The remarkable capabilities of Large Language Models (LLMs) often need to be tailored for specific applications, requiring the integration of new knowledge or the acquisition of new skills. While full fine-tuning is a powerful adaptation method, it is computationally expensive and can lead to a degradation of general reasoning abilities, a phenomenon known as catastrophic forgetting. A range of alternative techniques exists, each with its own trade-offs. In-Context Learning (ICL) is fast but limited by context length, while Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) offer a middle ground by minimizing parameter changes. However, the challenge of catastrophic forgetting persists, raising questions about the best adaptation strategy for a given task. This paper presents a comparative analysis of Supervised Finetuning (SFT), LoRA, and ICL in data-scarce scenarios. We find that LoRA provides the most effective balance, successfully instilling new skills with minimal impact on the base model's general knowledge. In contrast, while SFT excels at skill acquisition, it is highly susceptible to catastrophic forgetting. ICL is effective for incorporating factual knowledge but struggles with complex skills. Our findings offer a practical framework for selecting an LLM adaptation strategy. We highlight the critical distinction between skill acquisition and knowledge integration, clarify the trade-offs between task-specific performance and the preservation of general capabilities.

We present a typed computer language, Doug, in which all typed programs may be proved to halt in polynomial time, encoded in a vector-symbolic architecture (VSA). Doug is just an encoding of the light linear functional programming language (LLFPL) described by (Schimanski2009, ch. 7). The types of Doug are encoded using a slot-value encoding scheme based on holographic declarative memory (HDM; Kelly, 2020). The terms of Doug are encoded using a variant of the Lisp VSA defined by (Flanagan, 2024). Doug allows for some points on the embedding space of a neural network to be interpreted as types, where the types of nearby points are similar both in structure and content. Types in Doug are therefore learnable by a neural network. Following (Chollet, 2019), (Card, 1983), and (Newell, 1981), we view skill as the application of a procedure, or program of action, that causes a goal to be satisfied. Skill acquisition may therefore be expressed as program synthesis. Using Doug, we hope to describe a form of learning of skilled behaviour that follows a human-like pace of skill acquisition (i.e., substantially faster than brute force; Heathcote, 2000), exceeding the efficiency of all currently existing approaches (Kaplan, 2020; Jones, 2021; Chollet, 2024). Our approach brings us one step closer to modeling human mental representations, as they must actually exist in the brain, and those representations' acquisition, as they are actually learned.

Traditional surgical skill acquisition relies heavily on expert feedback, yet direct access is limited by faculty availability and variability in subjective assessments. While trainees can practice independently, the lack of personalized, objective, and quantitative feedback reduces the effectiveness of self-directed learning. Recent advances in computer vision and machine learning have enabled automated surgical skill assessment, demonstrating the feasibility of automatic competency evaluation. However, it is unclear whether such Artificial Intelligence (AI)-driven feedback can contribute to skill acquisition. Here, we examine the effectiveness of explainable AI (XAI)-generated feedback in surgical training through a human-AI study. We create a simulation-based training framework that utilizes XAI to analyze videos and extract surgical skill proxies related to primitive actions. Our intervention provides automated, user-specific feedback by comparing trainee performance to expert benchmarks and highlighting deviations from optimal execution through understandable proxies for actionable guidance. In a prospective user study with medical students, we compare the impact of XAI-guided feedback against traditional video-based coaching on task outcomes, cognitive load, and trainees' perceptions of AI-assisted learning. Results showed improved cognitive load and confidence post-intervention. While no differences emerged between the two feedback types in reducing performance gaps or practice adjustments, trends in the XAI group revealed desirable effects where participants more closely mimicked expert practice. This work encourages the study of explainable AI in surgical education and the development of data-driven, adaptive feedback mechanisms that could transform learning experiences and competency assessment.

