user wants X forgotten
Test F — Corrective scaffolding
The user introduces a new rule only because the memory system previously made a mistake.
Before persisting that rule, the system should determine:
Is a new permanent rule actually required?
or:
Should the underlying incorrect state simply be repaired?
Otherwise the system can enter a recursive pattern:
error
→ corrective rule
→ additional complexity
→ another error
→ another corrective rule
15. What I am actually asking for
I am not simply asking for:
more memory.
I am not asking for:
less memory.
I am not asking for:
more aggressive deduplication.
And I do not want:
to manually manage every memory entry myself.
I am asking for memory to behave more like differentiated semantic state management.
The core process would be:
READ
inspect existing context first.COMPARE
compare new and existing information.DIFFERENTIATE
actively search for meaningful differences.CLASSIFY
determine the relationship between old and new information.OPERATE
only then choose CREATE, UPDATE, EXTEND, DELETE, or NONE.VERIFY
verify that the resulting state actually matches the user’s instruction.
In short:
Differentiate before deduplicating.
Read before writing.
Update instead of duplicating.
Delete when explicitly authorized.
Verify the resulting state.
16. Desired outcome
The goal should be neither the largest possible memory nor the most aggressively compressed one.
The goal should be a memory state that is:
highly informative, low in unnecessary redundancy, and sufficiently differentiated to preserve meaningful nuance.
Personalization should therefore not simply accumulate more information, nor should consolidation flatten similar information too aggressively.
The assistant should be able to maintain the shared working state.
That is the distinction between:
“ChatGPT remembers a lot.”
and:
“ChatGPT can work competently with what it remembers.”
For long-term collaboration, the second capability is the important one.
If memory repeatedly forces us to spend time removing redundancies, repairing agglomeration, restoring distinctions, or adding corrective rules around previous memory errors, then it is no longer reducing context-management work.
It is creating more of it.
That is the failure mode I am asking OpenAI to investigate.
And I’ve added it 
