# SEABED: SouthEast Asian Benchmark for Evaluating Audio Reasoning

arXiv: https://arxiv.org/abs/2609.22586 · HTML: https://mlsanity.com/paper/2609.22586/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning.
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 18 Sep 2026.

## Placed in

- LLM reasoning / [Audio-language model reasoning](https://mlsanity.com/lr/audio-language-model-reasoning/)

## Builds on (most cited first, 12 of 12)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2410.19168/) (2024)
- [MMAR: A Challenging Benchmark for Deep Reasoning in Speech, Audio, Music, and Their Mix](https://mlsanity.com/paper/2505.13032/) (2025)
- [MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2506.04779/) (2025)
- [Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models](https://mlsanity.com/paper/2503.02318/) (2025)
- [MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence](https://mlsanity.com/paper/2508.13992/) (2025)
- [Measuring Audio's Impact on Correctness: Audio-Contribution-Aware Post-Training of Large Audio Language Models](https://mlsanity.com/paper/2509.21060/) (2025)
- [SeaLLMs-Audio: Large Audio-Language Models for Southeast Asia](https://mlsanity.com/paper/2511.01670/) (2025)
- [Contextual Paralinguistic Data Creation for Multi-Modal Speech-LLM: Data Condensation and Spoken QA Generation](https://mlsanity.com/paper/2505.13338/) (2025)
- [MedMosaic: A Challenging Large Scale Benchmark of Diverse Medical Audio](https://mlsanity.com/paper/2605.00969/) (2026)
- [LOTUSDIS: A Thai far-field meeting corpus for robust conversational ASR](https://mlsanity.com/paper/2509.18722/) (2025)
- [PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark](https://mlsanity.com/paper/2603.14456/) (2026)

## Abstract (arXiv)

Modern audio-language models are no longer judged only on what words they can transcribe, but on whether they can reason over what they hear: recovering meaning that lives in tone and prosody, telling dialects and regional languages apart, and resolving ambiguity that the written form leaves open. This capability is now measured by a growing family of audio-reasoning benchmarks, but almost entirely in English and on general-domain audio. Southeast Asia (SEA) is served instead by benchmarks that inherit an English task taxonomy of recognition, translation, and paralinguistic classification, and therefore test whether a model hears SEA speech rather than whether it can reason from it. We introduce SEABED, an audio-first question answering dataset designed to benchmark language and audio reasoning models on SEA speech. SEABED comprises a suite of six audio-reasoning tasks built entirely from real, openly available SEA speech corpora, yielding 5,404 question-answer pairs. We evaluate six frontier and region-specific audio LLMs: even the state-of-the-art model Gemini 3.5 Flash achieves only 50.3% weighted average accuracy. SEABED evaluates not only answer accuracy, but also whether models' stated reasoning is grounded in the audio evidence. A sample of the benchmark data is available here: https://huggingface.co/datasets/CentificAIResearch/SEABED.