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Amortized agentic policy discovery enables personalized test-time scaling to satisfy complex, multidimensional user requirements, outperforming single-objective optimization on unseen profiles.
A novel contrastive feedback mechanism allows LLM agents to learn skill proposals that are aligned with the current actor's behavior without requiring expensive skill evaluations.
Robots can now reliably navigate through buildings with glass, avoiding collisions and phantom obstacles by accurately mapping transparent surfaces.
Across seven representative adapted baselines, Grounding2Route substantially outperforms existing methods in all metrics, and a substantial gap to human demonstrations remains, highlighting the difficulty of Map2Route and the considerable headroom for future progress.
