Part-X-MLLM: Part-aware 3D Multimodal Large Language Model
Authors: Chunshi Wang
Junliang Ye
Yunhan Yang
YANG LI
Zizhuo Lin
Jun Zhu
Zhuo Chen
Yawei Luo
Chunchao Guo
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Computer Vision
3D Vision-language Modeling
Part-aware 3D understanding
Multimodal Large Language Model
Primary area: foundation or frontier models, including LLMs

Abstract: We introduce Part-X-MLLM, a native 3D multimodal large language model that unifies diverse 3D tasks by formulating them as programs in a structured, executable grammar. Given an RGB point cloud and a natural language prompt, our model autoregressively generates a single, coherent token sequence encoding part-level bounding boxes, semantic descriptions, and edit commands. This structured output serves as a versatile interface to drive downstream geometry-aware modules for part-based generation and editing. By decoupling the symbolic planning from the geometric synthesis, our approach allows any compatible geometry engine to be controlled through a single, language-native frontend. We pre-train a dual-encoder architecture to disentangle structure from semantics and instruction-tune the model on a large-scale, part-centric dataset. Experiments demonstrate that our model excels at producing high-quality, structured plans, enabling state-of-the-art performance in grounded Q&A, compositional generation, and localized editing through one unified interface. Project page: https://chunshi.wang/Part-X-MLLM/

Published as a conference paper at ICLR 2026
PART-X-MLLM: PART-AWARE LARGE LANGUAGE MODEL
3D MULTIMODAL
Chunshi Wang*.1.2 Junliang Yel.+.233 Yunhan Yang*,,4 Yang Liº, Zizhuo Lin'
Jun Zhu Zhuo Chen?, Yawei Luo Chunchao Guo ZZeejinng University, Tencent Hunyuan, TTsinghua University, "The University of Hong Kong
arm
dirig, fons
forrein vobiele with proméscnt front and rear corgo racka.
nose
engged notdd with Mlacky
Grounding
Captlening
monnted dark for all-zeernis meharie.
Part-Gen
Editing
Figure 1: Part-X-MLLM is natively 3D, part-aware multimodal large language model that provides comprehensive understanding of 3D shapes and supports wide range of 3D understanding tasks. also seamlessly integrates with diffusion-based pipelines, enabling semantically precise part-aware 3D shape generation and editing.
ABSTRACT
We introduce Part-X-MLLM, a native 3D multimodal large language model that unifies diverse 3D tasks by formulating them programs in structured, ecutable grammar. Given an RGB point cloud and a natural language prompt, our model autoregressively generates a single. coherent token sequence encod- ing part-level bounding boxes, semantic descriptions, and edit commands. This structured output serves as versatile interface drive downstream geometry- aware modules for part-based generation and editing. By decoupling the sym- bolic planning from the geometric synthesis, our approach allows any compati- ble geometry engine to be controlled through single, language-native frontend. We pre-train a dual-encoder architecture to disentangle structure from seman- ties and instruction-tun the model on large-scale, part-centric dataset. Exper- iments demonstrate that our model excels at producing high-quality, structured plans, enabling state-of-the-art performance in grounded Q&A, compositional generation, and localized editing through one unified interface. Project page:
https:://chi
wang Paart X-MLLM/
Equal contribution. 'Corresponding Author. Project Leader.

---

Published as a conference paper at ICLR 2026
INTRODUCTION
The creation of rich, interactive 3D worlds a cornerstone of modern visual computing. While re- cent advances in generative Al have solved the creation of holistic 3D shapes. they largely treat assets as static, monolithic forms. This results in a fundamental limitation we term "structural opaque- the model perceives a 3D object a single, indivisible block of geometry rather than collection of distinct components. Such opaqueness prevents downstream applications from accessing or manipulating specific parts (e.g., editing just "the chair's left leg"), thereby hindering fine-grained control in animation and editing. Real-world objects are inherently assemblies of mean- ingful parts. Unlocking true 3D interaction, therefore, demands native LLM-based interface capa- ble of reasoning about this substructure. Unlike approaches that rely on external adapters, our model adopts native strategy by treating 3D structure an intrinsic part of its language processing ge- ometric parts and edit commands as native tokens alongside natural text.
Current 3D Multimodal Large Models (MILMs)) fall short of this goal. Scene-level 3D MILMS align point clouds with language and perform captioning or Q&A Xu et al.] (2023) Qi et al. (2024b a). but they largely treat objects as monolithic and lack persistent part identifiers, grounded references, and executable outputs. On the generative side, geometry-oriented
(2024); Hong et al.
models offer high-fidelity asset synthesis via structured 3D latents Xiang et al.
(2024); Zhao et al.
(2025c); HHunyaand et al. (2025b) or tokenized 3D representations Wang et al. (2024) Ye et al.
(025a), yet expose limited semantic addressability. Part pipelines either lift segmentations to 3D Liu et al. (2024a); Chen et al. (2025a); Yang et al. (2024b); Liu et al. (2025a); Yang et al. (2025a) prone to view inconsistencies and weak 3D) constraints- or generate parts natively in 3D al. (2025b); Zhang et al. (2025); Yang et al. (2025b) without unified language interface. Editing methods increasingly operate in 3D space Li ct al. (2025), but are not themselves language- native frontends. There is still no model that (i) understands and names parts, (ii) grounds references to persistent bounding box (BBox), and (iii) compiles executable add/delete/modify programs while delegating to strong geometry engines-with controllable semantic granularity (from coarse labels to fine descriptionn) a single instruction-followii interface.
We address this challenge with Part-X-MLILM, native 3D part-aware Multimodal Large Lan- guage Model that reframes 3D interaction as language modeling problem. Our core insight is that spectrum of disparate tasks-generation,, editing, and question answering-can be unified under a grammar of parts.
single, geometry- input into structured program, emitting single token sequence of part-level bounding boxes, per- sistent references, semantic descriptions, and edit operators. This discrete, language-nativn native interface provides three concrete benefits. (1) Stable part identity and grounding: tokens carry persistent references to parts via BBox symbols, enabling precise, auditable reasoning and manipulation across steps and tasks. (2) Controllable semantic granularity: the same program can surface either coarse labels or fine descriptions on demand, and our post-hoc clustering supports user-controlled merging of parts. (3) Separation of structure and semantics: a dual-encoder design decouples geometry (X (XYZ+n)mmlls) from appearance (RGB), avoiding the representational conflict observed in single- encoder ablations and yielding consistent gains on box listing, multi-part grounding, and part Q&A. Because the output program model-agnostic, any geometry module can be driven by this token interface-turing language into a universal control surface for 3D assets. Empirically, the resulting plans enable strong part grounding. compositional generation, and localized editing across 11 task families on our UniPart-Benae establishing a general paradigm for part-centric 3D intelligence.
translates user instructions and 3D visual
Our contributions are summarized follows:
We introduce Part-X-MLLM, a native 3D part-aware MLLM that unifies generation, edit- ing, and reasoning single geometry-aware program in part grammar with persistent BBox tokens providing a language- model-agnoste control surface for 3D assets.
We propose dual-encoder architecture that decouples structure (XYZ+normals) from appearance (RGB), avoiding representational conflicts and delivering consistent gains over a single-encoder baseline across grounding. captioning, and part Q&A
We enable semantic granularity control by clustering part bounding boxes using text semantics, allowing seamless transition between coarse components and fine- ame-grained parts under the same programmatic interface.
2

