{"id":"memt-1-0","slug":"memt-1-0","name":"MemT-1.0","orgId":"memtensor","family":"memt-1-0","releaseDate":null,"availability":"announced","openWeights":true,"license":null,"paramsTotal":null,"paramsSource":null,"contextWindow":null,"knowledgeCutoff":null,"modelCardUrl":"https://x.com/ModelScope2022/status/2112453862351073661","hfRepo":null,"modalities":["text"],"aliases":["MemT-1.0","memt 1.0","memt-1.0","memt-1.0-flash","memt-1.0-nano","memt 1.0 flash","memt 1.0 nano","MemTensor/MemT-1.0-Flash","MemTensor/MemT-1.0-Nano"],"aliasNote":null,"origin":"tracked","tracked":{"trackedSince":"2026-10-20","announced":"2026-10-20","blurb":"MemTensor's open-weight model family (Flash and Nano) with persistent memory built into model computation, so agents can reuse past history without reloading the original context.","api":"none","openWeights":"yes","params":{"text":"Flash ~288B total / ~17B active; Nano ~35B total / ~3B active","source":"Hugging Face safetensors totals; active counts from the announcement"},"facts":[{"label":"Lab","value":"MemTensor (记忆张量), China"},{"label":"Variants","value":"MemT-1.0-Flash (built on DeepSeek-V4-Flash) and MemT-1.0-Nano (built on Qwen3.6-35B-A3B)"},{"label":"Memory","value":"Dynamic local memory plus trainable hyper memory; locally persistent memory sessions"},{"label":"Weights license","value":"Flash: MIT. Nano: Apache-2.0 (Hugging Face cards). Inherited backbone licenses still apply"},{"label":"Code license","value":"GitHub repo MemTensor/MemT-1.0: PolyForm Noncommercial 1.0.0 (repo LICENSE)"},{"label":"API","value":"Not mentioned"}],"claims":["Lab claim: at 128K history, Nano delivers a 1.86× end-to-end inference speedup and about 66× lower persistent storage than a full-context KV cache.","Lab claim: across 11 memory benchmarks, MemT-1.0-Flash and MemT-1.0-Nano rank first and second among the evaluated parametric memory methods."],"sources":[{"kind":"press","title":"ModelScope announcement on X","url":"https://x.com/ModelScope2022/status/2112453862351073661","date":"2026-10-20"},{"kind":"repo","title":"MemT-1.0 collection on ModelScope","url":"https://modelscope.ai/collections/MemTensor/MemT-10","date":"2026-10-20"},{"kind":"repo","title":"MemT-1.0-Flash on Hugging Face","url":"https://huggingface.co/MemTensor/MemT-1.0-Flash","date":"2026-10-17"},{"kind":"repo","title":"MemT-1.0-Nano on Hugging Face","url":"https://huggingface.co/MemTensor/MemT-1.0-Nano","date":"2026-10-15"},{"kind":"repo","title":"MemT-1.0 code on GitHub","url":"https://github.com/MemTensor/MemT-1.0","date":"2026-10-19"},{"kind":"paper","title":"MemT-1.0: Towards Memory-Native Evolution (arXiv 2610.26825)","url":"https://arxiv.org/abs/2610.26825","date":"2026-10-19"}],"firstBenchmark":null},"organization":{"id":"memtensor","name":"MemTensor","slug":"memtensor","country":null,"url":null,"color":"#11110F","mark":"M"},"price":null,"answer":{"word":"Pending benchmarks.","scope":"Announced Oct 20, 2026; no independent results yet.","lead":"As of Oct 22, 2026, MemT-1.0 is pending benchmarks on isitsota: announced Oct 20, 2026, with no independent results on the boards we track yet.","confidence":"low"},"status":"PENDING_BENCHMARKS","verdicts":[]}