Large language models (LLMs) are poised to significantly impact software development, especially in the Open - Source Software (OSS) sector. To understand this impact, we first outline the mechanisms through which LLMs may influence OSS through code development, collaborative knowledge transfer, and skill development . W e then e mpirically examine how LLMs affect OSS developers' work in these three key areas . Leveraging a natural experiment from a temporary ChatGPT ban in Italy, we employ a Difference - in - Differences framework with two - way fixed effects to analyze data from all OSS developers on GitHub in three similar countries -- Italy, France, and Portugal -- totaling 88,022 users. We find that access to ChatGPT increases developer productivity by 6.4%, knowledge sharing by 9.6%, and skill acquisition by 8.4%. These benefits vary significantly by user experience level: n ovice developers primarily experience productivity gains, whereas more experienced developers benefit more from improved knowledge sharing and accelerated skill acquisition. In addition, we f ind that LLM - assisted learning is highly context - dependent, with the greatest benefits observed in technically complex, fragmented, or rapidly evolving contexts . We show that the productivity effects of LLMs extend beyond direct code generation to include enhanced collaborative learning and knowledge exchange among developers -- dynamics that are essential for gaining a holistic understanding of LLMs' impact in OSS. Our findings offer critical managerial implications: strategically deploying LLMs can accelerat e novice developers' onboarding and productivity, empower intermediate developers to foster knowledge sharing and collaboration, and support rapid skill acquisition -- together enhancing long - term organizational productivity and agility.

Generative skill acquisition enables embodied agents to actively learn a scalable and evolving repertoire of control skills, crucial for the advancement of large decision models. While prior approaches often rely on supervision signals from generalist agents (e.g., LLMs), their effectiveness in complex 3D environments remains unclear; exhaustive evaluation incurs substantial computational costs, significantly hindering the efficiency of skill learning. Inspired by recent successes in verification models for mathematical reasoning, we propose VERGSA (Verifying Embodied Reasoning in Generative Skill Acquisition), a framework that systematically integrates real-time verification principles into embodied skill learning. VERGSA establishes 1) a seamless extension from verification of mathematical reasoning into embodied learning by dynamically incorporating contextually relevant tasks into prompts and defining success metrics for both subtasks and overall tasks, and 2) an automated, scalable reward labeling scheme that synthesizes dense reward signals by iteratively finalizing the contribution of scene configuration and subtask learning to overall skill acquisition. To the best of our knowledge, this approach constitutes the first comprehensive training dataset for verification-driven generative skill acquisition, eliminating arduous manual reward engineering. Experiments validate the efficacy of our approach: 1) the exemplar task pool improves the average task success rates by 21%, 2) our verification model boosts success rates by 24% for novel tasks and 36% for encountered tasks, and 3) outperforms LLM-as-a-Judge baselines in verification quality.

Real-world robot manipulation in dynamic unstructured environments requires lifelong adaptability to evolving objects, scenes and tasks. Traditional imitation learning relies on static training paradigms, which are ill-suited for lifelong adaptation. Although Continual Imitation Learnin (CIL) enables incremental task adaptation while preserving learned knowledge, current CIL methods primarily overlook the intrinsic skill characteristics of robot manipulation or depend on manually defined and rigid skills, leading to suboptimal cross-task knowledge transfer. To address these issues, we propose Skill Prompts-based HiErarchical Continual Imitation Learning (SPECI), a novel end-to-end hierarchical CIL policy architecture for robot manipulation. The SPECI framework consists of a multimodal perception and fusion module for heterogeneous sensory information encoding, a high-level skill inference module for dynamic skill extraction and selection, and a low-level action execution module for precise action generation. To enable efficient knowledge transfer on both skill and task levels, SPECI performs continual implicit skill acquisition and reuse via an expandable skill codebook and an attention-driven skill selection mechanism. Furthermore, we introduce mode approximation to augment the last two modules with task-specific and task-sharing parameters, thereby enhancing task-level knowledge transfer. Extensive experiments on diverse manipulation task suites demonstrate that SPECI consistently outperforms state-of-the-art CIL methods across all evaluated metrics, revealing exceptional bidirectional knowledge transfer and superior overall performance.

