Large Language Models (LLMs) are increasingly used to answer everyday questions, yet their performance on culturally grounded and dialectal content remains limited across languages and their varieties. We propose a comprehensive method that (i) translates Modern Standard Arabic (MSA) multiple-choice questions (MCQs) into English and several Arabic dialects, (ii) converts them into open-ended questions (OEQs), (iii) benchmarks a range of zero-shot and fine-tuned LLMs under both MCQ and OEQ settings, and (iv) generates chain-of-thought (CoT) rationales to fine-tune models for step-by-step reasoning. Using this method, we extend an existing dataset in which QAs are parallelly aligned across language varieties, making it, to our knowledge, the first of its kind. A large portion of the resulting test set is further validated through targeted human annotation and native-speaker post-editing. We conduct extensive experiments with both open and closed models. Our findings show that (i) models underperform on Arabic dialects, showing persistent gaps in culturally grounded and dialect-specific knowledge; (ii) Arabic-centric models perform well on MCQs but struggle with OEQs; and (iii) CoT improves judged correctness while yielding mixed n-gram-based metrics.

Conversational AI is moving beyond isolated text prompts toward sustained, multimodal interaction. In real conversations, users clarify goals, revise requests, interrupt responses, switch topics, and introduce new evidence while expecting systems to preserve context across turns. This makes multi-turn dialogue a distinct challenge requiring systems to maintain and update memory, ground responses across modalities, tools, and external knowledge, and adapt across languages and cultures. This study reviews ***multi-turn conversational AI*** across text-only dialogue, AudioLLMs and speech-native systems, multimodal and omni-modal systems, and tool-augmented agents. We organize the literature around *datasets* and *benchmarks*, *modeling paradigms*, *training strategies*, *evaluation setups*, and *cross-cutting challenges*. Our analysis shows that support for multiple modalities has advanced faster than the ability to sustain coherent interaction across a session. Despite stronger capabilities to perceive, speak, and act across modalities, current systems still struggle with persistent memory, cross-turn grounding, full-duplex interaction, robust evaluation, and cultural alignment. We conclude with a research agenda for systems that can remember, revise, ground, speak, listen, act, and adapt across turns, modalities, and cultures. ([https://github.com/faiza-sfa/multiturn-conversational-ai-survey](https://github.com/faiza-sfa/multiturn-conversational-ai-survey))

Audio large language models (AudioLLMs) enable instruction following over speech and general audio, but progress is limited by the scarcity of *diverse, conversational, and instruction-aligned* speech-text data. This gap is particularly pronounced for persona-grounded and dialectal interactions, where collecting real multi-speaker recordings remains costly and slow. We introduce **MENASpeechBank**, a reference speech bank comprising **∼18K high-quality utterances** from **124 speakers** spanning multiple MENA countries, covering English, Modern Standard Arabic (MSA), and regional Arabic varieties. We develop a controllable data pipeline that *(i)* constructs persona profiles enriched with World Values Survey (WVS) inspired attributes, *(ii)* defines a taxonomy driven ∼5Kconversational scenarios, *(iii)* matches personas to scenarios via semantic similarity, *(iv)* generates ∼417K role-play conversations with an LLM where the user speaks as the persona and the assistant behaves as a helpful agent, and *(v)* produces speaker-conditioned user-turn audio (synthetic) from reference recordings to preserve speaker diversity. We evaluate synthetic and human recorded conversations and provide an analysis. We will make the MENASpeechBank available for the community.([https://huggingface.co/datasets/QCRI/MenaSpeechBank](https://huggingface.co/datasets/QCRI/MenaSpeechBank))

We present an overview of the **ImageEval 2026** shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) **AynVQA**, covering spoken visual question answering and image-grounded hallucination detection in English and Modern Standard Arabic (MSA), and (ii) **CRAI-Bench**, evaluating the cultural accuracy of text-to-image generation. A total of 14 teams participated in the test phase, with 12 teams submitting system description papers. Participating systems used a range of approaches, including zero-shot prompting, fine-tuning of vision-language models, speech-recognition pipelines, ensembling, and score calibration. We describe the task setup, datasets, evaluation procedure, and participating systems, and summarize the main results across the different tracks. All datasets and evaluation scripts from the shared task are released to the research community. The shared task highlights challenges in culturally grounded multimodal evaluation, particularly for Arabic speech and image-text reasoning.

We present Aslema, our system for NADI 2026 Shared Task 5, which consists of two subtasks: intent recognition and slot filling. We evaluate four omni LLMs in a zero-shot setting and compare them with fine-tuned models. Our results show that fine-tuning consistently outperforms zero-shot inference. We further explore synthetic data augmentation by using an LLM to generate culturally grounded Tunisian Derja utterances, followed by voice cloning to generate synthetic speech. Incorporating this synthetic data improves performance on both tasks. Our final submitted system, based on Qwen3-Omni-30B and trained with a mixture of original and synthetic data, achieves 86.8% intent accuracy and 34.7 WER on the devtest split. On the official test set it ranks 1st in slot filling (59.5 CoER) and 4th among 5 teams in intent recognition (66.1% accuracy). We release our experimental scripts and the synthetic dataset to support further research.