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A new AI model called MetaPerch learns to identify bird and animal species from their sounds by also paying attention to metadata like where and when recordings were made. This approach outperforms models trained on acoustic data alone, because it learns that certain species appear in certain places at certain times—knowledge that helps it recognize calls even when recording conditions are poor or the species is in an unusual location.

Researchers built a mathematical model that tracks how the human mind moves from sensing the world to making decisions—treating perception, thought, and action as linked gears rather than a black box. The model reveals how attention, memory, and intention each shape behavior in ways that can be measured and understood. In a test with simulated movement therapy, a controller using this model kept people engaged better than simpler approaches.

When major crypto and traditional finance crises hit, different types of crypto firms responded in opposite ways—retail customers pulled money out while institutional players withdrew funds—and stablecoins didn't protect everyone equally. By tracking actual transaction records from registered Austrian crypto firms, researchers found that during the Silicon Valley Bank collapse, stablecoins revealed hidden two-tier systems where some users couldn't access their money as promised.

Researchers built a speech recognition system that generates complete transcripts in parallel rather than one word at a time, using a technique called diffusion that refines rough guesses over eight steps. The system achieved 6.6% word error rate on a standard benchmark — competitive with conventional approaches — while using a clever training method to connect audio features with text generation, and works across multiple languages with a single adapter.

When engineers test the ground to predict earthquake damage, they often deploy multiple measurement arrays at different sizes to probe deeper. This paper solves a longstanding problem: how to properly measure the uncertainty in those velocity measurements when data comes from mismatched array sizes. The fix works well for both simple and complex soil structures, producing reliable uncertainty estimates even when the raw velocity values themselves are uncertain.

Researchers automated the process of shrinking and optimizing the artificial intelligence that controls nano-drones, cutting memory use in half and speeding up the drone's decision-making by 1.6 times. The optimized system let a Crazyflie nano-drone fly twice as fast as before, avoid obstacles more sharply, and navigate turns—all while using less than 2% of the drone's power budget.

AI agents waste enormous amounts of computing power on simple tasks by re-reading files and dependencies they've already seen, treating a one-line code edit like a full codebase audit. Researchers developed E3, a method that makes agents estimate task difficulty first, then expand their search only if something goes wrong—cutting costs by 85% and file inspections by 92% while maintaining 100% success rates on code-editing tasks.

When charged particles emit photons, they don't have a single well-defined energy boundary—instead they fade out gradually at the edge, following a mathematical power law. Rey shows this fuzzy edge comes directly from soft photons we can't measure individually, and that the exact shape of this edge encodes information about how those unmeasured photons behave.

Scientists have developed a new method called the Ensemble Controlled-flow Filter that updates forecasts from dynamical systems when observations are complex, indirect, or only accessible through simulation. Unlike traditional filtering methods that assume observations are clean and straightforward, this approach works with messy real-world measurement mechanisms—including those that produce multiple possible outcomes or require running expensive computer simulations to interpret.

A validated system for automatically sorting teacher feedback comments by topic and sentiment, built in 2019, still works well in 2026 even with newer AI models. Surprisingly, the newest frontier models don't improve sentiment classification over much cheaper alternatives, and the system transfers successfully to English feedback, suggesting the original protocol is robust across time and languages.

When an IoT device starts behaving suspiciously, security systems can now detect it and boot it from the network in about a third of a second—without needing specialized equipment. The system uses standard protocols already installed in most networks, combines three different detection methods into a single model, and can permanently revoke a device's access while also disconnecting its active session.

Researchers built an AI system to schedule work in factories that build house modules, where long waits for concrete to cure and paint to dry create bottlenecks that traditional scheduling ignores. The AI reaches within 4% of the best possible schedule and outperforms both standard scheduling rules and genetic algorithms, even as factories get more congested.