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Multimodal agents can now reason, plan, and execute actions more effectively by integrating perception as a core component, not just an auxiliary interface.
LLM-as-a-judge can be made far more reliable by explicitly modeling the aggregation weights of sub-features in a tree structure, achieving near-human agreement on complex writing tasks.
Overcoming infrastructure limitations, not algorithmic capability, is the key to unlocking the potential of Embodied AI for Science in the Global South.
