The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While multilingual benchmarks have expanded, both in size and languages, many rely on translations of English datasets, failing to capture cultural nuances. In this work, we propose Kaleidoscope, as the most comprehensive exam benchmark to date for the multilingual evaluation of vision-language models. Kaleidoscope is a large-scale, in-language multimodal benchmark designed to evaluate VLMs across diverse languages and visual inputs. Kaleidoscope covers 18 languages and 14 different subjects, amounting to a total of 20,911 multiple-choice questions. Built through an open science collaboration with a diverse group of researchers worldwide, Kaleidoscope ensures linguistic and cultural authenticity. We evaluate top-performing multilingual vision-language models and find that they perform poorly on low-resource languages and in complex multimodal scenarios. Our results highlight the need for progress on culturally inclusive multimodal evaluation frameworks.

Generative AI (GenAI) models have become vital across industries, yet current evaluation methods have not adapted to their widespread use. Traditional evaluations often rely on benchmarks and fixed datasets, frequently failing to reflect real-world performance, which creates a gap between lab-tested outcomes and practical applications. This white paper proposes a comprehensive framework for how we should evaluate real-world GenAI systems, emphasizing diverse, evolving inputs and holistic, dynamic, and ongoing assessment approaches. The paper offers guidance for practitioners on how to design evaluation methods that accurately reflect real-time capabilities, and provides policymakers with recommendations for crafting GenAI policies focused on societal impacts, rather than fixed performance numbers or parameter sizes. We advocate for holistic frameworks that integrate performance, fairness, and ethics and the use of continuous, outcome-oriented methods that combine human and automated assessments while also being transparent to foster trust among stakeholders. Implementing these strategies ensures GenAI models are not only technically proficient but also ethically responsible and impactful.

We introduce Ring-topology Decentralized Federated Learning (RDFL) for distributed model training, aiming to avoid the inherent risks of centralized failure in server-based FL. However, RDFL faces the challenge of low information-sharing efficiency due to the point-to-point communication manner when handling inherent data heterogeneity. Existing studies to mitigate data heterogeneity focus on personalized optimization of models, ignoring that the lack of shared information constraints can lead to large differences among models, weakening the benefits of collaborative learning. To tackle these challenges, we propose a Divide-and-conquer RDFL framework (DRDFL) that uses a feature generation model to extract personalized information and invariant shared knowledge from the underlying data distribution, ensuring both effective personalization and strong generalization. Specifically, we design a \textit{PersonaNet} module that encourages class-specific feature representations to follow a Gaussian mixture distribution, facilitating the learning of discriminative latent representations tailored to local data distributions. Meanwhile, the \textit{Learngene} module is introduced to encapsulate shared knowledge through an adversarial classifier to align latent representations and extract globally invariant information. Extensive experiments demonstrate that DRDFL outperforms state-of-the-art methods in various data heterogeneity settings.

Figure 1 shows the heatmap depicting performance of 16 combinations of KD for 14 metrics. For brevity, we also report the mean of all 14 metrics and group-wise metrics (N-gram metrics, embedding based metrics and Oracle-LLM metrics) in Figure 1. We systematically analyze the results and organize our findings as impact of (i) SFT (RQ1) (ii) SFT on teacher and student (RQ1) (iii) vocabulary and KD algorithm (RQ2) (iv) performance metrics groups (RQ3) 3.1 Impact of SFT We organize analysis with vocabulary as starting point: 3.1.1 Llama Consider the bar plots which depicts Llama as the teacher in Figure 1 i.e., the bars denoting (Llama, V anilla KD) and (Llama, DSKD). We observe that SFT of teacher/student/both results in improvement of performance irrespective of the training algorithm (first bar vs the subsequent 3 bars). The improvement is statistically significant (refer to H S train, H T train, H T,S trainin Table 3). Here, we observe that NH is rejected for most metrics (13 out of 14 for V anilla KD and 8 or 9 out of 14 for DSKD) with SFT of student or teacher or both for Llama vocabulary, irrespective of algorithms.

This article presents the results of a pilot study involving the reception of a fictional short story translated from English into Dutch under four conditions: machine translation (MT), post-editing (PE), human translation (HT) and original source text (ST). The aim is to understand how creativity and errors in different translation modalities affect readers, specifically regarding cognitive load. Eight participants filled in a questionnaire, read a story using an eye-tracker, and conducted a retrospective think-aloud (RTA) interview. The results show that units of creative potential (UCP) increase cognitive load and that this effect is highest for HT and lowest for MT; no effect of error was observed. Triangulating the data with RTAs leads us to hypothesize that the higher cognitive load in UCPs is linked to increases in reader enjoyment and immersion. The effect of translation creativity on cognitive load in different translation modalities at word-level is novel and opens up new avenues for further research. All the code and data are available at https://github.com/INCREC/Pilot_to_MT_or_not_to_MT

