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Leading image generation models fail over 40% of goal-driven visual reasoning tasks, but decoupling explicit visual planning from generation boosts task completion by up to 14.6 points without fine-tuning.
This work introduces ToolCompass, a post-training framework that guides tool trialing by organizing tool-call representations according to shared functions and jointly reduces intra-function variation across domains and increases inter-function separation.
OmniRSCLIP achieves strong performance across heterogeneous remote sensing modalities while preserving the advantages of RGB-based models.
LEAP reveals that separating evidence evaluation can drastically enhance the accuracy and interpretability of probabilistic forecasts in LLM applications.
