AI can now remember, update, and forget facts without any external retrieval. A new paradigm for native memory in foundation models has just been published. Title: Metis: Memory Foundation Model 🔍 Overview Current external memory systems like RAG face three fundamental limitations: decoupling from the model backbone, inability to propagate gradients through discrete memory operations, and added inference latency. Metis integrates memory natively into Transformer blocks — analogous to how Chain-of-Thought became intrinsic to LLMs — eliminating external module dependency while enabling learned, end-to-end memory behavior. 🛠 Problem and Approach Two core components are introduced. The Local Memory Block maintains a dense memory network updated across inference steps via exponential moving average, with learned importance scoring and Top-ρ token selection. The Hyper Memory Block uses static learned parameters to enable memory transformation through the forward pass. Four memory operations — Remember, Update, Forget, and Reflect — are executed purely through forward computation without any background gradient updates. 📊 Experimental Results On the MemOps benchmark (no-context setting), Metis-27B achieves 24.76% average performance: · Baseline Qwen3.5-27B (no context): 1.69% · Test-time training Temp-LoRA-27B: 9.70% · Parametric memory δ-Mem: 4.38% On the Metis internal test set, the model reaches 73.77% average with Reflect (multi-hop reasoning) at 93.44%. It outperforms all no-context baselines across model sizes and tasks. 💡 Practical Significance Native memory avoids the retrieval, ranking, and prefilling overhead of RAG through parallelizable computation, keeping inference latency low. Domain adaptation via post-training is supported. Model checkpoints and code are publicly available on GitHub (MemTensor/Metis) and HuggingFace. #LLM# #AIAgent#