Generating chemically valid 3D molecules is hindered by discrete bond topology: small local bond errors can cause global failures (valence violations, disconnections, implausible rings), especially for drug-like molecules with long-range constraints. Many unconditional 3D generators emphasize coordinates and then infer bonds or rely on post-processing, leaving topology feasibility weakly controlled. We propose Hierarchy-Guided Latent Topology Flow (HLTF), a planner-executor model that generates bond graphs with 3D coordinates, using a latent multi-scale plan for global context and a constraint-aware sampler to suppress topology-driven failures. On QM9, HLTF achieves 98.8% atom stability and 92.9% valid-and-unique, improving PoseBusters validity to 94.0% (+0.9 over the strongest reported baseline). On GEOM-DRUGS, HLTF attains 85.5%/85.0% validity/valid-unique-novel without post-processing and 92.2%/91.2% after standardized relaxation, within 0.9 points of the best post-processed baseline. Explicit topology generation also reduces "false-valid" samples that pass RDKit sanitization but fail stricter checks.

Competing objectives arise when a model's capabilities

These challenges have spawned the new task of'Subject-Drive Text-to-Image Generation', which is the core task of our paper aims to solve. Though the mined clusters already contain (image, alt-text) information, the alt-text's noise level is For example, the generation model believes'teapot' should contain a's in-context generation that demonstrates its skill set. Results generated from a single model . Subject (image, text) and editing key words are annotated, with detailed template in the Appendix. Such manual modification process is time-consuming.