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Reverse engineering intelligence with interpretability.
We build the tooling to debug and improve ML models by pin-pointing issues and simulating fixes, from reducing hallucinations to ensuring safety, based on mechanistic interpretability.
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-//}]_\CjNeurology for machine learning.
From wound imaging and ECG to bridges between human brains and LLMs, we map how models prefer, feel, refuse, and remember, then build the tools frontier labs need to train, interpret, and steer them.
Learn morewith CLI build models with same precision as writing code.
▋
locate, debug, simulate and improve.
build transformers & llms
layered attention
simulate lora
low-rank weight adaptation
The science: Mechanistic interpretability
Mechanistic interpretability reverse-engineers how neural networks compute, not just what they output. Aquin applies sparse autoencoders, logit lens, activation patching, and causal tracing to expose which features fire, which layers encode a concept, and which circuits produce each token. Stop guessing why a model hallucinated, drifted, or refused, trace the answer back to the exact prompt span that caused it and patch at the source.
circuit attribution
geography / capitals → Paris
feature space
Work with us
Interpretability tooling, custom SAE databases, mechanistic audits, circuit reports, and hands-on research, experiments, and studies for teams of all sizes. Reach us at aquin@aquin.app
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