開源權重+私有數據讓前沿模型通過金融測試

bridgewater and thinking machines lab fine-tuned the open-weight qwen3-235b model on internal financial data, achieving 84.7% accuracy on document triage tasks compared to 78.2% for top proprietary models. the project highlighted that frontier models like gpt and claude failed to meet the 80% deployment threshold because the necessary 'judgment' data exists only in private corporate archives, not public training sets. the fine-tuned model operates at nearly 14 times lower cost than commercial alternatives, offering a blueprint for companies to build competitive ai without sharing sensitive trade secrets with large labs.