To recognize and mitigate the harms of generative AI systems, it is crucial to consider who is represented in the outputs of generative AI systems and how people are represented. A critical gap emerges when naively improving who is represented, as this does not imply bias mitigation efforts have been applied to address how people are represented. We critically examined this by investigating gender representation in occupation across state-of-the-art large language models. We first show evidence suggesting that over time there have been interventions to models altering the resulting gender distribution, and we find that women are more represented than men when models are prompted to generate biographies or personas. We then demonstrate that representational biases persist in how different genders are represented by examining statistically significant word differences across genders. This results in a proliferation of representational harms, stereotypes, and neoliberalism ideals that, despite existing interventions to increase female representation, reinforce existing systems of oppression.

Curriculum learning is a training method in which an agent is first trained on a curriculum of relatively simple tasks related to a target task in an effort to shorten the time required to train on the target task. Autonomous curriculum design involves the design of such curriculum with no reliance on human knowledge and/or expertise. Finding an efficient and effective way of autonomously designing curricula remains an open problem. We propose a novel approach for automatically designing curricula by leveraging the learner's uncertainty to select curricula tasks. Our approach measures the uncertainty in the learner's policy using relative entropy, and guides the agent to states of high uncertainty to facilitate learning. Our algorithm supports the generation of autonomous curricula in a self-assessed manner by leveraging the learner's past and current policies but it also allows the use of teacher guided design in an instructive setting. We provide theoretical guarantees for the convergence of our algorithm using two time-scale optimization processes. Results show that our algorithm outperforms randomly generated curriculum, and learning directly on the target task as well as the curriculum-learning criteria existing in literature. We also present two additional heuristic distance measures that could be combined with our relative-entropy approach for further performance improvements.

Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have led to significant progress in 2D body pose estimation. However, achieving a good balance between accuracy, efficiency, and robustness remains a challenge. For instance, CNNs are computationally efficient but struggle with long-range dependencies, while ViTs excel in capturing such dependencies but suffer from quadratic computational complexity. This paper proposes two ViT-based models for accurate, efficient, and robust 2D pose estimation. The first one, EViTPose, operates in a computationally efficient manner without sacrificing accuracy by utilizing learnable joint tokens to select and process a subset of the most important body patches, enabling us to control the trade-off between accuracy and efficiency by changing the number of patches to be processed. The second one, UniTransPose, while not allowing for the same level of direct control over the trade-off, efficiently handles multiple scales by combining (1) an efficient multi-scale transformer encoder that uses both local and global attention with (2) an efficient sub-pixel CNN decoder for better speed and accuracy. Moreover, by incorporating all joints from different benchmarks into a unified skeletal representation, we train robust methods that learn from multiple datasets simultaneously and perform well across a range of scenarios -- including pose variations, lighting conditions, and occlusions. Experiments on six benchmarks demonstrate that the proposed methods significantly outperform state-of-the-art methods while improving computational efficiency. EViTPose exhibits a significant decrease in computational complexity (30% to 44% less in GFLOPs) with a minimal drop of accuracy (0% to 3.5% less), and UniTransPose achieves accuracy improvements ranging from 0.9% to 43.8% across these benchmarks.

Medical education faces challenges in scalability, accessibility, and consistency, particularly in clinical skills training for physician-patient communication. Traditional simulation-based learning, while effective, is resource-intensive, difficult to schedule, and often highly variable in feedback quality. Through a collaboration between AI, learning science, and medical education experts, we co-developed MedSimAI, an AI-powered simulation platform that enables deliberate practice, self-regulated learning (SRL), and automated assessment through interactive patient encounters. Leveraging large language models (LLMs), MedSimAI generates realistic clinical interactions and provides immediate, structured feedback using established medical evaluation frameworks such as the Master Interview Rating Scale (MIRS). In a pilot study with 104 first-year medical students, we examined engagement, conversation patterns, and user perceptions. Students found MedSimAI beneficial for repeated, realistic patient-history practice. Conversation analysis revealed that certain higher-order skills were often overlooked, though students generally performed systematic histories and empathic listening. By integrating unlimited practice opportunities, real-time AI assessment, and SRL principles, MedSimAI addresses key limitations of traditional simulation-based training, making high-quality clinical education more accessible and scalable.

-- Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the algorithm. Backpropagation is the most popular method of training artificial neural networks. However, while artificial neural networks are inspired by biological nervous systems, it has long been observed that backpropagation is not biologically plausible [1]-[3]. Several biologically plausible alternatives to backpropagation have been proposed in the literature, among them so-called energy-based learning algorithms [4]- [11]. These algorithms apply to energy-based models, which come equipped with some generalized notion of energy, and associate to each input a minimum of this energy. The basic idea is to probe the system in two states, one free and one clamped, or dictated by the training data, and use the energy difference between these states as a cost function. An iterative procedure is then applied to minimise this cost function. Several clamping mechanisms and iterative procedures have been defined, among them Contrastive Learning [4], [5], [12], Equilibrium Propagation [7], Coupled Learning [9] and Temporal Contrastive Learning [13]. These algorithms all resemble gradient descent, where the gradient of the cost function is replaced by a gradient-like quantity which may be computed in a distributed manner across a network. The energy-based learning paradigm is particularly suited to learning in analog electronic devices, as they have a natural notion of generalized energy: the heat dissipated by electrical resistance (in this case, a power rather than energy). M. A. Huijzer, B. Besselink, and H.J. van Waarde are with the Bernoulli Institute for Mathematics, Computer Science, and Artificial Intelligence, University of Groningen, Groningen, The Netherlands; email: m.a.huijzer@rug.nl; Chaffey was with the Control Group, Department of Engineering, University of Cambridge, UK, and is now with the School of Electrical and Computer Engineering, University of Sydney, Australia; email: thomas.chaffey@sydney.edu.au. This is, in part, due to the ability of analog circuits to perform inference many times faster than conventional neural networks [20]-[22].

