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Applies Item Response Theory (IRT) to training data difficulty assessment and dynamic scheduling, directly serving hierarchical teaching and personalized learning path design.
First to combine artificial crowd responses with IRT for model-independent global difficulty scoring, providing an interpretable standard for educational data.
Medium-High difficulty. Requires understanding of IRT. Suggested to read with code implementation.
Addresses "training data acquisition" by proposing a model-based HTML parser, significantly improving extraction quality and building a 7.3T token corpus.
Proves high-quality extraction outweighs filtering. AICC model improves by 1.08pp on average, offering a paradigm-shifting tool for data construction.
Medium difficulty. Focus on model architecture and data pipeline. Suitable for data engineering researchers.
Challenges "memory as association", proposing that Transformers encode knowledge via a geometric embedding space, providing a new foundation for Agent memory.
First systematic demonstration of geometric memory emergence, linking it to spectral bias, offering a blueprint for interpretable memory systems.
High theoretical depth. Suggest reading abstract and conclusion first, focusing on visualizations.
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