# SEAR: Spoofing Evidence-Grounded Audio Reasoning Benchmark for Audio Language Models

arXiv: https://arxiv.org/abs/2609.39847 · HTML: https://mlsanity.com/paper/2609.39847/

## 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 18 of its 24 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Placed in

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

## Builds on (most cited first, 18 of 18)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [Qwen2-Audio Technical Report](https://mlsanity.com/paper/2407.10759/) (2024)
- [GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities](https://mlsanity.com/paper/2406.11768/) (2024)
- [ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild](https://mlsanity.com/paper/2210.02437/) (2022)
- [MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction](https://mlsanity.com/paper/2604.27393/) (2026)
- [ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech](https://mlsanity.com/paper/2502.08857/) (2025)
- [ALLM4ADD: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detection](https://mlsanity.com/paper/2505.11079/) (2025)
- [CodecFake: Enhancing Anti-Spoofing Models Against Deepfake Audios from Codec-Based Speech Synthesis Systems](https://mlsanity.com/paper/2406.07237/) (2024)
- [Where are we in audio deepfake detection? A systematic analysis over generative and detection models](https://mlsanity.com/paper/2410.04324/) (2024)
- [MOSS-Audio Technical Report](https://mlsanity.com/paper/2606.01802/) (2026)
- [AudioToolAgent: An Agentic Framework for Audio-Language Models](https://mlsanity.com/paper/2510.02995/) (2025)
- [Interpretable All-Type Audio Deepfake Detection with Audio LLMs via Frequency-Time Reinforcement Learning](https://mlsanity.com/paper/2601.02983/) (2026)
- [AuTAgent: A Reinforcement Learning Framework for Tool-Augmented Audio Reasoning](https://mlsanity.com/paper/2602.13685/) (2026)
- [TriDF: Evaluating Perception, Detection, and Hallucination for Interpretable DeepFake Detection](https://mlsanity.com/paper/2512.10652/) (2025)
- [Towards Robust Speech Deepfake Detection via Human-Inspired Reasoning](https://mlsanity.com/paper/2603.10725/) (2026)
- [HoliAntiSpoof: Audio LLM for Holistic Speech Anti-Spoofing](https://mlsanity.com/paper/2602.04535/) (2026)
- [Audio Language Model for Deepfake Detection Grounded in Acoustic Chain-of-Thought](https://mlsanity.com/paper/2603.28021/) (2026)

## Abstract (arXiv)

Audio language models (ALMs) are increasingly used for audio deepfake detection (ADD), yet existing benchmarks assess their verdicts or rationale plausibility without verifying the underlying acoustic evidence. To address this issue, we first introduce spoofing evidence-grounded audio reasoning (SEAR), a four-task AQA benchmark to evaluate ALM-based ADD through acoustic evidence identification and quantification, deepfake detection, and forensic rationale generation. We further propose a bona-fide-based acoustic evidence agent (BAEA), which equips a frozen ALM with controlled acoustic tools under textscfixed or textscadaptive evidence-acquisition policies. Experiments with six ALMs reveal a clear gap between plausible rationales and verifiable acoustic evidence reasoning, while BAEA-textscFixed improves final verdicts and forensic rationales on both evaluation partitions. Controlled interventions further show that misleading evidence degrades both detection and grounding performance.