Technical assurance system for credible research
The project team has embedded a multi-layer credible guarantee mechanism in the system architecture: firstly, a result verification module is set up to trace the source and check the timeliness of the retrieved data; secondly, a knowledge boundary detector is set up to provide a clear indication when the problem is out of the scope of the model's knowledge; and lastly, a signature mechanism is used to ensure the integrity of the output results. The technical program adopts a double-checking design - static rule checking (e.g., citation format specification) and dynamic semantic checking (e.g., contradictory statement detection) running in parallel.
In terms of ethical considerations, the system proactively identifies potentially biased data and generates a fairness assessment report in . /evaluate/score.json to generate a fairness assessment report. This design concept makes it particularly suitable for research support in high-risk areas such as medicine and law, and several medical research organizations have already incorporated it into their pre-study workflow.
This answer comes from the articleDeepResearcher: driving AI to study complex problems based on reinforcement learningThe
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