STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D Intelligence

Despite rapid progress in Multi-modal Large Language Models and Large Audio-Language Models, existing audio benchmarks largely test semantics that can be recovered from text captions, masking deficits in fine-grained perceptual reasoning. We formalize audio 4D intelligence that is defined as reasoning over sound dynamics in time and 3D space, and introduce ST AR-Bench to measure it. ST AR-Bench combines a Foundational Acoustic Perception setting (six attributes under absolute and relative regimes) with a Holistic Spatio-Temporal Reasoning setting that includes segment reordering for continuous and discrete processes and spatial tasks spanning static localization, multi-source relations, and dynamic trajectories. Our data curation pipeline uses two methods to ensure high-quality samples. For foundational tasks, we use procedurally synthesized and physics-simulated audio. For holistic data, we follow a four-stage process that includes human annotation and final selection based on human performance. Unlike prior benchmarks where caption-only answering reduces accuracy slightly, ST AR-Bench induces far larger drops (-31.5% temporal, -35.2% spatial), evidencing its focus on linguistically hard-to-describe cues. Evaluating 19 models reveals substantial gaps compared with humans and a capability hierarchy: closed-source models are bottlenecked by fine-grained perception, while open-source models lag across perception, knowledge, and reasoning. Our ST AR-Bench provides critical insights and a clear path forward for developing future models with a more robust understanding of the physical world. As a fundamental modality of human perception, audio serves a pivotal role in communication, aesthetic appreciation, and situational awareness, complementing the limitations of visual perception. With the rise of Multimodal Large Language Models (MLLMs) (Comanici et al., 2025; Achiam et al., 2023) and especially Large Audio-Language Models (LALMs) (Chu et al., 2024; Goel et al., 2025), these models have shown impressive capabilities in understanding audio, representing a crucial step toward diverse applications such as embodied intelligence (Paul et al., 2022). To drive progress, a series of audio benchmarks has been introduced (Y ang et al., 2024; Sakshi et al., 2025), covering traditional tasks like Automatic Speech Recognition (ASR) and sound event classification.