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On October 20, 2026, Memory Tensor open-sourced and released the MemT 1.0 series of memory-native models. The series includes two models: Nano has 36B parameters with 3B activated, based on Qwen3.6-35B-A3B; Flash has 288B parameters with 17B activated, based on DeepSeek-V4-Flash. The models add a memory branch parallel to attention in selected Transformer modules, encoding experience as updatable matrix states that participate in subsequent generation. On 500 SWE-bench Verified tasks, Nano achieved an average success rate of 69.73% at key checkpoints, versus 66.07% for the no-experience baseline; under a 128K long history, end-to-end inference speed is approximately 1.86 times that of the full-context approach, and the persistent state footprint per trajectory is one sixty-sixth of the full-context KV Cache. There are currently no subsequent development facts.
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Memory Tensor Open-Sources MemT 1.0 Series of Memory-Native ModelsDevelopments
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Oct 20
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Memory Tensor Open-Sources MemT 1.0 Series of Memory-Native Models
Memory Tensor has open-sourced the MemT 1.0 series of memory-native models, comprising two variants: Nano with 36B parameters and 3B activated, and Flash with 288B parameters and 17B activated, built on the Qwen3.6-35B-A3B and DeepSeek-V4-Flash bases, respectively. The models add a memory branch parallel to attention within selected Transformer modules, encoding experience into an updatable matrix state that participates in subsequent generation. Across 500 SWE-bench Verified tasks, Nano achieved an average success rate of 69.73% at key checkpoints, versus a no-experience baseline of 66.07%; under a 128K long history, end-to-end inference speed is approximately 1.86 times that of the full-context approach, and the persistent state footprint per trajectory is one sixty-sixth that of the full-context KV Cache.