Reasoning-augmented search agents such as Search-R1, trained via reinforcement learning with verifiable rewards (RLVR), demonstrate remarkable capabilities in multi-step information retrieval from external knowledge sources. These agents address the limitations of their parametric memory by dynamically gathering relevant facts to address complex reasoning tasks. However, existing approaches suffer from a fundamental architectural limitation: they process search queries strictly sequentially, even when handling inherently parallelizable and logically independent comparisons. This sequential bottleneck significantly constrains computational efficiency, particularly for queries that require multiple entity comparisons. To address this critical limitation, we propose ParallelSearch, a novel reinforcement learning framework that empowers large language models (LLMs) to recognize parallelizable query structures and execute multiple search operations concurrently. Our approach introduces dedicated reward functions that incentivize the identification of independent query components while preserving answer accuracy through jointly considering correctness, query decomposition quality, and parallel execution benefits. Comprehensive experiments demonstrate that ParallelSearch outperforms state-of-the-art baselines by an average performance gain of 2.9% across seven question-answering benchmarks. Notably, on parallelizable questions, our method achieves a 12.7% performance improvement while requiring only 69.6% of the LLM calls compared to sequential approaches.

Put bluntly, picking the best movies of 2023 was tough. The double-whammy of Barbie and Oppenheimer gave the box office a long-overdue, post-Covid-19 jolt, only to be followed by a pair of months-long strikes in Hollywood that shut down production on nearly all the films in the works for 2024 and beyond. Even now, with the strikes over, the industry is scratching its head at what happened and what's to come. Still, amidst all the noise, 2023 provided a wealth of quietly beautiful films. Even as Hollywood fretted over the possibility of artificial intelligence upending filmmaking and giving writing and acting gigs to bots, it's impossible to watch the movies on this list and not feel such a possibility is faintly ridiculous.

In an intriguing paper certain to catch the eye of senior Pentagon officials, a company claims that an artificial intelligence program it designed allowed drones to repeatedly and convincingly "defeat" a human pilot in simulations in a test done with the Air Force Research Lab (AFRL). A highly experienced former Air Force battle manager, Gene Lee, tried repeatedly and failed to score a kill and "he was shot out of the air by the reds every time after protracted engagements." All of the missile battles were executed beyond Beyond Visual Range. "It seemed to be aware of my intentions and reacting instantly to my changes in flight and my missile deployment. It knew how to defeat the shot I was taking. It moved instantly between defensive and offensive actions as needed," Lee, who oversaw the F-35A, F-22 and Global Hawk systems for Air Combat Command until 2011, said in an article published by University of Cincinnati Magazine.

"The Advanced Computational Methods Center, University of Georgia," see Nute, Donald. "An AI-Based Methodology for Factory Design" see Fisher, Edward L. "Artificial Intelligence: A Rand Perspective," see Klahr, Philip. "Artificial Intelligence and Ethics: An Exercise in the Moral Imagination," see LaChat, Michael R. "AI in Manufacturing at Digital," see Lynch, Frank. "Artificial Intelligence Research and Applications at the NASA Johnson Space Center," see Healey, Kathleen Jurica. "Artificial Intelligence Research in Progress at the Courant Institute, New York University," see Davis, Ernest.

Complex computer software may be the key to correctly diagnosing and treating patients with various diseases. Dr. Nick Ernest, a UC graduate who beat the Air Force in a simulated game of aerial combat with his artificial intelligence (AI) system, is now applying the concept to the human body. In a proof of concept study, Ernest harnessed the power of his Psibernetix AI program to determine if bipolar patients could benefit from a certain medication. Using fMRIs of bipolar patients, the software looked at how each patient would react to lithium. The computer software predicted with 100 percent accuracy how patients would respond.

The findings open a world of possibility for using AI, or machine learning, to treat disease, researchers said. David Fleck, an associate professor at the UC College of Medicine, and his co-authors used artificial intelligence called "genetic fuzzy trees" to predict how bipolar patients would respond to lithium. Bipolar disorder, depicted in the TV show "Homeland" and the Oscar-winning "Silver Linings Playbook," affects as many as six million adults in the United States or 4 percent of the adult population in a given year. "In psychiatry, treatment of bipolar disorder is as much an art as a science," Fleck said. "Patients are fluctuating between periods of mania and depression. Treatments will change during those periods. It's really difficult to treat them appropriately during stages of the illness."

Artificial intelligence (AI) developed by a University of Cincinnati doctoral graduate was recently assessed by subject-matter expert and retired United States Air Force Colonel Gene Lee - who holds extensive aerial combat experience as an instructor and Air Battle Manager with considerable fighter aircraft expertise - in a high-fidelity air combat simulator. The artificial intelligence, dubbed ALPHA, was the victor in that simulated scenario, and according to Lee, is "the most aggressive, responsive, dynamic and credible AI I've seen to date." Details on ALPHA - a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

While Google was building an artificial intelligence that could beat a grandmaster at the ancient game of Go, University of Cincinnati alum took a different tack. They designed an AI that could take on a fighter pilot. Dubbed ALPHA, this system recently beat retired United States Air Force Colonel Gene Lee in multiple flight simulator trials, as the researchers explain in a paper recently published in the Journal of Defense Management. The idea isn't to replace human fighter pilots. According to Nicholas Ernest, a University of Cincinnati alum and the founder of Psibernetix, the company that developed ALPHA, this AI may ultimately act as a kind of digital assistant that provides real-time advice to pilots.

In the military world, fighter pilots have long been described as the best of the best. As Tom Wolfe famously wrote, only those with the "right stuff" can handle the job. Now, it seems, the right stuff may no longer be the sole purview of human pilots. A pilot A.I. developed by a doctoral graduate from the University of Cincinnati has shown that it can not only beat other A.I.s, but also a professional fighter pilot with decades of experience. In a series of flight combat simulations, the A.I. successfully evaded retired U.S. Air Force Colonel Gene "Geno" Lee, and shot him down every time.

Would you trust an artificial intelligence to fly an armed combat jet? Software called ALPHA is being used to fly uncrewed jets in simulations and could one day help pilots in real-world missions. ALPHA's developers claim, that unlike many AI systems, its behaviour can be verified at each step, meaning it won't act unpredictably. ALPHA was developed by Psibernetix in Ohio as a training aid for the US air force. It was originally designed to fly aircraft in a virtual air combat simulator, but has now been turned into a friendly co-pilot system that can help human pilots using the simulator.