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.
Instead of exploring independently, agents could:
- see what others had tried - reuse working strategies - avoid known failure modes
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.
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.
Full write-up: https://x.com/christinetyip/status/2039040161439224157
Detailed results: https://ensue-network.ai/lab/ane?view=strategies https://ensue-network.ai/lab/ane
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.
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