This paper documents the first shared task at Interspeech 2026 dedicated to evaluating Chain-of-Thought reasoning quality in Large Audio Language Models (LALMs). Rather than measuring only final answer accuracy, the challenge introduces MMAR-Rubrics — an instance-level evaluation protocol that scores the factuality and logical coherence of the reasoning chains themselves. With 156 competing teams from 18 countries, it represents the most comprehensive community effort yet to benchmark explainable audio intelligence.
The "black-box" problem in audio AI has been a persistent gap. Models can identify what they hear, but rarely explain why they reached that conclusion in a verifiable way. By shifting the evaluation target from output correctness to reasoning process quality, this challenge reframes what it means for an audio model to "understand" — a conceptual leap with broad implications for trust, debugging, and deployment in high-stakes domains like medical audio analysis or legal transcription.
The results reveal a clear architectural split: agent-based systems outperform single models on reasoning quality by leveraging iterative tool orchestration and cross-modal analysis. This confirms a pattern emerging across AI domains — that tool-augmented, multi-step reasoning pipelines produce more interpretable and defensible outputs than monolithic end-to-end models. Meanwhile, single models are closing the gap fast through reinforcement learning and more sophisticated data pipelines, suggesting convergence is likely within 1-2 years.
The MMAR-Rubrics protocol itself may be the most durable contribution here. If adopted broadly, it could become a standard for evaluating reasoning transparency across multimodal AI — not just audio. The challenge also underscores that the next frontier in AI benchmarking isn't just "did it get the right answer" but "can we follow its thinking." That's a fundamentally different and more demanding bar, one that pushes the field toward genuinely interpretable AI systems.