Author

Hung-Yi Lee

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Preprint Sep 2026

When Capabilities Fail to Compose: Diagnosing the Compositionality Gap in Large Audio-Language Models

Large audio-language models (LALMs) perform strongly on individual audio tasks, but whether these capabilities can be reliably composed remains underexplored. We conduct a controlled diagnostic study of capability composition in LALMs, requiring models to integrate audio-attribute recognition, cue-conditioned segment s...

Chien-Feng Liu, Chih-Kai Yang, Bo-Han Feng et al. · 0 citations

Preprint Sep 2026

AudioICL-Bench: A Benchmark for Large Audio Language Model In-Context Learning

AudioICL-Bench is introduced, a diagnostic benchmark whose per-episode rules are resampled so that no correct answer is recoverable from prior knowledge, and its nine tasks are organized along two axes that separate what must be learned from demonstrations from what must be perceived in the signal, enabling failures to...

Jia-Hung Chen, Yi-Cheng Lin, Kai-Wei Chang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Tri-PvP: Exposing Modality Bias in Omni-Modal Large Language Models through Perceptual-Propositional Evidence Conflicts

Tri-PvP, an 8,000-sample tri-modal conflict benchmark crossing vision, audio, and text, is introduced, revealing a systematic asymmetry in evidence-form bias: models exhibit a stronger bias toward perceptual signal in vision but propositional in audio.

Yen-Ting Piao, Shu-Yun Chen, Chin-Hui Chu et al. · 0 citations

Preprint Jul 2026

Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models

IAAN, Identifying and Amplifying Acoustic Neurons, a training-free and label-free method that scores each feed-forward neuron in the audio encoder by contrasting its activation on the real waveform with that on a noise reference lacking the real audio's acoustic information, improves average accuracy and improves a mod...

Yu-Han Huang, Chih-Kai Yang, Ke-Han Lu et al. · 0 citations