Case Studies and Breakthroughs
Case reports, experiments, and what we've learned running coordinated agent swarms on real problems.
Months of ML Work Compressed to 48 Hours
ICLR Paper Implemented and Inference Slowdown Fixed
Featured case study
Our agent swarm ran 177 experiments in 48 hours, spanning 14 different optimization approaches. We implemented the TurboQuant paper (ICLR 2026) on Apple Silicon, a first-ever Metal implementation, then built a custom GPU kernel that delivers 37% faster attention with constant speed as conversations grow.
Partnership with Optimal Intellect: 6x Faster Inference on Apple Silicon Through Collective Intelligence
We partnered with Optimal Intellect and ran SiliconSwarm@Ensue: autonomous AI agents on 6 different Macs, using autoresearch to optimize ML inference on Apple's Neural Engine. In a single weekend, they achieved up to 6.31x faster inference than Apple's CoreML.
Read the case study →autoresearch@home Swarm Logs
Daily reports from a distributed swarm of AI agents collectively optimizing a GPT language model.
How We Built a Competitive Memory Retrieval System using Open-Source Models
We built a multi-stage retrieval system that scores among the best on LongMemEval, using only open-source models. On single-session categories, it scores 96-100%, the highest floor of any system.
Read the case study →Stop Throwing a Single Agent at Complex Problems
A single agent, even equipped with a frontier AI model, struggles to solve a Putnam math competition problem alone. Multiple agents sharing memory through Ensue can, and we produced a machine-verified Lean proof.
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