The population dynamics of molecules, cells, and organisms are governed by a number of unknown forces. In the last decade, population dynamics have predominantly been modeled with Wasserstein gradient flows. However, since gradient flows minimize free energy, they fail to capture important dynamical properties, such as periodicity. In this work, we propose a change in perspective by considering dynamics that minimize a population-level action under a damped Wasserstein Lagrangian. By deriving the corresponding Hamiltonian equations of motion, we formalize Wasserstein Lagrangian Mechanics, a structured class of second-order dynamics that encompasses classical mechanics, quantum mechanics, and gradient flows. We then propose WLM as the first algorithm that learns these second-order dynamics from observed marginals, without specifying the Lagrangian. By directly learning the population mechanics, WLM can both forecast and interpolate unseen marginals, and outperforms existing gradient flow and flow matching methods across a wide range of dynamics, including vortex dynamics, embryonic development, and flocking.

A visitor looks at the copy of Michelangelo's Last Judgment by Robert le Voyer at the Louvre in Paris, 14 April 2026. A visitor looks at the copy of Michelangelo's Last Judgment by Robert le Voyer at the Louvre in Paris, 14 April 2026. To be human is to live with friction. That's something AI boosters will never understand We're being sold a world where there's no room for reflection or spontaneity. H ow fast do you have to strike a match to get it to light?

Distributed reinforcement learning trains on data from stale, buggy, or mismatched actors, producing actions with high surprisal (negative log-probability) under the learner's policy. The core difficulty is not surprising data per se, but \emph{negative learning from surprising data}. High-surprisal failures can dominate the update direction despite carrying little useful signal, while high-surprisal successes reveal opportunities the current policy would otherwise miss. The \textit{Delightful Policy Gradient} (DG) separates these cases by gating each update with delight, the product of advantage and surprisal, suppressing rare failures and amplifying rare successes without behavior probabilities. Under contaminated sampling, the cosine similarity between the standard policy gradient and the true gradient collapses, while DG's grows as the policy improves. No sign-blind reweighting, including exact importance sampling, can reproduce this effect. On MNIST with simulated staleness, DG without off-policy correction outperforms importance-weighted PG with exact behavior probabilities. On a transformer sequence task with staleness, actor bugs, reward corruption, and rare discovery, DG achieves roughly $10{\times}$ lower error. When all four frictions act simultaneously, its compute advantage is order-of-magnitude and grows with task complexity.

This work was done when Mingyu was at MIT.

In this section, we provide proof for the disentanglement identifiability of the inferred exogenous variable. Our proof consists of three main components. Then we have ( f, T, λ) ( f, T, λ) . The conditional V AE, in this case, inherits all the properties of maximum likelihood estimation. The following proof is based on the reduction to absurdity.

How skis meet snow be the difference between winning gold or silver. Breakthroughs, discoveries, and DIY tips sent six days a week. Every ski and snowboarding event at the 2026 Winter Olympics is won through a combination of sheer athleticism, quick thinking, creativity, and persistence. But like so many other sports, competitors know their choice of equipment can mean the difference earning the gold or silver medal. A ski is built for function over form, and manufacturers have spent decades adapting and honing their products to ensure wearers get the best results.

How much do you know about friction? Jennifer R. Vail's charming, if sometimes technical, biography of the force showcases its amazing and largely overlooked role in everything from climate change to dark matter, says Karmela Padavic-Callaghan IN 2009, World Aquatics banned a specific type of swimsuit from all international competitions in water sports, ruling that it gave athletes an unfair advantage. The development of this swimsuit included using NASA's testing facilities and sophisticated computer software. Some versions had ultrasonically welded seams instead of traditional stitches. Swimmers who wore the suit broke 23 of the 25 world records set at the Beijing Olympics in 2008.

Modern social life often begins on screen. Digital profiles invite us to inspect the lives - and social circles - of friends, colleagues and strangers. Before meeting someone new, chances are we may have scanned their Instagram, LinkedIn or dating profile, forming assumptions from a carefully curated snapshot of their life. Somewhere along the way, we've forgotten the value of a considered, human introduction as the foundation for genuine connection. Bridget Jones's Shazza had it right when she said that making introductions with thoughtful details can go a long way: mentioning an unexpected talent or hobby, highlighting a mutual interest or sharing a funny anecdote.

AI based mental health diagnosis is often judged by benchmark accuracy, yet in practice its value depends on how psychologists respond whether they accept, adjust, or reject AI suggestions. Mental health makes this especially challenging: decisions are continuous and shaped by cues in tone, pauses, word choice, and nonverbal behaviors of patients. Current research rarely examines how AI diagnosis interface design influences these choices, leaving little basis for reliable testing before live studies. We present SimClinician, an interactive simulation platform, to transform patient data into psychologist AI collaborative diagnosis. Contributions include: (1) a dashboard integrating audio, text, and gaze-expression patterns; (2) an avatar module rendering de-identified dynamics for analysis; (3) a decision layer that maps AI outputs to multimodal evidence, letting psychologists review AI reasoning, and enter a diagnosis. Tested on the E-DAIC corpus (276 clinical interviews, expanded to 480,000 simulations), SimClinician shows that a confirmation step raises acceptance by 23%, keeping escalations below 9%, and maintaining smooth interaction flow.

Bridging the sim-to-real gap remains a fundamental challenge in robotics, as accurate dynamic parameter estimation is essential for reliable model-based control, realistic simulation, and safe deployment of manipulators. Traditional analytical approaches often fall short when faced with complex robot structures and interactions. Data-driven methods offer a promising alternative, yet conventional neural networks such as recurrent models struggle to capture long-range dependencies critical for accurate estimation. In this study, we propose a Transformer-based approach for dynamic parameter estimation, supported by an automated pipeline that generates diverse robot models and enriched trajectory data using Jacobian-derived features. The dataset consists of 8,192 robots with varied inertial and frictional properties. Leveraging attention mechanisms, our model effectively captures both temporal and spatial dependencies. Experimental results highlight the influence of sequence length, sampling rate, and architecture, with the best configuration (sequence length 64, 64 Hz, four layers, 32 heads) achieving a validation R2 of 0.8633. Mass and inertia are estimated with near-perfect accuracy, Coulomb friction with moderate-to-high accuracy, while viscous friction and distal link center-of-mass remain more challenging. These results demonstrate that combining Transformers with automated dataset generation and kinematic enrichment enables scalable, accurate dynamic parameter estimation, contributing to improved sim-to-real transfer in robotic systems