---

Published as a conference paper at ICLR 2026
We establish UniPart-Bench, 30k-entry part-centric benchmark spanning 11 task fami- lies with geometric and linguistic metrics, and use it to rigorously evaluate plan quality and downstream performance.
RELATED WORK
2.1 3D MULTIMODAL UNDERSTANDING AND GENERATION
Early 3D MLLMs align point clouds with language for 3D captioning, QA, and reasoning. in- cluding PointLLM Xu et al. (024), 3D-LLM Hong et al. (202), Point-BERT Yu et al. (2022). GPT4Point Qi et al. (2024b), and ShapeLLM Qi al. (2024a). However, point clouds' sparsity and limited detail constrain high-fidelity, editable asset creation. Recent work addresses this through geometry-aware latents: TRELLIS Xiang et al. (2024) employs structured sparse voxel latents with rectified flow for unified decoding to meshes/
Hunyuan31 2.x [Zhao et al. (202c)); Hunyuan3D al. (2025b) provides a production-ready pipeline with PBR materials. Discretiza- tion enables autoregression: LLaMA-Mesh Wang et al. (2024) feeds OBJ text to LLMs but ignores mesh topology, DeepMesh Zhao et al. (2025a) addresses this with auto-regressive artist-mesh cre- ation enhanced by reinforcement learning. while ShapeLLM-One Oommi Ye et al. (2025a) compresses 3D into discrete tokens for unified text/image/31) understanding and generation. Despite these ad- vances, most systems remain object- or scene-level Wang et al. (2025): Min et al. (2024); Miao et al.) (2055b: Existing methods often lack persistent part identities, grounded references, and executable outputs for downstream geometry engines. We address this by introducing language-natic inter- face that outputs tokenized bounding boxes and edit programs, enabling part-aware and high-fidelity generation and editing.
2.2 PART GENERATION
2D-drivee pipelines extract multi-view then lift to 3D: Part123 Liu al. (2024a) and PhyCAGE Yan et al.] (2024)) uses Kiriloo et al.] (2023) masks, PartGen Chen et al. (0025)) segments/inpaints with inconsistency issues, SAMPart3D Yang al. 2024b) and PartField Liu et al. (2025a) distill priors, and HoloPart Yang et al. (2025a) completes parts with diffusion. These methods suffer from weak 3D constraints. Direct 3D approaches include: PASTA Li al. (2024a)
for primitive composition, AutoPartGen [hen et al.
(025b) for autoregressive generation, Part- Packer Tang al. (2025) and Frankenstein Yan et al. (222a)) for efficient part representation with constrained space usage, BANG ZZang et al. (2025) for exploded views, and Assembler /Zhao et al. (2025b) for assembly sampling. OmniPart Yang et al. (2025b) unifies these approaches via autore- gressive box planning followed by TRELLIS bRaseed synthesis. X-Part Yan et al. (2025) scale up vecset-based part generation conditioned on semantics provided My ct al. (2025)
2.3 3D EDITING
Optimization-basel editing utilizes SDS: DreamFusion Poole et al. (2022) and DreamReward Ye al. (2024) enables text-to-3D generation, V and Instruct-NeRF2Naat Haque et al. (2023) edits mu multi- fi-views using InstructPix Brooks et al.)
(2023) adds volumetric regularization.
(202)) 1. and 2 Miao et al. (2025a) extends text-driven editing to volumetric video. Faster alternatives include: Shap-Editor Chen et al. (2024b) feed-forward latent editing, MVEdit Chen et (2024a) as training-fres 3D adapter, and PrEd-
(023) while optimizing NeRF Mildenhall MeddMMilenhall
itor3D |trkog/Ekkoç et al. (2025) using DDPM inversion with 2D-to-3D lifting. FocalDreamer Li et al. (2024b) enables part-wise assembly, VoxHammer Li et al. (2025) performs training- free latent edit- ing, NANO3D Ye et al. (2025b) achieves precise and coherent 3D editing without requiring masks in a training-free free manner, and Make- Your-3D Liu et al. (2024b) customizes subjects via model co- evolution. Yet these methods typically tool-side: they do not provide language- native model that reasons about parts and emits executable edit programs with precise spatial grounding. We target this gap by coupling part-aware planning interface with strong geometry backends.
3

---

Published as a conference paper at ICLR 2026
Part Sing Cap Ground
Intersetive Part Editing
Part-Aware 3D Generation
BBox's Inko.
Edit Promplé
Generation ion
Adodyo
Head
(captions cottoggyyy
(000) (300)
pproept:
Ebono
Part-X- Part-X-MLLM
pessssas casssasaagnaa tagnasacea
Language Editing
cedits
Structure Encoder
Semantic
Text Tokenizer
<boso
Ediling
Encoder
xyz
+norm
(409600 63
(10240,6) 6)
+rgb
(Prompts
<bux>
Avers with Bloners
Figure 2: The Part-X-MLLM Framework. Our pipeline begins by encoding geometry and ap- pearance features separately using a dual-encoder architecture, which are then fused together with text prompts. These combined features are passed to an autoregressive decoder that generates program-like token sequence representing a plan (e.g.. bounding boxes, edit commands). Finally, specialized geometry heads execute this plan enable part-aware generation and editing.
3 METHODOLOGY
An overview of framework shown in Figure 2 Our methodology centers three key design choices: unified architecture that processes geometry and language, multi-stage training curricu- lum that systematically builds model capabilities, and the use of powerful, pre-existing geometry engines as execution backends.
3.1 MOTIVATION
Modern 3D applications demand more than holistic shape synthesis-they require precise, language- driven control over semantically meaningful parts. For example, artists want to swap handles without touching the body; roboticists need reason about graspable subcomponents; and downstream pipelines rely on consistent, addressable structure for animation and simulation. Prior systems either focus on scene-level understanding or provide powerful but siloed generators/editors with bespoke interfaces. Our goal is native, part-centric MLLM that treats parts first-class citizens and exposes a single, executable interface that is intuitive, auditable, and robust across categories.
3.2 UNIFIED ARCHITECTURE FOR PART-AWARE PLANNING
Dual 3D Encoders. To capture both geometric structure and visual appearance, employ dual- pathway encoder. A Structure Encoder processes the raw point cloud geometry (XYZ and normals) to extract structural tokens. A parallel Semantic Encoder processes RGB color information to produce appearance tokens. This dual representation allows the model to disambiguate parts that may be structurally similar but visually distinct (e.g., identical chair legs of different colors).
Structured Planning Language and Autoregressive Decoder. decoder-only transformer, ini- tialized from a pretrained I.I.M takes the fused sequence of structural, semantic, and text tokens as input. trained autoregressively generate a program-lik, like output that follows our structured planning language. This language defines special tokens for part representation (c.g.. <boxs> and <boxe>, representing box-start and box-end, wrapping six quantized coordinate tokens) and edit operations (e.g., <adds>, <dels>, <mods>). By formulating the output program, unify diverse tasks into single instruction- follloiinn problem, where the model's goal always to gen- erate the correct token sequence representing the plan.
3.3 DOWNSTREAM GEOMETRY INTERFACES
Our model's 's structured output is designed to be consumed by downstream modules capable in- terpreting its geometric and semantic content.
4

---

Published as a conference paper at ICLR 2026
Captioning
breadstiik Part-aware Mesh Generation
Find every
Give (details/ (dctals/cateeorr)
boundding
about thés regions
Generation
Eaax
mndu
brusbet
How mony breudaticks basket?
Q&A with Grounding braod
Answer w/ grounding
Diecretmmaton lon 125 blus X
There appear to be breadsticks (a83 in basket.
(rayttonn)
Ireand
want to replace basket liner with blue one.
Mose chockered buskat liuer
Auto-Lorated Mesh Editing
Sonegres
Editing Hoad
"A Loma, shiny breadstick sprinkled with conrse salt 6n basket."
Editea
Part- X-MLLM Unified Pipeline
Mesh
Figure 3: Task realization with a planning language. decoder oulputs program tokens that unify diverse interactions: (Top) part-aware generation guided by bounding boxes; (Middle) grounded Q&A whose answers embed BBox tokens; (Bottom) auto-located 3D editing executed via cuboid masks and commands. The numbered circles (e.g.. denote the corresponding task types.
Part-Aware Synthesis. For generation, the planned bounding boxes and optional part text passed to synthesis module, which treats the boxes as spatial guides to generate high-fidelity, part-based assets (e.g., in mesh, 3DGS, or NeRF format).
Localized Editing. For editing, the emitted program and associated bounding boxes are used to define cuboid masks for localized manipulation, enabling precise edits while preserving untouched regions.
3.4 END-TO-END TASK REALIZATION
To make the workflow concrete, Figure illustrates how our structured planning language realizes four representative tasks.
of bounding boxes and optional part text. A synthesis module then uses these boxes as spatial guides to generate a part-based asset. Q&A with Grounding: Answers are augmented with BBox tokens, yielding language outputs that carry explicit, persistent references to parts. Auto-located 3D Editing; The model localizes the instruction by generating bounding boxes and edit command (e.g., <adds>). A downstream editing module then uses this program to apply a masked edit.
Part-aware Mesh Generation: The decoder generates a program containing
Semantic Granularity Control. Beyond these core tasks, our box-an box-and-text representation enables dynamic control over semantic granularity. By clustering part bounding boxes based on the sim- ilarity their associated text descriptions (using CLIP embeddings), we can progressively merge fine-grained parts into coarser semantic components. This allows users to control the level of detail in the generated output without manual intervention, such as pre-defining the number parts (cf. PartPacker) manually merging masks (cf. OmniPart). A qualitative example is shown in Figure with the full algorithm detailed in the appendix.
3.5 MULTI-STAGE INSTRUCTION TUNING
We adopt a two-stage curriculum. The first stage pretrains structure-awarc encoder for robust geometry understanding. The second stage performs full instruction tuning, integrating a semantic encoder and aligning a powerful LLM with specialized task grammar.
Stage 1: Geometry-Onke BBox Pretraining. We initialize the structure encoder with the Hunyuan 2.1 3D Shape VAE Encoder. Each training sample is fixed-size RGB-less point cloud of shape (40960, 6) containing (x. y, 2) coordinates and surface normals. The encoder downsamples features by 20x produce latent of length 2048. To force bounding-box knowledge into the encoder,
5

