T2I-SEH: A Self-Evolving Text-to-Image Harness via Effect-Conditioned Harness Learning

Text-to-image (T2I) generation has recently been exploring a shift from conventional single-shot model invocation toward agentic systems, but its runtime control relies heavily on certain predefined strategies. The self-evolving paradigm presents a promising direction; nevertheless, existing approaches still depend on accurate state interpretation and reliable experience transfer. We propose T2I-SEH, a self-evolving T2I harness that aims to determine when prior experience can be leveraged and to what extent it could influence subsequent actions. The core design is Effect-Conditioned Harness Learning (ECHL) which quantifies paired intervention gains, weights well-supported historical effects according to contextual similarity and uncertainty, and yields cost-aware diagnostic beliefs. This formulation emphasizes relevant and reliable experience, while preserving prior beliefs when sufficient evidence is unavailable. ECHL then integrates cost-adjusted utility bounds with Monte Carlo estimates to determine whether to intervene, acquire additional evidence, or retain the current image. Finally, diagnostic generations refine action-specific beliefs and consolidate observed effects for future reuse. Experiments on the compositional generation task across five architecturally diverse T2I generators demonstrate that T2I-SEH enables more effective intervention selection at substantially lower action cost. In addition, when the task aligns with available interventions, resulting benefits transfer into improved generation quality effectively as reflected in the official GenEval 2 scores. Further, evaluations on CoReBench and DPG-Bench show T2I-SEH’s robustness to the task–intervention mismatch by selectively exploiting residual cross-task utility when beneficial and otherwise defaulting to the null intervention at negligible action cost.