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arXiv:2512.15649v2 Announce Type: replace-cross Abstract: The computational and memory overheads associated with expanding the context window of LLMs severely limit...
arXiv:2512.19399v1 Announce Type: cross Abstract: Interpretability methods for large language models (LLMs) typically derive directions from textual supervision, which can...
arXiv:2512.19908v1 Announce Type: new Abstract: The rise of AI has fueled growing concerns about “hype” in machine learning papers, yet...
arXiv:2510.02025v2 Announce Type: replace Abstract: Evaluations of large language models (LLMs)’ creativity have focused primarily on the quality of their...
arXiv:2512.18748v1 Announce Type: cross Abstract: The performance of automatic code documentation generation models depends critically on the quality of the...
arXiv:2512.18658v1 Announce Type: new Abstract: Before closing venture capital financing rounds, lawyers conduct diligence that includes tying out the capitalization...
arXiv:2512.18190v1 Announce Type: cross Abstract: This paper proposes the External Hippocampus framework, which models language model reasoning from a cognitive...
arXiv:2511.07044v2 Announce Type: replace Abstract: Mental health disorders affect over one-fifth of adults globally, yet detecting such conditions from text...
arXiv:2512.17260v1 Announce Type: new Abstract: Large language models have recently made significant progress to generate rigorous mathematical proofs. In contrast,...


