# SAR-LM: Symbolic Audio Reasoning with Large Language Models

arXiv: https://arxiv.org/abs/2511.06483 · HTML: https://mlsanity.com/paper/2511.06483/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2025.

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

- [The Interspeech 2026 Audio Reasoning Challenge: Evaluating Reasoning Process Quality for Audio Reasoning Models and Agents](https://mlsanity.com/paper/2602.14224/) (2026)
- [How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation](https://mlsanity.com/paper/2603.19195/) (2026)
- [SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models](https://mlsanity.com/paper/2606.06907/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have advanced in text and vision, but their reasoning on audio remains limited. Most existing methods rely on dense audio embeddings, which are difficult to interpret and often fail on structured reasoning tasks. Caption-based approaches, introduced in recent benchmarks such as MMAU, improve performance by translating audio into text, yet still depend on dense embeddings as input, offering little insight when models fail. We present SAR-LM, a symbolic audio reasoning pipeline that builds on this caption-based paradigm by converting audio into structured, human-readable features across speech, sound events, and music. These symbolic inputs support both reasoning and transparent error analysis, enabling us to trace failures to specific features. Across three benchmarks, MMAU, MMAR, and OmniBench, SAR-LM achieves competitive results, while prioritizing interpretability as its primary contribution.