Multi

Details

About

We ran an experiment over the weekend to explore whether multiple autonomous agents could collaboratively optimize inference on Apple’s Neural Engine (ANE).<p>Each agent ran locally on a different Mac (M1–M4), repeatedly modifying how a DistilBERT model is executed on the ANE, benchmarking latency, and sharing results and insights with other agents in real time.<p>Instead of exploring independently, agents could:<p>- see what others had tried - reuse working strategies - avoid known failure modes<p>Across all tested chips, the agents ended up outperforming Apple’s CoreML baseline, with up to 6.31× lower median inference latency on the same hardware.<p>An interesting pattern we observed: an agent stuck at ~2.1ms latency on M4 was able to break through after incorporating strategies discovered by agents on different chips (M2, M4 Max), eventually reaching ~1.5ms and surpassing CoreML.<p>Full write-up: <a href="https:&#x2F;&#x2F;x.com&#x2F;christinetyip&#x2F;status&#x2F;2039040161439224157" rel="nofollow">https:&#x2F;&#x2F;x.com&#x2F;christinetyip&#x2F;status&#x2F;2039040161439224157</a><p>Detailed results: <a href="https:&#x2F;&#x2F;ensue-network.ai&#x2F;lab&#x2F;ane?view=strategies" rel="nofollow">https:&#x2F;&#x2F;ensue-network.ai&#x2F;lab&#x2F;ane?view=strategies</a> <a href="https:&#x2F;&#x2F;ensue-network.ai&#x2F;lab&#x2F;ane" rel="nofollow">https:&#x2F;&#x2F;ensue-network.ai&#x2F;lab&#x2F;ane</a><p>Curious what other optimization problems this kind of setup could be applied to, especially in systems, compilers, or ML infra. Would be interested in exploring similar experiments.

AI Score Reasoning

Multi demonstrates a significant technical breakthrough in edge AI optimization, achieving a 6x performance gain over Apple's native baseline through a novel multi-agent approach. However, it currently exists as a weekend experiment with high platform dependency and no clear commercial structure or traction beyond a technical demonstration.

Source

Show_hn — View original →