July's corpus holds 6,479 papers, against 12,021 in June. The decline touches all twenty tracked topics without exception: it says something about collection, not about the state of research. What remains readable is the order in which topics fall back.
Only one comes through the month nearly intact: quantum computing algorithms and architecture, 69 papers in July against 79 in June, while the whole corpus loses close to half its volume. It is also the only theme the weekly issues saw growing, two weeks in a row.
At the other end, the largest topics are the ones giving up the most ground: large language models (344 papers against 773), multimodal machine learning (311 against 684), adversarial robustness (211 against 464) and image synthesis (171 against 378). All fall back further than the corpus average.
Three quantum papers noted over the month: Cautious optimism for deep parameterized quantum circuits Towards quantum machine learning for assessing the resilience of post-quantum cryptography Approximate Quantum State Preparation Through Proximal Policy Optimization