We present ArA-DF 2026, the first shared task for Arabic speech deepfake detection, held at ArabicNLP-2026. Unlike existing benchmarks that conflate all distribution shift into a single evaluation set, ArA-DF 2026 separates dialect generalization (Track 1: unseen Arabic dialects and speakers) from acoustic robustness (Track 2: codec compression, noise, reverberation, re-encoding). We release 43,500 labeled training utterances spanning MSA and regional dialects, plus blind evaluation sets of 144,210 (Track 1) and 127,746 (Track 2) utterances, ranking systems by EER. Fourteen teams competed on Track 1 and twelve on Track 2, with eight system description papers submitted. The best submissions cut EER from 14.54% to 0.87% (Track 1) and from 27.11% to 0.66% (Track 2). Across independent submissions, three findings recur: augmentation and data curation move performance far more than architecture; simpler pooling heads often outperform graph-attention back-ends once augmentation is applied; and external synthetic Arabic speech with mismatched generation artifacts hurts rather than helps. Residual errors concentrate on short utterances and on spoofed audio misclassified as bona fide.

We present Octopus, a first family of modular speech-language models designed for Arabic-English ASR, dialect identification, and speech translation. Built on Whisper-V3 and enhanced with large language models like ALLaM, LLaMA, and DeepSeek, Octopus bridges speech and text through a lightweight projection layer and Q-Former. To broaden its scope beyond speech, Octopus integrates BEATs, a general-purpose audio encoder allowing it to understand both linguistic and acoustic events. Despite its simplicity, this dual-encoder design supports robust performance across multilingual and code-switched scenarios. We also introduce TinyOctopus, a distilled variant using smaller models (Distil-Whisper + LLaMA3-1B / DeepSeek-1.5B), achieving competitive results with just a fraction of the parameters. Fine-tuning on synthetic code-switched data further boosts its performance. Octopus demonstrates the power of compact, extensible architectures in Arabic-centric speech modeling and sets the stage for unified multilingual audio-language understanding.