Tencent Hunyuan open-sources Hy-Memory-30B-A3B under Apache 2.0: a roughly 3B-active memory writer averaging 55.49 across four memory frameworks, ahead of Claude Opus 5 at 54.62
Deciding what to remember and how is being handed to a dedicated small model. Which step it replaces and how it compares with large models.
// Key points
- It directly replaces the general-purpose LLM on the write path inside Mem0, MemOS and Hy-Memory, deciding what to keep, how precisely to represent it and how new evidence updates old memories.
- Average across the four frameworks: 55.49 for Hy-Memory-30B-A3B, 54.62 for Claude Opus 5, 53.64 for DeepSeek V4.1 Flash and 50.37 for Qwen3.6-35B-A3B.
- The evaluation swaps only the memory writer and holds everything else fixed; each benchmark uses a fixed 20% subsample, and EverMemOS is held out from training.
- It ships under Apache 2.0, tested with vLLM v0.31.0 and served through an OpenAI-compatible API.
Builder's takeMemory writing is a high-frequency background call, and running it on a flagship model is the most expensive and least sensible choice. I'd swap it in on Mem0 for a week for the user-preference memory in AI Cloud Drive; as my 10-17 post on aipan's cost bill notes, long-run cost piles up in repeated background steps like this. The scores come from 20% subsamples, so check on your own data first.
// Background · from #Open models
Full timeline →- Oct 18 DeepSeek releases V4.1-Flash-1017 under MIT: DeepSWE v1.1 rises from 74.2 to 77.0 and Terminal-Bench 4.0 from 31.2 to 42.3
- Oct 18 NVIDIA releases Dory1.0, a looped 4B model that trades compute for accuracy by loop count: SWE-bench Verified goes from 50.3 at one loop to 61.8 at four
- Oct 18 NVIDIA and AI4Bharat open a 2.8 trillion-token Indic pretraining corpus covering 22 languages, along with the pipeline that generated it