TriViewBench: Controlled Complexity Scaling for Multi-View Structural Reasoning in MLLMs Yu-Yang Chen1,2 and Lan-Zhe Guo1,2 School of Intelligence Science and Technology, Nanjing University, China National Key Laboratory for Novel Software Technology, Nanjing University, China [email protected], [email protected] 1
arXiv:2606.26029v1 [cs.CV] 24 Jun 2026
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Abstract. Multimodal Large Language Models (MLLMs) demonstrate strong performance on standard visual question answering benchmarks, yet their scalability under controlled structural complexity remains poorly understood. We introduce TriViewBench, a controlled three-view visual reasoning benchmark constructed from synthetic 3D scenes with explicitly parameterized object count and occlusion. The benchmark contains 1,923 scenes and over 14K Question-Answer (QA) pairs organized into four complexity levels and three reasoning categories: Local Decision, Object Counting, and Global Recovery. We evaluate 18 open- and closed-source MLLMs under a unified prompting protocol. All 18 models exhibit an identical capability hierarchy without exception (Local Decision > Object Counting > Global Recovery), and performance degrades monotonically with complexity: Local Decision tasks decline modestly (12.11% relative drop), while Object Counting degrades substantially (59.14%) and Global Recovery collapses severely (80.02%). Error analysis on Object Counting reveals two mechanistically independent failure modes: single-view tasks are dominated by undercounting due to occlusion blindness, whereas the multi-view task reverses to overcounting due to cross-view identity confusion. Chain-of-Thought (CoT) prompting yields near-zero overall benefit (∆ = −0.16%) and its effect on Global Recovery is strongly capability-gated, suggesting that the bottleneck lies in cross-view spatial representation rather than reasoning strategy. These findings reveal fundamental scalability limitations in current MLLMs and position TriViewBench as a controlled diagnostic framework for analyzing structural reasoning failures. Keywords: Multi-view Benchmark · Controllable Scene Generation · Multimodal Reasoning
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Introduction
Multimodal Large Language Models (MLLMs) achieve strong performance across various visual question answering tasks [11, 13, 29]. In multi-view settings, however, reliable reasoning demands more than recognizing individual objects: models must align identities across views, resolve occlusion by using complementary viewpoints, and reconstruct coherent structural representations from fragmented
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Occlusion
Level 1
Level 2
high
Level 3
Level 4
Count
high
low
Local Decision
low
(tower_height_max) Q: What is the maximum number of objects stacked in a single tower? (z_relation) Q: In the 3D space, is the purple cylinder in front of or behind the cyan cube?
Object Counting
Top z
x
y
Side
Front
Top view
(all_view_count) Q: How many objects are there in the scene in total, considering all views? (front_view_count) Q: Looking only at the front, how many objects can you see?
Global Recovery
Front view
Side view
(tower_height_list) Q: Please list the number of objects in each tower, sorted from tallest to shortest.
Fig. 1: Overview of TriViewBench. Left: Four complexity levels defined by object count and occlusion density with three-view rendering. Center: Examples of questions in three reasoning categories (Local Decision, Object Counting, and Global Recovery). Right: Overall performance comparison between humans and representative MLLMs.
