Path to the realization of multi-source verification techniques
DeepResearcher's built-in cross-platform verification system is its core advantageous feature. By integrating Google, Bing and other search engine APIs, the system uses multi-threaded parallel retrieval technology to obtain information and build a confidence assessment model. The technical documentation shows that its validation process contains three key steps: firstly, clustering the results from different sources through semantic similarity algorithms, then using statistical significance tests to exclude outlier data, and finally generating confidence scores based on bibliometric features.
Experimental data show that the mechanism can reduce the recognition rate of misinformation by 831 TP3T. In tests in specialized fields such as healthcare, the accuracy of warning on contradictory data reaches 91.21 TP3T. the system will also be . /outputs/research_log.txt to completely record the validation process and decision-making basis, which meets the requirement of academic research reproducibility.
This answer comes from the articleDeepResearcher: driving AI to study complex problems based on reinforcement learningThe
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