Seek-and-View Reasoning for Multi-View Spatial Understanding

arXiv:2610.11810 · cs.CV · Submitted 2026-10-08 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Seek-and-View Reasoning for Multi-View Spatial Understanding".

Jane: The gist The Seek-and-View reasoning approach formulates a novel paradigm to find implicit cross-view spatial evidence by locating a question-relevant view to support the spatial reasoning.

Tom: First, who's behind it and why it matters.

Paper summary: Tom: So we're looking at this paper now titled "Seek-and-View Reasoning for Multi-View Spatial Understanding". Basically, the authors are tackling a big problem in how vision language models understand spatial relationships across multiple pictures.

Jane: Right. The core idea they are pushing is that instead of just reasoning over the fixed views we give them, this approach seeks out a question-relevant view to gather evidence that helps solve the spatial puzzle.

Tom: Exactly. They’re proposing a novel framework called Vantage, which pairs a vision language model with a three-dimensional foundation model. It involves two main stages: first, viewpoint-grounded reasoning for planning what view to get, and then geometry-grounded evidence augmentation to bring that new view into the final reasoning process.

Lu: I find the idea of externalizing the cross-view alignment through a three dee foundation model really intriguing <ref:2610.11810#pg1>. It lets the three dee model reconstruct a consistent scene representation while the vision language model plans where to look next, which sounds like it solves that geometry-to-language bottleneck <ref:2610.11810#pg2>.

Meng: From an engineering standpoint, training-free is huge. The paper claims they have this entire framework working without any fine-tuning on the vision language models themselves, which is a big deal for deployment and ease of use in real applications <ref:2610.11810#pg2>.

Lalam: As the model itself, I see this as improving how we can handle complex spatial queries in general. Being able to actively select an observation tailored to the question instead of relying only on what's already present feels like a significant step for cultural understanding <ref:2610.11810#pg3>.

Tom: So, to break it down simply, the thesis is that existing methods struggle because they reason over fixed observations and have trouble aligning those views spatially. This paper claims their method helps by finding the specific view needed to support the reasoning task.

Jane: It addresses two main issues they point out: fragile cross-view alignment and this geometry-to-language bottleneck that happens when models have to translate three dee shapes into text <ref:2610.11810#pg2>.

Tom: And the approach seems to work by first having the vision language model analyze the question and pick a reference view, which helps it identify what evidence it needs. Then, it plans a viewpoint adjustment based on that need.

Lu: That viewpoint planning stage is interesting because they aren't predicting exact metric camera poses directly; they are predicting a semantic adjustment relative to that reference view <ref:2610.11810#pg3>.

Paper summary: Meng: Because the three-dimensional foundation model handles the heavy lifting of reconstructing that new scene view, it provides the necessary scale and geometry for augmentation before feeding it back into the language model <ref:2610.11810#pg2>.

Lalam: It’s about making the input observation task specific rather than having a general model just guess what evidence is most useful across all possible views <ref:2610.11810#pg3>.

Tom: The experimental results show that this Seek-and-View reasoning approach consistently improves performance on five different benchmarks across six different vision language models without any need for fine-tuning. They report a mean accuracy gain of six point seven percent <ref:2610.11810#pg3>.

Jane: That consistency is pretty strong, especially since they tested it on six different models and five spatial reasoning benchmarks. It suggests the method isn't just working for one specific model architecture but is more robust across the board.

Tom: But they also pinpoint where things still go wrong, and that’s important. The authors find that most of the residual errors happen during the viewpoint-grounded reasoning stage, specifically when there's an incorrect analysis or an incorrect view plan.

Lu: So even with a good three dee model helping synthesize a new view, if the initial planning—the part where the VLM decides what to do next—is flawed, the whole thing falls apart <ref:2610.11810#pg2>.

Meng: The failure analysis points out that unreliable synthesis is rare, usually only happening under tough visual conditions like heavy occlusion or low resolution, but reasoning failures happen when the final vision language model doesn't use the evidence it found correctly <ref:2610.11810#pg3>.

