arXiv Machine Learning
EvoAudio: Recursive Self-Improvement for Audio Understanding
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arXiv AI
Jul 14
MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models
arXiv:2603. 09714v2 Announce Type: replace-cross Abstract: While multi-audio understanding is critical for large audio-language models (LALMs), it remains underexplored.
arXiv Computation and Language
3d ago
Symbiotic Architecture for Post-Hoc Audio Extension of Frozen Language Models
The paper introduces a symbiotic architecture that equips large language models with audio‑understanding abilities without fine‑tuning their weights. It uses an injector module to write audio‑conditioned vectors into the LLM’s key‑value cache, allowing the model to act as an audio language model while keeping the backbone unchanged. The approach improves scalability—since injection cost depends on the injector width—and preserves the LLM’s original text performance, outperforming conventional frozen‑LLM methods and approaching fine‑tuned ALM results on audio tasks.
arXiv AI
Jun 16
EChO-Agent: Evidence Chain Orchestration Agent for Audio Reasoning
arXiv:2606. 15141v1 Announce Type: cross Abstract: While LALMs show promise on audio question answering, they fail to focus on question-relevant segments of audio and provide a clear, checkable reasoning process when dealing with complex audio reasoning.
arXiv AI
Jun 9
Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound
arXiv:2502. 16584v2 Announce Type: replace-cross Abstract: Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs).
arXiv AI
Sep 10
Audio-Maestro: Enhancing Large Audio-Language Models with Tool-Augmented Reasoning
arXiv:2510.11454v2 Announce Type: replace-cross Abstract: Recent advancements in large multimodal models (LMMs) have shown strong capabilities in audio understanding. However, most systems rely solel...
arXiv AI
Jun 30
ORCA: Open-ended Response Correctness Assessment for Audio Question Answering
arXiv:2512. 09066v2 Announce Type: replace-cross Abstract: Reliable assessment of the abilities of large audio language models (LALMs) is essential to advancing the state of the art.