Dialogue data has been a key source for understanding learning processes, offering critical insights into how students engage in collaborative discussions and how these interactions shape their knowledge construction. The advent of Large Language Models (LLMs) has introduced promising opportunities for advancing qualitative research, particularly in the automated coding of dialogue data. However, the inherent contextual complexity of dialogue presents unique challenges for these models, especially in understanding and interpreting complex contextual information. This study addresses these challenges by developing a novel LLM-assisted automated coding approach for dialogue data. The novelty of our proposed framework is threefold: 1) We predict the code for an utterance based on dialogue-specific characteristics -- communicative acts and communicative events -- using separate prompts following the role prompts and chain-of-thoughts methods; 2) We engaged multiple LLMs including GPT-4-turbo, GPT-4o, DeepSeek in collaborative code prediction; 3) We leveraged the interrelation between events and acts to implement consistency checking using GPT-4o. In particular, our contextual consistency checking provided a substantial accuracy improvement. We also found the accuracy of act predictions was consistently higher than that of event predictions. This study contributes a new methodological framework for enhancing the precision of automated coding of dialogue data as well as offers a scalable solution for addressing the contextual challenges inherent in dialogue analysis.

Large language models and autonomous AI agents have evolved rapidly, resulting in a diverse array of evaluation benchmarks, frameworks, and collaboration protocols. However, the landscape remains fragmented and lacks a unified taxonomy or comprehensive survey. Therefore, we present a side-by-side comparison of benchmarks developed between 2019 and 2025 that evaluate these models and agents across multiple domains. In addition, we propose a taxonomy of approximately 60 benchmarks that cover general and academic knowledge reasoning, mathematical problem-solving, code generation and software engineering, factual grounding and retrieval, domain-specific evaluations, multimodal and embodied tasks, task orchestration, and interactive assessments. Furthermore, we review AI-agent frameworks introduced between 2023 and 2025 that integrate large language models with modular toolkits to enable autonomous decision-making and multi-step reasoning. Moreover, we present real-world applications of autonomous AI agents in materials science, biomedical research, academic ideation, software engineering, synthetic data generation, chemical reasoning, mathematical problem-solving, geographic information systems, multimedia, healthcare, and finance. We then survey key agent-to-agent collaboration protocols, namely the Agent Communication Protocol (ACP), the Model Context Protocol (MCP), and the Agent-to-Agent Protocol (A2A). Finally, we discuss recommendations for future research, focusing on advanced reasoning strategies, failure modes in multi-agent LLM systems, automated scientific discovery, dynamic tool integration via reinforcement learning, integrated search capabilities, and security vulnerabilities in agent protocols.

Generative Artificial Intelligence (GenAI) tools such as ChatGPT are emerging as a revolutionary tool in education that brings both positive aspects and challenges for educators and students, reshaping how learning and teaching are approached. This study aims to identify and evaluate the key competencies students need to effectively engage with GenAI in education and to provide strategies for lecturers to integrate GenAI into teaching practices. The study applied a mixed method approach with a combination of a literature review and a quantitative survey involving 130 students from South Asia and Europe to obtain its findings. The literature review identified 14 essential student skills for GenAI engagement, with AI literacy, critical thinking, and ethical AI practices emerging as the most critical. The student survey revealed gaps in prompt engineering, bias awareness, and AI output management. In our study of lecturer strategies, we identified six key areas, with GenAI Integration and Curriculum Design being the most emphasised. Our findings highlight the importance of incorporating GenAI into education. While literature prioritized ethics and policy development, students favour hands-on, project-based learning and practical AI applications. To foster inclusive and responsible GenAI adoption, institutions should ensure equitable access to GenAI tools, establish clear academic integrity policies, and advocate for global GenAI research initiatives.

- The field of natural language processing (NLP) has dramatically expanded within the last decade. Many human-being applications are conducted daily via NLP tasks, starting from machine translation, speech recognition, text generation and recommendations, Part-of-Speech tagging (POS), and Named-Entity Recognition (NER). However, low-resourced languages, such as the Central-Kurdish language (CKL), mainly remain unexamined due to shortage of necessary resources to support their development. The POS tagging task is the base of other NLP tasks; for example, the POS tag set has been used to standardized languages to provide the relationship between words among the sentences, followed by machine translation and text recommendation. Specifically, for the CKL, most of the utilized or provided POS tagsets are neither standardized nor comprehensive. To this end, this study presented an accurate and comprehensive POS tagset for the CKL to provide better performance of the Kurdish NLP tasks. The article also collected most of the POS tags from different studies as well as from Kurdish linguistic experts to standardized part-of-speech tags. The proposed POS tagset is designed to annotate a large CKL corpus and support Kurdish NLP tasks. The initial investigations of this study via comparison with the Universal Dependencies framework for standard languages, show that the proposed POS tagset can streamline or correct sentences more accurately for Kurdish NLP tasks.

This paper investigates the knowledge of language models from the perspective of Bayesian epistemology. We explore how language models adjust their confidence and responses when presented with evidence with varying levels of informativeness and reliability. To study these properties, we create a dataset with various types of evidence and analyze language models' responses and confidence using verbalized confidence, token probability, and sampling. We observed that language models do not consistently follow Bayesian epistemology: language models follow the Bayesian confirmation assumption well with true evidence but fail to adhere to other Bayesian assumptions when encountering different evidence types. Also, we demonstrated that language models can exhibit high confidence when given strong evidence, but this does not always guarantee high accuracy. Our analysis also reveals that language models are biased toward golden evidence and show varying performance depending on the degree of irrelevance, helping explain why they deviate from Bayesian assumptions.