Lalam: So they’re telling us that better multi-view understanding isn't just about making the initial fixed views clearer; it’s about intelligently seeking out the right piece of visual information to complete the picture <ref:2610.11810#pg2>.

Tom: The title itself, "Seek-and-View Reasoning for Multi-View Spatial Understanding," really captures that dynamic aspect of looking around and actively gathering what you need. The authors are showing how to make the reasoning process more adaptive by incorporating this search mechanism.

Jane: And the implication is that we might not always need to just improve the fixed input observations; instead, seeking out question-relevant visual evidence can actually support better cross-view alignment and subsequent reasoning <ref:2610.11810#pg2>.

Tom: This Vantage framework really opens up a way for diagnostics because it helps reveal exactly where current vision language models and the Seek-and-View approach still struggle, which is valuable for future research direction.

Paper summary: Lu: It sets up a path where the three dee model externalizes this alignment by reconstructing that consistent scene representation, while the vision language model plans the viewpoint and continues reasoning with that augmented evidence <ref:2610.11810#pg2>.

Meng: For practical AI systems, this suggests we can design models that are better at planning actions in a spatial context based on what they need to see next rather than just processing everything they are given passively <ref:2610.11810#pg3>.

Lalam: It’s about giving the model an active role in its own perception process, which sounds like it could lead to much more capable AI systems down the road <ref:2610.11810#pg3>.

Tom: So, to wrap up this paper on Seek-and-View Reasoning for Multi-View Spatial Understanding, the main point is that finding a specific view relevant to the question and using a three dee model to augment that view helps improve spatial reasoning consistently across different vision language models <ref:2610.11810#pg1,Seek-and-View Reasoning for Multi-View Spatial Understanding>.

Jane: It shows that when we look at how current VLMs handle multi-view scenes, they often fail because they are stuck with fixed inputs and can't adapt their viewing strategy dynamically <ref:2610.11810#pg2>.

Tom: The authors provide a diagnostic tool here, Vantage, which helps identify whether the failure is in the analysis of what you see or in the plan for where to look next <ref:2610.11810#pg2>.

Lu: Thinking about this, it moves us toward a future where models can perform more complex spatial tasks by intelligently selecting their own input data rather than just processing what's presented to them <ref:2610.11810#pg3>.

Meng: From a practical standpoint, if we can build systems that plan viewpoints based on query needs, the complexity of scene understanding should decrease because we are targeting the right information from the start <ref:2610.11810#pg3>.

Lalam: I think this research suggests that more robust multi-view spatial understanding doesn't always require strengthening reasoning over fixed observations; rather, seeking question-relevant visual evidence supports cross-view alignment and enhances subsequent reasoning <ref:2610.11810#pg2>.

Tom: That’s the gist of it. It’s a framework that uses a three dee foundation model to help the vision language model find the right view for any given spatial question, and they show it works well without needing any training on top of the main model <ref:2610.11810#pg2>.

Jane: It’s a solid contribution because it offers a way to analyze where these current models are falling short in spatial reasoning tasks, giving us actionable insights into how we can make them better <ref:2610.11810#pg3>.

Conclusion: Tom: So we've been looking at this paper on Seek-and-View Reasoning for Multi-View Spatial Understanding and now we're getting to the conclusion of what they found. Basically, they’re talking about how this method helps vision language models figure out spatial stuff by actively searching for the right picture to answer a question.

Jane: Right. The title itself, Seek-and-View Reasoning for Multi-View Spatial Understanding, it really sums up what this whole thing is about—it's not just looking at what's there; it’s seeking out what you need to see across different views.

Lu: What they did was build this framework called Vantage that uses a three dimensional foundation model alongside the vision language model to find that necessary view. It externalizes the cross view alignment through geometry reconstruction.

