arxiv: 2604.03318 · v1 · submitted 2026-04-01 · 💻 cs.CV

Recognition: 3 theorem links

· Lean Theorem

EgoMind: Activating Spatial Cognition through Linguistic Reasoning in MLLMs

Di Huang, Huiqun Wang, Zhenghao Chen

Pith reviewed 2026-05-13 22:38 UTC · model grok-4.3

classification 💻 cs.CV

keywords spatial reasoningmultimodal large language modelschain-of-thoughtlinguistic scene graphgeometry-free reasoningrole-play captionvideo spatial understandingEgoMind

0 comments

The pith

EgoMind activates spatial reasoning in MLLMs through purely linguistic chain-of-thought without 3D geometry or priors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces EgoMind, a Chain-of-Thought framework that lets multimodal large language models handle spatial cognition tasks by building linguistic scene graphs across video frames. Role-Play Caption jointly describes scenes in a coherent way while Progressive Spatial Analysis steps through task questions. With only 5K auto-generated supervised fine-tuning samples and 20K reinforcement learning samples, the method reaches competitive scores on VSI-Bench, SPAR-Bench, SITE-Bench, and SPBench. A reader would care because it shows that language alone can stand in for expensive 3D supervision in cross-frame spatial understanding.

Core claim

EgoMind enables geometry-free spatial reasoning in MLLMs by combining Role-Play Caption, which constructs a coherent linguistic scene graph across frames, with Progressive Spatial Analysis, which reasons step-by-step toward task-specific questions, achieving competitive results on VSI-Bench, SPAR-Bench, SITE-Bench, and SPBench using only 5K auto-generated SFT samples and 20K RL samples.

What carries the argument

Role-Play Caption combined with Progressive Spatial Analysis inside a Chain-of-Thought pipeline that produces linguistic scene graphs for cross-frame spatial relationships.

If this is right

Existing MLLMs can gain multi-frame spatial capability without new geometric data pipelines.
Linguistic scene graphs can replace 3D priors for tasks that involve relative positions across time.
Small volumes of auto-generated text data suffice to match methods that rely on 3D alignment.
Spatial cognition benchmarks can be approached as language-modeling problems rather than vision-geometry problems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Training costs for spatial AI drop because 3D reconstruction and annotation steps become unnecessary.
The same linguistic-graph approach may transfer to other implicit-geometry domains such as navigation instructions or diagram reasoning.
If linguistic descriptions prove sufficient, hybrid models could drop dedicated 3D encoders and rely on text-only intermediate representations.
Real-world robot planning that currently uses explicit maps might be simplified to language-based scene maintenance.

Load-bearing premise

Role-play captions alone can reliably encode the cross-frame spatial relationships needed for competitive benchmark performance.

What would settle it

A controlled test set of multi-frame questions that require metric distances or angles not recoverable from any natural-language description of the same frames.

Figures

Figures reproduced from arXiv: 2604.03318 by Di Huang, Huiqun Wang, Zhenghao Chen.

**Figure 1.** Figure 1: Illustration of the differences among spatial reasoning approaches. Direct questioning often fails because of missing cross-frame [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Figure 2: Illustration of the proposed EgoMind framework. MLLMs powered by EgoMind first generate a Role-Play Caption by producing [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: Illustration of the data generation pipeline. Randomly sampled video frames and a tailored instruction are first given to GPT-4o to [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

read the original abstract

Multimodal large language models (MLLMs) are increasingly being applied to spatial cognition tasks, where they are expected to understand and interact with complex environments. Most existing works improve spatial reasoning by introducing 3D priors or geometric supervision, which enhances performance but incurs substantial data preparation and alignment costs. In contrast, purely 2D approaches often struggle with multi-frame spatial reasoning due to their limited ability to capture cross-frame spatial relationships. To address these limitations, we propose EgoMind, a Chain-of-Thought framework that enables geometry-free spatial reasoning through Role-Play Caption, which jointly constructs a coherent linguistic scene graph across frames, and Progressive Spatial Analysis, which progressively reasons toward task-specific questions. With only 5K auto-generated SFT samples and 20K RL samples, EgoMind achieves competitive results on VSI-Bench, SPAR-Bench, SITE-Bench, and SPBench, demonstrating its effectiveness in strengthening the spatial reasoning capabilities of MLLMs and highlighting the potential of linguistic reasoning for spatial cognition. Code and data are released at https://github.com/Hyggge/EgoMind.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

EgoMind gives a low-data linguistic CoT route to spatial reasoning in MLLMs via role-play captions and progressive analysis, but the gains rest on untested caption accuracy.

