Large Language Model (LLM)-based agents are increasingly applied to tasks requiring structured reasoning, tool use, and environmental adaptation, such as data manipulation, multistep planning, and computer-use automation. However, despite their versatility, current training paradigms for model weight optimization methods, like PPO and GRPO, remain relatively impractical with their high computational overhead for rollout convergence. In addition, the resulting agent policies are difficult to interpret, adapt, or incrementally improve. To address this, we investigate creating and refining structured memory of experiential learning of an agent from its environment as an alternative route to agent optimization. We introduce BREW (Bootstrapping expeRientially-learned Environmental knoWledge), a framework for agent optimization for downstream tasks via KB construction and refinement. In our formulation, we introduce an effective method for partitioning agent memory for more efficient retrieval and refinement. BREW uses task graders and behavior rubrics to learn insights while leveraging state-space search for ensuring robustness from the noise and non-specificity in natural language. Empirical results on real world, domain-grounded benchmarks -- OSWorld, $τ^2$Bench, and SpreadsheetBench -- show BREW achieves $10-20\%$ improvement in task precision, $10-15\%$ reduction in API/tool calls leading to faster execution time, all while maintaining computational efficiency on par with base models. Unlike prior work where memory is treated as static context, we establish the KB as a modular and controllable substrate for agent optimization -- an explicit lever for shaping behavior in a transparent, interpretable, and extensible manner.

Since 2004, the number of American adults who've enjoyed a daily cup of joe has increased 37 percent. Breakthroughs, discoveries, and DIY tips sent every weekday. Whether you're an early bird or a night owl, coffee is probably part of your daily routine. Since 2004, the number of American adults who've enjoyed a daily cup of java has jumped up 37 percent, the highest level in more than 20 years, according to the National Coffee Association . But coffee is hardly a new invention.

With holiday music playing, cookies baking and everyone filled with joy and cheer, home sweet home is the place to be this season. While you're busy shopping for Black Friday deals, keep in mind that there are home and kitchen steals on Amazon that make great gifts for anyone on your list. Here are four house and home deals ahead of Black Friday 2023. See this list of four Amazon deals that could make household duties like cooking and cleaning just a little bit easier. The Roborock Q5 series is a self-emptying robotic vacuum that's for sale ahead of Black Friday on Amazon.com.

AI that mimics aspects of the behaviour of flies seeking food can be used to design new beer recipes. It can also accurately recreate an existing brew using alternative ingredients when supplies are unstable. Mohammad Majid al-Rifaie at the University of Greenwich, UK, and his colleagues say that brewers traditionally follow existing recipes or create new ones using the time-consuming process of trial and error. He says that the AI tool is intended to "flip this whole equation around, so instead of …

Companies developing artificial intelligence (AI)-powered marketing tools typically claim that their solutions drive strategic decision-making better than software without an algorithmic component. But -- as is often the case -- the reality is more complicated. AI learns to make predictions from large amounts of high-quality data, and so can be hamstrung (e.g., make mistakes) if that data is not available. The complex nature of marketing stacks, which sprawl across disparate, disconnected systems, can put up logistical roadblocks to implementation. Brew, a Tel Aviv, Israel-based strategic marketing platform, claims its approach is different from the rest in that it's more holistic.

Did you miss a session at the Data Summit? Companies developing artificial intelligence (AI)-powered marketing tools typically claim that their solutions drive strategic decision-making better than software without an algorithmic component. But -- as is often the case -- the reality is more complicated. AI learns to make predictions from large amounts of high-quality data, and so can be hamstrung (e.g., make mistakes) if that data is not available. The complex nature of marketing stacks, which sprawl across disparate, disconnected systems, can put up logistical roadblocks to implementation.

Smart home automation has become quite popular in recent years, moving from a luxury for the rich to a staple in many homes. The most popular smart home devices are speakers and thermostats, but a growing number of people are adopting other smart devices like door locks and security cameras. Residential smart home automation has become a massive industry, and it's not hard to implement. For those who only want a couple of devices, it's easy to set up without a professional. However, for those who want an entire network of connected devices, it's better to hire a professional.

A tea bag is an extraordinary thing. Each small sachet contains a mix of leaves from different producers and different places. Hundreds of factors can affect the flavour of each leaf, from the amount of sunlight and rainfall to the type of soil it was grown in, how it was plucked and how it was dried. Yet when you drink a cup of your favourite brew, you expect it to taste exactly like the last one. Tetley, a British teamaker, boasts that its basic blend has had the same distinctive taste since the company was set up in 1822.

When Joe Brew worked for the Florida Department of Health as an epidemiologist for two years starting in 2013, he helped with syndromic surveillance, meaning he had the arduous job of reviewing the symptoms of patients coming into the emergency departments from all across the state. The goal of such work: to detect an abnormal spike of symptoms in an area that may indicate there's a public health concern. Public health authorities worldwide continue to use this type of surveillance. The outbreak of a novel pathogen in Wuhan, China in late 2019, for instance, was in part detected by a large uptick of patients coming to the hospital with symptoms of a respiratory infection, with unknown etiology. But Brew says this system fails to prevent the transmission of a virus like SARS-CoV-2 because by the time patients arrive at the hospital, they have likely already been infectious for a matter of days.

Is there a magic formula for brewing the perfect beer? If there is, then given the drink's timeless popularity, whoever finds it is likely to be very successful. It's a question that the world's largest brewer is hoping to answer with the help of artificial intelligence (AI). The Amazing Ways The Brewers of Budweiser Are Using Artificial Intelligence To Transform The Beer ... [ ] Industry AB InBev – producer of renowned brews including Budweiser, Stella Artois, and Corona - is building machine learning into key areas of its business, as it seeks to bring one of the world's oldest industries into the digital age. The company has invested in a raft of data-driven initiatives with the aim of improving everything from how it brews beer to how it manages its relationships with customers and markets its products to the public. It began its steps towards digital transformation several years ago by establishing what it refers to as its Beer Garage – a Silicon Valley-based hub of innovation, where it researches, develops, and tests technology-driven solutions.