Yesterday I saw a new research paper about δ-mem and integrated with openclaw

Improves agent response quality by 7-32%. Without context window increases depending what you pass it. 7% not passing anything. This project is not yet usable for anything outside mlx qwen3:4b yet until people train adaptors for it. I recommend asking your claw to check huggingface so you know when they drop! The original paper claimed up to 30%, but I found a way to get even better results up to 32% with openclaw agent use. Benchmark normalized and made with a n of 15. GitHub for plugin: [https://github.com/elimaine/openclaw-delta-mem-mlx-plugin](https://github.com/elimaine/openclaw-delta-mem-mlx-plugin) Clawhub: [https://clawhub.ai/plugins/@elimaine/openclaw-delta-mem-mlx](https://clawhub.ai/plugins/@elimaine/openclaw-delta-mem-mlx) Original paper: [https://arxiv.org/abs/2605.12357](https://arxiv.org/abs/2605.12357) I did a ton of benchmark testing you can read about here, if you get bored of the tables scroll down to the graph which shows the important bits. Benchmark results have probably changed though since I made a bunch of improvements/hardenings. [https://github.com/elimaine/delta-mem-mlx-sidecar-w-openclaw/blob/main/wiki/Benchmark-Findings.md](https://github.com/elimaine/delta-mem-mlx-sidecar-w-openclaw/blob/main/wiki/Benchmark-Findings.md) TLDR: pass qmd vsearch for adapter attention state. 32% improvement at cost of 30-61% slowdown. My project is only usable on Apple Silicon using mlx. Porting it to CUdA would be easy and faster. Once qwen3.6:27b δ-mem mlx adaptor get released this will be the best local stack on the planet (higher parameters excluded). Happy experimenting lobsters!