Multitask Representation Learning (MRL) has emerged as a prevalent technique to improve sample efficiency in Reinforcement Learning (RL). Empirical studies have found that training agents on multiple tasks simultaneously within online and transfer learning environments can greatly improve efficiency. Despite its popularity, a comprehensive theoretical framework that elucidates its operational efficacy remains incomplete. Prior analyses have predominantly assumed that agents either possess a pre-known representation function or utilize functions from a linear class, where both are impractical. The complexity of real-world applications typically requires the use of sophisticated, non-linear functions such as neural networks as representation function, which are not pre-existing but must be learned. Our work tries to fill the gap by extending the analysis to \textit{unknown non-linear} representations, giving a comprehensive analysis for its mechanism in online and transfer learning setting. We consider the setting that an agent simultaneously playing $M$ contextual bandits (or MDPs), developing a shared representation function $\phi$ from a non-linear function class $\Phi$ using our novel Generalized Functional Upper Confidence Bound algorithm (GFUCB). We formally prove that this approach yields a regret upper bound that outperforms the lower bound associated with learning $M$ separate tasks, marking the first demonstration of MRL's efficacy in a general function class. This framework also explains the contribution of representations to transfer learning when faced with new, yet related tasks, and identifies key conditions for successful transfer. Empirical experiments further corroborate our theoretical findings.

-- Reinforcement learning (RL) has achieved remarkable success in various robotic tasks; however, its deployment in real-world scenarios, particularly in contact-rich environments, often overlooks critical safety and stability aspects. Policies without passivity guarantees can result in system instability, posing risks to robots, their environments, and human operators. In this work, we investigate the limitations of traditional RL policies when deployed in contact-rich tasks and explore the combination of energy-based passive control with safe RL in both training and deployment to answer these challenges. Firstly, we introduce energy-based constraints in our safe RL formulation to train passivity-aware RL agents. Secondly, we add a passivity filter on the agent output for passivity-ensured control during deployment. We conduct comparative studies on a contact-rich robotic maze exploration task, evaluating the effects of learning passivity-aware policies and the importance of passivity-ensured control. The experiments demonstrate that a passivity-agnostic RL policy easily violates energy constraints in deployment, even though it achieves high task completion in training. The results show that our proposed approach guarantees control stability through passivity filtering and improves the energy efficiency through passivity-aware training. A video of real-world experiments is available as supplementary material. I. INTRODUCTION In recent years, RL has earned increasing attention and success in addressing complex decision-making and control problems, especially in robotic applications [1]. From manipulation tasks to autonomous navigation, RL offers the potential to achieve unprecedented performance by learning optimal control policies.

Facial expressions are vital in human communication and significantly influence outcomes in human-robot interaction (HRI), such as likeability, trust, and companionship. However, current methods for generating robotic facial expressions are often labor-intensive, lack adaptability across contexts and platforms, and have limited expressive ranges--leading to repetitive behaviors that reduce interaction quality, particularly in long-term scenarios. We introduce Xpress, a system that leverages language models (LMs) to dynamically generate context-aware facial expressions for robots through a three-phase process: encoding temporal flow, conditioning expressions on context, and generating facial expression code. We demonstrated Xpress as a proof-of-concept through two user studies (n=15x2) and a case study with children and parents (n=13), in storytelling and conversational scenarios to assess the system's context-awareness, expressiveness, and dynamism. Results demonstrate Xpress's ability to dynamically produce expressive and contextually appropriate facial expressions, highlighting its versatility and potential in HRI applications.

The introduction of Large Language Models (LLMs) has significantly impacted various fields, including education, for example, by enabling the creation of personalized learning materials. However, their use in Systematic Reviews (SRs) reveals limitations such as restricted access to specialized vocabularies, lack of domain-specific reasoning, and a tendency to generate inaccurate information. Existing SR tools often rely on traditional NLP methods and fail to address these issues adequately. To overcome these challenges, we developed the ``NeuroLit Navigator,'' a system that combines domain-specific LLMs with structured knowledge sources like Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). This integration enhances query formulation, expands search vocabularies, and deepens search scopes, enabling more precise searches. Deployed in multiple universities and tested by over a dozen librarians, the NeuroLit Navigator has reduced the time required for initial literature searches by 90\%. Despite this efficiency, the initial set of articles retrieved can vary in relevance and quality. Nonetheless, the system has greatly improved the reproducibility of search results, demonstrating its potential to support librarians in the SR process.

In recent years, online lecture videos have become an increasingly popular resource for acquiring new knowledge. Systems capable of effectively understanding/indexing lecture videos are thus highly desirable, enabling downstream tasks like question answering to help users efficiently locate specific information within videos. This work proposes PreMind, a novel multi-agent multimodal framework that leverages various large models for advanced understanding/indexing of presentation-style videos. PreMind first segments videos into slide-presentation segments using a Vision-Language Model (VLM) to enhance modern shot-detection techniques. Each segment is then analyzed to generate multimodal indexes through three key steps: (1) extracting slide visual content, (2) transcribing speech narratives, and (3) consolidating these visual and speech contents into an integrated understanding. Three innovative mechanisms are also proposed to improve performance: leveraging prior lecture knowledge to refine visual understanding, detecting/correcting speech transcription errors using a VLM, and utilizing a critic agent for dynamic iterative self-reflection in vision analysis. Compared to traditional video indexing methods, PreMind captures rich, reliable multimodal information, allowing users to search for details like abbreviations shown only on slides. Systematic evaluations on the public LPM dataset and an internal enterprise dataset are conducted to validate PreMind's effectiveness, supported by detailed analyses.