visual cues [22, 27, 28]. Whether current MLLMs can maintain this level of reasoning as scene complexity increases in a controlled and measurable way remains an open question. Existing visual reasoning benchmarks provide valuable progress but exhibit several structural limitations that make this question difficult to answer [14, 19, 25]. Most benchmarks are constructed from unconstrained real-world images [6, 14, 22, 27]. While such data offers diversity, scene complexity is not explicitly parameterized: object count, spatial arrangement, and occlusion degree vary simultaneously and unpredictably, so performance degradation cannot be cleanly attributed to specific factors. Most benchmarks also operate in singleview settings, which limit the demand for cross-perspective integration [7,12,19]. Furthermore, real-image benchmarks typically lack precise object-level geometry and visibility annotations, preventing fine-grained decomposition of errors into local, counting, or structural failures. To address these limitations, we introduce TriViewBench, a controlled multi-view benchmark built from synthetic 3D scenes with explicitly parameterized object count and occlusion. Each scene is rendered from front, side, and top-down viewpoints, enforcing cross-view reasoning and structural consistency. The benchmark contains 1,923 scenes and over 14K Question-Answer (QA) pairs organized into four complexity levels and three reasoning categories: Local Decision, Object Counting, and Global Recovery. By varying complexity axes independently and structuring tasks by reasoning demand, TriV-
TriViewBench
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iewBench enables direct measurement of performance scaling and interpretable failure analysis. Figure 1 provides an overview of the benchmark design, question taxonomy, and performance comparison between humans and representative MLLMs. We evaluate 18 open- and closed-source MLLMs under a unified prompting protocol. The results reveal four consistent findings. First, all models exhibit an identical capability hierarchy: Local Decision > Object Counting > Global Recovery, forming a stable gradient across architectures, parameter scales, and model families. Second, performance degrades monotonically with complexity at markedly different rates across categories: Local Decision tasks decline modestly (12.11% relative drop from Level 1 to 4), Object Counting degrades substantially (59.14%), and Global Recovery deteriorates severely (80.02% relative drop). Third, error analysis on Object Counting uncovers two mechanistically distinct failure modes: single-view tasks are dominated by undercounting, as models fail to accurately perceive partially occluded objects from a single view; the multiview task reverses to overcounting, as models lose cross-view object identity and count the same physical object multiple times. These two modes are comparable in magnitude but opposite in direction, confirming that they arise from independent perceptual mechanisms. Fourth, Chain-of-Thought (CoT) prompting yields negligible overall gain (∆ = −0.16%), with its effect on Global Recovery being strongly capability-gated: large open-source models (≥14B) improve by an average of +17.03%, while small models (≤3B) uniformly deteriorate. Even with CoT, no model approaches human performance, indicating that the root limitation is cross-view spatial representation rather than reasoning strategy. In summary, the main contributions of this work are: – We introduce TriViewBench, a controlled multi-view benchmark with independently parameterized object count and occlusion, providing a systematic diagnostic framework for structural reasoning under complexity scaling. – We establish a universal capability hierarchy across all 18 evaluated MLLMs (Local Decision > Object Counting > Global Recovery) and quantify the differential degradation rates across categories. – We identify two mechanistically independent counting failure modes, namely single-view occlusion blindness and cross-view identity confusion, through error direction analysis. These findings reveal distinct perceptual bottlenecks that emerge at comparable magnitudes but in opposite directions. – We show that CoT prompting does not resolve structural scaling failures and that its benefit on Global Recovery is capability-gated, pointing to cross-view spatial representation as the fundamental bottleneck.
