I fitted the new δ-mem research for apple silicon using mlx and openclaw integration! My findings

So I’ve been nerding out hard about memory, and have started looking for ways of dynamically changing the weights outside of context and loras. Luckily, this morning I checked my news feed and saw this new paper on δ-mem! δ-mem paper results (Qwen3-4B-Instruct) are promising. - base model vs base δ-mem: `1.10x` (correct answers) - MemoryAgentBench: `1.31x` - LoCoMo: `1.20x` It improves model attention direction without using context or a lora with 20% better answers from their tests (using LoCoMo)! And I matched agentbench at 30% by using qmd injected memory.