World2Mind: Cognition Toolkit for Allocentric Spatial Reasoning in Foundation Models

TL;DR

An Allocentric-Spatial Tree that uses elliptical parameters to model the top-down layout of landmarks accurately and a three-stage reasoning chain comprising tool invocation assessment, modality-decoupled cue collection, and geometry-semantics interwoven reasoning are introduced.

Abstract

Achieving robust spatial reasoning remains a fundamental challenge for current Multimodal Foundation Models (MFMs). Existing methods either overfit statistical shortcuts via 3D grounding data or remain confined to 2D visual perception, limiting both spatial reasoning accuracy and generalization in unseen scenarios. Inspired by the spatial cognitive mapping mechanisms of biological intelligence, we propose World2Mind, a training-free spatial intelligence toolkit. At its core, World2Mind leverages 3D reconstruction and instance segmentation models to construct structured spatial cognitive maps, empowering MFMs to proactively acquire targeted spatial knowledge regarding interested landmarks and routes of interest. To provide robust geometric-topological priors, World2Mind synthesizes an Allocentric-Spatial Tree (AST) that uses elliptical parameters to model the top-down layout of landmarks accurately. To mitigate the inherent inaccuracies of 3D reconstruction, we introduce a three-stage reasoning chain comprising tool invocation assessment, modality-decoupled cue collection, and geometry-semantics interwoven reasoning. Extensive experiments demonstrate that World2Mind boosts the performance of frontier models, such as GPT-5.2, by 5%~18%. Astonishingly, relying solely on the AST-structured text, purely text-only foundation models can perform complex 3D spatial reasoning, achieving performance approaching that of advanced multimodal models.

Figures (3)

• Figure 1: Overview of foundation models performing allocentric spatial reasoning via the proposed World2Mind toolkit. Given egocentric video or multi-view observations, the model first assesses the necessity of tool invocation and subsequently passes key parameters (e.g., instances of interest) to World2Mind to drive the generation of spatial cognitive maps. World2Mind integrates an efficient pipeline for 3D reconstruction and semantic-geometry alignment, returning the required structured spatial knowledge through targeted projection and rendering mechanisms. Furthermore, the model conducts geometry-semantics interwoven reasoning based on both the raw visual observations and the geometric cues provided by World2Mind, ultimately yielding highly reliable answers.
• Figure 2: Performance comparison under the text-only model ("blind") setting. we report the performance gap on the VSI-Bench (Tiny subset) between foundation models relying solely on commonsense reasoning and those leveraging world2mind to acquire structured spatial knowledge for allocentric reasoning.
• Figure 3: Complete reasoning trace under World2Mind.