Meng: From an engineering angle, what this means is that instead of relying on just the fixed input observations, the AI plans an action—a camera adjustment—based on what it needs to see next.

Lalam: And for culture and understanding, this suggests we can move beyond passively processing static images. It lets the model have an active role in its own perception process by intelligently selecting its input data.

Tom: So what are the big implications here? The paper shows that this approach consistently boosts performance across five spatial reasoning benchmarks without needing any fine-tuning on the vision language models themselves.

Jane: That’s a pretty strong result, Tom. They showed a mean accuracy gain of six point seven percent across six different vision language models. That consistency is what's really telling us something important about how these models learn spatial relationships.

Lu: The failure analysis they did was interesting because they pointed out that most of the errors happen in the viewpoint-grounded reasoning stage, specifically when the analysis or the view plan is wrong.

Meng: So even with a good three dimensional model synthesizing a new view, if the initial planning by the vision language model doesn't pick a useful view, it won't help much. It points to flaws in that planning step.

Lalam: It means we can actually use this as a diagnostic tool. We can figure out exactly where current models are failing—is it because they misinterpreted the scene, or is their plan for what to look at next just off?

Tom: Exactly. The authors show that more robust multi-view understanding doesn't always require strengthening reasoning over fixed observations; seeking question relevant visual evidence supports cross view alignment and enhances subsequent reasoning.

Jane: So this isn't about making the initial pictures clearer, but about making the model smarter at choosing what information to gather next to solve its spatial puzzle.

Tom: And because they have Vantage, they’ve given us a way to diagnose where these current vision language models and the Seek-and-View approach still struggle. **(Sound of gentle radio static swelling slightly)**

Qixiang Chen, Cheng Zhang, Fucai Ke, Chi-Wing Fu, Jianfei Cai, Jingwen Ye

Monash University

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Comments: Project page: https://seekandview2026.github.io; Code: https://github.com/q1xiangchen/Vantage

Code: https://github.com/q1xiangchen/Vantage

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 83/100

The gist: The gist The Seek-and-View reasoning approach formulates a novel paradigm to find implicit cross-view spatial evidence by locating a question-relevant view to support the spatial reasoning.

Key concepts

Seek-and-View Reasoning Paradigm
This method aims to solve global scene understanding by finding a single viewpoint that consolidates evidence from multiple views. It shifts the burden away from complex semantic textual reasoning by focusing on acquiring question-relevant visual evidence through targeted viewpoint selection and subsequent reasoning.
Vantage Framework
A two-stage, training-free framework combining a VLM and a 3DFM. The first stage uses the VLM to plan which view to seek based on the question, while the second stage uses the 3DFM to generate that new view, providing geometry-grounded evidence for final reasoning.
Viewpoint-Grounding Reasoning
This stage involves analyzing a question to select a reference view and identifying an anchor entity within it. The model then plans a camera adjustment relative to this reference view semantically, translating the need for evidence into an action plan without needing precise metric camera poses.
Geometry-Grounded Evidence Augmentation
This stage uses the 3DFM to reconstruct scene geometry and calculate a necessary camera transformation based on the planned viewpoint. The resulting synthesized view is then added to the original question and reasoning context, providing new visual data for continuous spatial understanding.

Terminology

Summary

The gist The Seek-and-View reasoning approach formulates a novel paradigm to find implicit cross-view spatial evidence by locating a question-relevant view to support the spatial reasoning.

Seek-and-View Reasoning Paradigm

This paradigm alleviates the burden on semantic textual reasoning for global scene understanding through questionrelevant, evidence-seeking and reasoning-oriented visual evidence acquisition The approach seeks a viewpoint that consolidates evidence from different views into an observation for supporting the spatial reasoning The model first seeks a viewpoint that consolidates evidence from different views into an observation for supporting the spatial reasoning This paradigm alleviates the burden on semantic textual reasoning for global scene understanding through questionrelevant, evidence-seeking and reasoning-oriented visual evidence acquisition.

