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Even state-of-the-art models only achieve pass rates below 60% on a new benchmark that spans 1,431 diverse tasks, exposing critical weaknesses in general AI capabilities.
GraspLLM achieves unprecedented zero-shot generalization on Text-Attributed Graphs, outperforming existing methods by effectively merging graph structure with LLM semantics.
Educational LLMs struggle with curriculum cognition, achieving only 57% accuracy on a new benchmark designed to test their understanding of structured knowledge.
Stop reinventing the wheel: OpenWorldLib offers a unified framework and codebase for advanced world models, finally bringing standardization to a fragmented field.
DataFlex makes data-centric LLM training dramatically easier, unifying disparate methods for data selection, mixing, and reweighting into a single, efficient, and reproducible framework.
Stop wrestling with finicky evaluation codebases: One-Eval lets you specify LLM evaluation tasks in natural language and automatically executes them end-to-end.
