arXiv:2509. 22363v4 Announce Type: replace Abstract: Large Audio Language Models (LALMs) integrate audio encoders with pretrained Large Language Models to perform complex multimodal reasoning tasks.
AU-Harness is an open-source toolkit designed to streamline and standardize the evaluation of Audio Language Models (LALMs). It addresses three key limitations of existing frameworks: slow processing pipelines, lack of multi-turn dialogue support, and absence of a scalable, unified evaluation system. The toolkit achieves a 77% reduction in evaluation time, enabling large-scale studies and providing standardized protocols for fair model comparison and in-depth analysis of audio reasoning capabilities.
AdaLoop is a lightweight recurrent module that adaptively determines how many latent refinement steps are needed for audio–question pairs, allowing deeper reasoning only when necessary. It shares a transformer block that iterates over the audio representation guided by the question, with a learned halting mechanism that exits the loop once the representation is ready. Adding fewer than 3 % of the base model’s parameters, AdaLoop improves average accuracy by 2.9 to 3.8 points across three distinct models, especially on perception-heavy subtasks.
UniAE-MoE is a unified audio encoder that uses a Mixture-of-Experts architecture to model cross‑domain audio representations. It integrates encoder components from Qwen2‑Audio and Audio‑Flamingo 3, enhances them with SwiGLU and shared experts, and applies a two‑stage instruction‑tuning strategy along with task‑specific data scaling. The model achieves state‑of‑the‑art results on the XARES‑LLM benchmark (0.802) and tops the Interspeech 2026 Audio Encoder Capability Challenge, demonstrating strong generalization across speech, music, and general audio tasks.
The paper introduces TORUS, a closed‑loop benchmark designed to evaluate unified audio models on their ability to understand the audio they generate or edit. TORUS contains 1,053 questions across 21 tasks and 8 audio domain settings, covering all three audio types. The authors evaluate both unified models and cascades of specialized models, finding that the best unified models answer only about 52.4% of questions about their own outputs, while GPT‑Audio achieves 53.6%.
arXiv:2609.23589v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are increasingly used for a broader range of audio reasoning tasks. These models typically incorporate audio repr...
arXiv:2606. 05121v1 Announce Type: cross Abstract: Audio is an inherently interactive modality, yet today's Large Audio Language Models (LALMs) are offline, and streaming audio models each handle only a single task such as streaming ASR or voice chatting.
arXiv:2606. 07643v1 Announce Type: cross Abstract: Recent advances in Omni-Multimodal Large Language Models (Omni-MLLMs) have enabled strong integration of vision, audio, and language.
ReasonAudio is a new benchmark designed to evaluate reasoning capabilities in text‑audio retrieval, addressing the gap left by existing semantic‑matching focused datasets. It tests four logical abilities—negation, temporal order, sound co‑occurrence, and sound duration—across five synthetic subtasks (1,000 queries over 10,000 composite clips) and one natural subtask (100 queries over 1,000 real‑world clips). Evaluation of 11 state‑of‑the‑art systems shows significant limitations, with the best model, OmniEmbed‑7B, scoring only 20.7 overall and 53.8% in a controlled setting, compared to 70.6% for its generative backbone and 95.6% for humans.
arXiv:2610.02181v1 Announce Type: new Abstract: We present OmniSeek, an agentic framework that transforms an Omni Large Language Model (Omni-LLM) into an active, multi-turn reasoning agent with nativ...
arXiv:2606. 11260v1 Announce Type: cross Abstract: Humans process rich auditory environments through tightly integrated cognitive capabilities such as audio perception, audio reasoning, and memory.
arXiv:2606. 14591v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have shown strong performance on a wide range of audio understanding tasks, yet they still struggle with complex audio reasoning.