Archive
135 articles published in this period.
Foundry forks freeze five properties of the Ethereum execution environment that are live, adversarial, and expensive in production: basefee, oracle prices, blockhash entropy, MEV competition, and TWAP accumulation. Each one silently misleads AI agents through testing.
Every EIP-4844 blob is secured by a KZG polynomial commitment — 48 bytes committing to 128 KB, verifiable in 50,000 gas. That efficiency required 140,416 people to collectively bury a secret for 69 days. Here's the math, the ceremony, and why Ethereum chose a different scheme for state.
When a perp DEX's insurance fund runs dry, ADL force-closes winning positions first — ranked by profit %. Chitra 2026 proves no mechanism can be solvent, fair, and revenue-neutral. Hyperliquid Oct 10 2025: $2.1B closed, $653M via ADL, $45–51.7M haircut in 12 min.
An on-chain agent's transactions prove what happened, not why. Three 2026 papers on trajectory anomaly detection show how to close that gap — and how the optimistic bisection game from rollups makes it slashable.
An AI agent holding a raw ECDSA key is one model compromise from total loss. FROST's two-round DKG means the full private key never exists anywhere — not during setup, not during signing. Here's the mechanism, the Ethereum gap, and how Lit Protocol PKPs deploy it today.
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.
Deposit ETH in Aave, borrow USDC, buy more ETH, repeat. Three loops creates 2.97× leverage from a single ETH — and all three positions share one liquidation trigger at −3% ETH price. The math, the cascade, and why AI yield optimizers find themselves here by default.
ElizaOS agents treat their memory store as ground truth — their own past. CrAIBench tests 685 attack cases across 33 Web3 action types and finds memory injection hits 55.1% ASR even on Claude Sonnet 3.7, while every off-the-shelf detector fails.
Bitcoin burns ~150 TWh/yr on SHA-256 nonces that prove nothing. Komargodski & Weinstein showed a Freivalds randomisation check turns any matmul into a valid proof-of-work at only 3/(2N) overhead — the same GPU seconds train your model and mine the block.
EIP-4844 blobs look like cheap on-chain storage, but they vanish in 18 days. Here is why DA windows break AI training pipelines and what to use instead.
Softmax costs 275 ZK constraints per element vs 4 for MatMul. ZK-DeepSeek proved Transformer inference is expressible in SNARKs. Lookup arguments (Lasso, LogUp) explain how — replacing in-circuit transcendental approximations with pre-committed tables cuts nonlinear costs 10–20×.
Probabilistic classifiers miss 30–40 % of policy violations. Lean 4 theorem provers and SMT solvers make certain guardrail tiers mathematically certain — here's how the four-layer policy stack works.