Archive
18 articles published in this period.
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.
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.
The textbook market-making model quotes symmetrically around mid. On a perpetual, holding inventory pays or charges funding every hour — a deterministic drift Avellaneda-Stoikov never sees. A 2026 HJB model that prices it cut inventory risk 36-38% on Hyperliquid ETH and BTC.
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.
Bond-and-slash verifiable AI assumes someone audits. But checking costs money and pays nothing, so a rational verifier won't — the verifier's dilemma. Proof of Sampling fixes it with a bounty, and the math says honest inference holds at a 0.74% sampling rate where nobody ever arbitrates.