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Even the best vision-language models struggle with predictive spatial reasoning, scoring less than 60% on a new benchmark designed to test anticipating unseen outcomes.
Visual-native skill cards, directly accessible to VLMs, unlock a self-reinforcing loop of policy improvement and skill accumulation, drastically boosting sample efficiency.
Personal LLM agents can significantly improve recommendation quality by mediating platform rankings with cross-platform history, but naive mediation introduces substantial harmful overrides.
This work finds that point clouds are substantially more reliable than RGB under zero-shot category shift, and designs a Multi-view Information Decoupling module to suppress heterogeneous information from multi-view point cloud projections and enhance point cloud feature quality.
