Privacy-preserving proof of inference is a key building block for AI safety. @DarioAmodei's call for AI slowdown & independent oversight raises a critical question / How can frontier labs prove their computations follow the rules, without revealing model weights, prompts, or private data?
Two complementary technologies are necessary to make this inference layer possible: Zero-Knowledge proofs (zk-SNARKs) can prove that Y = M(x), while keeping M and x private, and verify compliance using explicit policies. Proof-of-useful-work (PoUW) can
tie that evidence to actual, timestamped GPU computation, with a public immutable record and economic incentives for participation.
@prlnet is partnering with @attestable to build this trust layer for AI. Together, our complementary technologies can make AI objectively accountable, making verification economically sustainable and self-funding.
Today, we are announcing @prlnet's new and ultra-efficient floating-point PoUW scheme for NVIDIA Blackwell chips.
Floating-point arithmetic has been a notorious obstacle to efficient verification of compute for decades. Pearl’s FP scheme brings proof-of-useful-work technology to Blackwell FP computations on frontier LLMs, with NEAR-ZERO additional overhead over end-to-end inference on optimized vLLM.
The FP network upgrade specification is now available (github & whitepaper) and will soon serve frontier models on Pearl x @togethercompute's endpoints in production.
Explore the implementation:
https://t.co/FjwwSVoLSP
Whitepaper: https://t.co/6BqeEqQRgl
Run Pearlified models on our endpoint:
https://t.co/6bykpZhFTk
Introducing Gemma-4-31B-it-Pearl on Together AI, Pearl Research Labs’ instruction-tuned checkpoint of Gemma 4 31B powered by @prlnet Proof of Useful Work protocol.
AI natives can now use this Pearl model as a serverless inference endpoint on Together AI, at a 25%+ discounted pricing.