World2Mind: Cognition Toolkit for Allocentric Spatial Reasoning in Foundation Models

• URL: http://arxiv.org/abs/2603.09774v1
• Date: Tue, 10 Mar 2026 15:12:14 GMT
• Title: World2Mind: Cognition Toolkit for Allocentric Spatial Reasoning in Foundation Models
• Abstract summary: Existing methods either overfit statistical shortcuts via 3D grounding data or remain confined to 2D visual perception.<n>We propose World2Mind, a training-free spatial intelligence toolkit.<n>We show that World2Mind boosts the performance of frontier models, such as GPT-5.2, by 5%18%.
• Score: 25.135316296678187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Disentangling 3D Modeling from Spatial Reasoning [56.3819096070689]
  We explore an alternative paradigm for spatial reasoning by explicitly disentangling 3D perception from reasoning.<n>Our key observation is that modern perception models excel at estimating continuous 3D geometry, whereas large language models (LLMs) are particularly effective at compositional and symbolic reasoning.<n>Motivated by these complementary strengths, we propose the Disentangled Spatial Reasoner (DiSR)<n>DiSR reconstructs the physical world into structured 3D evidence using off-the-shelf expert perception models and fine-tunes an LLM with LoRA to perform reasoning solely over this explicit geometric evidence.
  arXiv  Detail & Related papers  (2026-08-05T14:32:48Z)
• OmniView-Space: Reinforcing Spatial Reasoning via Multi-Perspective Spatial Mapping [69.48762031013514]
  We propose OmniView-Space, a framework designed to maintain spatial consistency through multimodal egocentric evidence.<n>Our approach consists of three core components: (1) Multi-Perspective Spatial Mapping (MPSM), which re-anchors reconstructed geometry into a query-aligned visual cognitive map and a textual spatial graph; (2) Tool-Guided Egocentric Reasoning, an interleaved policy trained to actively select the ego anchor required by the query and request the corresponding MPSM evidence; and (3) Cognitive-Map Distillation, which uses MPSM-generated trajectories and ego-frame rewards to train the model to reason with self-generated cognitive maps
  arXiv  Detail & Related papers  (2026-07-01T12:45:12Z)
• AlloSpatial: Agentic Harness Framework for Spatial Reasoning in Foundation Models [33.84590534650041]
  AlloSpatial is an agentic framework for allocentric spatial cognition in foundation models.<n>World2Mind is a plug-and-play cognitive mapping sandbox that converts egocentric observations into structured allocentric priors.<n> experiments show that AlloSpatial improves proprietary models by 5%-18% in a training-free setting.
  arXiv  Detail & Related papers  (2026-06-08T02:55:05Z)
• From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data [56.41479248637621]
  We introduce a novel 3D representation learning paradigm, namely textbfPRISM, for textbfPre-training.<n>PRISM learns embeddings by textbfRecovering the textbfIntrinsic textbfSurface geodesic textbfMetric.<n> Experiments demonstrate that our approach shows satisfactory accuracy, robustness, and high efficiency in geodesic distance prediction.
  arXiv  Detail & Related papers  (2026-06-01T13:54:26Z)
• Dual-Pathway Geometry-Aware MLLM for Spatial Intelligence [63.11502936651325]
  We propose GAMSI, a dual-pathway Geometry-Aware MLLM for Spatial Intelligence.<n>It takes only RGB images as input while internalizing both forms of geometric prior within a unified autoregressive backbone.<n>GAMSI achieves state-of-the-art performance on seven spatial intelligence benchmarks.
  arXiv  Detail & Related papers  (2026-05-25T01:33:19Z)
• GeoSense: Internalizing Geometric Necessity Perception for Multimodal Reasoning [51.63457948949102]
  We develop a framework to overcome the limited spatial understanding of Multimodal Large Language Models (MLLMs)<n>Our framework endows the model with an awareness of perceptual insufficiency, empowering it to autonomously engage geometric features in reasoning when 2D cues are deemed insufficient.
