Zehao Wang

MLLMs can achieve 10% gains on multimodal reasoning benchmarks by using ground-truth anchored data curation and scaffold-stripping to avoid cognitive drift during self-evolution.

ML evaluation harnesses, the unsung heroes of model development, are plagued by surprisingly mundane software engineering issues like missing documentation and unimplemented features, hindering reliable assessment.