Scientific breakthroughs typically emerge through the surprising violation of established research ideas, yet quantifying surprise has remained elusive because it requires a coherent model of all contemporary scientific worldviews. Deep neural networks like large language models (LLMs) are arbitrary function approximators tuned to consistently expect the expressions and ideas on which they were trained and those semantically nearby. This suggests that as LLMs improve at generating plausible text, so the perplexity or improbability a text sequence would be generated by them should come to better predict scientific surprise and disruptive importance. Analyzing over 2 million papers across multiple disciplines published immediately following the training of 5 prominent open LLMs, here we show that higher perplexity scores systematically predict papers that receive more variable review ratings, longer editorial delays, and greater reviewer uncertainty. The most perplexing papers exhibit bimodal outcomes: disproportionately represented among the most celebrated scientific achievements and also the most discounted. High-perplexity papers tend to be published in journals with more variable impact factors and receive fewer short-term citations but in prestigious venues that bet on long-term impact. They also generate more interdisciplinary engagement portending long-term influence, and are more likely to have been supported by speculative funders like DARPA versus the NIH. Interestingly, we find the opposite pattern for humanities research, where the least surprising work is the most celebrated and cited. Our findings reveal that computational measures of corpus-wide linguistic surprise can forecast the reception and ultimate influence of scientific ideas, offering a scalable approach to recognize and generate potentially transformative research that challenge conventional scientific thinking.