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9 hours ago After a year of headlines claiming AI would replace millions of jobs, companies are quietly discovering something else instead. AI isn’t eliminating human work. It’s redistributing human judgment, away from routine tasks and into the narrow zones where ambiguity is high, mistakes are costly, and …
Abstract Scientific progress depends on the ability of researchers to synthesize the growing body of literature. Can large language models (LLMs) assist scientists in this task? Here we introduce OpenScholar, a specialized retrieval-augmented language model (LM)1 that answers scientific queries by identifying relevant passages from 45 million open-access papers and synthesizing citation-backed responses.
Large language models (LLMs) often fail to scale their performance on long-context tasks performance in line with the context lengths they support. This gap is commonly attributed to retrieval failures—the models' inability to identify relevant information in the long inputs.
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