Verace V1 replaces four standard transformer subsystems (attention, KV cache, mixture-of-experts, fixed-depth compute) with continuous, manifold-constrained alternatives: linear-cost attention via an orthogonal state update, O(1) memory with no KV cache, no cross-device expert dispatch, and real per-token adaptive depth. Every manifold and conservation claim is backed by an executable test, not just derived on paper. Reference implementation only, no pretrained weights yet.
👋 Hey Product Hunt,
I'm Krrish, founder of Verace. We're a small AI lab based in Jaipur,
India, and Verace V1 is the first thing we're putting out into the open. 🚀
Verace V1 replaces attention, the KV cache, mixture-of-experts, and
fixed-depth compute with continuous, manifold-constrained alternatives,
each one backed by an executable test, not just a derivation on paper. ✅
It's a reference implementation, not a trained model. No pretrained
weights, no benchmark claims. Just the architecture, the tests, and an
honest write-up of what worked and what didn't . 🔍
📄 Full paper: https://verace.in/research/verac...
💻 Code: https://github.com/Verace-Pvt-Lt...
Would love feedback 🙏, especially from anyone who's worked on
manifold-constrained optimization or linear attention variants.
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Incredible