GUI agents powered by LLMs show promise in interacting with diverse digital environments. Among these, video games offer a valuable testbed due to their varied interfaces, with adventure games posing additional challenges through complex, narrative-driven interactions. Existing game benchmarks, however, lack diversity and rarely evaluate agents on completing entire storylines. To address this, we introduce FlashAdventure, a benchmark of 34 Flash-based adventure games designed to test full story arc completion and tackle the observation-behavior gap: the challenge of remembering and acting on earlier gameplay information. We also propose CUA-as-a-Judge, an automated gameplay evaluator, and COAST, an agentic framework leveraging long-term clue memory to better plan and solve sequential tasks. Experiments show current GUI agents struggle with full story arcs, while COAST improves milestone completion by bridging the observation-behavior gap. Nonetheless, a marked discrepancy between humans and best-performing agents warrants continued research efforts to narrow this divide.

Detective fiction, a genre defined by its complex narrative structures and character-driven storytelling, presents unique challenges for computational narratology, a research field focused on integrating literary theory into automated narrative generation. While traditional literary studies have offered deep insights into the methods and archetypes of fictional detectives, these analyses often focus on a limited number of characters and lack the scalability needed for the extraction of unique traits that can be used to guide narrative generation methods. In this paper, we present an AI-driven approach for systematically characterizing the investigative methods of fictional detectives. Our multi-phase workflow explores the capabilities of 15 Large Language Models (LLMs) to extract, synthesize, and validate distinctive investigative traits of fictional detectives. This approach was tested on a diverse set of seven iconic detectives - Hercule Poirot, Sherlock Holmes, William Murdoch, Columbo, Father Brown, Miss Marple, and Auguste Dupin - capturing the distinctive investigative styles that define each character. The identified traits were validated against existing literary analyses and further tested in a reverse identification phase, achieving an overall accuracy of 91.43%, demonstrating the method's effectiveness in capturing the distinctive investigative approaches of each detective. This work contributes to the broader field of computational narratology by providing a scalable framework for character analysis, with potential applications in AI-driven interactive storytelling and automated narrative generation.

Large language models (LLMs) trained on a substantial corpus of human knowledge and literature productively work with a large array of facts from that corpus. Surprisingly, they are also able to re-create the behaviors of personae that are captured within the corpus. By forming teams of simulated personae, supplying contexts that set the stage, and providing gentle prompts, one can move through scenarios that elicit expert behavior to perform meaningful cognitive work. The power of this strategy is demonstrated with two examples, one attacking factuality of LLM responses and the other reproducing a very recently published result in quantum optics.

While your body is lying in bed, your mind may be strolling around the manicured gardens of a manor house or the gritty streets of Victorian London. But now you can see some of the most iconic homes in literature with your own eyes, thanks an artificial intelligence (AI). These include Pemberley House, Mr Darcy's lavish estate in'Pride and Prejudice', and the residence of the world's most famous detective, Sherlock Holmes. Book lovers at Hammonds Furniture used the text-to-image software Midjourney to bring fictional homes to life in celebration of World Book Day 2023 - but how many of them can you guess? Jay Gatsby's mansion in'The Great Gatsby' (pictured) is described as a'colossal affair by any standard' and an'imitation of some Hôtel de Ville in Normandy' Daisy Buchanan's estate in'The Great Gatsby' (pictured) is described as a'cheerful red-and-white Georgian Colonial mansion', as well as'elaborate', 'bright' and'rosy-coloured' The above two houses are depictions of those from'The Great Gatsby', a novel set in 1922 that follows the life of mysterious millionaire Jay Gatsby.

"I felt there's a gap between what I learned in school, and what I actually do, and I also feel very insecure sometimes," she said. "I didn't know a lot of other data scientists who worked in the industry, so I wished I could have a community and talk to them." Essentially, said Liu, a data scientist takes something raw and translates it into something meaningful. The power of data science, she explained, is making sense of the past to make a recommendation for the future. "A data scientist is basically someone who solves a business problem with data," she explained.

Scientists have identified the five key personality traits that are common among famous psychopaths, including serial killer Ted Bundy, disgraced fraudster Bernie Madoff and robber Clyde Barrow. The US academics looked for shared traits in six men - Ted Bundy, Bernie Madoff, Clyde Barrow, James Bond, Sherlock Holmes and Chuck Yeager - who have previously been identified as psychopathic. They found that Bundy, Madoff and Barrow are all psychopaths guilty of callousness, manipulativeness, dishonesty, arrogance and cruelty. However, Bond, Holmes and Yeager likely are not psychopaths, and may have been misidentifed in the past due to their fearlessness and boldness, the experts say. Clyde Barrow (1910-1934): Along with Bonnie Parker, Clyde Barrow went on almost two-year crime spree that spanned several US states.

In the past quarter century, enrollment in college English departments has sunk like the Pequod in Moby Dick. Meanwhile enrollment in science programs has skyrocketed. Elon Musk, not Herman Melville, is the role model of the digital economy. But it doesn't have to be that way, says Angus Fletcher, 44, an English professor at Ohio State University. Fletcher is part of "group of renegades," he says, who are on a mission to plug literature back into the electric heart of contemporary life and culture. Fletcher has a plan--"apply science and engineering to literature"--and a syllabus, Wonderworks: The 25 Most Powerful Inventions in the History of Literature, his new book. Before the England-born Fletcher got his Ph.D. in literature at Yale, he earned an undergraduate degree in neuroscience, followed by a four-year stint in a neurophysiology lab at the University of Michigan. He switched careers when he realized the biology of the brain wouldn't take him far enough toward understanding our need for stories. "What's special about the human brain is its power of storytelling," Fletcher says.