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Related Work
Multimodal Large Language Models. Recent MLLMs combine vision encoders with large language models to perform diverse visual tasks, including image captioning, visual question answering, and scene understanding [3,5,10,15,17,18,24]. Model scale and architectural design have been shown to influence reasoning
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capability, though their relative contributions remain contested [3, 4, 24]. TriViewBench tests these factors by evaluating 18 models spanning a wide range of scales and architectures under identical conditions, finding that architectural family predicts performance more reliably than parameter count alone. Visual Reasoning Benchmarks. Most existing visual reasoning benchmarks are built around single images [2, 16, 19, 20, 30], which limits the demand for crossview information integration: models need only process one perspective at a time and are never required to reconcile observations across viewpoints or resolve occlusion by consulting complementary angles. A growing body of work has extended evaluation to multi-image or multi-view settings to address this gap. MuirBench [23] covers 12 multi-image task categories and finds that even GPT4o achieves only 68.0% accuracy. MMSI-Bench [27] targets multi-image spatial intelligence and finds that the strongest open-source model attains only 30.7% accuracy against a human baseline of 97.2%, with six domain experts spending over 300 hours crafting 1,000 questions manually. All-Angles Bench [28] evaluates multi-view understanding across real-world scenes and identifies cross-view occlusion as a central failure mode. These benchmarks demonstrate that multiview reasoning poses genuine challenges to current MLLMs, but their reliance on real-world imagery introduces two interrelated limitations. First, collecting and annotating multi-view real-world data is labor-intensive: establishing groundtruth answers for spatial relations, occlusion states, and structural attributes requires careful manual verification for each scene. Second, and more fundamentally, scene complexity in real images is uncontrolled. Object count, spatial arrangement, and occlusion degree vary simultaneously and without explicit parameterization. When accuracy drops, it is impossible to attribute failure to specific causal factors or to measure how performance scales with individual complexity dimensions. TriViewBench addresses both limitations through synthetic, parameterized scene generation: ground-truth annotations are derived automatically from scene metadata without any manual labeling, and complexity axes are varied independently, enabling direct attribution of observed failures to object cardinality, occlusion severity, or their combination. Synthetic and Controllable Benchmarks. CLEVR [12] showed that controlled synthetic generation enables precise failure diagnosis invisible to real-image evaluations, establishing a paradigm that has since informed numerous diagnostic benchmarks. In the domain of Object Counting, VLMCountBench [9] shows that MLLMs fail under compositional counting scenarios, consistent with our findings. TriViewBench extends this controlled paradigm to a three-view setting, adding occlusion as an explicit complexity axis and introducing a cross-view structural reasoning dimension absent from prior synthetic benchmarks. Spatial Reasoning and CoT Prompting. Prior work probes spatial reasoning from complementary angles: 3DSRBench [19] exposes degradation under noncanonical viewpoints across 12 question types, Spatial457 [25] diagnoses 6D spatial reasoning through a cascading evaluation structure across five difficulty lev-
TriViewBench
Stage 1: Data Construction
Stage 2: Question Construction
Controlled Scene Generation & Tri-View Rendering
Automated Question Generator
Controlled Scene Generation Automated Scene Generator
Scene Config
• • •
Object Count • Tower Count • Occlusion Level •
Object Shape Color of Shapes …
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tower_height_max x_relation y_relation z_relation
Output: QA Pairs Global Recovery
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tower_height_list front_hidden_count side_hidden_count front_occluded_ref side_occluded_ref
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Stage 3: Model Evaluation
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Output: • 3 View Images • Structured Annotation
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Input: • Images • Questions
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Predicted Answer
Fig. 2: Illustration of the TriViewBench construction pipeline. The workflow comprises three main stages: (1) Data Construction: generating synthetic 3D scenes from parameterized configs and rendering three-view images with structured annotations; (2) Question Construction: automatically synthesizing QA pairs across three reasoning Categories (Local Decision, Object Counting, and Global Recovery) based on scene metadata; (3) Model Evaluation: benchmarking 18 MLLMs under various complexity levels and prompting protocols.
els, and MM-Spatial [6] advances 3D spatial understanding via multi-view and metric depth inputs, targeting spatial relationship prediction, metric estimation, and 3D grounding. These benchmarks reveal important limitations in model spatial awareness, but focus primarily on metric estimation, orientation, and 3D grounding rather than the structural reconstruction and cross-view identity resolution that characterize TriViewBench’s task design. CoT prompting [21,26,31] has been shown to improve multi-step reasoning in language tasks. Our evaluation finds that CoT yields near-zero overall benefit (∆ = −0.16%) and that its effect on the hardest category is strongly capability-gated, pointing to spatial representation rather than reasoning strategy as the primary bottleneck.
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TriViewBench
In this section, we present TriViewBench, a controlled benchmark for evaluating multi-view structural reasoning. We first introduce the benchmark construction process in Sec. 3.1, and then provide an overview of its task organization and distribution in Sec. 3.2.
