Retrieval-Augmented Language Models boost task performance, owing to the retriever that provides external knowledge. Although crucial, the retriever primarily focuses on semantics relevance, which may not always be effective for generation. Thus, utility-based retrieval has emerged as a promising topic, prioritizing passages that provides valid benefits for downstream tasks. However, due to insufficient understanding, capturing passage utility accurately remains unexplored. This work proposes SCARLet, a framework for training utility-based retrievers in RALMs, which incorporates two key factors, multi-task generalization and inter-passage interaction. First, SCARLet constructs shared context on which training data for various tasks is synthesized. This mitigates semantic bias from context differences, allowing retrievers to focus on learning task-specific utility for better task generalization. Next, SCARLet uses a perturbation-based attribution method to estimate passage-level utility for shared context, which reflects interactions between passages and provides more accurate feedback. We evaluate our approach on ten datasets across various tasks, both in-domain and out-of-domain, showing that retrievers trained by SCARLet consistently improve the overall performance of RALMs.

Linear temporal logic (LTL) is widely used in industrial verification. LTL formulae can be learned from traces. Scaling LTL formula learning is an open problem. We implement the first GPU-based LTL learner using a novel form of enumerative program synthesis. The learner is sound and complete. Our benchmarks indicate that it handles traces at least 2048 times more numerous, and on average at least 46 times faster than existing state-of-the-art learners. This is achieved with, among others, novel branch-free LTL semantics that has $O(\log n)$ time complexity, where $n$ is trace length, while previous implementations are $O(n^2)$ or worse (assuming bitwise boolean operations and shifts by powers of 2 have unit costs -- a realistic assumption on modern processors).

Sylvester Stallone wants his daughters, Sistine, Scarlet and Sophia, to be ready for anything. In new clips from the second season of their Paramount reality series, "The Family Stallone," Stallone spoke about his two eldest daughters, Sophia and Sistine, moving to New York, calling it "traumatic" as he recalled his own experiences with robbery, car accidents, and more. "Since you guys have moved to New York, it's made me very uneasy. You know I'm paranoid anyway because I have a responsibility as a father to do everything I can," he told them early in the episode. The girls then joked about him being "the most paranoid person on the planet," with the youngest daughter Scarlet saying "he is crazy!"

Looking at the latest TRDR Pocket video games console, you would never think it hosted one of the most sophisticated AI voice assistants on the market today. The £399 Gameboy-like device, which is the second handheld console developed by British company Go Games, has a boxy, retro appearance, with a shiny aluminium body and a 3.5-inch touchscreen display. It runs Android, giving users access to over 600,000 games and apps from the Google Play Store, including Fortnite and Call of Duty. The original TRDR Pocket, which was released in 2021 in partnership with American rapper and social media influencer Soulja Boy, shipped over 100,000 units. However, the reviews were far from glowing - with some pointing out it was almost identical to the Retroid Pocket, while others complained that the small touch screen made it awkward to type text and play certain games like shooters.

It appears as though "Scarlet" and "Violet" take heavy inspiration from that game; some open-world traveling features teased for the two upcoming games, like flying, appear nearly identical in function and animation to their "Arceus" counterpart. It's still unclear if "Scarlet" and "Violet's" catching mechanics will take inspiration from "Arceus," which included not just the classic catching mechanics involving turn-based battling, weakening and throwing a Poké Ball, but also a "Pokémon Go"-like hybrid that involved sneaking up and throwing a perfectly aimed Poké Ball before the battle even began. "Scarlet" and "Violet's" battles and UI were only briefly showcased during the Pokémon Presents.

Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas. We consider the problem of learning LTL formulas for classifying traces; despite a growing interest of the research community, existing solutions suffer from two limitations: they do not scale beyond small formulas, and they may exhaust computational resources without returning any result. We introduce a new algorithm addressing both issues: our algorithm is able to construct formulas an order of magnitude larger than previous methods, and it is anytime, meaning that it in most cases successfully outputs a formula, albeit possibly not of minimal size. We evaluate the performances of our algorithm using an open source implementation against publicly available benchmarks.

Reuters – The new Apple iPhone has something missing: the small socket millions of us have used for years to plug in headphones. While some fans opposed the widely anticipated move – one online petition urging Apple to keep the headphone jack drew more than 300,000 signatures – equipment suppliers and experts heralded a change in how users will interact with their devices. Axing the jack, they say, paves the way for discreet, bean-sized earbuds that can simultaneously translate, filter out unwanted noise or let us control other devices by voice – and drive up the value of the so-called'hearables' market to 16 billion within five years. It's the vision of the futuristic 2013 movie "Her", where a human has a love affair with a disembodied voice in his ear. But some who follow the industry say it's closer than many think, noting improvements in wireless technologies, materials, artificial intelligence and battery life.