{
  "id": 1229,
  "url": "https://arxiv.org/abs/2606.11260v1",
  "title": "RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark",
  "summary": "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",
  "authors": "Hongyu Jin, Siyi Wang, Yang Xiao, Jiaheng Dong, Shihong Tan, Kaiyuan peng et al.",
  "category": "research",
  "topics": "transparency,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-09T02:38:17.000Z",
  "fetched_at": "2026-07-14T14:15:07.844Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1229",
  "original_url": "https://arxiv.org/abs/2606.11260v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}