SearchGen-Corpus-1M

SearchGen-20K benchmark and teach-then-search co-training framework for world-knowledge-grounded visual generation

Researchers introduce SearchGen-20K and SearchGen-Bench, a dataset of 20,839 prompts across 12 failure categories targeting the world-knowledge bottleneck in visual generation, paired with a 1M-item multimodal search corpus. Frontier open visual generators score only 21–28/100 on the new benchmark, a gap invisible to existing evaluations. The paper proposes a teach-then-search co-training framework that discovers a model's evolving knowledge boundary and uses search tools selectively, achieving monotonic improvement and laying groundwork for recursive self-improvement in agentic visual generation. All datasets and corpora are released publicly.