EAGOR reformulates embodied 360-degree directional reasoning as recursive Bayesian estimation on a spherical manifold using spherical harmonics, achieving training-free, rotation-equivariant target tracking.
Mind meets space: Rethinking agentic spatial intelligence from a neuroscience-inspired per- spective
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Recent advances in agentic AI have led to systems capable of autonomous task execution and language-based reasoning, yet their spatial reasoning abilities remain limited and underexplored, largely constrained to symbolic and sequential processing. In contrast, human spatial intelligence, rooted in integrated multisensory perception, spatial memory, and cognitive maps, enables flexible, context-aware decision-making in unstructured environments. Therefore, bridging this gap is critical for advancing Agentic Spatial Intelligence toward better interaction with the physical 3D world. To this end, we first start from scrutinizing the spatial neural models as studied in computational neuroscience, and accordingly introduce a novel computational framework grounded in neuroscience principles. This framework maps core biological functions to six essential computation modules: bio-inspired multimodal sensing, multi-sensory integration, egocentric-allocentric conversion, an artificial cognitive map, spatial memory, and spatial reasoning. Together, these modules form a perspective landscape for agentic spatial reasoning capability across both virtual and physical environments. On top, we conduct a framework-guided analysis of recent methods, evaluating their relevance to each module and identifying critical gaps that hinder the development of more neuroscience-grounded spatial reasoning modules. We further examine emerging benchmarks and datasets and explore potential application domains ranging from virtual to embodied systems, such as robotics. Finally, we outline potential research directions, emphasizing the promising roadmap that can generalize spatial reasoning across dynamic or unstructured environments. We hope this work will benefit the research community with a neuroscience-grounded perspective and a structured pathway. Our project page can be found at Github.
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Backward token warping in ViT-based MLLMs enables reliable reasoning from nearby viewpoints by preserving semantic coherence better than pixel-wise warping or fine-tuning baselines.
LLMind uses bio-inspired non-uniform sampling via a Mobius module and closed-loop semantic feedback to retain 82-97% of full-resolution VLM performance with only 1-5% of pixels on VQA benchmarks.
citing papers explorer
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EAGOR: Embodied Reasoning in Omni-direction
EAGOR reformulates embodied 360-degree directional reasoning as recursive Bayesian estimation on a spherical manifold using spherical harmonics, achieving training-free, rotation-equivariant target tracking.
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Token Warping Helps MLLMs Look from Nearby Viewpoints
Backward token warping in ViT-based MLLMs enables reliable reasoning from nearby viewpoints by preserving semantic coherence better than pixel-wise warping or fine-tuning baselines.
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LLMind: Bio-inspired Training-free Adaptive Visual Representations for Vision-Language Models
LLMind uses bio-inspired non-uniform sampling via a Mobius module and closed-loop semantic feedback to retain 82-97% of full-resolution VLM performance with only 1-5% of pixels on VQA benchmarks.