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: github.com/pearl-research-la… Whitepaper: pearlresearch.ai/Pearl_White… Run Pearlified models on our endpoint: pearlresearch.ai/