@inproceedings{nguyen-etal-2026-au,
title = "{AU}-Harness: An Open-Source Toolkit for Efficient and Unified Evaluation of {A}udio{LLM}s",
author = "Nguyen, Hoang H and
Surapaneni, Sidharth and
Suresh, Akshay Kalkunte and
Mehta, Jash and
Tiwari, Aman and
Bamgbose, Oluwanifemi and
Mahajan, Khyati and
Shah, Jash and
Radhakrishna, Shruthan and
Madhusudhan, Sathwik Tejaswi and
Yadav, Vikas and
Rajeswar, Sai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1338/",
pages = "28360--28380",
ISBN = "979-8-89176-462-0",
abstract = "Large Audio Language Models (LALMs) are rapidly advancing, but evaluating them remains challenging due to inefficient and non-standardized toolkits that limit fair comparison and systematic assessment. Existing evaluation frameworks exhibit three critical limitations: (1) slow and inefficient processing pipeline that bottlenecks large-scale studies, (2) inadequate multi-turn dialogue support, leaving fundamental questions about cross-turn context integration and performance dynamics over extended conversations in LALMs unanswered; and (3) the absence of unified and scalable evaluation framework capable of keeping pace with the rapid growth of both LALMs and audio benchmarks. To address these issues, we introduce **AU-Harness**, an efficient and comprehensive evaluation framework for LALMs. Our empirical studies demonstrate that AU-Harness achieves 77{\%} reduction in evaluation processing time over existing evaluation toolkits, enabling large-scale LALM evaluations previously considered impractical. We provide standardized evaluation protocols and flexible configurations for fair model comparison across diverse scenarios. AU-Harness unlocks a range of in-depth analyses difficult to conduct without a unified foundation, including multi-turn dialogue dynamics, enabling the study of true audio reasoning capabilities in existing LALMs. AU-Harness provides both practical evaluation tools and insights into model limitations, advancing systematic LALM development."
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<abstract>Large Audio Language Models (LALMs) are rapidly advancing, but evaluating them remains challenging due to inefficient and non-standardized toolkits that limit fair comparison and systematic assessment. Existing evaluation frameworks exhibit three critical limitations: (1) slow and inefficient processing pipeline that bottlenecks large-scale studies, (2) inadequate multi-turn dialogue support, leaving fundamental questions about cross-turn context integration and performance dynamics over extended conversations in LALMs unanswered; and (3) the absence of unified and scalable evaluation framework capable of keeping pace with the rapid growth of both LALMs and audio benchmarks. To address these issues, we introduce **AU-Harness**, an efficient and comprehensive evaluation framework for LALMs. Our empirical studies demonstrate that AU-Harness achieves 77% reduction in evaluation processing time over existing evaluation toolkits, enabling large-scale LALM evaluations previously considered impractical. We provide standardized evaluation protocols and flexible configurations for fair model comparison across diverse scenarios. AU-Harness unlocks a range of in-depth analyses difficult to conduct without a unified foundation, including multi-turn dialogue dynamics, enabling the study of true audio reasoning capabilities in existing LALMs. AU-Harness provides both practical evaluation tools and insights into model limitations, advancing systematic LALM development.</abstract>
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%0 Conference Proceedings
%T AU-Harness: An Open-Source Toolkit for Efficient and Unified Evaluation of AudioLLMs
%A Nguyen, Hoang H.
%A Surapaneni, Sidharth
%A Suresh, Akshay Kalkunte
%A Mehta, Jash
%A Tiwari, Aman
%A Bamgbose, Oluwanifemi
%A Mahajan, Khyati
%A Shah, Jash
%A Radhakrishna, Shruthan
%A Madhusudhan, Sathwik Tejaswi
%A Yadav, Vikas
%A Rajeswar, Sai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F nguyen-etal-2026-au
%X Large Audio Language Models (LALMs) are rapidly advancing, but evaluating them remains challenging due to inefficient and non-standardized toolkits that limit fair comparison and systematic assessment. Existing evaluation frameworks exhibit three critical limitations: (1) slow and inefficient processing pipeline that bottlenecks large-scale studies, (2) inadequate multi-turn dialogue support, leaving fundamental questions about cross-turn context integration and performance dynamics over extended conversations in LALMs unanswered; and (3) the absence of unified and scalable evaluation framework capable of keeping pace with the rapid growth of both LALMs and audio benchmarks. To address these issues, we introduce **AU-Harness**, an efficient and comprehensive evaluation framework for LALMs. Our empirical studies demonstrate that AU-Harness achieves 77% reduction in evaluation processing time over existing evaluation toolkits, enabling large-scale LALM evaluations previously considered impractical. We provide standardized evaluation protocols and flexible configurations for fair model comparison across diverse scenarios. AU-Harness unlocks a range of in-depth analyses difficult to conduct without a unified foundation, including multi-turn dialogue dynamics, enabling the study of true audio reasoning capabilities in existing LALMs. AU-Harness provides both practical evaluation tools and insights into model limitations, advancing systematic LALM development.
%U https://aclanthology.org/2026.findings-emnlp.1338/
%P 28360-28380
Markdown (Informal)
[AU-Harness: An Open-Source Toolkit for Efficient and Unified Evaluation of AudioLLMs](https://aclanthology.org/2026.findings-emnlp.1338/) (Nguyen et al., Findings 2026)
ACL
- Hoang H Nguyen, Sidharth Surapaneni, Akshay Kalkunte Suresh, Jash Mehta, Aman Tiwari, Oluwanifemi Bamgbose, Khyati Mahajan, Jash Shah, Shruthan Radhakrishna, Sathwik Tejaswi Madhusudhan, Vikas Yadav, and Sai Rajeswar. 2026. AU-Harness: An Open-Source Toolkit for Efficient and Unified Evaluation of AudioLLMs. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 28360–28380, Budapest, Hungary. Association for Computational Linguistics.