Your ChatGPT assistant now builds a profile of you in the background. Not based on what you tell it to remember. Based on everything you have ever said to it.

OpenAI rolled out Dreaming V3 on June 4, 2026. It replaces the old system where you manually saved facts ("Remember I like Python") with an automatic background process that reads across years of your past conversations and synthesizes a working model of who you are, what you care about, and what you are currently working on. One billion monthly active users are affected.

What actually changed

Before Dreaming V3, ChatGPT memory had two layers. You could save specific facts, and a background process called Dreaming V0 (introduced April 2025) could reference broader chat history but could not function as a standalone memory system.

Dreaming V3 replaces both. An asynchronous background process synthesizes a single, coherent "memory state" for each user out of their raw sources: past chats, files, connected apps. At inference time, the relevant slice gets injected into the system prompt. Every new conversation starts with a context window that already contains what the system has synthesized about you from past sessions.

OpenAI achieved a roughly 5x reduction in compute cost for the dreaming process. That is what makes the free-tier rollout possible. Plus and Pro users in the US got access first, with free and international expansion in the coming weeks.

How well it works

OpenAI shared three internal benchmarks for Dreaming V3 performance:

Metric Score
Factual recall 82.8%
Preference adherence 71.3%
Time-sensitive accuracy 75.1%

The 82.8% factual recall number is the one to focus on. It means the system correctly retrieves specific facts from your past conversations roughly 4 out of 5 times. The remaining 18% is where it either retrieves the wrong thing, retrieves nothing, or synthesizes something plausible but incorrect.

For preference adherence at 71.3%, the system follows your stated preferences about 7 out of 10 times. That means 30% of the time, the system either ignores or forgets a preference you explicitly told it about.

Time-sensitive accuracy at 75.1% means the system updates its understanding of time-dependent information (you went to Singapore, not going to Singapore) correctly about 3 out of 4 times.

The deletion problem nobody is talking about

Here is what makes this different from a normal settings update. Deleting a conversation does not delete the synthesized memory derived from it. The memory is stored in a separate data layer, and the source conversation is just one input into the synthesis. You need to go to the Memory Summary Page and delete the memory entry, AND delete the source conversation, to fully purge data.

Even after deletion, OpenAI retains logs of saved memories for up to 30 days for "debugging and safety."

There is also what researchers call "context bleed." Information from one session (say, a health question or a financial discussion) influences unrelated future sessions. This is a feature of the architecture, not a bug. The system is designed to carry forward context across conversations.

Privacy controls are more fragmented than you think

You need to manage three separate settings to maintain privacy:

  • Saved Memories: Disables memory synthesis and reference to past chat history.
  • Temporary Chat: A "clean slate" mode that does not use or update memory.
  • Improve the model: A separate toggle that governs if data is used for training.

Turning off memory does not disable the "Improve the model" setting. Users who want both (no persistent memory and no training contribution) must adjust each control independently.

Tenable Research warned in 2025 that because memories are appended to the system prompt, malicious prompts from third-party sources (linked webpages, documents) could potentially exfiltrate persistent memory data. That vulnerability still applies to Dreaming V3.

Community reaction

Power users are not happy. A top post on r/ChatGPTPro titled "How to avoid ChatGPT's damaging upgrade to saved memories" calls Dreaming V3 "a serious downgrade for anyone who manages saved memories manually." The post provides a workaround to restore legacy memory before OpenAI removes the option.

On r/ChatGPTcomplaints, users are reporting that the "Don't mention this again" command only suppresses future references. The underlying data remains in the memory layer. The distinction between "suppressing mention" and "deleting data" is not made clear in the UI.

The broader pattern across Reddit: users feel like they are losing control over something they thought they had ownership of. The old system was clunky but transparent. The new system is smoother but opaque.

Why the timing matters

The EU AI Act's transparency requirements take effect August 2, 2026. OpenAI is currently facing a class-action lawsuit regarding data-sharing practices and has previously faced GDPR fines from Italian regulators.

Dreaming V3 launched two months before those transparency requirements kick in. OpenAI is establishing a new memory architecture with a billion users before regulators can define what "transparency" means for systems that synthesize user profiles in the background.

What surprised me

The 5x compute reduction is genuinely impressive engineering. Synthesizing memory from years of conversation history for a billion users, and doing it cheaply enough to offer for free, is a real technical achievement.

But the thing that actually matters is not the benchmarks. It is the control model. The old system was a mess, but you knew what it remembered because you saved each fact yourself. The new system builds a profile of you automatically, stores it in a separate layer you cannot fully inspect, and does not delete it when you delete the source conversations.

The 82.8% recall number means 18% of the time the system gets something wrong about you, and you may not know which 18%. The 71.3% preference adherence means nearly a third of your explicit preferences are being ignored or forgotten. Those numbers are good enough to feel useful and bad enough to feel unsettling.

What I keep thinking about is the power user workaround. When your most engaged users are writing guides on how to avoid your latest feature, that tells you something about who this feature actually serves.

Sources