Archive
135 articles published in this period.
An on-chain agent acts on facts it reads from an RPC it has to trust. ZK coprocessors swap that trusted read for a proof — and the punchline is succinctness: proving a query over a year of blocks verifies for the same flat ~300k gas as one slot. The mechanism, the anchor problem, real-time proving.
A perp has no expiry, so only the funding rate pegs it to spot. On Hyperliquid that's an hourly cash flow AI agents now harvest delta-neutral — a $10k position at +0.02%/8h pays ~21.9%/yr. The mechanism, the carry math on real rates, and why it's carry, not alpha.
An MCP tool's description is trusted context the user never sees. Hide an instruction there and a wallet-signing agent will route your USDC to an attacker. MCPTox: the best-aligned model still refused under 3% of these attacks — so the defense can't live in the model.
Decentralized AI networks have to turn many validators' subjective quality scores into one reward number. Bittensor's Yuma Consensus uses a stake-weighted median and clips the rest — robust to a <50% bloc. Then a parasite shows up: copying the consensus pays better than producing it.
Decentralized RL splits the actor from the learner across the internet: the policy a worker acts with runs several steps behind the one that learns. INTELLECT-2 held reward at 4 steps stale; SparrowRL cut the broadcast 79x. The bandwidth and staleness taxes, and what the chain secures.
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.
Secure multi-party computation runs a transformer without any party seeing your prompt — at minutes per query and gigabytes per token. BumbleBee's BERT-base: 6.4 GB, 2.55 min; LLaMA-7B: ~8 min/token. The bytes don't go where you'd think, and it's why 'blind' networks fall back to TEEs.
An AI agent that retrieves from an untrusted vector DB can't tell the true top-k from a cherry-picked or fabricated one. Re-running the search needs the whole corpus. The 2026 fix: commit the snapshot, then prove the k-th distance is a boundary, not the sort. V3DB proves it 22x faster.
On-chain AI loves to say a model is 'stored on-chain.' But a chain commits to a 32-byte hash for cents; keeping the 140 GB it points to retrievable is a separate, recurring, surprisingly centralized bill. The storage math, erasure coding, and the retrieval wall.
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.
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.
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.