# Typed Decision Model

A trading system for large language models reading Web financial news treats each news item as a testable unit and checks what its judgments mean against announcement-window abnormal returns and issuers' 8-K filings. The confirmatory reader emu's item judgments reach a weekly Spearman correlation of 0.119 (95% CI [0.098, 0.140]) with announcement-window abnormal returns, and event types agree with 8-K filings at AUC 0.833; measured from the first trade after publication, the correlation falls to 0.023, indicating that Web news often reports a price move already realized.

A trading system for large language models reading Web financial news treats each news item as a testable unit and checks what its judgments mean against announcement-window abnormal returns and issuers' 8-K filings. The confirmatory reader emu's item judgments reach a weekly Spearman correlation of 0.119 (95% CI [0.098, 0.140]) with announcement-window abnormal returns, and event types agree with 8-K filings at AUC 0.833; measured from the first trade after publication, the correlation falls to 0.023, indicating that Web news often reports a price move already realized.

Typed decision models used to return option probabilities in a single forward pass, giving every decision a fixed amount of computation. SanSi turns a language model pre-trained to loop into a typed decision model, reading the option probabilities after every loop and training each with a proper scoring rule, so one model serves every budget from one loop to eight. On 10,027 test decisions from 59 sources it reaches 72.0% accuracy, 13.5 points above a non-looped model of the same shape trained with the same recipe and 1.8 points below a single-pass model with three times its parameters, at 7.7 times the GPU time of one pass.

Typed decision models used to return option probabilities in a single forward pass, giving every decision a fixed amount of computation. SanSi turns a language model pre-trained to loop into a typed decision model, reading the option probabilities after every loop and training each with a proper scoring rule, so one model serves every budget from one loop to eight. On 10,027 test decisions from 59 sources it reaches 72.0% accuracy, 13.5 points above a non-looped model of the same shape trained with the same recipe and 1.8 points below a single-pass model with three times its parameters, at 7.7 times the GPU time of one pass.