MemT 1.0 Open-Sourced: Memory Built Into Architecture, Leads Tests

Few things frustrate AI users more than a moment they know all too well: you spend half a day correcting an AI until it gets the task right, then you open a new session and it promptly falls into the same pit again. Memory Tensor has just released and open-sourced MemT 1.0, a “memory-native” large model built specifically to fix this problem. One test in the technical report is especially telling. A user first gives the original date of an exhibition, then settles on rescheduling it to Sunday. Along the way, they casually mention a Monday alternative that was never adopted. The two then talk for more than 500 further rounds, covering roughly 66,000 tokens, and the exhibition is never mentioned again. In a “closed-book” state, with no access to the original text, the model still accurately states both the original and rescheduled times. It also notes that the venue, ticket prices and contact person have not changed, and it clearly separates the Sunday decision from the Monday alternative.

This kind of scenario exposes the weakness of conventional approaches. Most current solutions attach an external memory bank to the model, store chat logs there, and retrieve them when needed. The report argues that with multi-version information spanning this long a period, external retrieval can easily mistake idle chatter for a final decision. MemT 1.0 takes a different approach. It splits the model's capacity into two parts: a backbone network that carries general cognition, and a dynamic memory state that evolves in real time with each interaction. At the end of every dialogue turn, the model refreshes this memory directly at the token level. A gating mechanism learned during training decides what is kept and what is overwritten, and the entire large model does not need to be retrained. To fit more detail into a fixed-size memory, the team also introduced a Mixture-of-Storage architecture, in which several “memory experts” take charge of reading and writing the storage space. These abilities are acquired through training. The team first ran continued pretraining on 20 billion tokens, then post-training on 14 billion tokens, with a “top-student” teacher model scoring the memory extractions.

Across 11 systematic memory benchmarks, the fully closed-book MemT-1.0-Flash and MemT-1.0-Nano average 50.6 and 47.0, respectively. That puts them 12.5 points and 8.9 points ahead of the strongest parameterized-memory baselines in their groups. The report includes two more specific examples. In a history of about 16,000 tokens, when a user asks about their own birthday, the model honestly says it doesn't know rather than making something up. In a conversation of 33,000 tokens, the model applies a rule set early in the exchange to check badges and registration status, and concludes that one of two people complies while the other is unregistered. All these figures come from the developer's technical report, and there are as yet no results from independent institutions to replicate them.

The most demanding tests were conducted in real coding scenarios. The team first distilled more than a thousand lessons from tens of thousands of successful trajectories. Then, at “stuck points” where the model encountered errors, empty returns or repeated ineffective actions, it injected those lessons and reran the task. This process yielded about 42,000 samples used to train the memory module. On 500 tasks from SWE-bench Verified, writing experience in advance raised Nano's average success rate from 66.07% to 69.33%, roughly on par with a control group that placed the same experience directly into the context (68.87%). In a live-work mode, the model writes experience only when it hits an error or is going in circles. Its average success rate reached 69.73%, slightly above the 69.13% of the text-experience group. On SWE-bench Multilingual, which covers nine programming languages, Nano rose from 60.00% to 63.56%. These comparisons also come from the developer's report.

For businesses, the most immediate change is that team know-how no longer has to be taught over and over. The report describes a common situation: project conventions and debugging routines must be explained again every time an engineer opens a new session, so the company effectively keeps paying for this repeated overhead. In MemT 1.0's vision, real business feedback continuously refreshes the experience library, and the model gradually internalizes the team's conventions, so veterans worry less and the AI stumbles into fewer old pitfalls. Cost matters just as much. In tests with two GPUs and single concurrency, when processing a 64K-token history, Nano's end-to-end inference speed is about 1.20 times that of a full-context approach. At a 256K history, the gain reaches about 3.99 times. With a 128K history, the persistent state stored for a single interaction is about one sixty-sixth of the full KV cache. The report also lists long-term companion scenarios such as digital twins, chronic disease management advisers and personalized personal trainers. These remain conceptual, however, and the report gives no timeline for deployment.