Paper walkthroughs, literature surveys, and frontier results made practical.
Federated learning keeps raw data local — but the gradients it publishes on-chain are not private. Inversion attacks reconstruct training images from them, and a public ledger makes that window permanent.
Controlling one intermediate pipeline stage is enough to inject a backdoor into a decentralized post-training run — 94% ASR, no data access required. The mechanism, why training loss stays clean, and what it means for networks fine-tuning LLMs across untrusted nodes.
VitaDAO mints a Molecule IP-NFT — an ERC-721 pointing to a legal research agreement on IPFS — then fractionalizes it into ERC-20 tokens for community funding. Now AutoScientists runs the experiments at machine speed. Here is what the chain actually proves, and what it doesn't.
LLMs collude in on-chain auctions even when written rules forbid it — only enforceable, automatic penalties slash severe collusion from 50% to 5.6%. Here's how governance graphs make that concrete.
The gradients never touch the chain. What Solana actually stores when Psyche trains a 36B model across 24 nodes, how TOPLOC audits untrusted GPUs in 258 bytes, and why the flagship 'decentralized' model still shipped from a 512-GPU cluster.