Testing δ-Mem on Apple Silicon: MLX Implementation and Benchmarks
A Reddit user implemented the δ-mem research paper (arXiv 2605.12357) for Apple Silicon using mlx and OpenClaw integration. The paper improves model attention direction without context or LoRA, reporting 20% better answers in their tests. The implementation used Qwen3-4B-Instruct via mlx and custom adapters.
Benchmark Results (normalized mlx tests, Qwen3-4B-Instruct on MacMini 64GB):
- Synthetic paper-style: Plain 0.5129, δ-mem 0.5129 (1.00x)
- LoCoMo-10 mini: Plain 0.0500, δ-mem 0.1833 (3.67x)
- OpenClaw replay: Plain 0.5701, δ-mem 0.6667 (1.17x)
Latency costs (vs plain):
- Synthetic: 1.013x
- LoCoMo-10 mini: 1.33x query / 1.50x total
- OpenClaw replay: 1.30x
Key links:
- GitHub repo with adapter: delta-mem-mlx-sidecar-w-openclaw
- MLX adapter on Hugging Face: delta-mem-qwen3-4b-instruct-mlx-adapter
Takeaways:
- Synthetic probes were flat (1.00x), but LoCoMo-mini showed strong relative gains (3.67x).
- OpenClaw-style replay showed a practically meaningful improvement (6/8 → 7/8 probes passed, 1.17x).
- The user notes Apple Silicon cannot run CUDA efficiently, so results are lower than paper benchmarks. Paper benchmarks (Qwen3-4B-Instruct) showed avg 1.10x vs frozen backbone, MemoryAgentBench 1.31x, LoCoMo 1.20x.
- The user is seeking help (or funding ~$6k) to train an adapter for larger models like Qwen3.6:27B.
Who it's for: Developers running local LLM agents on Apple Silicon who want to experiment with δ-mem weight modulation to improve memory/context performance.
📖 Read the full source: r/LocalLLaMA
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