The field of multimodal spatial reasoning is rapidly advancing, with a focus on developing models that can effectively integrate and reason about multiple sources of information, such as vision, language, and audio. Recent research has highlighted the importance of spatial awareness and the need for models to be able to actively acquire and integrate new information through interaction.

Notable papers in this area have introduced new benchmarks and datasets, such as CLEVR-AVR and SIGBench, which are designed to evaluate the spatial reasoning capabilities of multimodal models. Other papers have proposed innovative approaches to spatial reasoning, including the use of grid-based schemas and auxiliary tasks such as action description prediction.

Some papers are particularly noteworthy for their innovative approaches and significant contributions to the field. For example, PhysVLM-AVR introduces a new task and benchmark for active visual reasoning, while Towards Physics-informed Spatial Intelligence with Human Priors presents a novel approach to integrating spatial intelligence into foundation models. GRAID proposes a high-fidelity data generation framework for enhancing spatial reasoning in vision language models. Benchmarking Egocentric Multimodal Goal Inference for Assistive Wearable Agents introduces a new benchmark for goal inference in multimodal contexts. Look and Tell presents a multimodal dataset for studying referential communication across egocentric and exocentric perspectives. A Cocktail-Party Benchmark introduces a new task and dataset for multi-modal context-aware recognition. Learning Spatial-Aware Manipulation Ordering proposes a unified spatial-aware manipulation ordering framework. Multimodal Spatial Reasoning in the Large Model Era provides a comprehensive survey and benchmarks for multimodal spatial reasoning tasks.