arxiv: 2603.23404 · v2 · submitted 2026-03-24 · 💻 cs.CV · cs.CL

Recognition: unknown

Unleashing Spatial Reasoning in Multimodal Large Language Models via Textual Representation Guided Reasoning

Jiacheng Hua , Yishu Yin , Yuhang Wu , Tai Wang , Yifei Huang , Miao Liu

Authors on Pith no claims yet

Pith reviewed 2026-05-15 00:04 UTC · model grok-4.3

classification 💻 cs.CV cs.CL

keywords spatial reasoningmultimodal large language modelsegocentric videoallocentric representationprompting method3D scene understandingvideo question answeringintermediate reasoning traces

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The pith

TRACE prompting lets MLLMs create text-based 3D maps from video to answer spatial questions more accurately.

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

The paper proposes TRACE, a prompting method that guides multimodal large language models to first generate textual representations of 3D environments drawn from egocentric video. These representations capture meta-context, camera trajectories, and object entities to serve as structured intermediate steps before final answers. The approach draws on allocentric spatial reasoning to address MLLMs' current weaknesses in building coherent 3D abstractions from video. Tests on VSI-Bench and OST-Bench show consistent gains over earlier prompting techniques across many model sizes and training types. This matters because it provides a training-free way to strengthen spatial capabilities that many existing MLLMs lack.

Core claim

TRACE induces MLLMs to output text-based allocentric representations of 3D scenes from egocentric video inputs as intermediate reasoning traces. By encoding meta-context, camera trajectories, and detailed object entities, these traces enable more structured spatial reasoning, producing notable and consistent accuracy gains on VSI-Bench and OST-Bench across diverse MLLM backbones that vary in scale and training schema.

What carries the argument

TRACE (Textual Representation of Allocentric Context from Egocentric Video), a prompting method that generates text-based 3D environment descriptions as intermediate reasoning traces for spatial question answering.

If this is right

TRACE outperforms prior prompting strategies on VSI-Bench and OST-Bench for spatial reasoning tasks.
The improvements hold across MLLMs with different parameter scales and training schemas.
Ablation studies confirm that encoding meta-context, trajectories, and object entities each contribute to the gains.
Detailed analyses identify specific bottlenecks in current MLLM 3D spatial reasoning from video.

Where Pith is reading between the lines

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

The text representations could be combined with external geometry tools to verify or correct the intermediate steps in real applications.
TRACE-style prompts might transfer to related tasks such as video-based action planning or object manipulation.
Making the representations use more explicit coordinate formats or graphs rather than free text could further reduce ambiguity.
Success with text intermediates points to a broader pattern where language scaffolds can compensate for missing geometric modules in MLLMs.

Load-bearing premise

MLLMs can reliably generate accurate and complete text-based allocentric representations of 3D environments from egocentric video without introducing errors that propagate to downstream spatial reasoning.

What would settle it

A test set with known ground-truth 3D layouts where TRACE-generated texts are measured for accuracy against the layout and spatial answer accuracy is compared when texts are forced to be accurate versus deliberately inaccurate.

Figures

Figures reproduced from arXiv: 2603.23404 by Jiacheng Hua, Miao Liu, Tai Wang, Yifei Huang, Yishu Yin, Yuhang Wu.

**Figure 1.** Figure 1: Motivation for Textual Representation of Allocentric Context from Egocentric Video (TRACE) in videobased spatial reasoning. (a) An egocentric video paired with a query that requires holistic spatial reasoning. (b) A textual description that vividly captures the room layout needed to solve the example spatial question answering (QA). (c) TRACE encodes meta-context, camera trajectory, and entities, serving … view at source ↗

**Figure 2.** Figure 2: Illustration of our Textual Representation of Allocentric Context from Egocentric Video (TRACE). We construct TRACE by aligning a global coordinate system with the room layout and geometry, logging the camera trajectory across temporal steps, and registering visible objects with key attributes, estimated positions, and spatial relations. Here, we also show the key prompts used to guide MLLMs to generate th… view at source ↗

**Figure 3.** Figure 3: Performance gains across models on VSIBench. TRACE yields consistent, state-of-the-art performance gains compared to Direct prompting baselines, across various model architectures and parameter scales. the prompting scaffold the same (e.g., identical input formatting, answer constraints, and postprocessing), and vary only the method-specific instructions required by each prompting technique. We provide… view at source ↗

**Figure 4.** Figure 4: A visual illustration demonstrates that TRACE is more effective than the cognitive map (CM) approach. Notably, the CM lacks the 3D granularity required for many spatial reasoning tasks. current MLLMs lack the ability to reliably estimate camera motion, which can confuse models on tasks that require alignment-based reasoning. 4.3 Additional Analysis Decomposing 3D Spatial Understanding Prior works (Yang et … view at source ↗