---

Published as a conference paper at ICLR 2026
pair it with lightweight autoregressive decoder whose task is to predict part-level bounding boxes from these latent features, with no textual semantics involved. After pretraining on 3.6M objects for 10 epochs, we retain the specialized structure encoder weights and discard the lightweight decoder. This stage domain-specializes the 3D encoder to reliably disentangle and localize part BBoxes.
Stage 2: Full Instruction Tuning with Dual-Encoder LLM. After pretraining the structure coder, we proceed directly to full instruction tuning with a more powerful Qwen 2.5 VL model. In this stage, we introduce the Semantic (RGB) Encoder, which has the same architecture as the structure encoder and processes a point cloud of shape (10240, 6) with (a, y, and (r, b) data capture appearance. We also extend the vocabulary with our task-specific special tokens (e.g., <boxs>/ <boxe>, <adds>/ (aals><<add>)) During this stage, we freeze the pretrained Structure En- coder from Stage and the original Qwen 2.5 VI. token embeddings. We then train only the new Semantic Encoder, the AR transformer layers of the Qwen 2.5 VI. decoder, and the embeddings for our newly added special tokens. This approach efficiently aligns the powerful language model with our dual-stream (geometry and appearance) conditioning and executable grammar, preserving its strong prior while adapting it for our specialized tasks.
3.6 IMPLEMENTATION AND EXECUTION BACKENDS
To translate plans into high-fieelity geometry, we use powerful, off-the-shelf models as execution backends. For part-aware generation, we use the synthesis module from OmniPart Yang et al. (2025b), feeding it our generated bounding boxes. For editing, we use the training- teining-free volumetric editor VoxHammer Li et al. (2055), providing it with cuboid mask derived from our planned BBox and the user's s instruction. This modular approach allows Part-X-MLLLM to serve as a universal, language-driven frontend for various SOTA geometry engines. The rich information encoded in the generated token probabilities also enables advanced downstream tasks, such as confidence-aware face segmentation (see Appendix A.5)
4 EXPERIMENTS
4.1 DATASET
We curate high-quality, part-centric 3D dataset comprising 85,771 distinct objects with an average of 23 parts per object. Each object is annotated with axis-aligned part bounding boxes (AABBs) and paired natural language annotations at two granularities: coarse part label (Q1) and fine- grained part description (Q2). At the object level, we include an overall caption and a small set of instruction-answer pairs for part-aware Q& A. All annotations follow the unified box-token grammar introduced in Section 3 enabling consistent scrialization of AABBs and edit programs.
Data construction follows a two-step pipeline: (1) a structured labeling stage collecting object-level and part-level texts and (2) a data building stage converting annotations into instruction-followinn samples across multiple task families (grounding, captioning. QA, editing). Concretely, we stantiate eleven task templates (Types 0-10) covering pure box listing. multi-part grounding with coarse/fine text, single-part grounding from name or description, box-to-text captioning, part-aware Q&A, and edit programs for seletion/moddiiicaioo//aad ministic file list partition (<x 99.5/0.5). Full details, prompt templates, sampling rules, and dataset statistics are provided in the supplementary material (Tables [0 and figures therein).
The train/test split is obtained by deter-
4.2 EVALUATION PROTOCOT.
Since existing benchmarks do not test for structured, part-aware, and executable program generation from language, we introduce UniPart-Bench a held-out set of 400 objects, to evaluate our model's core capabilities. Our evaluation focuses on the quality of the structured plans generated by the model, as measured by the accuracy of the predicted BBox layouts. For downstream tasks, the generated plans passed to external geometry modules. For generation, forward the BBoxes to a synthesis head; for editing, we provide the instruction and a cuboid mask derived from the planned BBox.

---

Published as a conference paper at ICLR 2026
4.3 PART-AWARD GENERATION AND EDITING
Bounding Box Generation. To evaluate the quality of our structured generation, we report BBox loU, Voxel Recall, and Voxel loU. Matching pairs each ground-truth box with its nearest predicted box. As baselines, we include PartField Liu et al. (2025a) by treating the voxel set as a point cloud and extracting BBox per predicted segment, and the generation model from OmniPart Yang et al. (2025)). Our model consumes RGB point cloud tokens and a text prompt and autoregressively emits an ordered list of bounding boxes following the box grammar of Section [3] For the PartField baseline, we treat voxels derived from the asset as point cloud and segment them at the ground- truth part count, then compute bounding boxes per segment for comparison.
Table I: Quantitative results for bounding box generation (%).
Method
Voxel recall
Voxel IoU t
Bbox ToU f
PartField Liu et al. (225aa)
69.65 72.32
46.04
37.33
OmniPart Yang et al. (0025b)
47.62
39.78
Part-X-MLLM (Ours)
74.11
48.74
42.55
Qualitative Generation and Editing Results. Figure visualizes our qualitative shape decom- position results, where model demonstrates superior performance in generating semantically herent and geometrically accurate part segmentations. It successfully captures fine-grained details and maintains structural integrity, outperforming baselines that often produce fragmented inac- curate decompositions. We also evaluate the model's ability to perform localized, language-driven edits. As shown in F Part-X-MLLM successfully interprets user instructions to add, remove, or modify specific parts, executing the edits while preserving the rest of the object's 's structure.
Input
PartCrafter
PartPacker
Ours
Figure 4: Qualitative shape decomposition results.
Semantic Granularity Control. As introduced in Section 3 framework supports controlling part granularity by semantically clustering bounding boxes. Figure (6 demonstrates this process, where algorithm progressively merges components based the CLIP similarity of their textual descriptions, reducing the part count from 22 down to 2. This automated process allows for flexible control over the level of detail without manual intervention.
Ablation Study: Dual vs. Single Encoder. We conduct an ablation study to validate our dual- encoder design, which processes geometric structure and visual appearance in separate pathways.