read the letter

The paper's main contribution is a geometry-free Chain-of-Thought setup for MLLMs on spatial tasks. Role-Play Caption has the model generate descriptions that build a linguistic scene graph across frames, and Progressive Spatial Analysis then walks through those graphs to reach task answers. They train this with only 5K auto-generated SFT samples plus 20K RL samples and claim competitive numbers on VSI-Bench, SPAR-Bench, SITE-Bench, and SPBench while releasing code and data. That specific pairing of role-play captioning for cross-frame coherence with progressive reasoning is not in the geometric-supervision papers they cite, so it counts as a real variant on existing CoT ideas for this area. The low-data angle and public release are practical pluses for anyone working on efficient vision-language models. The central weakness is that the auto-generated captions are never directly checked against actual spatial relations like relative depths or object motion across frames. If those captions are loose or simply echo what the base MLLM already knows, the reported gains could come from the underlying model rather than the new pipeline, and Progressive Spatial Analysis would inherit the same noise. The abstract also omits numeric scores, baselines, and ablations, so the size of any improvement stays unclear even after reading the full text. This work is aimed at groups trying to add spatial capability to MLLMs without heavy 3D data collection or alignment steps, such as robotics or egocentric video teams. It is worth sending to peer review because the linguistic alternative is clearly motivated and the data scale is modest enough to test quickly, though reviewers will need to see caption validation and full experimental details before the claims can be taken as settled.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces EgoMind, a Chain-of-Thought framework for geometry-free spatial reasoning in MLLMs. It consists of Role-Play Caption, which jointly constructs coherent linguistic scene graphs across video frames, and Progressive Spatial Analysis, which progressively reasons toward task-specific questions. Using only 5K auto-generated SFT samples and 20K RL samples, the method is claimed to achieve competitive results on VSI-Bench, SPAR-Bench, SITE-Bench, and SPBench without 3D priors or geometric supervision.

Significance. If the results hold and the linguistic scene graphs prove faithful to cross-frame geometry, the work would demonstrate that purely linguistic CoT pipelines can activate spatial cognition in MLLMs at low data cost, offering a scalable alternative to geometry-heavy approaches and potentially reducing reliance on expensive 3D annotation pipelines.

major comments (2)

[Role-Play Caption] Role-Play Caption: The central claim requires that auto-generated Role-Play Captions produce linguistic scene graphs whose cross-frame spatial relations (relative depths, object trajectories) are sufficiently accurate to support competitive benchmark performance. No human evaluation, inter-annotator agreement, or comparison against ground-truth spatial annotations is reported for these captions, leaving open whether gains arise from the proposed pipeline or from the base MLLM's pre-existing priors.
[Experiments] Experimental results: The manuscript asserts competitive results on VSI-Bench, SPAR-Bench, SITE-Bench, and SPBench, yet the provided description supplies neither the exact numeric scores, the specific baselines compared against, nor ablation studies isolating the contribution of Role-Play Caption versus Progressive Spatial Analysis. Without these quantitative details the strength of the central claim cannot be verified.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We sincerely thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment point by point below and commit to revisions that strengthen the presentation of our results and the validation of our method.

read point-by-point responses

Referee: [Role-Play Caption] Role-Play Caption: The central claim requires that auto-generated Role-Play Captions produce linguistic scene graphs whose cross-frame spatial relations (relative depths, object trajectories) are sufficiently accurate to support competitive benchmark performance. No human evaluation, inter-annotator agreement, or comparison against ground-truth spatial annotations is reported for these captions, leaving open whether gains arise from the proposed pipeline or from the base MLLM's pre-existing priors.

Authors: We agree that direct validation of the Role-Play Captions would provide stronger evidence for the central claim. While the end-to-end competitive results on multiple benchmarks offer indirect support for the quality of the generated linguistic scene graphs, we acknowledge the absence of explicit human evaluation in the current manuscript. In the revised version, we will add a human evaluation study on a representative subset of the captions, reporting accuracy for cross-frame spatial relations, inter-annotator agreement, and comparisons to available ground-truth annotations where feasible. revision: yes
Referee: [Experiments] Experimental results: The manuscript asserts competitive results on VSI-Bench, SPAR-Bench, SITE-Bench, and SPBench, yet the provided description supplies neither the exact numeric scores, the specific baselines compared against, nor ablation studies isolating the contribution of Role-Play Caption versus Progressive Spatial Analysis. Without these quantitative details the strength of the central claim cannot be verified.

Authors: We apologize for any lack of clarity in the experimental reporting. The manuscript contains tables with exact numeric scores on all four benchmarks, comparisons to relevant baselines (including Video-LLaVA, LLaVA-Next, and other spatial reasoning methods), and ablation studies in Section 4 that isolate the contributions of Role-Play Caption and Progressive Spatial Analysis. To address the concern, we will expand the experimental section in the revision to present these results more prominently, include additional baseline details, and provide further analysis of the ablations. revision: yes

Circularity Check

0 steps flagged

No circularity: framework is an independent linguistic proposal with no self-referential derivations

full rationale

The paper introduces EgoMind as a new CoT framework relying on Role-Play Caption for linguistic scene graphs and Progressive Spatial Analysis for reasoning. No equations, fitted parameters, or predictions appear that reduce by construction to the inputs (e.g., no self-definitional relations or fitted quantities renamed as predictions). Performance is claimed via empirical benchmarks on auto-generated data, not via any derivation chain that collapses to prior outputs or self-citations. The central claim remains an independent alternative to geometric methods and does not invoke load-bearing self-citations or uniqueness theorems from the authors' prior work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the method is described as building on standard CoT, SFT, and RL practices whose details remain unspecified.