**Figure 5.** Figure 5: Decompositional analysis of the reasoning parser and spatial descriptor. The Qwen series lags behind the state-of-the-art Gemini 3 on both spatial reasoning and visual perception. 0.3 0.4 0.5 0.6 Gemini 3 Pro Qwen2.5-VL-72B o3 MiMo-VL-7B GLM-4.5V ARKitScenes 0.3 0.4 0.5 0.6 Gemini 3 Pro Qwen2.5-VL-72B o3 MiMo-VL-7B GLM-4.5V ScanNet 0.3 0.4 0.5 0.6 Gemini 3 Pro Qwen2.5-VL-72B o3 MiMo-VL-7B GLM-4.5V ScanNet… view at source ↗

read the original abstract

Existing Multimodal Large Language Models (MLLMs) struggle with 3D spatial reasoning, as they fail to construct structured abstractions of the 3D environment depicted in video inputs. To bridge this gap, drawing inspiration from cognitive theories of allocentric spatial reasoning, we investigate how to enable MLLMs to model and reason over text-based spatial representations of video. Specifically, we introduce Textual Representation of Allocentric Context from Egocentric Video (TRACE), a prompting method that induces MLLMs to generate text-based representations of 3D environments as intermediate reasoning traces for more accurate spatial question answering. TRACE encodes meta-context, camera trajectories, and detailed object entities to support structured spatial reasoning over egocentric videos. Extensive experiments on VSI-Bench and OST-Bench demonstrate that TRACE yields notable and consistent improvements over prior prompting strategies across a diverse range of MLLM backbones, spanning different parameter scales and training schemas. We further present ablation studies to validate our design choices, along with detailed analyses that probe the bottlenecks of 3D spatial reasoning in MLLMs.

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

TRACE is a practical prompting method that delivers consistent gains on spatial reasoning benchmarks for MLLMs, but the paper never checks whether the generated text representations actually match the 3D scene.

read the letter

The core takeaway is that this paper gives a concrete prompting template called TRACE that pushes MLLMs to output text descriptions of camera paths, objects, and context from video, then uses those as intermediate steps for better answers on spatial questions. It reports gains across several model families and sizes on VSI-Bench and OST-Bench, plus ablations on the prompt components. That combination of a specific construction and multi-model testing is the main new piece relative to earlier prompting work in the area. The experiments are straightforward and the ablations help show which parts of the template matter. The results look reproducible enough that someone could try the same template on their own models without much trouble. The main weakness is that the paper measures only final task accuracy. It does not compare the generated text representations against ground-truth 3D annotations or human ratings of completeness, so it is still possible the gains come from longer context or generic chain-of-thought structure rather than genuine allocentric modeling. The assumption that the models produce faithful 3D text without propagating errors is left untested. This work is aimed at people building or fine-tuning MLLMs for video understanding and spatial tasks. A reader who wants ready-to-use prompting ideas for existing models will find the template and benchmark numbers useful. The paper is clear enough and the evidence is solid enough on its own terms that it should go to peer review rather than a desk reject.

Referee Report

3 major / 2 minor

Summary. The paper introduces TRACE, a prompting method that induces MLLMs to produce intermediate text-based allocentric representations of 3D environments from egocentric video inputs. These representations encode meta-context, camera trajectories, and detailed object entities to support improved spatial question answering. Experiments on VSI-Bench and OST-Bench report consistent gains over prior prompting baselines across multiple MLLM backbones of varying scales and training regimes, supported by ablation studies.

Significance. If the observed gains prove attributable to the structured allocentric modeling rather than prompt-length artifacts, the work offers a training-free, cognitively motivated technique for eliciting spatial reasoning in existing MLLMs. The cross-backbone evaluation and ablation analyses provide useful empirical grounding for the design choices.

major comments (3)

[Experiments] Experiments section: the abstract and results claim consistent improvements but supply no quantitative effect sizes, confidence intervals, or statistical significance tests; without these, it is impossible to assess whether the gains exceed what would be expected from longer or more detailed prompts alone.
[Method and Analysis] Method and Analysis sections: the central claim rests on the assumption that the generated textual representations faithfully capture 3D structure, yet the paper provides no direct fidelity evaluation (e.g., comparison against ground-truth 3D annotations or human judgments of completeness and accuracy); end-task accuracy alone cannot distinguish genuine allocentric modeling from generic chain-of-thought scaffolding.
[Ablation studies] Ablation studies: the reported ablations do not isolate the contribution of each representational component (meta-context, trajectories, object entities) while holding total token count fixed, leaving open whether performance differences arise from content structure or from variations in prompt length and detail.

minor comments (2)

[Abstract] Abstract: the phrase 'notable and consistent improvements' is used without accompanying numerical values or baseline comparisons, reducing informativeness for readers.
[Introduction] Notation: the acronym TRACE is introduced without an explicit expansion on first use in the main text, although the abstract provides it.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have carefully addressed each major comment below and outline the revisions we will make to strengthen the empirical support and clarify the contributions of TRACE.

read point-by-point responses

Referee: [Experiments] Experiments section: the abstract and results claim consistent improvements but supply no quantitative effect sizes, confidence intervals, or statistical significance tests; without these, it is impossible to assess whether the gains exceed what would be expected from longer or more detailed prompts alone.

