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Effective visual tool use hinges on causally grounded supervision, not just imitating tool calls, reshaping how we train multimodal agents.
DG-CFG not only enhances image generation quality but also reduces sampling costs by achieving target metrics with fewer steps, challenging the conventional wisdom of fixed guidance schedules.
Integrating 3D geometry, topological grammar, and physicochemical descriptors leads to a 20.6% reduction in prediction error for molecular properties, all while using under one million parameters.