---

Published as a conference paper at ICLR 2026
Replace the head with black hair and vertical eyes.
Replace the trowsers with black jeans.
Replace the left shoe with green-soled, polished-style shoe.
Replace central sphere Add appropriate padding to the base Remove the spherical support in the smooth red sphere.
of the turntable.
middle of the lamp.
Figure 5: Qualitative results for part-aware editing. Our model successfully interprets natural language instructions to perform localized edits, while preserving the integrity of the original object.
Original Meeh
Merge Step
Merge Step
Merge Step 3
Merge Step 4
#Components
#Camponents-18
#Conponents<I8
#Conponent sv6
#Conponents
Figure 6: Semantic granularity control via part clustering. By clustering parts based the semantic similarity of their descriptions, we can progressively merge fine-grained components into coarser structures. The number of components is automatically reduced from 22 to 2.
We compare our full model against single-encoder variant that consumes unified point cloud with fused geometry (XYZ) and color (RGB) information. As shown in Table dual-encoder archi- tecture consistently outperforms the single-encoder baseline across all evaluated tasks. For geometric tasks like box listing. the dual encoder improves IoU by a significant margin (+7.06). For munguage-intensive tasks such as Part QA and Multi-Part Grounding, across all metrics. This suggests that forcing a single encoder to handle both structural and se- mantic information creates conftict, whereas decoupling these responsibilities into two specialized encoders is more effective and robust design choice.
observe uniform gains
Table 2: Ablation study on the dual-encoder architecture. We compare our full model against a single-encoder variant. All metrics are reported on UniPart-Bench.
Task
Model
IoU
SBERT
SimCSE 1 BLEU-I1
ROUGE-L
METEOR 1
Dual Encoder (Ours)
75.53
Pure Box Listing
Single Enceder
68.47
A Gain
+.006
Dual Encoder (Ours)
72.82
$5.60
$4,19
35,55
35.58
18.09
Multi-Part Grounding
Single Encoder
69.78
54.18
53.53
33.95
33.97
17.27
Goin
4.3.44
+1.60
+0.82
(Ours)
40.54
Part QA
Single Encoder
54.24
78.44
83.13
39.29
41.31
33.06
A Gain
+1.20
+0.54
+1.12
+1.25
+0.95
+1.18
4.4 PART AND OBJECT UNDERSTANDING
Part Understanding Q&A. To evaluate part-level understanding and reasoning, we test on UniPart-Bench. We report sentence-level similarities (SBERT, SimCSE) and token-level metrics (BLEU-1, ROUGE-L, METEOR), Results in Table show consistent gains of our method part- level Q&A. We observe substantial gains over the strongest baseline across all metrics: compared to the best non-ours scores, Part-X-M Part-X-MILLM improves by +17.7 SBERT, +25.8 SimCSE, +17.2 BLEU-1,
8

---

Published as a conference paper at ICLR 2026
+9.7 ROUGE-L, and +9.8 METEOR. These gains reflect stronger part-level grounding and reason- ing enabled by our box grammar and instruction tuning.
Table Part understanding Q&A on UniPart-Bench.
Model
SBERT
SimCSE
BLEU-1
ROUGE-L
METEOR
GPT-S
GPT4Point Qi ct al. 2024bb
48.32
45.17
15.16
22.55
16.19
36.99
PointL 7B Xu al. 2024
61.30
58 $8.48
21.78
29.26
22.45
48.68
PointLLM- 13B Xu el al. 2024
56.36
51.47
21.40
29.16
21.80
55.83
ShapeLLM 10neLLMM-3BP 13B et al. 20024
61.19
57.26
23.32
32.56
24.45
42.21
ShapeLLM-Ommn- Ye cl 2025a
57.35
51.16
22.77
29.57
23.24
46.19
MiniGITT3D Tang al. 2024
58.02
53.63
21.05
28.66
22.55
50.38
Part-X-MLLLA (Ours)
78.98
84.25
40.54
42.26
34.24
60.77
Overall 3D Object Captioning. Unlike part-level captioning, this benchmark probes holistic ob- ject understanding UniPart-Bench. We report SBERT, SimCSE, BLEU-1, ROUGE-L, and ME- TEOR following P...tL..M On overall object captioning, our model also outperforms the best prior scores, with absolute improvements of +4.3 SBERT, +2.5 SimCSE, + +18.3 BLEU-1, +19.1 ROUGE- L, and +33.3 METEOR. The large gains on token-based metrics suggest stronger lexical coverage and structure in object-level descriptions.
Table 4: Overall 3D object captioning on UniPart-Bench.
Model
SBERT
SimCSE
BLEU-1
ROUGE-L
METEOR
GPT-5
GPT4Point
2024b
25.60
27.00
11.50
12.00
12.70
26.34
PointLLMM
2024
42.79
42.44
11.58
14.39
16.90
44.03
PointLLM 13
43.51
43.12
13.54
15.74
17.45
44.22
ShapeLLM- 13B al.
25.15
27.14
12.14
12.84
32.24
Shapel.LM-Omm- Ye al. 2025a
31.18
31.93
17.79
19.04
14.30
30.01
MiniGPT--DD Tang et al. 2024
49.52
49.44
7.75
10.23
17.24
48.75
Part-X-MLLM (Ours)
53.82
51.97
36.04
38.11
30.71
55.88
Qualitative Understanding Results. Figure 7 provides qualitative examples for overall object captioning. Our model generates more accurate and detailed descriptions compared baselines. For instance, our model correctly identifies an object as "pink teddy bear mascot costume with a purple bow tie," while other models provide less specific or incorrect descriptions. Additional qualitative results for part-aware Q&A, demonstrating our model's strong grounding capabilities, are provided the appendix (Figure
Input
GT
PointLLM
SbapeLLM
ShapeLLM-One
A pōnk, feddy bear mascot costamee with
partrays on endoaring carloon character designed resembble monee..
The 3D structure appoors to he a faturiatic vehicle with slect design.
A 911 model of bare. ieafless tree with roots and
pink Loddly bear costume with a purple bow
Overall Description
Thas is a 3D model of toy cartoon character that has brown and targe, expressine eyes. Notably, it's
A chibi-style troiitional Chinesee clothing with hair ornament.
The 3D structure appears futuristic, adstract design with mis of metallic and organic elements.
This 3D model carloun-like like figure representing girt.
A chibi character weering hat and traditional clotking.
jacket
structure appeors to be a fregmented or abstract representation ereature, o, possibly a horse.
women with curly hair moseing diessa for mask.
The 9D model represents a vibrant cantoon wearing bright red dress.
This a three- dimensional model of female cartoon character a nibmant red shirt.
sommen with eurly brosnn hair wearing colorful striped halter dress.
Figure 7: Qualitative results for overall object captioning.

