ReasonAudio: A Benchmark for Evaluating Reasoning Beyond Matching in Text-Audio Retrieval

Evaluation of 11 state-of-the-art retrieval systems reveals substantial limitations: the best-performing model, OmniEmbed-7B, achieves an overall score of 20.7. It comes from a paper posted to arXiv on 28 September 2026. Existing audio retrieval benchmarks primarily assess semantic matching, lacking comprehensive evaluation of the logical reasoning capabilities required by complex queries. We introduce ReasonAudio, a benchmark for reasoning-intensive Text-Audio Retrieval that evaluates four abilities: negation, temporal order, sound co-occurrence, and sound duration. It comprises five synthetic subtasks with 1,000 queries over 10,000 composite audio clips and a natural subtask with 100 queries over 1,000 real-world clips. In a controlled experiment designed to reduce the influence of sound-event matching, OmniEmbed-7B attains 53.8% average accuracy, compared with 70.6% for its generative backbone, Qwen2.5-Omni-7B-Thinker, and 95.6% for humans. Our results highlight the challenges of reasoning-intensive audio retrieval and reveal a performance gap between OmniEmbed-7B and its generative backbone.

Why it counts

Evaluation of 11 state-of-the-art retrieval systems reveals substantial limitations: the best-performing model, OmniEmbed-7B, achieves an overall score of 20.7.

Sources

Primary source: primary source

What is not known

This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.