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Benchmark Construction Process
As illustrated in Fig. 2, TriViewBench is built through three stages: data construction, question construction, and model evaluation. Data Construction. Scenes are generated using a parameterized pipeline built on top of Kubric [8], an open-source framework for procedural scene generation. Each scene is controlled by two independent axes of complexity, yielding four levels: Level 1 (LowCount_LowOcclusion), Level 2 (LowCount_HighOcclusion), Level 3 (HighCount_LowOcclusion), and Level 4 (HighCount_HighOcclusion). The count axis distinguishes LowCount scenes (1–5 objects) from HighCount scenes (6–10 objects). The occlusion axis is defined through the front and side views only, since vertical stacking can produce near-zero top-down visibility, and the top view is conventionally reserved for spatial localization. LowOcclusion requires all objects to have visibility ratios above 60% in both lateral views. HighOcclusion requires at least one object to fall below 30% visibility in either view; crucially, the same object must exceed 60% visibility in the other view, ensuring that every question remains answerable from the provided images. For each scene, three virtual cameras render aligned front, side, and top-down images. A structured JSON annotation is generated alongside, recording a rich set of metadata including world coordinates, camera parameters, object properties, spatial relations between objects, and per-view bounding boxes and visibility ratios. These annotations serve as the sole ground truth for all downstream QA generation, guaranteeing answer correctness and uniqueness by construction. Question Construction. QA pairs are generated automatically from the structured annotations using deterministic templates, with no manual labeling required. We define 13 fine-grained task types grouped into three categories. Local Decision covers pairwise spatial relation judgment (relative position along x/y/z axes) and comparative attribute queries (identifying the tallest tower), both of which can be resolved from locally visible objects without requiring complete scene enumeration. Object Counting requires fine-grained enumeration within or across views, including single-view counting, tower-level counting from the top perspective, and cross-view identity matching. Global Recovery demands comprehensive cross-view synthesis, with the primary task being full tower height distribution recovery; this category also includes occlusion-specific sub-tasks (hidden object counting and occluded object identification) that appear exclusively in HighOcclusion levels (Level 2 and Level 4). Because all answers are derived deterministically from annotations, ambiguity and labeling error are eliminated by design. Model Evaluation. In the evaluation stage, the three rendered images and a question are jointly provided to the model. The textual response is compared against the ground-truth answer derived from the annotation.
TriViewBench
Global tower_height_list Full height distribution recovery. Recovery front/side_hidden_count† Reason about fully occluded objects. † front/side_occluded_ref Identify partially obscured objects.
3.2
24 .
Ob to
p_
13 to w .6 % er_ cou nt
1 3. 6% vie w_c ou nt
Total questions: 14,174
5 4.3 %
je ct
C o u n ti n g
n t_
2
Glo ba
Loc al
fro
tow er
Single-view visible object counting. Count towers from top perspective. Cross-view identity matching.
1.0
ation z_rel 6.0%
Object front/side_view_count Counting top_tower_count all_view_count
Identify the maximum tower height. Relative 3D spatial relations.
very eco lR %
n atio rel y_ .4% 5
tower_height_max x/y/z_relation
side _hid den_ front_ 1.6% coun t hidde n_co 1.4% unt
1 3.6 % o unt a ll _ v i e w _ c
Local Decision
Description
ion cis De 7%
Category Sub-type
tower_hei ght_ max 8.0% x_ rel a 5.3 tio %
n
list ht_ eig _h .6% 13
side_occluded_ref 2.3% ref cluded_ front_oc2.2%
Table 1: Taxonomy of reasoning tasks. † denotes sub-types that appear only in HighOcclusion levels (Level 2 and 4).
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% 1 3.6 nt cou ew_
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Fig. 3: Task distribution.