---

Published as a conference paper at ICLR 2026
4.5 SENSITIVITY TO POINT CLOUD RESOLUTION
We evaluate the robustness of Part-X-MI.IM by randomly downsampling the input point clouds to various ratios (from 5% to 100%) and measuring performance across Q&A, Captioning, and Part- Level Mesh generation tasks.
Table 5: Sensitivity analysis on input point density.
Part Q&A
Overall Caption
Part-Level Mesh
Density
SBERT
SimCSE
B-1
R-L
MET
SBERT
SimCSE
B-1
R-L
MET
CD L
F-0.1
F-0.05
5%
51.83
54.26
27.38
29.92
22.85
79
24.87
15.06
17.30
8.49
0.2590
0.3188
0.3169
25%
50%
78.83
83.93
39.27
40.75
32.66
54.04
50.71
37.43
39.78
31.21
0,2318
0.6489
0.5647
0.2240
0.6547
100%
78.98
84.25
40.54
42.26
34.24
53.82
51.97
36.04
38.11
30.71
0.2226
0.6506
0.5671
As presented in Table 5 our model demonstrates remarkable robustness to input sparsity. Perfor- mance metrics across linguistic understanding and geometric generation tasks remain stable even when the input density is reduced to 25%. This indicates that Part-X-MLLM, is capable of extract- ing and reasoning about critical 3D structures from sparse data, ensuring reliable performance across varying input resolutions.
CONCLUSION
Part-X-MLLM casts 3D interaction executable program generation: from RGB point clouds and text it emits single sequence of part AABBs that geometry engines execute, unifying generation, QA, and localized editing, and improving Voxel Recal//ooU and BBox IoU on UniPart-Bench, Ap- pendix A.3.5 A.3.5 supports controllable granularity.
Limitations and Future Works. Longer sequences slow inference; simple compaction and hi- crarchical grouping mitigate latency. Our confidence-based segmentation from BBoxes remains relatively shallow; incorporating stronger features could improve segmentation quality. Fine-tuning on 3D tasks may reduce the base LLM's general language capabilities. In the future, we plan to scale our native part-based planning capability to full indoor scene synthesis, effectively extending the bounding-bol box grammar from object parts to room-level furniture layouts.
ETHICS STATEMENT
This work presents Part-X-MLLM, part-aware 3D multimodal model that outputs executable pro- grams (e.g., tokenized AABBs and edit commands). Training uses a blend of publicly available and professionally sourced 3D assets and annotations, subjected to rigorous quality filtering and license review; we avoid personal or biometric data. The model's outputs are grounded and auditable, and the system is intended for research and creative terface with usage guidelines. We acknowledge residual risks such as inherited dataset biases and domain shift and will monitor and update the service accordingly. The authors declare no conflicts of interest.
We will provide public API and online in-
REPRODUCIBILITY STATEMENT
We detail the structured planning grammar, architecture, training curriculum, and evaluation pro- tocol to enable replication. We will open-source the model checkpoints and the UniPart-Bench introduced in this paper, together with evaluation scripts for BBox loU and voxel metries. config- uration files, prompts/converters for data construction, and complete training/inference code with seeds. A public API and online interface will also be available for lightweight validation.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang. Peng Wang, Shijie Wang, Jun Tang. Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang. Zhibo Yang. Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report, 2025. URL
abs 25022 13923
Tim Brooks, Aleksander Holynski, and Alexci A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 18392-18402, 2023.
Hansheng Chen, Ruoxi Shi, Yulin Liu, Bokui Shen, Jiayuan Gu, Gordon Wetzstein, Hao Su, and Leonidas Guibas. Generic 3d diffusion adapter using controlled multi-view editing. arXiv preprint arXiv:2403.12033 2024a.
Minghao Chen, Junyu Xie, Iro Laina, and Andrea Vedaldi. Shap-editor: Instruction-guit -uidded latent 3d editing in seconds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 26456-26466, 2024b.
Minghao Chen, Roman Shapovalov, Iro Laina, Tom Monnier, Jianyuan Wang, David Novotny, and Andrea Vedaldi. Partgen: Part-level 3d generation and reconstruction with multi-view diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 5881- 5892, 2025a.
Minghao Chen, Jianyuan Wang, Roman Shapovalov, Tom Monnier, Hyunyoung Jung, Dilin Wang. Rakesh Ranjan, Iro Laina, and Andrea Vedaldi. Autopartgen: Autogressive 3d part generation and discovery. arXiv preprint arXiv:2507. 133007,..34... 2025b.
Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani. Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of anno- tated objects, 2022. URL http:/ss//aaxiii.rrrrrreesee/eee http:////xxvvv....eae...e abss abs/abbs//2222
08051]
Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objaverse-xl: A universe of 10m+ 3d objects, 2023. URL https: /ttss////xiv.... org/ abs/ 2307. 05663
Ziya Erkoç, Can Gümeli, Chaoyang Wang, Matthias Nießner, Angela Dai, Peter Wonka, Hsin-Ying Ying Lee. and Peiye Zhuang. Preditor3d: Fast and precise 3d shape editing. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 640-649, 2025.
Chongjian Ge, Chenfeng Xu, Yuanfeng Ji, Chensheng Peng, Masayoshi Tomizuka, Ping Luo, Mingyu Ding, Varun Jampani, and Wei Zhan. Compgs: Unleashing compositionality for com- positional text-to-3d via dynamically optimizing 3d gaussians, 2024. URL htt.h:tps:.. //attp::/arxiv..
org/ htt//bb/244000 20723
Ayaan Haque, Matthew Tancik, Alexei A Efros, Aleksander Holynski, and Angjoo Kanazawa. Instruct-nerf2herff: Editing 3d scenes with instructions. In Proceedings of the IEEE/CVF in- lernational conference on computer vision, pp. 19740-19750, 2023.
Yining Hong, Haoyu Zhen, Peihao Chen, Shuhong Zheng, Yilun Du, Zhenfang Chen, and Chuang Gan. 3d-llm: Injecting the 3d world into large language models. Advances Neural Information Processing Systems, 06:20482-20494, 2023.
Team Hunyuan3D, Shuhui Yang, Mingxin Yang, Yifei Feng, Xin Huang, Sheng Zhang, Zebin He, Di Luo, Haolin Liu, Yunfei Zhao, Qingxiang Lin, Zeqiang Lai, Xianghui Yang. Huiwen Shi, Zibo Zhao, Bowen Zhang, Hongyu Yan, Lifu Wang, Sicong Liu, Jihong Zhang, Meng Chen, Liang Dong, Yiwen Jia, Yulin Cai, Jiaao Yu, Yixuan Tang, Dongyuan Guo, Junlin Yu, Hao Zhang, Zheng Ye, Peng He, Runzhou Wu, Shida Wei, Chao Zhang, Yonghao Tan, Yifu Sun, Lin Niu, Shirui Huang, Bojian Zheng, Shu Liu, Shilin Chen, Xiang Yuan, Xiaofeng Yang. Kai Liu, Jianchen Zhu, Peng Chen, Tian Liu, Di Wang, Yuhong Liu, Linus, Jie Jiang, Jingwei Huang, and Chunchao
11

---

Published as a conference paper at ICLR 2026
Guo. Hunyuan3d 2.1: From images high- 3d assets with production-ready pbr material, 2025a. URL hattps://arxiv..oo www.aiiww.......aao.oooooooooooo http://abs/255..554/
Team Hunyuan3D, Shuhui Yang, Mingxin Yang. Yifei Feng, Xin Huang, Sheng Zhang, Zebin He, Di Luo, Haolin Liu, Yunfei Zhao, et al. Hunyuan3d 2.1: From images to high-fidelity 3d assets with production-ready pbr material. arXiv preprint arXiv:2506. 2222256661442. 15442, 2025b.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceed- ings of the IEEE/CVF international conference on computer vision, pp. 401 4015-4026, 2023.
Lin Li, Zehuan Huang, Haoran Feng, Gengxiong Zhuang, Rui Chen, Chunchao Guo, and Lu Sheng. Voxhammer: Training-free precise and coherent 3d editing in native 3d space. arXiv preprint arXiv:2508. 19247, 2025.
Songlin Li, Despoina Paschalidou, and Leonidas Guibas. Pasta: Controllable part-aware shape generation with autoregressive transformers. arXiv preprint arXiv:2407.13677 2024a.
Yuhan Li, Yishun Dou, Yue Shi, Yu Lei, Xuanhong Chen, Yi Zhang, Peng Zhou, and Bingbing Ni. Focaldreamer: Text-driven 3d editing via focal-fusion assembly. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pp. 9279-3287, 2024b.
Anran Liu, Cheng Lin, Yuan Liu, Xiaoxiao Long, Zhiyang Dou, Hao-Xiang Guo, Ping Luo, and Wenping Wang. Part123: part-aware 3d reconstruction from single-view image. ACM SIG- GRAPH 2024 Conference Papers, pp. 1-12, 2024a.
Fangfu Liu, Hanyang Wang, Weiliang Chen, Haowen Sun, and Yueqi Duan. Make-your-3de Fast and consistent subject-driven 3d content generation. In European Conference on Computer Vision, pp. 389 406. Springer, 2024b.
Minghua Liu, Mikaela Angelina Uy, Donglai Xiang, Hao Su, Sanja Fidler, Nicholas Sharp, and Jun Gao. Partfield: Learning 3d feature fields for part segmentation and beyond. arXiv preprint arXiv:2504. 1451, 2025a.
Yifan Liu, Zhiyuan Min, Zhenwei Wang, Junta Wu, Tengfei Wang, Yixuan Yuan, Yawei Luo, and Chunchao Guo. Worldmirror: Universal 3d world reconstruction with any-prior prompting. arXiv preprint arXiv:2510. 10726, 2025b.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. URL https:
Changfeng Ma, Yang Li, Xinhao Yan, Jiachen Xu, Yunhan Yang. Chunshi Wang, Zibo Zhao, Yan- wen Guo, Zhuo Chen, and Chunchao Guo. P3-sam: Native part segmentation. arXiv preprint arXiv:2509.0613 2025.
Qiaowei Miao, Kehan Li. Yawei Luo, and Yi Yang. 2 2 deditor: Zero-shot text-driven volumetric video editing with spatiotemporal consistency via single 2d diffusion. IEEE Transactions on Cir- cuits and Systems for Video Technology. PP:1-1, 2025a. doi: 10.1 D0.109/9SSVT222333222722
Qiaowei Miao, Kehan Li, Jinsheng Quan, Zhiyuan Min, Shaojie Ma, Yichao Xu, Yi Yang, Ping Liu, and Yawei Luo. Advances in 4d generation: A survey, 2025b. URL
abs/ fas/2500..0 14501
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021.
Zhiyuan Min, Yawei Luo, Jianwen Sun, and Yi Yang. Epipolar-free 3d gaussian splatting for gen- eralizable novel view synthesis. Advances in Neural Information Processing Systems, 37:39573- 39596, 2024.
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint a2X9 14988, 2022.
12