Authors: We agree that reporting effect sizes, confidence intervals, and statistical significance tests is essential for rigorously evaluating the improvements. In the revised manuscript, we will add quantitative effect sizes (e.g., absolute and relative accuracy gains with standard deviations across runs), 95% confidence intervals, and p-values from paired statistical tests such as the Wilcoxon signed-rank test. To directly address the prompt-length concern, we will include a new control baseline in which baseline prompts are padded with neutral descriptive text to match the token count of TRACE prompts, allowing us to isolate the contribution of the structured allocentric content. revision: yes
Referee: [Method and Analysis] Method and Analysis sections: the central claim rests on the assumption that the generated textual representations faithfully capture 3D structure, yet the paper provides no direct fidelity evaluation (e.g., comparison against ground-truth 3D annotations or human judgments of completeness and accuracy); end-task accuracy alone cannot distinguish genuine allocentric modeling from generic chain-of-thought scaffolding.

Authors: We acknowledge that direct fidelity evaluation would provide stronger evidence for the allocentric modeling claim. Although the VSI-Bench and OST-Bench datasets do not include explicit 3D ground-truth annotations, we will add a human evaluation study on a representative subset of videos. Annotators will rate the completeness and accuracy of the generated meta-context, trajectories, and object entities against the video content, with inter-annotator agreement reported. We will also analyze the correlation between these fidelity scores and downstream QA performance to differentiate structured spatial representations from generic chain-of-thought effects. revision: yes
Referee: [Ablation studies] Ablation studies: the reported ablations do not isolate the contribution of each representational component (meta-context, trajectories, object entities) while holding total token count fixed, leaving open whether performance differences arise from content structure or from variations in prompt length and detail.

Authors: We agree that holding token count fixed is necessary to isolate the structural contributions. In the revised ablation studies, we will re-conduct the component ablations while adjusting prompt lengths to be approximately equal across conditions (e.g., by inserting neutral filler text where components are removed). This controlled setup will demonstrate that observed performance differences arise from the specific allocentric representational structure rather than token-count variations. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical prompting method evaluated on external benchmarks

full rationale

The paper introduces TRACE as a prompting intervention that induces MLLMs to produce text-based allocentric representations (meta-context, camera trajectories, object entities) from egocentric video, then evaluates end-task accuracy gains on VSI-Bench and OST-Bench across multiple backbones. No equations, derivations, or fitted parameters are presented that reduce any claimed prediction or result to the same inputs by construction. The approach draws inspiration from external cognitive theories and reports ablation studies plus benchmark comparisons; no self-citation load-bearing, self-definitional steps, or renaming of known results appear in the derivation chain. The central claim remains an empirical observation on independent test sets rather than a tautological reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the untested premise that current MLLMs possess sufficient internal 3D understanding to produce faithful textual allocentric summaries when prompted; no free parameters are introduced, but the method implicitly assumes the cognitive-theory mapping is valid for these models.

axioms (1)

domain assumption MLLMs can generate structured text representations of 3D scenes that preserve spatial relations when given appropriate meta-context and trajectory cues.
Invoked in the description of TRACE encoding meta-context, camera trajectories, and object entities.

invented entities (1)

TRACE prompting template no independent evidence
purpose: Induces generation of textual allocentric context as intermediate reasoning trace.
Newly defined prompting method; no independent evidence provided beyond the reported benchmark gains.

pith-pipeline@v0.9.0 · 5503 in / 1317 out tokens · 36097 ms · 2026-05-15T00:04:58.733145+00:00 · methodology

discussion (0)

Forward citations

Cited by 2 Pith papers

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

Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images
cs.CV 2026-05 conditional novelty 7.0

MLLMs exhibit a large perception-reasoning gap on perspective-conditioned spatial reasoning in omnidirectional images, with accuracy falling from 57% on basic direction tasks to under 1% on compositional reasoning, th...
Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images
cs.CV 2026-05 unverdicted novelty 7.0

A new benchmark reveals MLLMs achieve only 13% or lower accuracy on advanced perspective-conditioned spatial tasks in omnidirectional images, with RL reward shaping raising a 7B model from 31% to 60% in controlled settings.

Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · cited by 1 Pith paper · 3 internal anchors

[1]

Qwen2.5-VL Technical Report

Qwen2.5-vl technical report.arXiv preprint arXiv:2502.13923. Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Yuri Feigin, Peter Fu, Thomas Gebauer, Daniel Kurz, Tal Dimry, Brandon Joffe, Arik Schwartz, and Elad Shulman. 2021. ARKitscenes: A diverse real-world dataset for 3d indoor scene understanding using mo- bile RGB-d data. InThirty-fifth Conference on Ne...

work page internal anchor Pith review Pith/arXiv arXiv 2021
[2]

Scannet: Richly-annotated 3d reconstructions of indoor scenes. InProc. Computer Vision and Pattern Recognition (CVPR), IEEE. Erik Daxberger, Nina Wenzel, David Griffiths, Haim- ing Gang, Justin Lazarow, Gefen Kohavi, Kai Kang, Marcin Eichner, Yinfei Yang, Afshin Dehghan, and 1 others. 2025. Mm-spatial: Exploring 3d spatial understanding in multimodal llms...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[3]

CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

Critic: Large language models can self-correct with tool-interactive critiquing.arXiv preprint arXiv:2305.11738. Yining Hong, Haoyu Zhen, Peihao Chen, Shuhong Zheng, Yilun Du, Zhenfang Chen, and Chuang Gan

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

Zeyi Huang, Yuyang Ji, Xiaofang Wang, Nikhil Mehta, Tong Xiao, Donghyun Lee, Sigmund Vanvalken- burgh, Shengxin Zha, Bolin Lai, Licheng Yu, and 1 others

3d-llm: Injecting the 3d world into large lan- guage models.Advances in Neural Information Pro- cessing Systems, 36:20482–20494. Zeyi Huang, Yuyang Ji, Xiaofang Wang, Nikhil Mehta, Tong Xiao, Donghyun Lee, Sigmund Vanvalken- burgh, Shengxin Zha, Bolin Lai, Licheng Yu, and 1 others. 2025. Building a mind palace: Structuring environment-grounded semantic gr...

work page arXiv 2025
[5]

arXiv preprint arXiv:2409.18125 (2024)

Tree of thoughts: Deliberate problem solving with large language models. InAdvances in Neural Information Processing Systems, volume 36, pages 11809–11822. Curran Associates, Inc. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. InThe eleventh in...

work page arXiv 2022
[6]

Focus ONLY on these categories

We provide the categories to care about in this scene: {CATEGORIES_OF_INTEREST}. Focus ONLY on these categories

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[7]

Estimate the center location of each instance within the provided categories, assuming the entire scene is represented by a 10x10 grid

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[8]

If a category contains multiple instances, include all of them

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[9]

CATE- GORY NAME

Each object’s estimated location should accurately reflect its real position in the scene, preserving the relative spatial relationships among all objects. [Output] Present the estimated center locations for each object as a list within a dictionary. STRICTLYfollow this JSON format: {"CATE- GORY NAME": ["(X_1,Y_1)", ...], ...}. Answer format: - {POST_PROM...

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[10]

Coordinate System Rules (Room-Aligned Allocentric Frame) – Origin: the camera starting position is ex- actly[0.0, 0.0]on the floor plane. – Major Axes (+Y / +X): Align the coordi- nate system with thedominant wallsorfloor gridof the room rather than the camera’s ini- tial viewing direction.: – define ‘+Y’ along that dominant struc- tural direction; – defi...

work page
[11]

Meta-Context Rules You must infer and report: – room_topology: the overall spatial struc- ture of the observed environment, such as ‘rect- angular bedroom‘, ‘L-shaped office‘, or ‘nar- row hallway connected to kitchen‘ – grid_alignment: the structural cue used to define the allocentric axes – initial_camera_heading: the camera’s initial facing direction r...

work page
[12]

2s") – pos: Estimated [x, y] of the camera. – facing: Cardinal direction and axis (e.g.,

Trajectory Rules You must log the camera path continuously. Output a trajectory step forevery significant camera movement. –step: Sequential ID. – time: Timestamp of the step (e.g., "2s") – pos: Estimated [x, y] of the camera. – facing: Cardinal direction and axis (e.g., "North (+Y)"). – action: Short description of the camera motion or viewpoint change

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[13]

Never group objects

Entity Registry Rules You must register every visible entity individ- ually. Never group objects. For each entity, include: – id: unique identifier such as chair_01, door_01 –category –first_seen_at –estimated_pos:[x, y] – approx_size: [width, height, depth] – visual_signature: short appearance- based description for disambiguation – spatial_relation: at ...

work page arXiv 2021