01 / The premise
Can a retrieval tool learn from the work it already performs without surrendering local control?
A fine-tuning and evaluation pipeline that extends scrt with self-supervised evolution, using retrieval outcomes as material for improvement.
02 / What took shape
- Self-supervised training loop
- Retrieval quality evaluation
- Paired with the scrt context engine
03 / Looking back
The useful residue.
The project separates adaptation from spectacle. Improvement requires a measurable feedback loop, carefully formed training material, and a stable baseline—not just a model call added to a pipeline.