Overview of TriViewBench
TriViewBench contains 14,174 QA pairs spanning 13 reasoning sub-types organized into three categories. The task taxonomy is summarized in Tab. 1, and the distribution across categories is shown in Fig. 3. Object Counting forms the largest portion (slightly over half), distributed evenly across single-view counting, cross-view identity matching, and top-view tower counting, ensuring balanced coverage of different aggregation scenarios. Local Decision constitutes approximately one quarter of the dataset, covering spatial relations along three axes and maximum tower height identification. Global Recovery accounts for the remainder, with full tower height reconstruction as its primary sub-task; occlusion-specific sub-types appear only in Level 2 and Level 4, forming a smaller subset. The two complexity axes, object count and occlusion severity, are varied independently, enabling controlled attribution of performance changes to each factor separately or their combination.
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Experiments
4.1
Evaluation Setup
Models. We evaluate 18 MLLMs spanning proprietary and open-source systems across a broad range of architectures and parameter scales. Proprietary models include GPT-4o [10], Gemini-2.5-Flash [5], and Claude-3.7-Sonnet [1]. Opensource models include two LLaVA-OneVision variants (0.5B, 7B) [15], three Qwen2.5-VL variants (3B–32B) [4], four Qwen3-VL variants (2B–32B) [3], and six InternVL3.5 variants (1B–38B) [24]. Inference regimes. Two prompting protocols are evaluated. Direct prompting instructs models to produce a concise final answer within a 128-token limit, measuring direct perceptual and structural reasoning without explicit intermediate reasoning. CoT prompting [26] allows extended intermediate reasoning up to 2,048 tokens. All experiments use greedy decoding (temperature = 0) to ensure reproducibility.
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Table 2: Evaluation results for 18 MLLMs on TriViewBench. For each level, the bestperforming proprietary model and the best-performing open-source model are both indicated in bold. Complexity Levels Model
Level 1 Level 2 Level 3 Level 4 Overall
Proprietary GPT-4o 86.41 Claude-3.7-Sonnet 84.16 Gemini-2.5-Flash 91.52 Open-source LLaVA-OneVision-Qwen2-0.5B 18.15 LLaVA-OneVision-Qwen2-7B 45.60 Qwen2.5-VL-3B-Instruct 47.21 Qwen2.5-VL-7B-Instruct 64.14 Qwen2.5-VL-32B-Instruct 67.21 Qwen3-VL-2B-Instruct 60.34 Qwen3-VL-4B-Instruct 69.55 Qwen3-VL-8B-Instruct 79.54 Qwen3-VL-32B-Instruct 86.04 InternVL3.5-1B 47.18 InternVL3.5-2B 56.78 InternVL3.5-4B 49.39 InternVL3.5-8B 40.84 InternVL3.5-14B 51.86 InternVL3.5-38B 56.55 Human Human Performance 99.67
68.70 72.41 83.35
50.74 67.99 81.90
42.76 54.90 65.94
60.35 68.62 79.55
21.64 28.17 28.40 42.36 49.38 48.10 58.80 64.93 69.39 34.32 38.53 45.66 40.91 46.68 46.19
14.69 18.07 14.34 22.74 35.30 38.61 43.61 55.58 54.26 24.94 29.55 40.35 37.52 39.54 39.28
15.91 18.34 20.37 25.05 32.10 33.59 40.75 46.68 43.02 25.90 26.87 34.11 28.43 32.45 34.98
17.48 26.49 26.59 37.05 44.59 44.07 52.00 60.32 61.38 32.26 36.76 41.71 36.34 41.79 43.37
99.00
99.67
98.67
99.25
Evaluation protocol. Accuracy is the primary metric across all tasks. For most question types, correctness is determined by exact string match. For Global Recovery (tower_height_list), which requires predicting an ordered list of tower heights, predictions and ground-truth lists are both sorted in descending order before comparison; a response is marked correct only if the full sorted sequence agrees exactly. Human baseline. Three participants independently answered a stratified random subset of 400 questions (100 per complexity level), achieving an overall accuracy of 99.25%. 4.2
Main Results
Table 2 presents the full benchmark results. We summarize our main findings as follows: All models degrade substantially as structural complexity increases. Across all 18 evaluated models, accuracy declines monotonically from Level 1 to
TriViewBench