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MemTensor Open-Sources MemT 1.0 Models With Trainable Internal Memory

1 reports 1 reporting sources 17 hours agoNew

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MemTensor has open-sourced its MemT 1.0 model series, adding trainable memory branches to selected Transformer modules so the models can encode, update and retrieve experience as internal dynamic states. The series includes MemT-1.0-Nano, with 36B total parameters and 3B active, and MemT-1.0-Flash, with 288B total parameters and 17B active. QuantumBit reported that, across 500 SWE-bench Verified tasks run three times each, Nano averaged a 69.73% success rate using internal experience, compared with 66.07% without experience and 69.13% with text experience. The article also acknowledged that text memory systems retain advantages in recalling details from long histories.

Generated by AI from reports · Updated 55 minutes ago

Latest development10/20 20:50 (UTC+8)
MemTensor releases MemT 1.0 models with native memory branches inside Transformer modules量子位

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10/20
  1. 量子位
    MemTensor releases MemT 1.0 models with native memory branches inside Transformer modules

    MemTensor has open-sourced MemT 1.0, which adds trainable memory branches to selected Transformer modules to encode, update, and retrieve experience as internal dynamic states. MemT-1.0-Nano has 36B total parameters with 3B active, while MemT-1.0-Flash has 288B with 17B active. Across 500 SWE-bench Verified tasks run three times each, Nano averaged 69.73% success versus 66.07% without experience and 69.13% with text experience; the article acknowledges that text memory systems retain advantages in recalling details from long histories.

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