A method to rapidly estimate extreme ship response events is developed in this paper. The method involves training by a Long Short-Term Memory (LSTM) neural network to correct a lower-fidelity hydrodynamic model to the level of a higher-fidelity simulation. More focus is placed on larger responses by isolating the time-series near peak events identified in the lower-fidelity simulations and training on only the shorter time-series around the large event. The method is tested on the estimation of pitch time-series maxima in Sea State 5 (significant wave height of 4.0 meters and modal period of 15.0 seconds,) generated by a lower-fidelity hydrodynamic solver known as SimpleCode and a higher-fidelity tool known as the Large Amplitude Motion Program (LAMP). The results are also compared with an LSTM trained without special considerations for large events.

We analyze the ability of computational units to retain past responses after parameter updates, a key property for system-wide continual learning. Neural networks trained with gradient descent lack this capability, prompting us to propose Modelleyen, an alternative approach with inherent response preservation. We demonstrate through experiments on modeling the dynamics of a simple environment and on MNIST that, despite increased computational complexity and some representational limitations at its current stage, Modelleyen achieves continual learning without relying on sample replay or predefined task boundaries.

Our framework demonstrates state-of-the-art performance across diverse long-horizon tasks, achieving strong generalization in both simulated and real-world scenarios. Videos and code are available at https://ghiara.github.io/DAHLIA/. I. INTRODUCTION Language-conditioned robotic manipulation is an emerging field at the intersection of robotics, natural language processing, and computer vision, which aims to enable robots to interpret human commands and perform complex tasks using multi-modal sensing [1]. Imitation learning (IL) and reinforcement learning (RL) have traditionally been the dominant approaches for training robotic manipulation policies. However, recent IL and RL methods are often constrained to narrow task distributions, leading to sampling inefficiency and high sensitivity to distributional shifts, which limits their ability to generalize to diverse and complex scenarios. Additionally, both IL and RL are data-driven, requiring large-scale expert demonstrations, yet Internet-scale data collection for embodied AI remains a substantial challenge. In contrast, the natural language processing domain has seen state-of-the-art (SOT A) LLMs like GPT [2] and Llama [3] achieve humanlike semantic understanding and common sense reasoning by training on massive datasets. Within embodied AI, LLMs offer a promising solution to bridge the gap between high-level language instructions and low-level robotic control, 1 Y uan Meng, Xiangtong Y ao, Haihui Y e, Yirui Zhou, and Alois Knoll are with the School of Computation, Information and Technology, Technical University of Munich, Germany. 2 Shengqiang Zhang is with the Center for Information and Language Processing, Ludwig Maximilian University of Munich, Germany. 3 Zhenshan Bing is with the State Key Laboratory for Novel Software Technology, Nanjing University, China.

Adapting Large Language Models (LLMs) to downstream tasks using Reinforcement Learning (RL) has proven to be an effective approach. However, LLMs do not inherently define the structure of an agent for RL training, particularly in terms of defining the action space. This paper studies learning a compact latent action space to enhance the controllability and exploration of RL for LLMs. We propose Controlling Large Language Models with Latent Actions (CoLA), a framework that integrates a latent action space into pre-trained LLMs. We apply CoLA to the Llama-3.1-8B model. Our experiments demonstrate that, compared to RL with token-level actions, CoLA's latent action enables greater semantic diversity in text generation. For enhancing downstream tasks, we show that CoLA with RL achieves a score of 42.4 on the math500 benchmark, surpassing the baseline score of 38.2, and reaches 68.2 when augmented with a Monte Carlo Tree Search variant. Furthermore, CoLA with RL consistently improves performance on agent-based tasks without degrading the pre-trained LLM's capabilities, unlike the baseline. Finally, CoLA reduces computation time by half in tasks involving enhanced thinking prompts for LLMs by RL. These results highlight CoLA's potential to advance RL-based adaptation of LLMs for downstream applications.

Large Language Models (LLMs) are increasingly relied upon for coding tasks, yet in most scenarios it is assumed that all relevant information can be either accessed in context or matches their training data. We posit that LLMs can benefit from the ability to interactively explore a codebase to gather the information relevant to their task. To achieve this, we present a textual environment, namely debug-gym, for developing LLM-based agents in an interactive coding setting. Our environment is lightweight and provides a preset of useful tools, such as a Python debugger (pdb), designed to facilitate an LLM-based agent's interactive debugging. Beyond coding and debugging tasks, this approach can be generalized to other tasks that would benefit from information-seeking behavior by an LLM agent.

Alpha School co-founder Mackenzie Price and a junior at the school Elle Kristine join'Fox & Friends' to discuss the benefits of incorporating artificial intelligence into the classroom. Will A.I. make schools'obsolete,' or does it present a new'opportunity' for the education system? STUDY BUDDY: A Texas private school is seeing student test scores soar to new heights following the implementation of an artificial intelligence "tutor." 'URGENT CALL': A new report from the Anti-Defamation League shows anti-Jewish and anti-Israel biases among AI large language models. ROBOTS SWARM: The automotive industry is undergoing a seismic shift driven by the integration of AI-powered humanoid robots into production lines. UBTech Robotics, in collaboration with Zeekr, has pioneered a groundbreaking initiative where swarm robots work together to build cars faster and more efficiently than ever before.

There's a good chance learning a new language is one of your New Year's resolutions, unless you're hoping Google Translate will be enough for your next international adventure. Either way, you'll need a reliable method to guide you through speaking and understanding the foreign language of your choosing. Fortunately, we're no longer confined to flashcards and textbooks as you can learn using your phone from the comfort of your couch. Many of the best language learning apps today offer a multi-tier approach, with AI-powered conversations, extensive vocab libraries and even podcasts you can listen to to help you master your target language. Whether you're just starting because you're just trying to understand what Bad Bunny means when he says "un verano en Nueva Yol," or you want to brush up on your Korean before that planned vacation, there's a language learning app to suit your needs.

Sometimes, all that's stopping you is a language barrier. If you're ready to tear that down and interact with more of the world, Babbel is ready to serve as your passport. Imagine learning the entirety of one semester of Spanish in just 15 hours. Researchers from City University of New York recently assessed Babbel's Spanish courses and discovered that the novice learners "acquired knowledge equivalent to one Spanish semester in 15h." That impressive distinction is easy to believe once you see Babbel's process.

For years, we've been told the future belongs to tech jobs, coding boot camps and college degrees that leave young Americans saddled with debt. But while artificial intelligence is shaking up white-collar professions, there's one sector AI won't be replacing anytime soon: blue-collar skilled trades. Let's face it, when your septic system blows up are you the one who is going to clean up the mess? That's right -- while office workers worry about ChatGPT taking their jobs, the demand for electricians, plumbers, welders, and mechanics is skyrocketing. Companies are desperate for skilled workers, wages are soaring, and many of these careers offer six-figure salaries without the need for a four-year degree.

Markowicz previously explained why they wrote the book in a Fox News Digital opinion piece, noting that in 2021, then-Democratic Virginia gubernatorial candidate Terry McAuliffe said, "I don't think parents should be telling schools what they should teach." "Taken on its own, the comment might even be benign. Sure, parental involvement in education had always been a prediction of student success. A 2010 study called'Parent Involvement and Student Academic Performance: A Multiple Mediational Analysis' by researchers at the Warren Alpert Medical School of Brown University and the University of North Carolina at Greensboro found'children whose parents are more involved in their education have higher levels of academic performance than children whose parents are involved to a lesser degree." But should parents be designing a curriculum?