Several emerging post-Bayesian methods target a probability distribution for which an entropy-regularised variational objective is minimised. This increased flexibility introduces a computational challenge, as one loses access to an explicit unnormalised density for the target. To mitigate this difficulty, we introduce a novel measure of suboptimality called 'gradient discrepancy', and in particular a 'kernel gradient discrepancy' (KGD) that can be explicitly computed. In the standard Bayesian context, KGD coincides with the kernel Stein discrepancy (KSD), and we obtain a novel charasterisation of KSD as measuring the size of a variational gradient. Outside this familiar setting, KGD enables novel sampling algorithms to be developed and compared, even when unnormalised densities cannot be obtained. To illustrate this point several novel algorithms are proposed, including a natural generalisation of Stein variational gradient descent, with applications to mean-field neural networks and prediction-centric uncertainty quantification presented. On the theoretical side, our principal contribution is to establish sufficient conditions for desirable properties of KGD, such as continuity and convergence control.

Abstract-- Recent research has highlighted the powerful capabilities of imitation learning in robotics. Leveraging generative models, particularly diffusion models, these approaches offer notable advantages such as strong multi-task generalization, effective language conditioning, and high sample efficiency. While their application has been successful in manipulation tasks, their use in legged locomotion remains relatively underexplored, mainly due to compounding errors that affect stability and difficulties in task transition under limited data. Online reinforcement learning (RL) has demonstrated promising results in legged robot control in the past years, which can provide valuable insights to address these challenges. In this work, we propose DMLoco, a diffusion-based framework for quadruped robots that integrates multi-task pretraining with online PPO finetuning to enable language-conditioned control and robust task transitions. Then, the policy is finetuned in simulation to ensure robustness and stable task transition for real-world deployment. By utilizing Denoising Diffusion Implicit Models (DDIM) for efficient sampling and T ensorRT for optimized deployment, our policy runs onboard at 50Hz, offering a scalable and efficient solution for adaptive, language-guided locomotion on resource-constrained robotic platforms.

Powerful artificial intelligence (AI) tools that have emerged in recent years -- including large language models, automated coding assistants, and advanced image and speech generation technologies -- are the result of monumental human achievements. These breakthroughs reflect mastery across multiple technical disciplines and the resolution of significant technological challenges. However, some of the most profound challenges may still lie ahead. These challenges are not purely technical but pertain to the fair and responsible use of AI in ways that genuinely improve the global human condition. This article explores one promising application aligned with that vision: the use of AI tools to facilitate and enhance education, with a specific focus on signal processing (SP). It presents two interrelated perspectives: identifying and addressing technical limitations, and applying AI tools in practice to improve educational experiences. Primers are provided on several core technical issues that arise when using AI in educational settings, including how to ensure fairness and inclusivity, handle hallucinated outputs, and achieve efficient use of resources. These and other considerations -- such as transparency, explainability, and trustworthiness -- are illustrated through the development of an immersive, structured, and reliable "smart textbook." The article serves as a resource for researchers and educators seeking to advance AI's role in engineering education.

The advancement of open-source text-to-image (T2I) models has been hindered by the absence of large-scale, reasoning-focused datasets and comprehensive evaluation benchmarks, resulting in a performance gap compared to leading closed-source systems. To address this challenge, We introduce FLUX-Reason-6M and PRISM-Bench (Precise and Robust Image Synthesis Measurement Benchmark). FLUX-Reason-6M is a massive dataset consisting of 6 million high-quality FLUX-generated images and 20 million bilingual (English and Chinese) descriptions specifically designed to teach complex reasoning. The image are organized according to six key characteristics: Imagination, Entity, Text rendering, Style, Affection, and Composition, and design explicit Generation Chain-of-Thought (GCoT) to provide detailed breakdowns of image generation steps. The whole data curation takes 15,000 A100 GPU days, providing the community with a resource previously unattainable outside of large industrial labs. PRISM-Bench offers a novel evaluation standard with seven distinct tracks, including a formidable Long Text challenge using GCoT. Through carefully designed prompts, it utilizes advanced vision-language models for nuanced human-aligned assessment of prompt-image alignment and image aesthetics. Our extensive evaluation of 19 leading models on PRISM-Bench reveals critical performance gaps and highlights specific areas requiring improvement. Our dataset, benchmark, and evaluation code are released to catalyze the next wave of reasoning-oriented T2I generation. Project page: https://flux-reason-6m.github.io/ .

The EdUKate project combines digital education, linguistics, translation studies, and machine translation to develop multilingual learning materials for Czech primary and secondary schools. Launched through collaboration between a major Czech academic institution and the country's largest educational publisher, the project is aimed at translating up to 9,000 multimodal interactive exercises from Czech into Ukrainian, English, and German for an educational web portal. It emphasizes the development and evaluation of a direct Czech-Ukrainian machine translation system tailored to the educational domain, with special attention to processing formatted content such as XML and PDF and handling technical and scientific terminology. We present findings from an initial survey of Czech teachers regarding the needs of non-Czech-speaking students and describe the system's evaluation and implementation on the web portal. All resulting applications are freely available to students, educators, and researchers.

