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Shanghai Innovation Institute
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Multi-agent prompt refinement can significantly boost text-to-video generation quality in complex scenarios, exceeding SOTA baselines by up to 3.28% on standard benchmarks.
LLMs can now generate coherent, diagram-rich explanations for K-12 STEM problems with high accuracy, opening new avenues for automated educational content creation.
Fine-tuning language models on role-specific representations bridges the semantic gap in cognitive diagnosis, substantially boosting performance across diverse educational tasks.
Forget hand-crafted student profiles: HACHIMI generates a million theory-aligned, quota-controlled student personas, offering a standardized synthetic population for educational LLM benchmarking and social-science simulations.