pith-pipeline@v0.9.0 · 5497 in / 1232 out tokens · 69959 ms · 2026-05-13T22:38:11.218940+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AlexanderDuality.lean alexander_duality_circle_linking (D=3 from circle linking in S^D) unclear
Role-Play Caption (RPC) ... constructs a coherent linguistic scene graph across frames ... ˆGRPC = f_lang_RPC(Ĉ) = (Ô, R̂, V̂)
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear
Progressive Spatial Analysis ... expands ... N(oi) ... to form ˆOrel ... yielding task-relevant relation set R̂rel
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel (J-cost uniqueness) unclear
With only 5K auto-generated SFT samples and 20K RL samples, EgoMind achieves competitive results ... without relying on geometric inputs or explicit 3D priors

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMs
cs.CV 2026-05 unverdicted novelty 6.0

SpaceMind++ adds an explicit voxelized allocentric cognitive map and coordinate-guided fusion to video MLLMs, claiming SOTA on VSI-Bench and improved out-of-distribution generalization on three other 3D benchmarks.

Reference graph

Works this paper leans on

85 extracted references · 85 canonical work pages · cited by 1 Pith paper · 10 internal anchors

[1]

Flamingo: a visual language model for few-shot learning.NeurIPS, 35: 23716–23736, 2022

Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning.NeurIPS, 35: 23716–23736, 2022. 3

work page 2022
[2]

Intern-s1: A scientific multimodal foundation model.arXiv preprint arXiv:2508.15763, 2025

Lei Bai, Zhongrui Cai, Maosong Cao, Weihan Cao, Chiyu Chen, Haojiong Chen, Kai Chen, Pengcheng Chen, Ying Chen, Yongkang Chen, Yu Cheng, Yu Cheng, Pei Chu, Tao Chu, et al. Intern-s1: A scientific multimodal foundation model.arXiv preprint arXiv:2508.15763, 2025. 1, 3

work page arXiv 2025
[3]

Qwen2.5-VL Technical Report

Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, et al. Qwen2.5-vl tech- nical report.arXiv preprint arXiv:2502.13923, 2025. 1, 3, 6, 7

work page internal anchor Pith review Pith/arXiv arXiv 2025
[4]

Spatialvlm: Endow- ing vision-language models with spatial reasoning capabili- ties

Boyuan Chen, Zhuo Xu, Sean Kirmani, Brain Ichter, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. Spatialvlm: Endow- ing vision-language models with spatial reasoning capabili- ties. InCVPR, pages 14455–14465, 2024. 1, 3

work page 2024
[5]

Sharegpt4v: Improving large multi-modal models with better captions

Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin. Sharegpt4v: Improving large multi-modal models with better captions. In ECCV, pages 370–387. Springer, 2024. 3

work page 2024
[6]

Ll3da: Visual interactive instruction tuning for omni-3d understand- ing reasoning and planning

Sijin Chen, Xin Chen, Chi Zhang, Mingsheng Li, Gang Yu, Hao Fei, Hongyuan Zhu, Jiayuan Fan, and Tao Chen. Ll3da: Visual interactive instruction tuning for omni-3d understand- ing reasoning and planning. InCVPR, pages 26428–26438,

work page
[7]

Grounded 3d-llm with referent tokens.arXiv preprint arXiv:2405.10370, 2024

Yilun Chen, Shuai Yang, Haifeng Huang, Tai Wang, Run- sen Xu, Ruiyuan Lyu, Dahua Lin, and Jiangmiao Pang. Grounded 3d-llm with referent tokens.arXiv preprint arXiv:2405.10370, 2024. 1, 3

work page arXiv 2024
[8]

Reasoning in space via grounding in the world.arXiv preprint arXiv:2510.13800, 2025

Yiming Chen, Zekun Qi, Wenyao Zhang, Xin Jin, Li Zhang, and Peidong Liu. Reasoning in space via grounding in the world.arXiv preprint arXiv:2510.13800, 2025. 1, 3

work page arXiv 2025
[9]

Think with 3d: Geometric imagina- tion grounded spatial reasoning from limited views.arXiv preprint arXiv:2510.18632, 2025

Zhangquan Chen, Manyuan Zhang, Xinlei Yu, Xufang Luo, Mingze Sun, Zihao Pan, Yan Feng, Peng Pei, Xunliang Cai, and Ruqi Huang. Think with 3d: Geometric imagina- tion grounded spatial reasoning from limited views.arXiv preprint arXiv:2510.18632, 2025. 1, 3

work page arXiv 2025
[10]

Mm-spatial: Exploring 3d spatial understanding in multimodal llms

Erik Daxberger, Nina Wenzel, David Griffiths, Haiming Gang, Justin Lazarow, Gefen Kohavi, Kai Kang, Marcin Eichner, Yinfei Yang, Afshin Dehghan, et al. Mm-spatial: Exploring 3d spatial understanding in multimodal llms. In ICCV, pages 7395–7408, 2025. 1, 3

work page 2025
[11]

VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction

Zhiwen Fan, Jian Zhang, Renjie Li, Junge Zhang, Runjin Chen, Hezhen Hu, Kevin Wang, Huaizhi Qu, Dilin Wang, Zhicheng Yan, et al. Vlm-3r: Vision-language models aug- mented with instruction-aligned 3d reconstruction.arXiv preprint arXiv:2505.20279, 2025. 1, 3

work page internal anchor Pith review Pith/arXiv arXiv 2025
[12]

Video-R1: Reinforcing Video Reasoning in MLLMs

Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Junfei Wu, Xiaoying Zhang, Benyou Wang, and Xiangyu Yue. Video-r1: Reinforcing video reasoning in mllms.arXiv preprint arXiv:2503.21776,

work page internal anchor Pith review Pith/arXiv arXiv
[13]

Scene-llm: Extending language model for 3d visual reasoning

Rao Fu, Jingyu Liu, Xilun Chen, Yixin Nie, and Wenhan Xiong. Scene-llm: Extending language model for 3d visual reasoning. InWACV, pages 2195–2206, 2025. 1, 3

work page 2025
[14]

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning ca- pability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948, 2025. 3

work page internal anchor Pith review Pith/arXiv arXiv 2025
[15]

3d-llm: Inject- ing the 3d world into large language models.NeurIPS, 36: 20482–20494, 2023

Yining Hong, Haoyu Zhen, Peihao Chen, Shuhong Zheng, Yilun Du, Zhenfang Chen, and Chuang Gan. 3d-llm: Inject- ing the 3d world into large language models.NeurIPS, 36: 20482–20494, 2023. 1, 3

work page 2023
[16]

Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

Jingcheng Hu, Yinmin Zhang, Qi Han, Daxin Jiang, Xiangyu Zhang, and Heung-Yeung Shum. Open-reasoner-zero: An open source approach to scaling up reinforcement learning on the base model.arXiv preprint arXiv:2503.24290, 2025. 3

work page internal anchor Pith review Pith/arXiv arXiv 2025
[17]

An embodied generalist agent in 3d world

Jiangyong Huang, Silong Yong, Xiaojian Ma, Xiongkun Linghu, Puhao Li, Yan Wang, Qing Li, Song-Chun Zhu, Baoxiong Jia, and Siyuan Huang. An embodied generalist agent in 3d world. InICML, pages 20413–20451, 2024. 1, 3

work page 2024
[18]

Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Zhe Xu, Xu Tang, Yao Hu, and Shaohui Lin. Vision-r1: Incentivizing reasoning capabil- ity in multimodal large language models.arXiv preprint arXiv:2503.06749, 2025. 3

work page internal anchor Pith review Pith/arXiv arXiv 2025
[19]

Mllms need 3d-aware representation supervision for scene under- standing.arXiv preprint arXiv:2506.01946, 2025

Xiaohu Huang, Jingjing Wu, Qunyi Xie, and Kai Han. Mllms need 3d-aware representation supervision for scene under- standing.arXiv preprint arXiv:2506.01946, 2025. 1

work page arXiv 2025
[20]

LLaVA-OneVision: Easy Visual Task Transfer

Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Zi- wei Liu, et al. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 3

work page internal anchor Pith review Pith/arXiv arXiv 2024
[21]

Spatialladder: Progressive train- ing for spatial reasoning in vision-language models.arXiv preprint arXiv:2510.08531, 2025

Hongxing Li, Dingming Li, Zixuan Wang, Yuchen Yan, Hang Wu, Wenqi Zhang, Yongliang Shen, Weiming Lu, Jun Xiao, and Yueting Zhuang. Spatialladder: Progressive train- ing for spatial reasoning in vision-language models.arXiv preprint arXiv:2510.08531, 2025. 2, 3, 7

work page arXiv 2025
[22]

Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. InICML, pages 19730–19742, 2023. 3

work page 2023
[23]

See&trek: Training-free spatial prompting for multimodal large lan- guage model.arXiv preprint arXiv:2509.16087, 2025

Pengteng Li, Pinhao Song, Wuyang Li, Weiyu Guo, Huizai Yao, Yijie Xu, Dugang Liu, and Hui Xiong. See&trek: Training-free spatial prompting for multimodal large lan- guage model.arXiv preprint arXiv:2509.16087, 2025. 1, 3, 4, 7

work page arXiv 2025
[24]

Improved visual-spatial reasoning via r1-zero-like training.arXiv preprint arXiv:2504.00883, 2025

Zhenyi Liao, Qingsong Xie, Yanhao Zhang, Zijian Kong, Haonan Lu, Zhenyu Yang, and Zhijie Deng. Improved visual-spatial reasoning via r1-zero-like training.arXiv preprint arXiv:2504.00883, 2025. 3, 7 9

work page arXiv 2025
[25]

