Automation

Opening — Why this matters now Scientific discovery has always been bottlenecked by one thing: human bandwidth. In scientific machine learning (SciML), where physics meets data-driven modeling, that bottleneck shows up as painstaking trial and error—architectures tuned by hand, loss functions adjusted by intuition, and results validated by weeks of computation. Enter AgenticSciML, a new framework from Brown University that asks a radical question: What if AI could not only run the experiment, but design the method itself? ...

Opening — Why this matters now AI agents are getting smarter—but not faster. Most large language model (LLM) systems still behave like cautious philosophers in a chess match: the world patiently waits while they deliberate. In the real world, however, traffic lights don’t freeze for an AI car mid-thought, and market prices don’t pause while a trading agent reasons about “the optimal hedge.” The new study Real-Time Reasoning Agents in Evolving Environments by Wen et al. (2025) calls this out as a fundamental flaw in current agent design—and offers a solution that blends human-like intuition with deliberative reasoning. ...

Opening — Why this matters now Large reasoning models like GPT-5 and s1.1-32B can solve Olympiad-level problems — but they’re computationally gluttons. Running them for every query, from basic arithmetic to abstract algebra, is like sending a rocket to fetch groceries. As reasoning models become mainstream in enterprise automation, the question is no longer “Can it reason?” but “Should it reason this hard?” ...

Why this matters now Every few months, another AI model promises to be more “aware” — but awareness is hard when memory is mush. Traditional large language models (LLMs) bury their knowledge across billions of parameters like a neural hoarder: everything is stored, but nothing is labeled. Updating a single fact means retraining the entire organism. The result? Models that can write essays about Biden while insisting he’s still president. ...

Opening — Why this matters now The academic world has been buzzing ever since a Nature paper claimed that large language models (LLMs) had made “mathematical discoveries.” Specifically, through a method called FunSearch, LLMs were said to have evolved novel heuristics for the classic bin packing problem—an NP-hard optimization task as old as modern computer science itself. The headlines were irresistible: AI discovers new math. But as with many shiny claims, the real question is whether the substance matches the spectacle. ...

Opening — Why this matters now In a world obsessed with productivity hacks and digital assistants, a new study offers a sobering reminder: being faster is not the same as being smarter. As tools like ChatGPT quietly integrate into workplaces and classrooms, the question isn’t whether they make us more efficient — they clearly do — but whether they actually reshape the human mind. Recent findings from the Universidad de Palermo suggest they don’t. ...

Opening — Why this matters now The AI world is obsessed with benchmarks. From math reasoning to coding, each new test claims to measure progress. Yet, none truly capture what businesses need from an agent — a system that doesn’t just talk, but actually gets things done. Enter Toolathlon, the new “decathlon” for AI agents, designed to expose the difference between clever text generation and real operational competence. In a world where large language models (LLMs) are being marketed as digital employees, Toolathlon arrives as the first test that treats them like one. Can your AI check emails, update a Notion board, grade homework, and send follow-up messages — all without breaking the workflow? Spoiler: almost none can. ...

AI’s impact on the workforce is no longer a speculative question—it’s unfolding in real time. But how do AI agents actually perform human work? A new study from Carnegie Mellon and Stanford, “How Do AI Agents Do Human Work?”, offers the first large-scale comparison of how humans and AI complete the same tasks across five essential skill domains: data analysis, engineering, computation, writing, and design. The findings are both promising and unsettling, painting a nuanced picture of a workforce in transition. ...

In Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness, researchers from Microsoft and UC Riverside reveal a surprisingly human flaw in autonomous AI systems: overconfidence. Like a digital version of Mr. Magoo—the well-meaning cartoon character who bumbles forward despite looming hazards—today’s computer-use agents (CUAs) often pursue tasks blindly, indifferent to feasibility or consequence. The Rise—and Risk—of GUI Agents CUAs represent the next frontier of automation: large multimodal models that control desktop interfaces to perform tasks like editing documents, sending emails, or configuring systems. Unlike chatbots, these agents act—clicking, typing, and navigating real operating systems. Yet this freedom exposes them to a unique failure pattern the authors term Blind Goal-Directedness (BGD)—the relentless drive to complete instructions without stopping to ask should this even be done? ...

From repetition to reasoning When early computer-use agents (CUAs) appeared, they promised to automate tedious digital workflows—clicking through files, formatting reports, or organizing spreadsheets. Yet anyone who has tried them knows the frustration: sometimes they succeed spectacularly, sometimes they click the wrong button and crash everything. Reliability, not intelligence, has been the missing link. A recent paper from Simular Research, “The Unreasonable Effectiveness of Scaling Agents for Computer Use,” shows that scaling these agents isn’t just about more compute—it’s about how we scale. Their method, Behavior Best-of-N (bBoN), turns the brute-force idea of “run many agents and hope one works” into a structured, interpretable, and near-human-level solution. ...