Voters in the US state of Massachusetts head to the polls on Tuesday for a primary election featuring a closely watched United States Senate contest between Democratic incumbent Senator Ed Markey and US Representative Seth Moulton. The race is the latest contest that pits the Democratic Party's progressive and moderate wings against each other. But in a twist, Moulton, the challenger, is part of the moderate New Democrat Coalition, while Markey, the longtime progressive incumbent, is fighting to hold on to his seat. What time do polls open and close in Massachusetts? The Massachusetts primary is Tuesday, September 1, 2026.

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How we picked 35 of the world's top young scientists and engineers Our 2026 Innovators Under 35 list will be out soon. Here's what we looked for as we sifted through 550 nominations from around the world. Next month, on September 8, will reveal its 2026 list of Innovators Under 35, recognizing 35 young people from around the world who are doing groundbreaking scientific work and building clever technical fixes for sticky problems. By finding the top young innovators globally and learning what they're focused on in their work, we aim to give readers a sense of what advances to expect in the years to come. As a newsroom, we also use this exercise to help us spot rising talent and get to know some of the best early-career researchers in the fields that we cover. The editors of published the first Innovators Under 35 list in 1999, and it's become a beloved annual tradition alongside our lists of 10 Breakthrough Technologies, 10 Climate Tech Companies to Watch, and (new this year) 10 Things That Matter in AI Right Now .

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Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Maxwell Dent's YouTube handle PlaqueBoyMax might be inspired by the platform's subscriber-and view-count plaques, but he's been racking up badges elsewhere, too. The 23-year-old Twitch creator has branched out across platforms through clippable moments from his streams, particularly from his series, in which he and his guests livestream from a recording studio.

We study the selection of agents based on mutual nominations, a theoretical problem with many applications from committee selection to AI alignment. As agents both select and are selected, they may be incentivized to misrepresent their true opinion about the eligibility of others to influence their own chances of selection. Impartial mechanisms circumvent this issue by guaranteeing that the selection of an agent is independent of the nominations cast by that agent. Previous research has established strong bounds on the performance of impartial mechanisms, measured by their ability to approximate the number of nominations for the most highly nominated agents. We study to what extent the performance of impartial mechanisms can be improved if they are given a prediction of a set of agents receiving a maximum number of nominations. Specifically, we provide bounds on the consistency and robustness of such mechanisms, where consistency measures the performance of the mechanisms when the prediction is accurate and robustness its performance when the prediction is inaccurate. For the general setting where up to k agents are to be selected and agents nominate any number of other agents, we give a mechanism with consistency 1 O 1k and robustness 1 1e O 1k .

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We study the selection of agents based on mutual nominations, a theoretical problem with many applications from committee selection to AI alignment. As agents both select and are selected, they may be incentivized to misrepresent their true opinion about the eligibility of others to influence their own chances of selection. Impartial mechanisms circumvent this issue by guaranteeing that the selection of an agent is independent of the nominations cast by that agent. Previous research has established strong bounds on the performance of impartial mechanisms, measured by their ability to approximate the number of nominations for the most highly nominated agents. We study to what extent the performance of impartial mechanisms can be improved if they are given a prediction of a set of agents receiving a maximum number of nominations. Specifically, we provide bounds on the consistency and robustness of such mechanisms, where consistency measures the performance of the mechanisms when the prediction is correct and robustness its performance when the prediction is incorrect. For the general setting where up to $k$ agents are to be selected and agents nominate any number of other agents, we give a mechanism with consistency $1-O\big(\frac{1}{k}\big)$ and robustness $1-\frac{1}{e}-O\big(\frac{1}{k}\big)$. For the special case of selecting a single agent based on a single nomination per agent, we prove that $1$-consistency can be achieved while guaranteeing $\frac{1}{2}$-robustness. A close comparison with previous results shows that (asymptotically) optimal consistency can be achieved with little to no sacrifice in terms of robustness.

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