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Evidence record 1229 · automatically gathered

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

Humans process rich auditory environments through tightly integrated cognitive capabilities such as audio perception, audio reasoning, and memory. Despite recent progress in large audio-language models (LALMs) across speech understanding and multimodal audio reasoning, current evaluation paradigms remain largely task- or modality-centric, focusing on end performance while overlooking underlying auditory cognitive behaviours. This reveals a fundamental gap between how auditory cognition is unders

Record details

Published: 9 June 2026
Source: arXiv
Category: Research
Topics: Transparency · Environment
Retrieved: 14 July 2026

source-onlyevidence status

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How to cite this record

ethics.ai (9 June 2026), “RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark,” evidence record 1229, https://ethics.ai/record/1229 (originally published by arXiv).

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