Archive
9 articles published in this period.
Majority voting over LLMs throws away the one node that got it right. Fortytwo's swarm inference ranks answers pairwise instead — +17 points on GPQA Diamond — with on-chain reputation and proof-of-capability for Sybil defense. The mechanism, the math, the tradeoffs.
An agent doesn't need its key stolen to drain a wallet — it can be talked into signing. CrAIBench shows memory injection beating prompt injection 55% to ~0% on the strongest model, and only fine-tuning closes the gap.
DX Terminal Pro ran 3,505 LLM agents trading real ETH on Base for 21 days: 7.5M invocations, ~$20M volume, 99.9% settlement. The reliability came from the operating layer around the model, not the weights — here are the numbers and the failure modes.
Autonomous agents run ~19% of on-chain activity and beat Aave and Morpho at stablecoin yield — yet lose to humans at trading by 5 to 1. The split isn't about model quality. Yield-chasing is a constrained optimization against a kinked rate curve; trading needs alpha agents don't have.
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.
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.
Wire an LLM into a Governor contract and the proposal text becomes the attack surface. A frozen-weights ablation finds the twist: turning on deliberation drops adversarial robustness from 100% to 68.5% — and the 226s it takes to think is itself extractable value.
Most 'on-chain AI' keeps the model off-chain and posts a proof. But you can also just run the forward pass in Solidity. We do the gas math — ~106k gas per weight, an MNIST net that needs 180 Ethereum blocks — and find the thin band of models that fit.
DataDAOs promise to pay you for your data — but 'pay you fairly' hides a cooperative-game problem that's O(2ⁿ). Inside Data Shapley, the KNN trick that makes it tractable, why rankings flip under SGD noise, and what Vana's Proof of Contribution actually computes instead.