The Enrollment Cliff Is Here. Which Schools Will Survive It? As the number of new high-school graduates drops, colleges will close, some will merge, and others may change beyond recognition. This series on the future of higher education started with a simple question: Should I still be contributing to my children's college funds? My first attempt to answer that question centered on the growing disillusionment with higher education in general.

Wide neural networks in the feature-learning regime drive modern deep learning, and yet they remain far less studied than their kernel-regime counterparts. We consider a critical yet under-explored difference between these two regimes: the regulariser and prior implied by gradient flow training. This canonical regularisation property is well-studied in kernel regime networks -- of all the infinite global minima, gradient flow selects exactly the vanishing ridge solution -- and underpins the celebrated NN-GP correspondence, precisely allowing the modelling of noise during training. However, we prove ridge regularisation biases gradient flow in feature-learning regime networks, even in the infinitesimal limit of vanishing regularisation. Over training, ridge distorts the inductive bias of the network, with a particular damage done to pretrained networks where the implicit prior is informative. We resolve this by axiomatising the canonical regulariser as a regime-agnostic function-space energy and lift, which uniquely identifies ridge in the kernel regime, and crucially generalises to the feature-learning regime. By studying the Riemannian geometry of feature-learning networks, we derive geodesic ridge from our framework, generalising ridge to the feature-learning regime. Correspondingly, we prove the canonical function-space prior is a Riemannian Gibbs Process, generalising the more familiar Gaussian Process. As a practical contribution, we propose arc ridge as a minimax-robust, scalable surrogate to geodesic ridge, revealing a deep relationship between early stopping and canonical regularisation across learning regimes. Finally, we demonstrate the consequences of our theory empirically on both image processing and NLP transfer-learning problems.

While diffusion has drawn considerable recent attention from the language modeling community, continuous diffusion has appeared less scalable than discrete approaches. To challenge this belief we revisit Plaid, a likelihood-based continuous diffusion language model (DLM), and construct RePlaid by aligning the architecture of Plaid with modern discrete DLMs. In this unified setting, we establish the first scaling law for continuous DLMs that rivals discrete DLMs: RePlaid exhibits a compute gap of only $20\times$ compared to autoregressive models, outperforms Duo while using fewer parameters, and outperforms MDLM in the over-trained regime. We benchmark RePlaid against recent continuous DLMs: on OpenWebText, RePlaid achieves a new state-of-the-art PPL bound of $22.1$ among continuous DLMs and superior generation quality. These results suggest that continuous diffusion, when trained via likelihood, is a highly competitive and scalable alternative to discrete DLMs. Moreover, we offer theoretical insights to understand the advantage of likelihood-based training. We show that optimizing the noise schedule to minimize the ELBO's variance naturally yields linear cross-entropy (information loss) over time. This evenly distributes denoising difficulty without any case-specific time reparameterization. In addition, we find that optimizing embeddings via likelihood creates structured geometries and drives the most significant likelihood gain.

People attend a jobs fair in London. Only 24% of the members of public surveyed thought AI was a positive thing for humanity. People attend a jobs fair in London. Only 24% of the members of public surveyed thought AI was a positive thing for humanity. One in three university students think AI will wipe out jobs so rapidly it will trigger civil unrest, according to a survey by King's College London (KCL).

This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support agentic behaviors in multi-robot environments. The talk concludes with key challenges encountered and practical lessons learned from ongoing research and development.

We study contextual dynamic pricing in a semiparametric scalar-index valuation model where the latent value is $v_t=μ_\ast(\mathsf c_t)+ξ_t$, with an unknown utility map $μ_\ast$ and an unknown additive noise distribution. The key decision object is the one-dimensional oracle price map $u\mapsto p^\ast(u)$ induced by the scalar index $u=μ_\ast(\mathsf c)$ and the noise tail. Under the $β$-Hölder smoothness of the tail function for $β\geq 2$ and a revenue-geometry condition that gives a unique, stable, interior maximizer, this oracle map is itself $(β-1)$-smooth. We exploit such structure through $\mathsf{ORBIT}$, a modular coarse-to-fine policy that takes a scalar pilot index as input, localizes a benchmark price in each active bin, and learns a local polynomial approximation of the oracle map inside a trust region via bandit convex optimization. For the baseline linear utility model $μ_\ast(\mathsf c)=\mathsf c^\topθ_\ast$, an adaptive elliptical exploration scheme constructs the required scalar pilot online without distributional assumptions on the contexts. The resulting policy achieves regret $\widetilde{O}\big(T^{\frac{2β-1}{4β-3}}+\sqrt{dT}\big)$. For fixed $d$, we establish a matching lower bound in the horizon dependence, unveiling that the nonparametric oracle-map learning term is minimax sharp. The same scalar-pilot interface also yields extensions to sparse high-dimensional linear utility and nonparametric Hölder utility.

Like clockwork each May, soon-to-be college graduates drift into New York City's Washington Square Park in caps and gowns, typically in purple, the school colour of nearby New York University. A sea of mostly 20-somethings gather for photographs that mark the moment when the predictability of collegiate life comes to a close and new graduates face the uncertainty of what's next. Julie Patel, who just finished a master's degree in public health, was one of those graduates. But a tight job market has dampened the joy of the graduation ceremony. Like millions of her peers around the country, she is headed into a precarious job market amid a surge in economic uncertainty driven by a range of reasons, including tariffs, the proliferation of artificial intelligence, global conflicts and, in her case, government funding cuts in her industry, slowing hiring, especially of new graduates.

My Son's Math Homework Is Essentially Just Pokémon Education games are taking over American classrooms. One afternoon earlier this year, my 11-year-old son was sitting at his laptop and working quietly on his math homework. At least, that's what he was supposed to be doing. When I glanced at his screen, equations were nowhere to be seen. He was controlling a monster in the midst of battle, casting magic spells to outduel an opposing player.

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Students at one of Russia's leading engineering universities are getting a lucrative offer: ditch their studies for a year, fly drones for the military and earn more than 5 million rubles ($68,275) in pay as well as free tuition on their return. Pamphlets distributed at Bauman Moscow State Technical University promise students who sign up for the unmanned systems forces will fly drones from far behind the front lines, but still qualify for combat veteran status. It's part of a broader push across Russia to recruit university and college students, using lavish signing bonuses, academic leave and even outright coercion to convince young men to join the fight. At least 270 institutions are actively promoting military contracts, according to the independent magazine Groza, which specializes in higher education and student issues. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.