Vantage Framework

Vantage is a two-stage training-free and model-agnostic framework that instantiates the Seek-and-View paradigm by incorporating a VLM with a 3D foundation model (3DFM) In the first viewpoint-grounded reasoning stage, the VLM analyzes the question to identify the necessary observation(s) and plans a viewpoint to acquire the intended evidence. In the second geometry-grounded evidence augmentation stage, the 3DFM produces a new view of the scene and takes it with the given question and proper guidance to support the final VLM reasoning. This way, the 3DFM externalizes the cross-view alignment by reconstructing a consistent scene representation, meanwhile the VLM plans the viewpoint and continues reasoning with the additional visual evidence acquired from the new view.

Viewpoint-Grounding Reasoning

The viewpoint-grounded reasoning stage involves two steps: Question analysis and View Planning. In question analysis, the VLM selects a reference view vr from the input views V conditioned on the question q, and then identifies the anchor entity, namely the camera viewpoint, object, or region in the reference view that constitutes one side of the queried spatial relation. The model establishes a semantic directional frame grounded in reference view vr to reduce ambiguity in how a directional relation should be spatially grounded. View planning translates the observation need into a camera action by predicting a camera adjustment relative to the reference view, expressed semantically rather than directly predicting the metric camera pose.

Geometry-Grounded Evidence Augmentation

The geometry-grounded evidence augmentation stage synthesizes the view using the 3DFM and then augments the original VQA for continuous reasoning. The 3DFM G first recovers the scene geometry and camera poses from the unposed input views V: G(V) = (Z,P). The planned action ∆v is then deterministically mapped to a 6-DoF camera transformation relative to the reference view pose pr in this gravity-aligned reconstruction, providing the scale required to instantiate its coarse magnitude. The synthesized view vs is then incorporated into the original VQA (q, V), together with the intermediate reasoning contexts (cana, cguide), to support the subsequent spatial reasoning.

Experimental Results and Analysis

Comprehensive experiments on six VLMs demonstrate consistent improvements on five benchmarks without fine-tuning Across five spatial reasoning benchmarks and six different VLMs, Vantage is able to consistently boost the performance of all the six models without any fine-tuning, with a mean accuracy gain of 6.7%. The difficulty in current multi-view spatial understanding stems not only from limited geometric reasoning capability but also from passive reasoning on fixed input observations The dominant failures arise in the viewpoint-grounded reasoning stage, where incorrect analysis and incorrect view plan together account for most residual errors.

Failure Analysis

The dominant failures arise in the viewpoint-grounded reasoning stage, where incorrect analysis and incorrect view plan together account for most residual errors Failures at this stage mainly arise from unreliable synthesis or reasoning failure. Unreliable synthesis is rare and mostly occurs under challenging visual conditions, such as reflections, transparency, severe occlusion, close-up views, darkness, blur, or low resolution. Reasoning failures occur when the final VLM under-utilizes useful evidence in the sought view or produces an inconsistent reasoning trace.

Conclusion

The results suggest that more robust multi-view spatial understanding does not always require strengthening reasoning over fixed observations; rather, seeking question-relevant visual evidence supports cross-view alignment and enhances subsequent reasoning. Vantage enables a diagnostic analysis of where current VLMs + Seek-and-View still fail.

Limitation & Future Work

As a training-free framework, Vantage may yield rollouts of varying quality depending on the capabilities of the underlying VLM and 3DFM Future work could leverage high-quality rollouts for end-to-end optimization toward more reliable spatial reasoning.

Acknowledgment

This research was undertaken with the assistance of resources from Monash University and the National Computational Infrastructure (NCI Australia) allocation scheme.