Objective: We aim to investigate long-term robotic surgical skill acquisition among surgical residents and the effects of training intervals and fatigue on performance. Methods: For six months, surgical residents participated in three training sessions once a month, surrounding a single 26-hour hospital shift. In each shift, they participated in training sessions scheduled before, during, and after the shift. In each training session, they performed three dry-lab training tasks: Ring Tower Transfer, Knot-Tying, and Suturing. We collected a comprehensive dataset, including videos synchronized with kinematic data, activity tracking, and scans of the suturing pads. Results: We collected a dataset of 972 trials performed by 18 residents of different surgical specializations. Participants demonstrated consistent performance improvement across all tasks. In addition, we found variations in between-shift learning and forgetting across metrics and tasks, and hints for possible effects of fatigue. Conclusion: The findings from our first analysis shed light on the long-term learning processes of robotic surgical skills with extended intervals and varying levels of fatigue. Significance: This study lays the groundwork for future research aimed at optimizing training protocols and enhancing AI applications in surgery, ultimately contributing to improved patient outcomes. The dataset will be made available upon acceptance of our journal submission.

Motor skill acquisition in fields like surgery, robotics, and sports involves learning complex task sequences through extensive training. Traditional performance metrics, like execution time and error rates, offer limited insight as they fail to capture the neural mechanisms underlying skill learning and retention. This study introduces directed functional connectivity (dFC), derived from electroencephalography (EEG), as a novel brain-based biomarker for assessing motor skill learning and retention. For the first time, dFC is applied as a biomarker to map the stages of the Fitts and Posner motor learning model, offering new insights into the neural mechanisms underlying skill acquisition and retention. Unlike traditional measures, it captures both the strength and direction of neural information flow, providing a comprehensive understanding of neural adaptations across different learning stages. The analysis demonstrates that dFC can effectively identify and track the progression through various stages of the Fitts and Posner model. Furthermore, its stability over a six-week washout period highlights its utility in monitoring long-term retention. No significant changes in dFC were observed in a control group, confirming that the observed neural adaptations were specific to training and not due to external factors. By offering a granular view of the learning process at the group and individual levels, dFC facilitates the development of personalized, targeted training protocols aimed at enhancing outcomes in fields where precision and long-term retention are critical, such as surgical education. These findings underscore the value of dFC as a robust biomarker that complements traditional performance metrics, providing a deeper understanding of motor skill learning and retention.

Motor skills, especially fine motor skills like handwriting, play an essential role in academic pursuits and everyday life. Traditional methods to teach these skills, although effective, can be time-consuming and inconsistent. With the rise of advanced technologies like robotics and artificial intelligence, there is increasing interest in automating such teaching processes using these technologies, via human-robot and human-computer interactions. In this study, we examine the potential of a virtual AI teacher in emulating the techniques of human educators for motor skill acquisition. We introduce an AI teacher model that captures the distinct characteristics of human instructors. Using a Reinforcement Learning environment tailored to mimic teacher-learner interactions, we tested our AI model against four guiding hypotheses, emphasizing improved learner performance, enhanced rate of skill acquisition, and reduced variability in learning outcomes. Our findings, validated on synthetic learners, revealed significant improvements across all tested hypotheses. Notably, our model showcased robustness across different learners and settings and demonstrated adaptability to handwriting. This research underscores the potential of integrating Reinforcement Learning and Imitation Learning models with robotics in revolutionizing the teaching of critical motor skills.

The human's cognitive capacity for problem solving is always limited to his/her educational background, skills, experiences, etc. Hence, it is often insufficient to bring solution to extraordinary problems especially when there is a time restriction. Nowadays this sort of personal cognitive limitations are overcome at some extend by the computational utilities (e.g. program packages, internet, etc.) where each one provides a specific background skill to the individual to solve a particular problem. Nevertheless these models are all based on already available conventional tools or knowledge and unable to solve spontaneous unique problems, except human's procedural cognitive skills. But unfortunately such low-level skills can not be modelled and stored in a conventional way like classical models and knowledge. This work aims to introduce an early stage of a modular approach to procedural skill acquisition and storage via distributed cognitive skill modules which provide unique opportunity to extend the limits of its exploitation.