Archive
135 articles published in this period.
DeFi interest rate formulas are open-source reward functions. An RL agent reading Aave's two-slope model can compute the optimal adversarial strategy analytically — no learned reward model required.
Every on-chain scheme that verifies an LLM by re-executing it and comparing digests assumes a forward pass is bit-for-bit reproducible. It isn't — Thinking Machines got 80 different answers to one prompt at temperature 0. Here's why, and what determinism costs.
zkML costs 1000x, optimistic schemes cost a challenge window. Hardware attestation verifies AI inference at under 7% overhead and ~$0.26 on-chain — if you're willing to trust Intel. Part 3 weighs the third leg of verifiable inference.
A new paper quantifies what everyone suspected: AI agent tokens trade at >10,000x the capital they actively manage. Here's the mechanics of why, and what it means for builders.
Every zkSNARK bottoms out in a pairing check. Before Pectra you had BN254 at ~100-bit security. EIP-2537 added BLS12-381 at 128 bits, and its pairings are cheaper: 32,600k+37,700 vs 34,000k+45,000 gas. G2 MUL dropped from millions to 22,500 gas.
Permissionless training networks pay peers for gradients they can't re-run. Proof-of-Learning was meant to verify the work — until it was forged for 3% of the training cost, then for one floating-point op per weight. Here's the mechanism, the attacks, and what's deployed instead.
AI agents now resolve most prediction markets straight off the web — UMA's bot hits 99.3% on sports and 72% on mention markets. The optimistic oracle's dispute game is the backstop, but it only catches errors someone is paid to catch. The numbers, and the silent-settlement gap.
A 15× spike on a $17M pool shifts its 30-minute v2 TWAP by 9.3% in one block. The arithmetic-mean accumulator is far weaker than v3's geometric tick accumulator. Covers the manipulation cost formula, the Inverse Finance case study, and minimum safe pool depth.
DiFR's seed-commitment round-trip is the last synchronization barrier to async verifiable inference. TOPLOC removes it: a locality-sensitive hash of intermediate activations compresses to 258 bytes per 32 tokens — 1,000× smaller than raw embeddings — and validates faster than inference ran.
An SP1 proof costs 327k gas on Ethereum mainnet. At a 30M-gas block target, a naive AI inference market maxes out at 91 verifications per block. SnarkPack's O(log N) aggregation collapses 1,000 proofs to a single 880k-gas verification — here's the mechanism, the math, and the latency tradeoffs.
Verification overhead gets the blame, but the real tax on decentralized LLM inference is the batch a single-tenant node can't fill. On identical 8x H100s, Llama-3.3-70B costs about $258 per million tokens at batch 1 and $2.30 at batch 256 — a 112x spread. Why, and how networks fight it.
PREVRANDAO costs roughly $88 per bit to bias. Chainlink VRF costs $2.43 per request. Here's how to choose the right randomness source for AI agents making on-chain decisions — and what breaks when you choose wrong.