Tensor-Networks

TL;DR for operators Running a sequence model in linear time does not settle the memory-design problem. A recurrent model still has to decide what shape its compressed history should take. RunningTensor1 replaces the matrix memory used by conventional linear attention with a higher-order tensor state. In the paper’s clearest matched ablation, an order-3 tensor state with the same ConcOnlyC projection averages 98.5 F1 on multi-query associative recall, versus 30.6 F1 for the matrix-state version. That gap makes memory geometry a serious architecture variable for workloads dominated by recovering many earlier associations. ...

TL;DR for operators Financial institutions do not suffer from a shortage of market ticks in the abstract. They suffer from a shortage of repeated histories. There is only one realised S&P 500 path, one realised liquidity crisis, one realised volatility regime sequence. Synthetic data is attractive because it promises more examples of rare-but-important behaviour without waiting politely for the next crisis to arrive. ...