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Quantum computers and simulators rely on Krylov methods to model systems that would otherwise be too large to handle. Knowing exactly how long these approximations remain trustworthy—not just in theory but with precise bounds—lets engineers set realistic expectations for how long a calculation can run before errors accumulate beyond tolerance. This is essential for deciding whether a quantum algorithm is practical for real problems.

Companies spending millions on AI training for their workforce may be wasting money if those programs don't address how employees' actual domain knowledge shapes their AI use. The findings suggest managers should focus on matching AI tools to senior experts and strategic teams rather than rolling out one-size-fits-all training—and that simply teaching people to use AI tools matters far less than what they already know about their work.

AI training consumes enormous computational resources and energy. This approach cuts the parameters needed by more than half while improving accuracy, which translates directly to cheaper training, faster inference, and lower energy costs. Since many real-world applications use models far more powerful than their tasks require, this adaptive scaling could make AI systems dramatically more efficient at deployment.

Entanglement of Formation is a central concept in quantum information theory, used in everything from quantum computing to quantum cryptography. If the mathematical framework physicists have been using doesn't actually work as promised, then calculations and proofs relying on that framework may be unreliable. This paper forces researchers to be more careful about when they can safely use these mathematical shortcuts and identifies which quantum states need special handling.

Bridges and buildings rely on spotting damage early to prevent catastrophic failure and plan repairs efficiently. Right now, structural inspections are mostly done by eye, which is slow and inconsistent. By putting AI-powered defect detection in the hands of engineers and inspectors without specialized programming skills, this tool could make inspections faster, more accurate, and cheaper—turning predictive maintenance from rare to routine across infrastructure.

Software companies spend enormous resources collecting and labeling training data for AI coding assistants. This work shows they can achieve better results with far less data, cutting costs and training time while actually improving the quality of the AI's solutions. It also reveals a blind spot: more data isn't always better if that data contains bad practices buried inside otherwise successful fixes.

Understanding how biological and engineered networks waste energy matters for designing efficient systems — from neural circuits to chemical reactions to supply chains. This work reveals that the efficiency loss follows a predictable formula across radically different system types, which could help engineers set realistic performance targets and predict where to intervene to reduce waste.

Distributional reinforcement learning helps AI agents make better decisions in uncertain environments by understanding the full range of what might happen, not just the typical outcome. This proof provides the first formal guarantee that the method works reliably, which gives engineers confidence to use it in real systems where data is expensive or risky to collect—like robotics or medical applications.

Companies increasingly deploy AI to search internal emails, reports, and knowledge bases, but there's been no realistic way to test whether these systems will actually work at scale before rolling them out. CorporateBench gives developers a standardized measure to catch failures before they happen in production, potentially preventing costly mistakes like AI systems giving executives wrong answers about contracts, policy, or business decisions.

As AI agents gain access to email, files, and payments, ordinary people will need ways to protect themselves from unintended actions. This research shows that the intuitive solution — letting people write their own permission rules once — actually fails in practice because people second-guess their own rules when faced with real decisions. Systems designers now know they need to either push users toward firmer commitments, or accept that protecting against AI overreach may require sustained human attention rather than automated policies.

If AI systems increasingly guide real investment decisions, being able to dial their bias up or down matters for transparency and control. Right now, different AI models have hidden preferences that aren't obvious to users. A calibration dial means investors and institutions could explicitly set and audit the AI's stance—or detect when a system's recommendations are being subtly skewed without permission.

Social platforms need to recommend friends to hundreds of millions of people simultaneously, but existing approaches either waste enormous amounts of memory or become too slow to serve recommendations in real time. This work makes both problems solvable without tradeoffs — the same system that fits in memory also responds fast enough for live use. Platforms can now deploy more sophisticated recommendation logic without building expensive new infrastructure.