Author

Jia-Heng Dong

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#artificial intelligence Jun 2026

RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark

RAIL is introduced, a human-centric benchmark grounded in the Cattell--Horn--Carroll (CHC) framework, which formalises auditory cognition into five core capabilities, and develops them into structured evaluation tasks with principled data curation and human-aligned evaluation protocols.

Hong-Yu Jin, Siyi Wang, Yang Xiao et al. · arXiv.org · 3 citations

#artificial intelligence Preprint Oct 2026

DuplexAct: How Interactive Are Frontier Full-Duplex Spoken Language Models?

This work introduces DuplexAct, a benchmark for evaluating interaction action taking in E2E FD-SLMs and defines a three-level action taxonomy spanning eight interaction actions and 24 corresponding interaction conditions, and develops action-specific evaluation metrics for both.

Jia-Heng Dong, Shi-Hong Tan, Gong-Ping Huang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Listen Then Reason: Perception-Grounded Test-Time Reinforcement Learning for Large Audio-Language Models

Perception-Grounded TTRL (PG-TTRL) is proposed, which aligns label-free test-time optimisation with perceptually grounded reasoning, encouraging the model to structure its reasoning more strongly on the audio input, showing the value of perceptual-grounding optimisation for test-time audio reasoning.

Jia-Heng Dong, Xiao-Feng Yu, Jean Honorio et al. · 0 citations