Researchers can study productive test-time refinement that transfers from coding work to tool-assisted deep research. Core capabilities include 27B dense model, 262K-token context, Feedback-driven reflection, and Multi-round solution refinement.
Public code or model weights are linked from the project, with setup instructions for running or adapting the system. Additional characteristics include Algorithmic programming, Machine-learning engineering, Deep-research workflows, and Apache-2.0 weights.
