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It is shown that persuasive interaction leaves a measurable, task-dependent behavioral footprint on downstream agent execution, and agents that are more susceptible to persuasive conversational context do not reliably predict the magnitude of the downstream behavioral shifts.
RoboTalk is introduced, a synthetic data-generation pipeline and dataset of 7,950 multimodal trajectories spanning 53 mobile-manipulation kitchen tasks for training small VLMs to communicate and coordinate and fine-tuning open-source models on this dataset can reach 77% success on novel held-out tasks.
CALM (Controller-Aware Language Models), a post-training framework that explicitly places controllers in the training loop, is introduced, showing that controller-aware post-training improves generalization across inference-time workflows beyond single-controller optimization.
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