Who Is the Real Kevin Warsh? Before the new Fed chairman got the job, he intimated that the central bank could cut interest rates, but last week he assumed the role of an inflation hawk. Kevin Warsh, the Republican financier who recently took over as the chairman of the Federal Reserve, holds economic views that could, kindly, be described as adaptable. Last summer, he said that the Fed had committed "the greatest mistake in macroeconomic policy in forty-five years" by allowing inflation to surge post- . This statement marked out Warsh as an inflation hawk, but late last year, after his name had surfaced as a possible candidate to succeed Jerome Powell as chair of the central bank, Warsh publicly argued that A.I. could generate big gains in productivity and be "structurally disinflationary."

One of the best-performing models in cosmology is also one with the least physical rationale behind it. Can a theory of quantum gravity illuminate what happened just after the big bang? Cosmic inflation is a problem. During the first tiny fraction of a second of the universe, it is generally believed that the universe expanded by a factor of around 10. And then, as quickly as it began, this exponential growth just stopped.

Adaptive specification search generates statistically significant backtests even under martingale-difference nulls. We introduce a falsification audit testing complete predictive workflows against synthetic reference classes, including zero-predictability environments and microstructure placebos. Workflows generating significant walk-forward evidence in these environments are falsified. For passing workflows, we quantify selection-induced performance inflation using an absolute magnitude gap linking optimized in-sample evidence to disjoint walk-forward realizations, adjusted for effective multiplicity. Simulations validate extreme-value scaling under correlated searches and demonstrate detection power under genuine structure. Empirical case studies confirm that many apparent findings represent methodological artifacts rather than genuine predictability.

Data leakage remains a recurrent source of optimistic bias in biomedical machine learning studies. Standard row-wise cross-validation and globally estimated preprocessing steps are often inappropriate for data with repeated measurements, study-level heterogeneity, batch effects, or temporal dependencies. This paper describes bioLeak, an R package for constructing leakage-aware resampling workflows and for auditing fitted models for common leakage mechanisms. The package provides leakage-aware split construction, train-fold-only preprocessing, cross-validated model fitting, nested hyperparameter tuning, post hoc leakage audits, and HTML reporting. The implementation supports binary classification, multiclass classification, regression, and survival analysis, with task-specific metrics and S4 containers for splits, fits, audits, and inflation summaries. The simulation artifacts show how apparent performance changes under controlled leakage mechanisms, and the case study illustrates how guarded and leaky pipelines can yield materially different conclusions on multi-study transcriptomic data. The emphasis throughout is on software design, reproducible workflows, and interpretation of diagnostic output.

Flexport CEO Ryan Petersen says the conflict is stranding cargo and threatening inflation. After years of chaos in the global supply chain, Ryan Petersen, CEO of the logistics company Flexport, felt 2026 might offer some modicum of order. The pandemic was firmly in the rearview mirror. Red Sea shipping channels--which had been closed due to the Gaza crisis--were finally opening. The Supreme Court struck down many of Donald Trump's tariffs, and some Flexport customers were hoping for refunds.

How dangerous is the US standoff with Venezuela? Is the US economy strong heading into 2026? As the United States economy heads into 2026, the report card emerging on its performance is complicated. By many measures, the world's largest economy appears to be in a strong position. After a tumultuous year marked by President Donald Trump's return to the White House and his swing towards tariffs and protectionism, recent growth has outpaced the expectations of most analysts.

The widespread adoption of Artificial Intelligence (AI) is likely to displace people from jobs in a similar way seen during the Industrial Revolution, the governor of the Bank of England has said. Andrew Bailey said the UK needed to have the training, education, [and] skills in place so workers could shift into jobs that use AI. He told the BBC Radio 4's Today programme people looking for a job would find securing employment a lot easier if they had such skills. However, he warned that there was an issue with younger, inexperienced professionals finding it difficult to secure entry-level roles due to AI. We do have to think about, what is it doing to the pipeline of people?

We develop a transparent and fully auditable LLM-based pipeline for macro-financial stress testing, combining structured prompting with optional retrieval of country fundamentals and news. The system generates machine-readable macroeconomic scenarios for the G7, which cover GDP growth, inflation, and policy rates, and are translated into portfolio losses through a factor-based mapping that enables Value-at-Risk and Expected Shortfall assessment relative to classical econometric baselines. Across models, countries, and retrieval settings, the LLMs produce coherent and country-specific stress narratives, yielding stable tail-risk amplification with limited sensitivity to retrieval choices. Comprehensive plausibility checks, scenario diagnostics, and ANOVA-based variance decomposition show that risk variation is driven primarily by portfolio composition and prompt design rather than by the retrieval mechanism. The pipeline incorporates snapshotting, deterministic modes, and hash-verified artifacts to ensure reproducibility and auditability. Overall, the results demonstrate that LLM-generated macro scenarios, when paired with transparent structure and rigorous validation, can provide a scalable and interpretable complement to traditional stress-testing frameworks.