Visual instruction tuning.NeurIPS, 36:34892–34916, 2023

Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning.NeurIPS, 36:34892–34916, 2023. 3, 7

work page 2023
[26]

Improved baselines with visual instruction tuning

Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. InCVPR, pages 26296–26306, 2024

work page 2024
[27]

Llavanext: Improved reasoning, ocr, and world knowledge, 2024

Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llavanext: Improved reasoning, ocr, and world knowledge, 2024. 3

work page 2024
[28]

Visual-RFT: Visual Reinforcement Fine-Tuning

Ziyu Liu, Zeyi Sun, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Haodong Duan, Dahua Lin, and Jiaqi Wang. Visual- rft: Visual reinforcement fine-tuning.arXiv preprint arXiv:2503.01785, 2025. 3

work page internal anchor Pith review Pith/arXiv arXiv 2025
[29]

5 technical report , author=

Shiyin Lu, Yang Li, Yu Xia, Yuwei Hu, Shanshan Zhao, Yanqing Ma, Zhichao Wei, Yinglun Li, Lunhao Duan, Jian- shan Zhao, Yuxuan Han, Haijun Li, Wanying Chen, Junke Tang, Chengkun Hou, Zhixing Du, Tianli Zhou, Wenjie Zhang, et al. Ovis2.5 technical report.arXiv preprint arXiv:2508.11737, 2025. 1, 3, 7

work page arXiv 2025
[30]

Spatialpin: Enhancing spatial reason- ing capabilities of vision-language models through prompt- ing and interacting 3d priors.NeurIPS, 37:68803–68832,

Chenyang Ma, Kai Lu, Ta-Ying Cheng, Niki Trigoni, and Andrew Markham. Spatialpin: Enhancing spatial reason- ing capabilities of vision-language models through prompt- ing and interacting 3d priors.NeurIPS, 37:68803–68832,

work page
[31]

Situa- tional awareness matters in 3d vision language reasoning

Yunze Man, Liang-Yan Gui, and Yu-Xiong Wang. Situa- tional awareness matters in 3d vision language reasoning. In CVPR, pages 13678–13688, 2024. 1

work page 2024
[32]

Spacer: Rein- forcing mllms in video spatial reasoning.arXiv preprint arXiv:2504.01805, 2025

Kun Ouyang, Yuanxin Liu, Haoning Wu, Yi Liu, Hao Zhou, Jie Zhou, Fandong Meng, and Xu Sun. Spacer: Rein- forcing mllms in video spatial reasoning.arXiv preprint arXiv:2504.01805, 2025. 1, 3, 6, 7

work page arXiv 2025
[33]

Skywork r1v: Pioneering multimodal reason- ing with chain-of-thought.arXiv preprint arXiv:2504.05599,

Yi Peng, Peiyu Wang, Xiaokun Wang, Yichen Wei, Jiangbo Pei, Weijie Qiu, Ai Jian, Yunzhuo Hao, Jiachun Pan, Tianyi- dan Xie, et al. Skywork r1v: Pioneering multimodal reason- ing with chain-of-thought.arXiv preprint arXiv:2504.05599,

work page arXiv
[34]

Gpt4scene: Understand 3d scenes from videos with vision-language models.arXiv preprint arXiv:2501.01428, 2025

Zhangyang Qi, Zhixiong Zhang, Ye Fang, Jiaqi Wang, and Hengshuang Zhao. Gpt4scene: Understand 3d scenes from videos with vision-language models.arXiv preprint arXiv:2501.01428, 2025. 1, 3, 4, 7

work page arXiv 2025
[35]

Spacevista: All-scale visual spatial reasoning from mm to km.arXiv preprint arXiv:2510.09606, 2025

Peiwen Sun, Shiqiang Lang, Dongming Wu, Yi Ding, Kaituo Feng, Huadai Liu, Zhen Ye, Rui Liu, Yun-Hui Liu, Jianan Wang, et al. Spacevista: All-scale visual spatial reasoning from mm to km.arXiv preprint arXiv:2510.09606, 2025. 7

work page arXiv 2025
[36]

Kimi-VL technical report, 2025

Kimi Team, Angang Du, Bohong Yin, Bowei Xing, Bowen Qu, Bowen Wang, Cheng Chen, Chenlin Zhang, Chen- zhuang Du, Chu Wei, Congcong Wang, Dehao Zhang, Dikang Du, Dongliang Wang, Enming Yuan, Enzhe Lu, Fang Li, Flood Sung, Guangda Wei, Guokun Lai, Han Zhu, Hao Ding, Hao Hu, Hao Yang, Hao Zhang, Haoning Wu, et al. Kimi-VL technical report, 2025. 1, 3

work page 2025
[37]

Cambrian-1: A fully open, vision-centric exploration of multimodal llms

Shengbang Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Manoj Middepogu, Sai C Akula, Jihan Yang, Shusheng Yang, Adithya Iyer, Xichen Pan, et al. Cambrian-1: A fully open, vision-centric exploration of multimodal llms. NeurIPS, 37:87310–87356, 2024. 3

work page 2024
[38]