---

Published as a conference paper at ICLR 2026
Zekun Qi, Runpei Dong, Shaochen Zhang, Haoran Geng, Chunrui Han, Zheng Ge, Li Yi, and Kaisheng Ma. Shapellm: Universal 3d object understanding for embodied interaction. In Euro- pean Conference on Computer Vision. pp. 214-211220 24-238.. Springer, 2024a.
Zhangyang Qi, Ye Fang, Zeyi Sun, Xiaoyang Wu, Tong Wu, Jiaqi Wang, Dahua Lin, and Heng- shuang Zhao. Gpt4point: A unified framework for loint-lagguage understanding and generation. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pp. 26417-
26427, 2024b.
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. Deepspeed: System opti- mizations enable training deep learning models with over 100 billion parameters. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp- 3505-3506, 2020.
Etai Sella, Gal Fiebelman, Peter Hedman, and Hadar Averbuch-Elor. Vox-e: Text-guided voxel editing of 3d objects. Proceedings of the IEEE/CVF international conference on computer vision, pp. 430-440, 2023.
Jiaxiang Tang, Ruijie Lu, Zhaoshuo Li, Zekun Hao, Xuan Li, Fangyin Wei, Shuran Song, Gang Zeng, Ming-Yu Liu, and Tsung-Yi Lin. Efficient part-level 3d object generation via dual volume packing. arXiv preprint arXiv:2506.0990 2025.
Yuan Tang, Xu Han, Xianzhi Li, Qiao Yu, Yixue Hao, Long Hu, and Min Chen. Minigpt-3d: Efficiently aligning 3d point clouds with large language models using 2d priors. arXiv preprint arXiv:2405.01413. 2024.
Chunshi Wang, Hongxing Li, and Yawei Luo. Sonicgauss: Position-aware physical sound synthesis for 3d gaussian representations, 2025.
Zhengyi Wang, Jonathan Lorraine, Yikai Wang, Hang Su, Jun Zhu, Sanja Fidler, and Xiaohui Zeng. Llama-mesh: Unifying 3d mesh generation with language models. arXiv preprint arXiv:2411.09595, 2024.
Tong Wu, Jiarui Zhang, Xiao Fu, Yuxin Wang, Liang Pan Jiawei Ren, Wayne Wu, Lei Yang, Jiaqi Wang, Chen Qian, Dahua Lin, and Ziwei Liu. Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation. In /EEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. arXiv preprint arXiv:2412.00 2024.
Runsen Xu, Xiaolong Wang, Tai Wang, Yilun Chen, Jiangmiao Pang, and Dahua Lin. Pointllm Empowering large language models to understand point clouds. In European Conference on Computer Vision, pp. 131-147. Springer, 2024.
Han Yan, Yang Li, Zhennan Wu, Shenzhou Chen, Weixuan Sun, Taizhang Shang, Weizhe Liu, Tian Chen, Xiaqiang Dai, Chao Ma, et al. Frankenstein: Generating semantic-come comanticcoompositionall 3d scenes in one tri-plane. In SIGGRAPH Asia 2024 Conference Papers, pp. 1-11, 2024a.
Han Yan, Mingrui Zhang, Yang Li, Chao Ma, and Pan Phycage: Physically plausible composi- tional 3d asset generation from a single image. arXiv preprint 1...i::2411188555 2024b.
Xinhao Yan, Jiachen Xu, Yang Li, Changfeng Ma, Yunhan Yang. Chunshi Wang. Zibo Zhao, Ze- qiang Lai, Yunfei Zhao, Zhuo Chen, et al. X-part: high fidelity and structure coherent shape decomposition. arXiv preprint arXiv:2509.066. 08643, 2025.
Ling Yang, Zixiang Zhang, Junlin Han, Bohan Zeng Runjia Li, Philip Torr, and Wentao Zhang. Semantic score distillation sampling for compositional text-to-3d generation, 2024a. URI.
https://erra http://axxxvv.r//bbooo org/abs..aoooo.ooooooo abs 24 2240.09000
Yunhan Yang, Xiaoyang Wu, Tong He, Hengshuang Zhao, and Xihui Liu. Sam3d: Segment anything 2306. 03908
in 3d scenes, 2023. URL. htt.htttss///axii.c.o
13

---

Published as a conference paper at ICLR 2026
Yunhan Yang, Yukun Huang, Yuan-Chen Guo, Liangjun Lu, Xiaoyang Wu, Edmund Y Lam, Yan-Pei Cao. and Xihui Liu. Sampart3d: Segment any part in 3d objects. arXiv preprint arXiv:2411.07184, 2024b.
Yunhan Yang, Yuan-Cher Guo, Yukun Huang, Zi-Xin Zou, Zhipeng Yu, Yangguang Li, Yan- Pei Cao, and Xihui Liu. Holopart: Generative 3d part amodal segmentation. arXiv preprint MXXi::2004009443 2025a.
Yunhan Yang, Yufan Zhou, Yuan-Chen Guo, Zi-Xin Zou, Yukun Huang, Ying-Tian Liu, Hao Xu, Ding Liang, Yan-Pei Cao, and Xihui Liu. Omnipart: Part-aware 3d generation with semantic decoupling and structural cohesion. arXiv preprint arXiv:2507.06165 2025b.
Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan, and Jun Zhu. Dreamreward: Text-to-3d generation with human preference. In European Conference on Computer Vision, pp. 259-276. Springer, 2024.
Junliang Ye, Zhengyi Wang. Ruowen Zhao, Shenghao Xie, and Jun Zhu. Shapellm-omni: A native multimodal 1lm for 3d generation and understanding. arXiv preprint arXiv:2506.01855. 2025a.
Junliang Ye, Shenghao Xie, Ruowen Zhao, Zhengyi Wang. Hongyu Yan, Wenqiang Zu, Lei Ma, and
Jun Zhu. Nano3d: A training-free approach for efficient 3d editing without masks, 2025b. URL
https: //arx// www.xvv.oog/aaae 2210. 15019
Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu. Point-bert: Pre-training 3d point cloud transformers with masked point modeling. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 19313-19322, 2022.
Longwen Zhang, Qixuan Zhang, Haoran Jiang. Yinuo Bai, Wei Yang, Lan Xu, and Jingyi Yu. Bang: Dividing 3d assets via generative exploded dynamics. ACM Transactions on Graphics (TOG), 44 (4):1-21, 2025.
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christo- pher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer. Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. Open pre-trained transformer language models, 2022.
Opt:
Ruowen Zhao, Junliang Ye, Zhengyi Wang. Guangce Liu, Yiwen Chen. Yikai Wang, and Jun Zhu. Deepmesh: Auto-regressive meth creation with reinforcement learning, 2025a. URL
http::s://xxxiv.rr//abasss aww/ 2503. 15265
Wang Zhao, Yan-Pei Cao, Jiale Xu, Yuejiang Dong, and Ying Shan. Assembler: Scalable 3d part assembly via anchor point diffusion. arXiv preprint arXiv:2506. 17074, 2025b.
Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang, Sheng Zhang, Xianghui Yang, et al. Hunyuan3d 2.0: Scaling diffusion models for high resolution textured 3d assets generation. arXiv preprint arXiv:2501. 12202, 2025c.
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhanghhi Feng, and Yongqiang Ma. Llamafactory: Unified efficient fine-tuning of 100+ language models. In Pro- ceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)) Bangkok, Thailand, 2024. Association for Computational Linguis- tics.
abs att.
Weipeng Zhong, Peizhou Cao, Yichen Jin, Luo Li. Wenzhe Cai, Jingli Lin, Zhaoyang Lyu, Tai Wang, Bo Dai, Xudong Xu, and Jiangmiao Pang. Internscenes: A large-scale interactive indoor scene dataset with realistic layouts. In arXiv, 2025.
Xiaoyu Zhou, Xingjian Ran, Yajiao Xiong, Jinlin He, Zhiwei Lin, Yongtao Wang, Deqing Sun, and Ming- Yang. Gala3d: Towards text-to-3d complex scene generation via layout-guided generative gaussian splatting, 2024. URL http:/tttt////aaaee
24022 07207
Yang Zhou, Zongjin He, Qixuan Li, and Chao Wang. Layoutdreamer: Physics-guided layout for text-to-3d compositional scene generation, 2025. URL httph:tttp//tttt httttp:://axxv..oggg. www.aaiww.o...////.0//0
01 9
14

