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Uncovering over 1,000 hidden dependencies in LLMs reveals a tangled web of licensing and documentation issues that could reshape our understanding of model transparency.
RAG's reputation for being ineffective in reasoning tasks is shattered by showing that retrieving the right data – intermediate "thinking traces" – unlocks substantial performance gains, even for state-of-the-art models.