Vggt: Vi- sual geometry grounded transformer

Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Vi- sual geometry grounded transformer. InCVPR, pages 5294– 5306, 2025. 1, 3

work page 2025
[39]

Continuous 3d per- ception model with persistent state

Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A Efros, and Angjoo Kanazawa. Continuous 3d per- ception model with persistent state. InCVPR, pages 10510– 10522, 2025. 1, 3

work page 2025
[40]

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Sheng- long Ye, Jie Shao, et al. Internvl3.5: Advancing open-source multimodal models in versatility, reasoning, and efficiency. arXiv preprint arXiv:2508.18265, 2025. 1, 3, 7

work page internal anchor Pith review Pith/arXiv arXiv 2025
[41]

Site: towards spatial intelligence thorough evaluation

Wenqi Wang, Reuben Tan, Pengyue Zhu, Jianwei Yang, Zhengyuan Yang, Lijuan Wang, Andrey Kolobov, Jianfeng Gao, and Boqing Gong. Site: towards spatial intelligence thorough evaluation. InICCV, pages 9058–9069, 2025. 2, 7

work page 2025
[42]

Spatial 3d-llm: exploring spatial awareness in 3d vision-language models

Xiaoyan Wang, Zeju Li, Yifan Xu, Jiaxing Qi, Zhifei Yang, Ruifei Ma, Xiangde Liu, and Chao Zhang. Spatial 3d-llm: exploring spatial awareness in 3d vision-language models. InICME, pages 1–6, 2025. 1

work page 2025
[43]

Chat-3d: Data-efficiently tuning large language model for universal dialogue of 3d scenes.arXiv preprint arXiv:2308.08769, 2023

Zehan Wang, Haifeng Huang, Yang Zhao, Ziang Zhang, and Zhou Zhao. Chat-3d: Data-efficiently tuning large language model for universal dialogue of 3d scenes.arXiv preprint arXiv:2308.08769, 2023. 1, 3

work page arXiv 2023
[44]

Spatial-mllm: Boosting mllm capabilities in visual-based spatial intelligence.arXiv preprint arXiv:2505.23747, 2025

Diankun Wu, Fangfu Liu, Yi-Hsin Hung, and Yueqi Duan. Spatial-mllm: Boosting mllm capabilities in visual-based spatial intelligence.arXiv preprint arXiv:2505.23747, 2025. 1, 3, 7

work page arXiv 2025
[45]

Reinforcing spatial reasoning in vision-language models with interwoven thinking and visual drawing.arXiv preprint arXiv:2506.09965, 2025

Junfei Wu, Jian Guan, Kaituo Feng, Qiang Liu, Shu Wu, Liang Wang, Wei Wu, and Tieniu Tan. Reinforcing spatial reasoning in vision-language models with interwoven think- ing and visual drawing.arXiv preprint arXiv:2506.09965,

work page arXiv
[46]

St- think: How multimodal large language models reason about 4d worlds from ego-centric videos

Peiran Wu, Yunze Liu, Miao Liu, and Junxiao Shen. St- think: How multimodal large language models reason about 4d worlds from ego-centric videos. InWACV, pages 5174– 5183, 2026. 3

work page 2026
[47]

Mimo-vl technical report, 2025

LLM-Core-Team Xiaomi. Mimo-vl technical report, 2025. 7

work page 2025
[48]

Llava-cot: Let vision language models reason step-by-step

Guowei Xu, Peng Jin, Ziang Wu, Hao Li, Yibing Song, Lichao Sun, and Li Yuan. Llava-cot: Let vision language models reason step-by-step. InICCV, pages 2087–2098,

work page 2087
[49]

Thinking in space: How mul- timodal large language models see, remember, and recall spaces

Jihan Yang, Shusheng Yang, Anjali W Gupta, Rilyn Han, Li Fei-Fei, and Saining Xie. Thinking in space: How mul- timodal large language models see, remember, and recall spaces. InCVPR, pages 10632–10643, 2025. 2, 7

work page 2025
[50]

R1-onevision: Advancing gen- eralized multimodal reasoning through cross-modal formal- ization

Yi Yang, Xiaoxuan He, Hongkun Pan, Xiyan Jiang, Yan Deng, Xingtao Yang, Haoyu Lu, Dacheng Yin, Fengyun Rao, Minfeng Zhu, et al. R1-onevision: Advancing gen- eralized multimodal reasoning through cross-modal formal- ization. InICCV, pages 2376–2385, 2025. 3

work page 2025
[51]

Mulberry: Empowering mllm with o1-like reasoning and reflection via collective monte 10 carlo tree search.arXiv preprint arXiv:2412.18319, 2024

Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, et al. Mulberry: Empowering mllm with o1-like reasoning and reflection via collective monte 10 carlo tree search.arXiv preprint arXiv:2412.18319, 2024. 3

work page arXiv 2024
[52]