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.O THE USE OF LARGE LANGUAGE MODELS (LLMS)
Large Language Models (LLMs) used exclusively for minor language editing- such im- proving grammar and readability and not for method design or experimental work. All technical contributions, including the methodology. equations, and results, solely the work of the authors.
A.1 IMPLEMENTATION DETAILS
Our framework is implemented based on LLaMA-Factory Zheng et al. (2024) and trained on cluster of 32 GPUs.
Data Construction Pipeline. We curated our training data by aggregating 3D assets from large- scale public repositories, primarily Objaverse Deitke et al. (2022) and Objaverse- -L Deitke et al. (023). To ensure high visual fidelity, we employed an aesthetic scoring model to filter low- quality noise-heavy meshes. Given that raw assets often contain overly fragmented components, we applied the Intersection-over-
(loU) merging strategy derived from PartPacker Tang et al.) (2025). For semantic annotation, established multi-view rendering pipeline to render both the holistic appearance of assets and the details of individual parts. These rendered images, including both the complete object renderings and part-specific renderings, were then processed by Qwen-2.5- VL Bai et al. (2025). which generated high-quality, lin-graiiee textual descriptions.
Model Architecture. We utilize Qwen-2.5- Bai al. 2025) the core multimodal back- bone for instruction following. The system features a Ancleen design initialized from the pre- trained VAE encoders of Hunyuan3D-22.. Hunyyaa33 et al.] (2025)). The Structure Encoder cepts geometric inputs (XYZ coordinates concatenated with surface normals) with a resolution of N 40, 960 points, projecting them into latent sequence of length 2.048. The Semantic Encoder processes appearance inputs (XYZ coordinates and RGB colors) with a resolution of N 10, 240 points, projecting them into a latent sequence of length 512. Both encoders align their feature dimen- sions to the LLM's embedding space via linear projection layers. Input point clouds are normalized to the range [-1, 1] along the longest axis, and bounding box coordinates are quantized into 128 discrete bins.
Training Protocol. Stage focuses on adapting the Structure Encoder for precise bounding box localization. We employ the open-source OPT-350M Zhang et al. (2022) as a lightweight autore- gressive decoder for this task. The model is trained for T0 epochs with a batch size of 128, using the Adam W Loshchilov & Hutter (201)) optimizer (learning rate x 10-1, weight decay x 10-5 and 5,000 warmup steps). To enhance robustness against scan imperfections, we apply random rotations (=15 and "Normal Drop" strategy, where surface normals are masked with a 50% probability. In Stage 2, the Structure Encoder is frozen to preserve the learned geometric priors, while the Seman- tie Encoder, the Qwen-2.5-VL. backbone, and special token embeddings are fine-tuned. This stage runs for 60,000 steps with global batch size of 128 using DeepSpeed ZeRO-2 Rasley et al. (2020) and oatle precision. We use AdamW Loshchilov & Hutter (2019) with cosine learning rate scheduler (peak LR 8 x 10-5) and enable sample packing with maximum sequence length of 5,120 optimize throughput.
A.2 TASK COMPARISON
Existing 3D models typically present a trade-off between task breadth and semantic granularity. As summarized in Table 6 understanding-focuss MLLMs (e.g.. ShapeLLM) lack generative ca- pabilities, while generative models (e.g.. ShapeLLM-Omni often operate at the coarse object level without supporting part-level grounding and editing. In contrast, Part-X-MLLA uniquely combines comprehensive understanding. fine-grain -raiind part-level operations, and localized editing capabilities in unified framework.
15

---

While quantitative metrics measure geometric alignment, they do not fully reflect human percep- tion of structural logic and editing intent. To address this, we conducted a user study with 32 participants with background in 3D vision. We randomly sampled 25 generated objects and 25 edit- ing instructions. Participants were asked to rate the results on a Likert scale from (Poor) to (Excellent) across four specific dimensions: Part Plausibility evaluates whether the decomposed parts in generation tasks are semantically reasonable and structurally sound (e.g., chair legs are attached the seat), while Generation Quality assesses the overall visual fidelity and complete- ness of the generated parts. For editing tasks, Instruction Fidelity measures whether the operation Published as a conference paper at ICLR 2026
Table 6: Comparison of capabilities with state-of-the-art. 3D models.
Understand
Grounding
Generation
Modity
Method
Cap
Q&A
Obj
Part
Obj
Part
Edit
Understanding & Reasoning MLLMS
PointLLM Xu et al.
(0224)
x
X
X
X
x
GPT4Point Qr al.
20240
X
X
ShapeLLM et al.
2024a
V
X
X
Unified/ / Generative Models
JLahA-Mesh) Wang (225cc)
2024
X
x
V
Hunyuan3D Zhao et al.
X
X
X
Shapel. M-0mm Yo cf al. 20225
X
X
X
Bart-Based Specialists
Part123 Liu et al. (2024a)
X
OmniPart Yang ct al.
2025h)
x
VoxHammer Li al. (2025
X
Ours
Part-X-MLLM
v
A.3
MORE EXPERIMENTAL. RESULTS
A.3.1 HUMAN EVALUATION
(addIdelete/moddify) aligns strictly with the text prompt, and Editing Quality evaluates the visual coherence of the edited result, including the preservation of non-edited regions. As shown in Table Part-X-MI.LM achieves an average score above across all metrics.
Table 7: Human Evaluation Results. We report the Mean Opinion Score (MOS) on scale of to (higher is better).
Task Type
Evaluation Metric
Score (1-5)
Struetural Plausibility
4.42 + 0.6
Part Generation
Generation Quality
4.25 + 0.7
Instruction Fidelity
4.03 = 0.5
Part Editing
Editing Quality
4.31 + 0.6
Table 8: Evaluation the PointLLM Benchmark.
Model
S-BERT'
SimCSE
BLEU-1 f ROUGE-L
METEOR
PointLLMM-BB Xu 22024
47.47
48.55
3.87
7.30
11.92
-.-.....--333330 2024
47.91
49.12
3,83
7.23
12.26
PointLLM-133
50.15
50.83
17.09
20.99
16.45
Part-X-ML.LM (Ours)
53.43
51.21
16.00
18.34
13.28
indicates PointLLM was prompted for shorter captions with no more than 20 words..
16

---

Published as a conference paper at ICLR 2026
A.3.2 ADDITIONAL EXPERIMENTS ON PUBLIC BENCHMARKS
As shown in Table Part-X-MLLM achieves superior semantic similarity scores, outperforming all baselines. While n- n-gram metrics are slightly lower than PointLLM-13B*. this reflects our model's shift toward structured, part-aware deseriptions. The high semantic scores confirm that Part-X- MLLM maintains factual correctness and demonstrates excellent generalization despite part-centric training.
Inpet
Bounding Boxes
Generated Mech
Inpot
Bounding
Doxcss
Gonerated
Mesh
(b) OmnatOejert30
Figure 8: Qualitative evaluation of generalization and robustness.
A.3.3 GENERALIZATION AND ROBUSTNESS ANALYSIS
Beyond standard object-level synthesis, we further investigate the model's capability to handle out- of-distribution data, as shown in Figure 8 First, explore scene-level composition using the InternScenes Zhong et al. (2025) dataset. This aligns with the emerging paradigm of compositional scene generation Zhou al. (2024) 2025 Yang et al. (2024a); Ge al. (2024); Liu al. (2025b),
where complex environments are constructed from distinct entities. Although trained on object parts, Part-X-MLLLM successfully generalizes to this domain by treating individual furniture items as components of room, generating plausible layouts and meshes in a zero-shot manner. Second, to address the domain gap between synthetic and realistic data, we evaluate the model on real- world scans from OmniObject3D Wu al. (2023). These inputs typically contain high-frequency noise, holes, and inconsistent normals. Our model demonstrates strong robustness by effectively filtering out these artifacts, producing precise bounding boxes and clean geometric reconstructions even under such challenging conditions.
A.3.4 ISOLATION OF PLANNING CAPABILITIES
To assess the contribution of our structured planning interface independent of the downstream ge- ometry kernels, conducted controlled comparison for the generation task. We compared our method against several baselines, including the native planner of OmniPart Yang et al. (2025b) and pipeline approaches using TRELLIS Xiang et al. (2024) combined with 3D segmentation tools (SAM3D Yang et al. (2023), PartField et al. (2025a). HoloPart Yang al. (2025a)).
Setup. For the "Part-X-MLLLL X-MLLM OmniPart" entry, we use our model to generate the bounding box plan from text, which is then fed into the frozen OmniPart synthesis decoder. This allows for direct "planner-to-planner" comparison with the original OmniPart method.
As shown in Table 9 when using the exact same generation backend, our planner outperforms the native OmniPart planner in Part-Level metrics (e-g. improving F-0.05 from 0.46 to 0.57). This dicates that Part-X-MLLM produces more geometrically accurate and semantically consistent part layouts (bounding boxes), which turn enables the backend to synthesize higher-fidelity compo- nents.
Operational Efficiency in Editing. Beyond quantitative generation quality, our contribution to the editing task lies in a fundamental shift in usability. Native geometry engines like VoxHammer require explicit, manually crafted 3D binary masks to identify edit regions- a process that typically demands manual modeling in professional software (e.g., Blender). Part-X-MLLMM bridges this
17

