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Reasoning in Space via Grounding in the World

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
3d spatial reasoning3d visual grounding
Abstract:

In this paper, we claim that 3D visual grounding is the cornerstone of spatial reasoning and introduce the Grounded-Spatial Reasoner (GS-Reasoner)\textit{Grounded-Spatial Reasoner (GS-Reasoner)} to explore the effective spatial representations that bridge the gap between them. Existing 3D LLMs suffer from the absence of a unified 3D representation capable of jointly capturing semantic and geometric information. This deficiency is manifested either in poor performance on grounding or in an excessive reliance on external modules, ultimately hindering the seamless integration of grounding and spatial reasoning. To address this, we propose a simple yet effective \emph{dual-path pooling} mechanism that tightly aligns geometric features with both semantic and positional cues, constructing a unified image patch-based 3D representation that encapsulates all essential information without extra tokens. Leveraging this holistic representation, GS-Reasoner is the first 3D LLMs that achieves autoregressive grounding entirely without external modules while delivering performance comparable to state-of-the-art models, establishing a unified and self-contained framework for 3D spatial reasoning. To further bridge grounding and spatial reasoning, we introduce the Grounded Chain-of-Thought (GCoT)\textit{Grounded Chain-of-Thought (GCoT)} dataset. This dataset is meticulously curated to include both 3D bounding box annotations for objects referenced in reasoning questions and step-by-step reasoning paths that integrate grounding as a core component of the problem-solving process. Extensive experiments demonstrate that GS-Reasoner achieves impressive results on 3D visual grounding, which in turn significantly enhances its spatial reasoning capabilities, leading to state-of-the-art performance.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes GS-Reasoner, a framework integrating 3D visual grounding with spatial reasoning through a dual-path pooling mechanism that unifies semantic and geometric features. It resides in the 'Grounded Spatial Reasoning Frameworks' leaf, which contains four papers including this one. This leaf sits within the broader 'Spatial Reasoning in Vision-Language Models' branch, indicating a moderately populated research direction focused on enhancing VLMs with 3D spatial cognition. The taxonomy reveals this is an active but not overcrowded area, with sibling papers like SpatialRGPT and MM-Spatial exploring similar integration challenges between grounding and reasoning.

The taxonomy structure shows neighboring leaves addressing complementary aspects: '3D Geometric Imagination and Limited-View Reasoning' explores geometric representations from constrained viewpoints, while 'Multi-Perspective and Allocentric Reasoning' examines viewpoint-dependent spatial understanding. The broader 'Reasoning-Centric Methods' branch encompasses question answering and scene understanding tasks, distinguishing this work from purely grounding-focused methods in the 'Grounding-Centric Methods' branch. The dual-path pooling approach appears to bridge these domains by creating representations that serve both localization and reasoning objectives, positioning the work at the intersection of grounding and spatial cognition research.

Among twenty-one candidates examined, the contribution-level analysis reveals mixed novelty signals. The semantic-geometric hybrid representation examined ten candidates with none clearly refuting it, suggesting this architectural choice may be relatively novel within the limited search scope. The GCoT dataset contribution examined ten candidates and found one refutable match, indicating prior work on grounded reasoning datasets exists. The unified GS-Reasoner framework examined only one candidate without refutation. These statistics reflect a focused semantic search rather than exhaustive coverage, so the absence of refutation should be interpreted cautiously as evidence of potential novelty rather than definitive originality.

Based on the limited search scope of twenty-one semantically similar papers, the work appears to offer incremental architectural contributions in representation design while operating in a moderately explored research direction. The taxonomy context suggests the integration of grounding and reasoning remains an active challenge, though the dataset contribution faces clearer prior work. A more comprehensive literature review would be needed to assess whether the dual-path pooling mechanism represents a significant departure from existing feature fusion strategies in 3D vision-language models.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
21
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: 3D visual grounding and spatial reasoning in 3D scenes. The field has evolved into several complementary branches that address different facets of understanding and localizing objects in three-dimensional environments. Grounding-Centric Methods focus primarily on mapping natural language descriptions to specific 3D regions or objects, often leveraging cross-modal alignment techniques and transformer architectures to handle complex referring expressions. Reasoning-Centric Methods emphasize higher-level cognitive processes, including spatial relationship understanding and multi-step inference, with works like SpatialRGPT[1] and MM-Spatial[38] developing frameworks that integrate vision-language models for grounded spatial reasoning. Affordance-Centric and Interaction Grounding explores how objects can be used or manipulated in scenes, while Datasets, Benchmarks, and Evaluation provides the empirical foundation through curated resources and standardized metrics. Surveys and Related Applications tie these threads together, contextualizing progress within broader vision-language research. Within the reasoning-centric landscape, a particularly active line of work addresses how to build frameworks that not only ground objects but also perform explicit spatial reasoning over relationships and scene structure. Reasoning in Space via[0] sits squarely in this cluster, emphasizing grounded spatial reasoning capabilities that go beyond simple object localization. It shares thematic overlap with SpatialRGPT[3], which similarly targets spatial relationship understanding through vision-language integration, and MM-Spatial[38], which explores multimodal spatial reasoning pathways. The main trade-offs in this area revolve around balancing end-to-end learning with modular, interpretable reasoning steps, and deciding whether to rely on large-scale pre-trained models or task-specific architectures. Open questions include how to scale reasoning to more complex, multi-hop queries and how to ensure robustness across diverse scene types and viewpoints.

Claimed Contributions

Semantic-geometric hybrid 3D scene representation with dual-path pooling

The authors introduce a unified image patch-based 3D representation that integrates semantic features from vision foundation models, geometric features from point cloud encoders, and 3D positional information through a dual-path pooling mechanism. This representation enables autoregressive 3D visual grounding without external modules while preserving both semantic and geometric information.

10 retrieved papers
Grounded Chain-of-Thought (GCoT) dataset

The authors construct a dataset containing 156k QA pairs with 3D bounding box annotations and chain-of-thought reasoning paths. The dataset bridges grounding and spatial reasoning by incorporating object localization as an intermediate step in the reasoning process, aligning with human cognitive patterns.

10 retrieved papers
Can Refute
GS-Reasoner framework for unified grounding and spatial reasoning

The authors develop GS-Reasoner, a 3D large language model that performs both visual grounding and spatial reasoning in an autoregressive manner without relying on external detectors or grounding modules. The framework demonstrates that grounding serves as a cornerstone for spatial reasoning by first identifying relevant objects before reasoning about their spatial relationships.

1 retrieved paper

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Semantic-geometric hybrid 3D scene representation with dual-path pooling

The authors introduce a unified image patch-based 3D representation that integrates semantic features from vision foundation models, geometric features from point cloud encoders, and 3D positional information through a dual-path pooling mechanism. This representation enables autoregressive 3D visual grounding without external modules while preserving both semantic and geometric information.

Contribution

Grounded Chain-of-Thought (GCoT) dataset

The authors construct a dataset containing 156k QA pairs with 3D bounding box annotations and chain-of-thought reasoning paths. The dataset bridges grounding and spatial reasoning by incorporating object localization as an intermediate step in the reasoning process, aligning with human cognitive patterns.

Contribution

GS-Reasoner framework for unified grounding and spatial reasoning

The authors develop GS-Reasoner, a 3D large language model that performs both visual grounding and spatial reasoning in an autoregressive manner without relying on external detectors or grounding modules. The framework demonstrates that grounding serves as a cornerstone for spatial reasoning by first identifying relevant objects before reasoning about their spatial relationships.

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