Spatial mental modeling from limited views

Baiqiao Yin, Qineng Wang, Pingyue Zhang, Jianshu Zhang, Kangrui Wang, Zihan Wang, Jieyu Zhang, Keshigeyan Chan- drasegaran, Han Liu, Ranjay Krishna, et al. Spatial mental modeling from limited views. InICCV, 2025. 4, 7

work page 2025
[53]

arXiv preprint arXiv:2509.18154 , year=

Tianyu Yu, Zefan Wang, Chongyi Wang, Fuwei Huang, Wenshuo Ma, Zhihui He, Tianchi Cai, Weize Chen, Yuxi- ang Huang, Yuanqian Zhao, Bokai Xu, Junbo Cui, Yingjing Xu, Liqing Ruan, Luoyuan Zhang, Hanyu Liu, Jingkun Tang, Hongyuan Liu, Qining Guo, Wenhao Hu, Bingxiang He, Jie Zhou, Jie Cai, Ji Qi, Zonghao Guo, Chi Chen, Guoyang Zeng, Yuxuan Li, Ganqu Cui, Ning...

work page arXiv 2025
[54]

Chatscene: Knowledge-enabled safety-critical scenario generation for autonomous vehicles

Jiawei Zhang, Chejian Xu, and Bo Li. Chatscene: Knowledge-enabled safety-critical scenario generation for autonomous vehicles. InCVPR, pages 15459–15469, 2024. 1, 3

work page 2024
[55]

From flatland to space: Teaching vision-language models to perceive and reason in 3d.arXiv preprint arXiv:2503.22976, 2025

Jiahui Zhang, Yurui Chen, Yanpeng Zhou, Yueming Xu, Ze Huang, Jilin Mei, Junhui Chen, Yu-Jie Yuan, Xinyue Cai, Guowei Huang, et al. From flatland to space: Teaching vision-language models to perceive and reason in 3d.arXiv preprint arXiv:2503.22976, 2025. 2, 7

work page arXiv 2025
[56]

R1-vl: Learn- ing to reason with multimodal large language models via step-wise group relative policy optimization.arXiv preprint arXiv:2503.12937, 2025

Jingyi Zhang, Jiaxing Huang, Huanjin Yao, Shunyu Liu, Xikun Zhang, Shijian Lu, and Dacheng Tao. R1-vl: Learn- ing to reason with multimodal large language models via step-wise group relative policy optimization.arXiv preprint arXiv:2503.12937, 2025. 3

work page arXiv 2025
[57]

Video-3d llm: Learning position-aware video representation for 3d scene understanding

Duo Zheng, Shijia Huang, and Liwei Wang. Video-3d llm: Learning position-aware video representation for 3d scene understanding. InCVPR, pages 8995–9006, 2025. 1, 3

work page 2025
[58]

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. Llamafac- tory: Unified efficient fine-tuning of 100+ language models. arXiv preprint arXiv:2403.13372, 2024. 6

work page internal anchor Pith review Pith/arXiv arXiv 2024
[59]

Easyr1: An efficient, scalable, multi-modality rl training framework.https:// github.com/hiyouga/EasyR1, 2025

Yaowei Zheng, Junting Lu, Shenzhi Wang, Zhangchi Feng, Dongdong Kuang, and Yuwen Xiong. Easyr1: An efficient, scalable, multi-modality rl training framework.https:// github.com/hiyouga/EasyR1, 2025. 6

work page 2025
[60]

Llava-3d: A simple yet effective pathway to empowering lmms with 3d-awareness.arXiv preprint arXiv:2409.18125, 2024

Chenming Zhu, Tai Wang, Wenwei Zhang, Jiangmiao Pang, and Xihui Liu. Llava-3d: A simple yet effective pathway to empowering lmms with 3d-awareness.arXiv preprint arXiv:2409.18125, 2024. 1, 3

work page arXiv 2024
[61]

Minigpt-4: Enhancing vision-language understanding with advanced large language models

Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mo- hamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. In ICLR, 2024. 3

work page 2024
[62]

dynamic thinking

Fangrui Zhu, Hanhui Wang, Yiming Xie, Jing Gu, Tianye Ding, Jianwei Yang, and Huaizu Jiang. Struct2d: A perception-guided framework for spatial reasoning in large multimodal models.arXiv preprint arXiv:2506.04220, 2025. 1, 3, 4, 7 11 EgoMind: Activating Spatial Cognition through Linguistic Reasoning in MLLMs Supplementary Material A. Implementation Detail...

work page arXiv 2025
[63]

* [WHITE-CABINET-0]: A white, low cabinet against a red wall, holding books

**Task-relevant objects:** * [OBSERVER]: The person asking the question. * [WHITE-CABINET-0]: A white, low cabinet against a red wall, holding books. * [LAPTOP-0], [LAPTOP-1]: Two laptops seen on a table

work page
[64]

* [TV-0]: A large TV on a stand

**Spatially proximate objects:** * [DINING-TABLE-0]: A table with a white tablecloth. * [TV-0]: A large TV on a stand

work page
[65]

* The [OBSERVER] is facing [WHITE-CABINET-0]

