# MMAE: A Massive Multitask Audio Editing Benchmark

arXiv: https://arxiv.org/abs/2606.07229 · HTML: https://mlsanity.com/paper/2606.07229/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 31 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 27 of 27)

- [Qwen3-Omni Technical Report](https://mlsanity.com/paper/2509.17765/) (2025)
- [Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains](https://mlsanity.com/paper/2507.17746/) (2025)
- [Qwen3.5-Omni Technical Report](https://mlsanity.com/paper/2604.15804/) (2026)
- [VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild](https://mlsanity.com/paper/2403.16973/) (2024)
- [AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models](https://mlsanity.com/paper/2304.00830/) (2023)
- [Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion](https://mlsanity.com/paper/2402.10009/) (2024)
- [Omni-Captioner: Data Pipeline, Models, and Benchmark for Omni Detailed Perception](https://mlsanity.com/paper/2510.12720/) (2025)
- [FluentSpeech: Stutter-Oriented Automatic Speech Editing with Context-Aware Diffusion Models](https://mlsanity.com/paper/2305.13612/) (2023)
- [Prompt-guided Precise Audio Editing with Diffusion Models](https://mlsanity.com/paper/2406.04350/) (2024)
- [FLAM: Frame-Wise Language-Audio Modeling](https://mlsanity.com/paper/2505.05335/) (2025)
- [Ming-UniAudio: Speech LLM for Joint Understanding, Generation and Editing with Unified Representation](https://mlsanity.com/paper/2511.05516/) (2025)
- [WavCraft: Audio Editing and Generation with Large Language Models](https://mlsanity.com/paper/2403.09527/) (2024)
- [SSR-Speech: Towards Stable, Safe and Robust Zero-shot Text-based Speech Editing and Synthesis](https://mlsanity.com/paper/2409.07556/) (2024)
- [AudioEditor: A Training-Free Diffusion-Based Audio Editing Framework](https://mlsanity.com/paper/2409.12466/) (2024)
- [Guiding Audio Editing with Audio Language Model](https://mlsanity.com/paper/2509.21625/) (2025)
- [Audio-Omni: Extending Multi-modal Understanding to Versatile Audio Generation and Editing](https://mlsanity.com/paper/2604.10708/) (2026)
- [MMEDIT: A Unified Framework for Multi-Type Audio Editing via Audio Language Model](https://mlsanity.com/paper/2512.20339/) (2025)
- [SpongeBob: Sync-Aware Harmonious Audio-Visual Generative Editing](https://mlsanity.com/paper/2605.25193/) (2026)
- [AudioMorphix: Training-free audio editing with diffusion probabilistic models](https://mlsanity.com/paper/2505.16076/) (2025)
- [Step-Audio-EditX Technical Report](https://mlsanity.com/paper/2511.03601/) (2025)
- [The Interspeech 2026 Audio Reasoning Challenge: Evaluating Reasoning Process Quality for Audio Reasoning Models and Agents](https://mlsanity.com/paper/2602.14224/) (2026)
- [Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling](https://mlsanity.com/paper/2605.13062/) (2026)
- [InstructAV2AV: Instruction-Guided Audio-Video Joint Editing](https://mlsanity.com/paper/2605.18467/) (2026)
- [CosyEdit: Unlocking End-to-End Speech Editing Capability from Zero-Shot Text-to-Speech Models](https://mlsanity.com/paper/2601.05329/) (2026)
- [AudioChat: Unified Audio Storytelling, Editing, and Understanding with Transfusion Forcing](https://mlsanity.com/paper/2602.17097/) (2026)
- [VoiceCraft-X: Unifying Multilingual, Voice-Cloning Speech Synthesis and Speech Editing](https://mlsanity.com/paper/2511.12347/) (2025)
- [Recomposer: Event-roll-guided generative audio editing](https://mlsanity.com/paper/2509.05256/) (2025)

## Built on by (most cited first, 5 of 5)

- [AgenticASR: Refining Speech Recognition in Real-World Scenarios via an Agentic Approach](https://mlsanity.com/paper/2607.28175/) (2026)
- [AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing](https://mlsanity.com/paper/2609.08936/) (2026)
- [TORUS: A Test of Rendering-Understanding Self-Coherence for Unified Audio Models](https://mlsanity.com/paper/2607.28896/) (2026)
- [dots.tts.edit: Precisely Controlled Speech Editing with a Continuous Autoregressive Model](https://mlsanity.com/paper/2608.02673/) (2026)
- [SEmoEdit: Probing and Harnessing the Editability of Pre-trained Speech Flows](https://mlsanity.com/paper/2609.34648/) (2026)

## Abstract (arXiv)

We introduce MMAE, a Massive Multitask Audio Editing benchmark, serving as the first comprehensive evaluation testbed designed for general-purpose instruction-based audio editing. Spurred by the shift toward intelligent creation, interactive editing has rapidly expanded from visual domains, pioneered by models like Nano-banana 2 for images and Gemini-Omni for video, into audio. However, the current evaluation infrastructure lags severely, remaining highly fragmented and restricted to specific subdomains or basic operations. Unlike existing benchmarks that are limited in scope, MMAE extends to a broad spectrum of real-world scenarios, encompassing 7 distinct audio modalities, including sound, speech, music, and their mixtures. Furthermore, we establish a comprehensive taxonomy spanning 6 levels of task complexity, from basic modifications to multi-hop reasoning and multi-round editing, 2 levels of granularity, and 8 distinct operation types. Meticulously curated through human-agent collaboration, MMAE comprises 2,000 high-fidelity samples paired with a pioneering rubric-based evaluation framework. By decomposing free-form tasks into 17,741 verifiable criteria, this robust rubric-based paradigm enables a precise, multi-dimensional assessment of both instruction following and context consistency. Our extensive evaluation of leading models reveals that current systems remain far from achieving reliable edits. Strikingly, the Exact Match Rate (EMR) consistently falls below 5% and plummets to an absolute 0% in complex, mixed-modality tasks, exposing critical bottlenecks in precise execution and structural robustness. We hope MMAE will serve as a catalyst for future advances in the intelligent creation community, providing a clear diagnostic roadmap and establishing a standardized, long-lasting evaluation paradigm for next-generation audio editing systems.