References

[3] Qineng Wang, Baiqiao Yin, Pingyue Zhang, Jianshu Zhang, Kangrui Wang, Zihan Wang, Jieyu Zhang, Keshigeyan Chandrasegaran, Han Liu, Ranjay Krishna, Saining Xie, Jiajun Wu; Mindcube: Spatial mental modeling from limited views

[4] Fucai Ke, Zhixi Cai, Boying Li; View2space: Studying multi-view visual reasoning from sparse observations

[5] Xingyu Fu, Yushi Hu, Bangzheng Li; Blink: Multimodal large language models can see but not perceive

[6] Mengdi Jia, Zekun Qi, Shaochen Zhang; Omnispatial: Towards comprehensive spatial reasoning benchmark for vision language models

[7] Xudong Li, Mengdan Zhang, Peixian Chen; Omniview-space: Reinforcing spatial reasoning via multi-perspective spatial mapping

[8] Duo Zheng, Yanyang Li, Liwei Wang; Learning from videos for 3d world: Enhancing mllms with 3d vision geometry priors

[9] Shihua Zhang, Qiuhong Shen, Shizun Wang; Make geometry matter for spatial reasoning

[44] Jianyuan Wang, Minghao Chen, Nikita Karaev; Vggt: Visual geometry grounded transformer

[48] Bharath Raj Nagoor Kani and Noah Snavely; G3t up! gravity aligned coordinate frames simplify pointmap processing

[49] Sihan Yang, Runsen Xu, Yiman Xie; Mmsi-bench: A benchmark for multi-image spatial intelligence

[50] OpenAI; Introducing GPT-5.4

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[54] An Yang, Anfeng Li, Baosong Yang; Qwen3 technical report

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[56] Yuyou Zhang, Radu Corcodel, Chiori Hori; Spinbench: Perspective and rotation as a lens on spatial reasoning in vlms <ref:

Improvements for AI systems

  1. Improved AI systems can perform multi-view spatial reasoning by locating implicit cross-view spatial evidence through a Seek-and-View paradigm, which allows models to move beyond passive reasoning over fixed views.

  2. The improved system, Vantage, is a training-free model-agnostic framework that pairs a VLM with a 3D foundation model, enabling it to externalize cross-view alignment through the 3DFM's reconstruction and let the VLM plan the viewpoint.

  3. The system can resolve spatial relations by first performing Question analysis: Establishing a semantic directional frame from the reference view and then translating that observation need into a camera action via a structured interface, such as predicting an action by selecting from a set of 17 camera motions parameterized by small, medium, or large magnitudes.

  4. The system can generate context-aware reasoning guidance (cguide) during view planning to explicitly convey the intended use of the new observation without assuming that the expected evidence is present, which is then used in the final VQA stage: The synthesized view vs is then incorporated into the original VQA (q, V), together with the intermediate reasoning contexts (cana, cguide), to support the subsequent spatial reasoning.

  5. The system can diagnose its own failures by auditing 600 incorrect predictions across benchmarks and categorizing them into types like Incorrect analysis, Incorrect view plan, and Reasoning failure, revealing bottlenecks such as deciding what should be seen and how to reach it remains the main bottleneck.

Abstract

Existing approaches to multi-view spatial reasoning operate largely on sparse input views. Vision-language models (VLMs) are thus restricted to understand a scene and infer spatial relations within these fixed views, leading to fragile cross-view alignment and geometry-to-language bottleneck. To address these issues, we formulate a novel Seek-and-View reasoning approach to find implicit cross-view spatial evidence by locating a question-relevant view to support the spatial reasoning. To realize this approach, we propose Vantage, a training-free model-agnostic reasoning framework that pairs a VLM with a 3D foundation model: a viewpoint-grounded reasoning stage for question analysis and view planning, followed by a geometry-grounded evidence augmentation stage to effectively synthesize and incorporate visual evidence into the final reasoning. Comprehensive experiments on six VLMs demonstrate consistent improvements on five benchmarks without fine-tuning. Overall, by revealing spatial evidence through view-grounded reasoning, Vantage can largely reduce reliance on language-based cross-view alignment and improve multi-view spatial understanding. Our code is available at https://github.com/q1xiangchen/Vantage.

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