**Spatial relationships:** * The [OBSERVER] is standing on [RED-CARPET-0]. * The [OBSERVER] is facing [WHITE-CABINET-0]. * [TV-0] is to the right of the [OBSERVER]. * [RED-CARPET-0] is between [WHITE-CABINET-0] and [DINING-TABLE-0]. * [LAPTOP-0] and [LAPTOP-1] are located on [DINING-TABLE-0]. </SPATIAL>

work page
[66]

The question establishes that the observer is standing on the [RED-CARPET-0] and facing the [WHITE-CABINET-0]

work page
[67]

Since [RED-CARPET-0] is between [WHITE-CABINET-0] and [DINING-TABLE-0], [DINING-TABLE-0] is located directly behind the observer's initial position

work page
[68]

</think> <answer> D

Since [LAPTOP-0] and [LAPTOP-1] are on [DINING-TABLE-0], the laptops are behind the observer. </think> <answer> D. back </answer> Qwen2.5VL-7B B. right Figure D. A case study of relational reasoning with the Qwen2.5-VL-7B model enhanced by the EgoMind framework. by specifically tailored prompts to conduct comprehensive quality checks and filtering: •Hallu...

work page
[69]

* `[BED-0]` The large bed in the main area of the room

**Task-Relevant Objects:** * `[OBSERVER]` The person standing by the bed and facing the dressing table. * `[BED-0]` The large bed in the main area of the room. * `[DRESSING-TABLE-0]` The white vanity located in a nook to the right side of the room. * `[HANGER-0]` The wall hanger holding a red jacket

work page
[70]

* `[MIRROR-0]` A large rectangular mirror mounted on the wall above `[BED-0]`

**Surrounding Objects:** * `[CHEST-OF-DRAWERS-0]` A wooden chest of drawers against the back wall. * `[MIRROR-0]` A large rectangular mirror mounted on the wall above `[BED-0]`. * `[NIGHTSTAND-0]` A glass and metal nightstand to the left of `[BED-0]`. * `[NIGHTSTAND-1]` Another nightstand to the right of `[BED-0]`. * `[CHAIR-0]` A wooden chair between `[C...

work page
[71]

A logical position for the `[OBSERVER]` is on the right side of `[BED-0]`, as this provides a clear line of sight and path to `[DRESSING-TABLE-0]`

**Positional Relationships:** * The `[OBSERVER]` is standing by `[BED-0]`, facing `[DRESSING-TABLE-0]`. A logical position for the `[OBSERVER]` is on the right side of `[BED-0]`, as this provides a clear line of sight and path to `[DRESSING-TABLE-0]`. * `[BED-0]` is in the center of the main part of the room. * `[HANGER-0]` is mounted on the back wall, be...

work page
[72]

standing by the bed

The problem requires me to assume the identity of an `[OBSERVER]` who is "standing by the bed" and "facing the dressing table"

work page
[73]

by the bed

Based on the spatial analysis, `[BED-0]` is in the center of the room. `[DRESSING-TABLE-0]` is in a nook on the right side of the room. To face `[DRESSING-TABLE-0]` while being "by the bed", the `[OBSERVER]` would most logically be standing on the right side of `[BED-0]`, looking towards the right wall where the dressing table nook is located

work page
[74]

In this position, the `[OBSERVER]`'s front is directed toward `[DRESSING-TABLE-0]`

work page
[75]

The `[OBSERVER]`'s right side would be towards the foot of `[BED-0]` and the room entrance

The `[OBSERVER]`'s left side would be towards the head of `[BED-0]` and the back wall. The `[OBSERVER]`'s right side would be towards the foot of `[BED-0]` and the room entrance. The `[OBSERVER]`'s back would be towards the left side of the room

work page
[76]

The `[HANGER-0]` is located on the back wall, near the head of `[BED-0]`

work page
[77]

Therefore, from the `[OBSERVER]`'s perspective (standing on the right of the bed, facing right), the `[HANGER-0]` is located behind them and to their left

work page
[78]

back-left

This corresponds to the "back-left" quadrant relative to the `[OBSERVER]`. </think> <answer> C. back-left </answer> Gemini-2.5-Pro (Original) A. back-right Task Figure F. A case study where Gemini 2.5 Pro is guided by an EgoMind CoT prompt to solve a complex spatial relationship problem. This case illustrates that our proposed framework can be used as a z...

work page
[79]

I will first traverse the room to identify all X, then map their coordinates to count them

**Plan Summary (No Tags):** - Start immediately after the `<reason>` tag. - **Goal:** Briefly explain the steps to solve the problem (e.g., "I will first traverse the room to identify all X, then map their coordinates to count them."). Summarize your plan in 3-5 concise sentences

work page
[80]

Ego-centric

**Role Play Caption (Using `<ROLE_PLAY_CAPTION>` tags):** - **Goal:** Narrate from a first-person perspective (like a camera operator or robot) what you see and hear in real-time. Follow the chronological order of the video frames. - **Requirements:** - **Perspective:** Fully adopt the "Ego-centric" view. Narrate your physical movement (e.g., "I move forw...

work page

Showing first 80 references.