Providing students with flexible and timely academic support is a challenge at most colleges and universities, leaving many students without help outside scheduled hours. Large language models (LLMs) are promising for bridging this gap, but interactions between students and LLMs are rarely overseen by educators. We developed and studied an LLM-powered course assistant deployed across multiple computer science courses to characterize real-world use and understand pedagogical implications. By Spring 2024, our system had been deployed to approximately 2,000 students across six courses at three institutions. Analysis of the interaction data shows that usage remains strong in the evenings and nights and is higher in introductory courses, indicating that our system helps address temporal support gaps and novice learner needs. We sampled 200 conversations per course for manual annotation: most sampled responses were judged correct and helpful, with a small share unhelpful or erroneous; few responses included dedicated examples. We also examined an inquiry-based learning strategy: only around 11% of sampled conversations contained LLM-generated follow-up questions, which were often ignored by students in advanced courses. A Bloom's taxonomy analysis reveals that current LLM capabilities are limited in generating higher-order cognitive questions. These patterns suggest opportunities for pedagogically oriented LLM-based educational systems and greater educator involvement in configuring prompts, content, and policies.

This innovative practice article reports on the piloting of vibe coding (using natural language to create software applications with AI) for English as a Foreign Language (EFL) education. We developed a human-AI meta-languaging framework with three dimensions: talking to AI (prompt engineering), talking through AI (negotiating authorship), and talking about AI (mental models of AI). Using backward design principles, we created a four-hour workshop where two students designed applications addressing authentic EFL writing challenges. We adopted a case study methodology, collecting data from worksheets and video recordings, think-aloud protocols, screen recordings, and AI-generated images. Contrasting cases showed one student successfully vibe coding a functional application cohering to her intended design, while another encountered technical difficulties with major gaps between intended design and actual functionality. Analysis reveals differences in students' prompt engineering approaches, suggesting different AI mental models and tensions in attributing authorship. We argue that AI functions as a beneficial languaging machine, and that differences in how students talk to, through, and about AI explain vibe coding outcome variations. Findings indicate that effective vibe coding instruction requires explicit meta-languaging scaffolding, teaching structured prompt engineering, facilitating critical authorship discussions, and developing vocabulary for articulating AI mental models.

The release of open-weight large language models (LLMs) creates a tension between advancing accessible research and preventing misuse, such as malicious fine-tuning to elicit harmful content. Current safety measures struggle to preserve the general capabilities of the LLM while resisting a determined adversary with full access to the model's weights and architecture, who can use full-parameter fine-tuning to erase existing safeguards. To address this, we introduce AntiDote, a bi-level optimization procedure for training LLMs to be resistant to such tampering. AntiDote involves an auxiliary adversary hypernetwork that learns to generate malicious Low-Rank Adaptation (LoRA) weights conditioned on the defender model's internal activations. The defender LLM is then trained with an objective to nullify the effect of these adversarial weight additions, forcing it to maintain its safety alignment. We validate this approach against a diverse suite of 52 red-teaming attacks, including jailbreak prompting, latent space manipulation, and direct weight-space attacks. AntiDote is upto 27.4\% more robust against adversarial attacks compared to both tamper-resistance and unlearning baselines. Crucially, this robustness is achieved with a minimal trade-off in utility, incurring a performance degradation of upto less than 0.5\% across capability benchmarks including MMLU, HellaSwag, and GSM8K. Our work offers a practical and compute efficient methodology for building open-weight models where safety is a more integral and resilient property.

This paper presents a personalized lecture concept using educational blocks and its demonstrative application in a new university lecture. Higher education faces daily challenges: deep and specialized knowledge is available from everywhere and accessible to almost everyone. University lecturers of specialized master courses confront the problem that their lectures are either too boring or too complex for the attending students. Additionally, curricula are changing more rapidly than they have in the past 10-30 years. The German education system comprises different educational forms, with universities providing less practical content. Consequently, many university students do not obtain the practical skills they should ideally gain through university lectures. Therefore, in this work, a new lecture concept is proposed based on the extension of the just-in-time teaching paradigm: Personalized and Demand-Based Education. This concept includes: 1) an initial assessment of students' backgrounds, 2) selecting the appropriate educational blocks, and 3) collecting ongoing feedback during the semester. The feedback was gathered via Pingo, ensuring anonymity for the students. Our concept was exemplarily tested in the new lecture "Practical Tools for Control Engineers" at the Karlsruhe Institute of Technology. The initial results indicate that our proposed concept could be beneficial in addressing the current challenges in higher education.

While assistive robots have much potential to help older people with frailty-related needs, there are few in use. There is a gap between what is developed in laboratories and what would be viable in real-world contexts. Through a series of co-design workshops (61 participants across 7 sessions) including those with lived experience of frailty, their carers, and healthcare professionals, we gained a deeper understanding of everyday issues concerning the place of new technologies in their lives. A persona-based approach surfaced emotional, social, and psychological issues. Any assistive solution must be developed in the context of this complex interplay of psychosocial and environmental factors. Our findings, presented as design requirements in direct relation to frailty, can help promote design thinking that addresses people's needs in a more pragmatic way to move assistive robotics closer to real-world use.