Archive
13 articles published in this period.
An autonomous agent that swaps in the open broadcasts its intent to every searcher in the mempool. We solve the optimal sandwich on a live Base pool, show why an agent's slippage default is the searcher's profit knob, and price the defenses.
A DeFi liquidation mints a fixed prize, and for years it went to whoever won the gas war, not the protocol. Aave's own data shows Chainlink SVR routing $675M of liquidations and clawing ~$16M back. Inside the recapture-auction mechanics — SVR, Oval, API3 — and what they don't fix.
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 DataDAO sold your data; you invoke the right to be forgotten. Deleting the file is easy, but the model already learned and the ledger can't be rewritten. ZK-APEX proves the unlearning ran in ~2h; UMA still pulls the 'forgotten' data back at MIA 0.97. Certified isn't forgotten.
A landmark 2025 result: ~250 poisoned documents backdoor an LLM whether it has 600M or 13B parameters — 0.00016% of the tokens. DataDAOs sell 'verifiable' training data, but on-chain provenance proves integrity, not purity. Here's the gap, and what actually narrows it.
Passive AMM liquidity is a short option that arbitrage bots exercise every block. LVR = σ²/8 prices the rent — at ETH's 63% realized vol that's ~5% of pool value a year, and most pools don't earn enough fees to cover it. The math, real numbers, and the auctions clawing it back.
A decentralized GPU network has to answer two questions an AWS invoice never raises: is this the GPU it claims, and did it actually do the work? io.net logged 327,000 registered GPUs and roughly 6,720 daily-verified. We dissect metadata trust, PoW puzzles, performance fingerprinting, and zkGPU-ID.
Virtuals' Agent Commerce Protocol sells an independent evaluator as the thing that makes agents trust each other's work. We read 62,882 jobs off Base — and in a 30-job sample, every one let the buyer grade itself. Not one used a third party.
You can copy open model weights bit-for-bit, so on-chain ownership can't be cryptographically enforced — only proven. Sentient's answer: fine-tune 24,576 secret key-response fingerprints into the weights and make scale the security parameter.
Most decentralized-AI networks vote or average. Allora bets the network should learn whom to trust per context — using forecasters that predict who's about to be wrong, mapped to weights through a softplus gradient. We dissect the mechanism, the math, and where it pays.
An LLM agent that writes its own working smart-contract exploit isn't hypothetical: A1 hits 63% on real exploited contracts and once pulled $8.59M from a single bug. The economics are the story — at a few dollars a scan, attackers profit at a tenth the contract value defenders need.
You pay per token for a named model; the provider picks the precision. FP8 quantization costs 0.6 MMLU points and is near-invisible to output auditing — so inference markets bond and attest instead of detect.