Benchmarking and Evolving the Capability Boundary in Agentic Visual Generation

Visual generators render complex scenes convincingly, yet they confidently fabricate the characters, symbols, and facts they never learned. This world-knowledge bottleneck is structural: generators learn from fixed corpora, whereas user requests are unbounded, evolving, and long-tailed. To measure and study it, we construct the first large-scale dataset SearchGen-20K, SearchGen-Corpus-1M and SearchGen-Bench featuring 20,937 production-level generation tasks paired with truth-grounded verifiers that check the requested content rather than image quality alone. Comparing open-weight generators with agent-powered frontier APIs, our analysis reveals a substantial gap of up to 35pp in handling production-level requests. Agentic visual generation, in which an agent retrieves evidence for the generator, is the natural remedy. Yet we identify two challenges: naive search can override generator capabilities due to search noise, and agent become stale when generator evolves its capability. In response, we showcase a minimal baseline approach that introduces a noise-resistant agentic protocol and co-adapts the generator and the agent. Data, code, and models are released for future research.