  arXiv  Detail & Related papers  (2026-03-11T03:32:12Z)
• SSR: Pushing the Limit of Spatial Intelligence with Structured Scene Reasoning [30.87517633729756]
  SSR is a framework designed for Structured Scene Reasoning.<n>It seamlessly integrates 2D and 3D representations via a lightweight alignment mechanism.<n>It achieves state-of-the-art performance on multiple spatial intelligence benchmarks.
  arXiv  Detail & Related papers  (2026-02-28T02:05:35Z)
• Thinking with Geometry: Active Geometry Integration for Spatial Reasoning [68.59084007360615]
  We propose GeoThinker, a framework that shifts paradigm passive fusion to active perception.<n>Instead of feature mixing, GeoThinker enables the model to selectively retrieve geometric evidence conditioned on its internal reasoning demands.<n>Our results indicate that the ability to actively integrate spatial structures is essential for next-generation spatial intelligence.
  arXiv  Detail & Related papers  (2026-02-05T18:59:32Z)
• From Perception to Action: Spatial AI Agents and World Models [0.0]
  We introduce a unified three-axis taxonomy connecting agentic capabilities with spatial tasks across scales.<n>Our analysis reveals three key findings mapped to these axes.<n>We conclude by identifying six grand challenges and outlining directions for future research.
  arXiv  Detail & Related papers  (2026-02-02T05:00:55Z)
• Video2Layout: Recall and Reconstruct Metric-Grounded Cognitive Map for Spatial Reasoning [19.549136366694572]
  Video2 is a framework for reconstructing metric-grounded spatial layouts from video.<n>The framework employs continuous object boundary coordinates to quantify inter-object physical and object size.<n>Our model, V2LO-7B, achieves an average improvement of 4.92% over the model trained on grid maps.
  arXiv  Detail & Related papers  (2025-11-20T08:57:14Z)
• MoRE: 3D Visual Geometry Reconstruction Meets Mixture-of-Experts [50.37005070020306]
  MoRE is a dense 3D visual foundation model based on a Mixture-of-Experts (MoE) architecture.<n>MoRE incorporates a confidence-based depth refinement module that stabilizes and refines geometric estimation.<n>It integrates dense semantic features with globally aligned 3D backbone representations for high-fidelity surface normal prediction.
  arXiv  Detail & Related papers  (2025-10-31T06:54:27Z)
• Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition [63.55828203989405]
  We introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds.<n>Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures.<n>We propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training.
  arXiv  Detail & Related papers  (2025-06-26T11:53:59Z)
• Aether: Geometric-Aware Unified World Modeling [49.33579903601599]
  Aether is a unified framework that enables geometry-aware reasoning in world models.<n>Our framework achieves zero-shot generalization in both action following and reconstruction tasks.<n>We hope our work inspires the community to explore new frontiers in physically-reasonable world modeling.
  arXiv  Detail & Related papers  (2025-03-24T17:59:51Z)
• Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene Understanding [58.38294408121273]
  We propose Cross-modal and Uncertainty-aware Agglomeration for Open-vocabulary 3D Scene Understanding dubbed CUA-O3D.<n>Our method addresses two key challenges: (1) incorporating semantic priors from VLMs alongside the geometric knowledge of spatially-aware vision foundation models, and (2) using a novel deterministic uncertainty estimation to capture model-specific uncertainties.
  arXiv  Detail & Related papers  (2025-03-20T20:58:48Z)
• PoseMamba: Monocular 3D Human Pose Estimation with Bidirectional Global-Local Spatio-Temporal State Space Model [7.286873011001679]
  We propose a purely SSM-based approach with linear correlations for complexityD human pose estimation in monocular video video.<n>Specifically, we propose a bidirectional global temporal-local-temporal block that comprehensively models human joint relations within individual frames as well as across frames.<n>This strategy provides a more logical geometric ordering strategy, resulting in a combined-local spatial scan.
  arXiv  Detail & Related papers  (2024-08-07T04:38:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.