Sumin Kim

Domain-specific agent evaluation no longer requires manual ground-truth curation: autonomous research and verification loops constrained by reusable expert rules yield grading rubrics that professional analysts prefer over expert baselines.

This work proposes a two-agent framework that first identifies the firms for which each alternative data channel is likely to be informative and then predicts revenue using firm- and channel-specific context and evaluates the framework across four commercial alternative data channels.

Text-guided 3D medical image segmentation just got a whole lot more practical: ESICA achieves state-of-the-art accuracy with a "Lite" variant that slashes parameter count without sacrificing performance.