LLM trading pipeline splits model duties

The architecture is more interesting than the trading claim: decomposing noisy financial analysis into specialized evaluators is a sane way to reduce prompt overload, but the alpha story needs real backtesting before anyone should trust it.

• –The strongest idea is hiding raw data from the Judge node, which forces it to reason over structured intermediate outputs instead of getting pulled into noisy social context.
• –Splitting social sentiment from tokenomics directly addresses a common LLM failure mode: treating engagement as evidence while missing mechanical supply pressure.
• –The weak point is consensus weighting; without calibration, the fourth model can turn three subjective scores into a cleaner-looking subjective score.
• –For developers, this is less a product launch than a useful infrastructure pattern for LLM-based decision systems.