Speech conveys complementary information through its linguistic content and acoustic characteristics. Speech-related tasks can exploit either source of information or combine them to improve performance. In this paper, we present Lynx_Id and Lynx_In, our systems for the NADI 2026 Spoken Dialect Identification and Intent Recognition tasks, respectively. Both systems build on an automatic speech recognition (ASR) model. Lynx_Id is a late-fusion system for dialect identification. It combines internal representations from the ASR encoder with predictions derived from the ASR transcript. Lynx_In is a two-branch text-based system that uses ASR transcriptions for intent recognition. One branch predicts the intent among the known classes, while the second acts as a gate to detect out-of-scope utterances. Lynx_Id achieved 53.53% accuracy and ranked third in the Spoken Dialect Identification task, while Lynx_In achieved a weighted F1 score of 76.86% and ranked first in the Intent Recognition task.

As large language models (LLMs) become increasingly integrated into daily life, ensuring their cultural sensitivity and inclusivity is paramount. We introduce PALM, a year-long community-driven project covering all 22 Arab countries. The dataset contains instruction–response pairs in both Modern Standard Arabic (MSA) and dialectal Arabic (DA), spanning 20 diverse topics. Built by a team of 44 researchers across the Arab world—each an author of this paper—PALM offers a broad, inclusive perspective. We use PALM to evaluate the cultural and dialectal capabilities of several frontier LLMs, revealing notable limitations: while closed-source LLMs generally perform strongly, they still exhibit flaws, and smaller open-source models face greater challenges. Furthermore, certain countries (e.g., Egypt, the UAE) appear better represented than others (e.g., Iraq, Mauritania, Yemen). Our annotation guidelines, code, and data are publicly available for reproducibility. More information about PALM is available on our project page: https://github.com/UBC-NLP/palm.