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Results show that EgoAlign establishes agent-centric spatial context directly from numerical pose without requiring textual situation descriptions, and achieves the strongest performance on SQA3D, Real-3DQA, and MSQA among compared methods while remaining highly efficient.
Recoverability-Aware Intervention Learning (RAIL), a training-time framework that learns how to generate rollouts based on the improvement produced by each intervention, consistently improves performance under limited rollout budgets.
The collaboration tax is formulated as the team-decentralisation loss of a two-player cooperative game with private information, with two propositions characterising its sign and its equivalence to a max-superadditivity violation.
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