The next frontier for AI agents is scientific research! Science’s Last Exam is a new benchmark designed to test whether an AI agent can carry out an entire research cycle on its own, not just solve isolated scientific problems. Instead of giving the model a question with a known path to the answer, SLE gives it a research objective, the required tools and materials, and a limited computational budget. From there, the agent has to decide what to do next. It needs to formulate hypotheses, choose experiments, run computations, interpret the results, react when something fails, and decide whether the evidence is strong enough to support a final conclusion. One of the tasks makes this especially clear. The agent has to identify the lowest dose of an antithrombotic drug that can suppress dangerous blood clots without disrupting normal hemostasis. That means it cannot simply find one dose that works once. It has to test different dosing intervals, vessel geometries, blood-flow conditions, and pharmacological response profiles, then use each result to decide what experiment is worth running next. Even after finding a workable regimen, it still has to ask whether the dose can be lowered further, whether the result holds under different conditions, and where the evidence stops being reliable. Once the agent submits its final result, the answer is frozen and evaluated against hidden checks defined in advance by domain experts. SLE also breaks the research process into individual capabilities such as hypothesis discovery, protocol planning, experiment observation, scientific computing, and scientific software operation, so it is possible to see not just whether an agent failed, but where it got stuck. The initial release includes 50+ selected tasks across nine scientific disciplines, with researchers from 38 institutions contributing to task design and review in their personal capacities. And the next step goes beyond simulation. SLE is also building wet-lab tasks where AI-designed experimental protocols will be executed in real laboratories, with the resulting data sent back to the agent for another round of reasoning and experimentation. That is where agent evaluation starts looking much closer to actual scientific research.