Jun Xiao

Budgeted adaptation under opponent shift can be solved by dual-counterfactual control that explicitly balances immediate adaptation value against future adaptation loss and local strategic risk.

Multimodal agents can now acquire evidence with verifiable optimality guarantees, reducing costly interactions by 90% while maintaining high decision accuracy.

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.