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This work evaluates six recent self-evolution methods and introduces CLR-Bench, a Curriculum of Latent Rules benchmark, which measures how well an agent learns, and this ability differs across models.
Complex questions in retrieval-augmented generation require evidence spread over several documents, and an answer is reliable only when the retriever finds all of it. Chain embedding retrievers collect such evidence step by step, re-encoding the question together with the passages retrieved so far, and we find that the...
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