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Like finding a complex recipe is just a twist on a basic one, researchers have shown that the Bethe-Feynman formula—used to estimate fission bomb yields—can be directly derived from Arthur Compton's 1941 approach. This resolves contradictions in prior derivations and makes the underlying physics more intuitive.

Think of video understanding as watching a movie—most AI models need to see every frame in high resolution, like reading every line of a script. VideoChat3 instead uses a novel 3D vision transformer (I3D-ViT) that adapts frame resolution to the action, skipping unnecessary details. With only 4B parameters, it outperforms larger open-source models on diverse video tasks, thanks to its efficient design and tailored training datasets. This makes advanced video AI more accessible, addressing scalability and reproducibility issues in current open-source models.

Imagine being near someone with the flu. You might worry about catching it, but infection isn't certain—it depends on how much virus they carry. This paper introduces a model where each contact exposes you, but infection probability depends on the infectious person's 'age of infection'—how long they've been sick, which correlates with viral load. A newer infection means higher viral load and higher risk. So what? This helps explain why some contacts lead to infection and others don't, allowing for more targeted public health measures.

Imagine trying to focus on your breath while a chatty friend distracts you. Suddenly you notice the chatter—that’s meta-awareness. A new computational model shows it works like a Global Neuronal Workspace ignition: when the brain detects a mismatch between current thoughts (distractor) and intended focus (orchestrator), it triggers a global broadcast. This bridges subjective meditation experiences with objective brain dynamics, potentially guiding better attention-training therapies.

Think of variational inference (VI) as a rough sketch of a complex economic model—fast but sloppy. A new approach, indirect variational inference (IVI), treats that sketch as an auxiliary model. By comparing the sketch to the real model, it measures and corrects the bias. Tests show flexible VI families paired with IVI yield reliable parameter estimates. So economists can use fast VI without losing accuracy.

Think of a student who, after each exam, writes down what worked and what didn’t, then uses those notes to study for the next test. SEED does something similar for AI: it has the policy itself analyze past complete episodes to generate concise natural-language "skills" (like "grab the wrench before opening the valve"). These skills are re-scored against current actions, creating a dense signal that helps the agent learn from sparse rewards. Because the skill analyzer evolves with the policy, the feedback stays relevant. The result: better performance on long, multi-step tasks like tool use or conversation.