1/ Enterprise AI is moving toward systems that operate continuously, not tools a human opens once in a while.
Models classify, agents retry, workflows branch, and thousands of decisions can happen between two human reviews.
Manual oversight cannot grow at the same rate.
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@inference_labs How DSperse works: Instead of proving an entire large ML model in zero-knowledge (which is very costly), DSperse allows selective verification of the most important model components…
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1/ Zero-knowledge verification for production vision models is now practical on consumer hardware.
Our paper "Targeted Zero-Knowledge Verification for Computer Vision Inference" is published in Springer's CVC 2026 proceedings (LNNS 2036).
How it works:
https://t.co/e562i3x3yd
@inference_labs That’s a major step for practical, verifiable AI. Making zero-knowledge verification feasible on consumer hardware could bring stronger trust and accountability to real-world computer vision. 🔥
The interesting part of physical AI is not that machines can move.
It is that they increasingly decide where to move, what to avoid, and what matters in the scene.
Computer vision becomes part of the control loop. That makes verifiable inference much more important.
@inference_labs Exactly. Once AI starts making real-world decisions, vision can’t just be accurate it needs to be trustworthy and verifiable. Physical AI needs both intelligence and accountability.