# Spatial Audio Question Answering and Reasoning on Dynamic Source Movements

arXiv: https://arxiv.org/abs/2602.16334 · HTML: https://mlsanity.com/paper/2602.16334/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning.
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Placed in

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

## Builds on (most cited first, 15 of 15)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2410.19168/) (2024)
- [Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models](https://mlsanity.com/paper/2507.08128/) (2025)
- [Recent Advances in Speech Language Models: A Survey](https://mlsanity.com/paper/2410.03751/) (2024)
- [Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model](https://mlsanity.com/paper/2501.07246/) (2025)
- [Vision Transformer with Quadrangle Attention](https://mlsanity.com/paper/2303.15105/) (2023)
- [BAT: Learning to Reason about Spatial Sounds with Large Language Models](https://mlsanity.com/paper/2402.01591/) (2024)
- [Mellow: a small audio language model for reasoning](https://mlsanity.com/paper/2503.08540/) (2025)
- [T-CLAP: Temporal-Enhanced Contrastive Language-Audio Pretraining](https://mlsanity.com/paper/2404.17806/) (2024)
- [Learning Spatially-Aware Language and Audio Embeddings](https://mlsanity.com/paper/2409.11369/) (2024)
- [OWL: Geometry-Aware Spatial Reasoning for Audio Large Language Models](https://mlsanity.com/paper/2509.26140/) (2025)
- [Towards Weakly Supervised Text-to-Audio Grounding](https://mlsanity.com/paper/2401.02584/) (2024)
- [SPUR: A Plug-and-Play Framework for Integrating Spatial Audio Understanding and Reasoning into Large Audio-Language Models](https://mlsanity.com/paper/2511.06606/) (2025)
- [The World is Not Mono: Enabling Spatial Understanding in Large Audio-Language Models](https://mlsanity.com/paper/2601.02954/) (2026)
- [Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification](https://mlsanity.com/paper/2507.12042/) (2025)

## Built on by (most cited first, 3 of 3)

- [Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding](https://mlsanity.com/paper/2606.10738/) (2026)
- [Spatio-Temporal Audio Language Modeling for Dynamic Sound Sources](https://mlsanity.com/paper/2606.14141/) (2026)
- [SonicWorld: Source-Centric Latent Dynamics for Spatial Audio Reasoning](https://mlsanity.com/paper/2610.17131/) (2026)

## Abstract (arXiv)

Spatial audio understanding aims to enable machines to interpret complex auditory scenes, particularly when sound sources move over time. In this work, we study Spatial Audio Question Answering (Spatial AQA) with a focus on movement reasoning, where a model must infer object motion, position, and directional changes directly from stereo audio. First, we introduce a movement-centric spatial audio augmentation framework that synthesizes diverse motion patterns from isolated mono audio events, enabling controlled and scalable training data generation. Second, we propose an end-to-end multimodal finetuning approach with a thinking mode, which allows audio-language models to produce explicit intermediate reasoning steps before predicting an answer. Third, we investigate the impact of query-conditioned source separation as a preprocessing stage and compare three inference regimes: no masking, an audio grounding model (AGM), and ground-truth masks. Our results show that reasoning amplifies the benefits of source separation, with thinking mode showing significant improvement of +5.1% when a single event is present in the question. These findings highlight the interplay between movement modeling, reasoning, and separation quality, offering new insights for advancing spatial audio understanding.