Sage-Taiyi: A Metacognitive Architecture for Scientific Discovery

Key points

Sage-Taiyi uses a two-level structure that connects object-level Target Tree, Idea Graph, and Evidence State with meta-level Cognitive State and Cognitive Repertoire through monitoring and control.
On Taiyi-Bench, Sage-Taiyi ranked first on all eight metrics of historical rediscovery and open-ended ideation, with an Overall score of 54.0 against 47.9 for the strongest baseline, AI Scientist-v2.
In chemistry, reaction-condition optimization raised the yield of cobalt-catalyzed carbonylation from trace amounts to 84%.
Photocatalytic allylic amination yield rose from 13% to 69%.
The system keeps revising its understanding of evidence, assumptions, and unresolved questions, and uses these assessments to adapt research targets and select reusable reasoning methods.

External research summaries. These are not HDATF publications or measured product results.