JAEGER: Joint 3D Audio-Visual Grounding and Reasoning in Simulated Physical Environments

JAEGER is a new AI framework that extends audio-visual large language models from 2D to 3D space, enabling spatial grounding and reasoning in physical environments through RGB-D observations and multi-channel audio. The researchers introduce Neural Intensity Vector (Neural IV) for enhanced directional audio analysis and release SpatialSceneQA, a 61k-sample benchmark for training and evaluation.

JAEGER addresses a fundamental limitation in current audio-visual AI systems: their restriction to 2D perception creates a dimensionality mismatch that prevents accurate spatial reasoning and sound source localization in complex 3D environments. By integrating depth sensing (RGB-D) with sophisticated multi-channel audio processing, the framework enables more natural and reliable interaction with physical spaces, a critical capability for embodied AI systems and real-world applications.

The development reflects broader trends in multimodal AI where researchers increasingly recognize that single-modality or simplified multi-modal approaches miss crucial environmental context. Prior systems treating audio as monaural data lose spatial information essential for understanding which sound sources originate from which directions—a problem JAEGER solves through its Neural IV representation, which learns robust directional cues even when multiple sound sources overlap or environmental conditions are adverse.

For the AI and robotics industries, this work demonstrates measurable progress toward systems that can navigate and reason about physical spaces with human-like spatial awareness. The public release of code, models, and datasets accelerates development in embodied AI, autonomous systems, and spatial reasoning tasks. The SpatialSceneQA benchmark provides standardized evaluation, enabling researchers to build incrementally on this foundation.

Future development should focus on real-world deployment testing, since the framework was trained and evaluated in simulated environments. Translation to actual physical spaces with real acoustic complexity and unpredictable sensor noise presents the next validation hurdle for practical robotics and immersive AI applications.

• →JAEGER extends audio-visual LLMs to 3D space, solving the dimensionality mismatch that prevented accurate spatial reasoning in complex environments
• →Neural Intensity Vector (Neural IV) enables robust direction-of-arrival estimation even with overlapping audio sources and poor acoustic conditions
• →SpatialSceneQA benchmark with 61k training samples facilitates large-scale development and standardized evaluation of 3D spatial reasoning systems
• →Experiments confirm explicit 3D modeling outperforms 2D-centric baselines across diverse spatial perception and reasoning tasks
• →Open-source release of code, models, and datasets accelerates research in embodied AI and spatial grounding applications