That revelation is anticipated, for if the characteristics of previous data are remembered perfectly ( e.g., via realistic generative replay), no forgetting should be expected for lifelong learning.

By "well-behaved" we mean the shape within kernels (see L175; shared between source

This should be emphasised similar to the argument in the introduction.

Summary and Contributions: This manuscript addresses the problem of continual learning in RNN. The authors propose a new learning rule that allows to organize the dynamics for different tasks into orthogonal subspaces. Using a set of neuroscience tasks, they show how this learning rule allows to avoid catastrophic interferences between tasks. By analyzing the dynamics of trained networks they provide evidence for why their learning rule is successful, it also allows them to discuss the problem of transfer learning. Strengths: - propose a new original solution to the problem of continual learning, which also allows them to address and understand under which conditions learning in one task can be transfered to learning off another task.

Is "Six Seven" Really Brain Rot? The viral phrase is easy to dismiss, but its ubiquity suggests something crucial about human nature. Recently, my wife was texting with a friend who lives in Singapore. The news from the other side of the world turned out to be that kids there had discovered "six seven." On Halloween, our friend reported, a boy with a handmade "six seven" jersey had earned applause as he made his way through her neighborhood--a place that's a long way from Sixty-seventh Street in Philadelphia, which the rapper Skrilla may have been referencing in his song "Doot Doot (6 7)," which came out last December.

Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks. However, prevailing RL fine-tuning methods predominantly rely on PPO and its variants. Though these algorithms are effective in general RL settings, they often exhibit suboptimal performance and vulnerability to distribution collapse when applied to the fine-tuning of LLMs.

This work was done when the second author was jointly with Google Research.

However, such "black-box" policies lack interpretability and reliability, and often,

The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). While TinyML initially focused on enabling simple inference tasks on microcontrollers, the emergence of TinyDL marks a paradigm shift toward deploying deep learning models on severely resource-constrained hardware. This survey presents a comprehensive overview of the transition from TinyML to TinyDL, encompassing architectural innovations, hardware platforms, model optimization techniques, and software toolchains. We analyze state-of-the-art methods in quantization, pruning, and neural architecture search (NAS), and examine hardware trends from MCUs to dedicated neural accelerators. Furthermore, we categorize software deployment frameworks, compilers, and AutoML tools enabling practical on-device learning. Applications across domains such as computer vision, audio recognition, healthcare, and industrial monitoring are reviewed to illustrate the real-world impact of TinyDL. Finally, we identify emerging directions including neuromorphic computing, federated TinyDL, edge-native foundation models, and domain-specific co-design approaches. This survey aims to serve as a foundational resource for researchers and practitioners, offering a holistic view of the ecosystem and laying the groundwork for future advancements in edge AI.

Integration of Large Language Models (LLMs) with Learning Management Systems (LMSs) can enhance task automation and accessibility in education. However, hallucination where LLMs generate inaccurate or misleading information remains a challenge. This study introduces the Dynamic Course Content Integration (DCCI) mechanism, which dynamically retrieves course content from Canvas LMS and structures it within an LLM's context window via prompt engineering, enabling the LLM-powered assistant, Ask ME, to deliver context-aware, curriculum-aligned responses while mitigating hallucinations. A mixed-methods pilot study grounded in Self-Determination Theory (autonomy, competence) and the Technology Acceptance Model (perceived usefulness, ease of use) evaluated DCCI's effectiveness with 120 first-year programming students at Eötvös Loránd University. The course focused on foundational programming patterns in C#, including writing program specifications. We analyzed 14,746 logged interactions and a post-course survey completed by 101 students. User satisfaction was measured via a 5-point Likert scale (turn-level ratings), while the survey assessed usability, engagement, and ethical concerns. Results indicated high satisfaction (mean 4.65/5) and strong recognition of Ask ME's ability to provide timely, contextually relevant answers to administrative and course-related queries. 78.06% agreed that Ask ME's Canvas integration reduced platform switching, improving usability, engagement, comprehension, and topic exploration. Many students reported reduced hesitation to ask questions and increased motivation for self-directed learning, though concerns about over-reliance on AI and reduced student-teacher interaction emerged. This study demonstrates that DCCI enhances LLM reliability, student satisfaction, and engagement in AI-driven educational automation, while highlighting the importance of balancing