Evaluation of socially unsafe content in spoken dialogues remains text-centric, missing prosody and transcription failures. We present LALM-as-a-Judge, which includes an open benchmark of 24,000 multi-turn spoken dialogues with one localized unsafe turn, generated out of 8 socially unsafe categories and 5 severity levels. We evaluate 6 large audio-language models (LALMs) as judges, open and closed-source, in text-only, audio-only, and multimodal setups by their sensitivity, severity-order specificity, and turn-position bias for socially harmful content in the dialogue. Results show that audio contributes non-lexical evidence beyond transcript semantics and that multimodal gains are not universal but can be text-anchored, balanced, conservative, and interfering, which we link to the audio pathway bottlenecks and fusion limits. We position the benchmark as diagnostic and derive practitioner guidance for model, modality, and prompts choices.
CLASH: Counterfactual Auditing of Lexical and Prosodic Reliance in Spoken Sarcasm Detection
Imperial College London（伦敦帝国理工学院）；New York University（纽约大学）；Technical University of Munich（慕尼黑工业大学）；Tencent Inc.（腾讯公司）；Wuhan University（武汉大学）；Nankai University（南开大学）
Can large audio language models understand child stuttering speech? speech summarization, and source separation
Department of Computer Science, University of Florida, Gainesville, FL, USA（佛罗里达大学计算机科学系）；Department of Computer Science, Lebanese American University（黎巴嫩美国大学计算机科学系）
Beyond Accuracy: ARIA-Rubrics for Evaluating Audio Reasoning in Large Audio Language Models
Imperial College London（帝国理工学院）；Technische Universität München（慕尼黑工业大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shanghai Jiao Tong University（上海交通大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
ORCA: Open-ended Response Correctness Assessment for Audio Question Answering
Brno University of Technology（布尔诺科技大学）；Universidad Autónoma de Madrid（马德里自治大学）；University of Buenos Aires（布宜诺斯艾利斯大学）；Tufts University（塔夫茨大学）；University of Maryland（马里兰大学）
Words at Play: Benchmarking Audio Pun Understanding in Large Audio-Language Models
School of Computer Science, University of Auckland（奥克兰大学计算机科学学院）；Singapore University of Technology and Design（新加坡科技设计大学）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学玛丽女王学院电子工程与计算机科学学院）；School of Statistics and Data Science, Nankai University（南开大学统计与数据科学学院）
Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models
Center for Language and Speech Processing (CLSP)（语言与语音处理中心）；Johns Hopkins University（约翰霍普金斯大学）；Group on Language, Audio & Music (GLAM)（语言、音频与音乐小组）；Imperial College London（伦敦帝国学院）
MSU-Bench: Towards Speaker-Centric Understanding in Conversational Multi-Speaker Scenarios
School of Software, Northwestern Polytechnical University（西北工业大学软件学院）；School of Intelligent Science and Technology, Nanjing University（南京大学智能科学与技术学院）；Shenzhen Loop Area Institute（深圳河套学院）；Base Model, Li Auto（理想汽车基础模型）
