MemOS teases memt1.0: memory-native MoE architecture, ~5% coding gains
CuriousSiopao · reddit · 2026-10-25
A developer argues the biggest pain with coding models isn't intelligence but amnesia: every session starts from zero, with project constraints and architecture decisions vanishing when the session ends. Vector search is an awkward patch, and stuffing retrieved chunks into prompts gets messy and hits context limits.
The author is watching MemOS's upcoming memt1.0, which takes a different approach:
- A memory-native mixture-of-experts architecture that integrates reading, writing, and updating memory directly into the core compute path
- Lets the model retain verified patterns and drop failed ones across steps
Preview data shows training across 36B and 293B checkpoints with roughly a 5% bump on coding benchmarks without ballooning compute costs. The model isn't public yet.
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