---

Published as a conference paper at ICLR 2026
Table 9: Quantitative Comparison of Part-Leevel and Overall-Level Generation.
Part-Level
Overall-
Method
CD
F-0.1 1
F-0.05
CD +
F-0.1
F-0.05
TRELLIS SAM3D Yang al.
(2023
0.58
0.25
0.20
0.11
0.89
0.72
TRELLIS PartField Liu al 2025a
0.24
0.60
0.42
0.11
0.89
0.72
TRELLIS PartField HoloPart Yang al. 2025a
0.24
0.61
0.43
0.09
0.90
0.74
Part123 Liu al. 2024a
0.47
0.28
0.14
0.42
0.36
0.20
OmniPart Yang et 20256
0.23
0.63
0,46
0.08
0.91
0.77
Part-X-MI.IM + OmniPart (Ours)
0.22
0.65
0.57
0.08
0.90
0.77
gap by acting as an intelligent semantic agent: it translates high-level natural language instructions
(e.g., "remove the armrests") directly into precise, geometrically grounded cuboid masks. This automates the entire workflow, transforming 3D editing from an expert-only, manual operation into an accessible, fully language-driven interaction where users simply "speak" to edit.
A.3.5 SEMANTIC PART CLUSTERING ALGORITHM
To enable dynamic control over semantic granularity, we introduce post-processing algorithm that clusters fine-grained part bounding boxes into coarser, semantically meaningful components. This process, illustrated in Figure operates without requiring manual intervention predefined number of target clusters. The algorithm follows three-step pipeline: feature extraction, clustering, and merging.
1. Feature Extraction. For each predicted part we extract its bounding box 6, = (Xmin Xmax) )a and textual description d,. A hybrid feature vector fü is then generated.
First, the semantic feature vector fgemss is obtained by encoding the description with pretrained CLIP model:
femm. CLIPPEncode(dL))
Next, we compute the spatial feature vector fapa.i from the bounding box's center c; (Xmin + Xmax) 22 and size s; Xmax Xmin- object produce f.pur.
The raw spatial vector is normalized across all N parts in the
fapat.i
(c,,8:),
Espar, Normalize(( ((
(2)
Finally, the semantic and spatial features are combined using a weighting factor a E [0, 1]. and the resulting vector is L2-normalized:
f = (1 ax) f.emm O afsuut.
(3)
a) )eem, arfpart
where @ denotes concatenation.
apply DBSCAN to the set of feature vectors {f.) DBSCAN groups points based on two parameters: a distance threshold and minimum number of points 'minPts'. A point f, is a core point if its e-neighborhood contains at least 'minPts' points. A cluster is formed by set of density-commected points, starting from core point and recursively expanding to all reachable neighbors. This approach allows us to automatically identify a variable number clusters K without prior specification, returning set of clusters C (C1. ..... Ck) and a set of noise points N.
Clustering.
Merging. For each cluster Ck E C, we compute single merged bounding box By (Xmin,k, X X. This is done by taking the component-wise minimum and maximum all bounding boxes b; E Ck:
X = min (Xmin,)),
X X = max (Xmax,i)-
(4)
ilb,EC,
dbbyECy
18

---

Published as a conference paper at ICLR 2026
The final output is set of K merged bounding boxes, representing coarser, saaaanticallyggouuppe decomposition the object.
This automated approach provides flexible and powerful way to adjust the granularity of the gen- crated 3D assets, bridging the gap between fine-grained part generation and high-level semantic understanding.
A.4 ADDITIONAL QUALITATIVE RESULTS
Figure prooovdes qualitative examples for part-aware question answering. Our model demonstrates strong grounding capabilities by providing detailed, box-annotated answers that accurately describe object parts in response to user queries.
Figure 9: Confidence-aware face segmentation. By leveraging the generated bounding boxes and their associated confidence scores, we can achieve high-quality, fine-grained face-level segmentation of 3D objects without any additional training.
A.5 CONFIDENCE SWWRDE FACE SEGMENTATION FROM BOUNDING BOXES
As mentioned in Section 3 the rich information encoded in model's autoregressive output can be leveraged for advanced downstream tasks beyond simple generation or editing. One such application fine-graiie graigra confidence-aue aware face segmentation, as shown in Figure This process requires no additional training and relies solely on the generated bounding boxes and the token probabilities from the decoding process.
The algorithm follows three-step process:
Confidence-Aware BBox Inference. During autoregressive decoding, the model generates sequence of tokens T (11, 12.....,4 (4) that represent series of bounding boxes. For each token i; the model also outputs probability distribution over the entire vocabulary, from which we derive confidence score. The confidence of a bounding box Bj. which is composed of sequence of tokens (typically 6), is calculated as the arithmetic mean of the probabilities of its constituent tokens:
Con K Σ P(I)te)))
This provides per-box confidence score that reflects the model's certainty in its prediction.
Face-to-Box Assignment. Given mesh with a set of faces F = f2..... i far first determine which faces belong which
and a set of
inferred bounding boxes B (B1, B Bx),
19

---

Published as a conference paper at ICLR 2026
Input
GT
PointLLM
Ours
He is wearing yellow puffer
vesf<boxs><hor cotoooxsxxxx--xxxbbbo -6>-bbx--33x-------- -ooox--9-boo--111eee -xxbbx-----bo--
wearing yellow puffer vest
<boxsx<box (5xxbxx-5xxxx------ -5xx-xxxxxxxxx------- 73x--xxxxx---bb
chirkboxs><box-- bbxx-xxxxxx---------- <hx--
over light blue long sleeved
8134box- 1100x-117550b 117 c8xcoxxe> over a blue
short choxs><hox 48>cbox--6 chox
Questions and Answers
dank Muee pentxboxsxxtox- bbxx-x-xx------
<bbbo--7Uxxbex- (ebbxx10>xxox--77 bbxxbx
beankersboxs><bboxe x-oo--45xbb---1ee 1><box-
Sooo--xxbo--77xb00 chores, and bright green
The men is wearing
yellow ahirt and
black ponts.
51><box-78 1obox--000xxxx-6600
dark blue
<boxs><box-
16><box-54>ce (ooo----xxx--->bbbe
74>cboxe>, and grees smeakers
What the man wearing?
553 Seccoxx--xxbb--1110 11><box-
36><box- 1aee----xxbxxxx 75> 75>-kxxe> <boxs><box-
70><box- 1- 1--box--bb5- 35xxbox-7xxxbbbb
15><boxe>< (oxxxxxss>xxxxxxxxxxxxx0000 1>ebox-
11xcoo--55xbboe 75><boxe>.
55ochox
This a modern-atyle sniper rifle.
rifle.
bolt-action sniper rific.
What type of Brearm ie this?
The object appears tall, rectongular structure with protruding section <boxs><box 12>cbox- 38xbox-899xbon
object consists a rectengular
The object coneiate of a large main casing
casing bbxss>ox--->obo----e 28>cbx-
(ooxs><boxx-1148 20bboxx----bx-2555
125><box- 1bbox-185><ee (85>xbox-922>bbb 92>cboxs> that resta
two long. parallei bers <boxs><box-
with large circuler fan assembly
What is the
fxchox-21<<box- 20xxbx---- chox-
212-bbx-- 112x
and <boxs><bux-
on one side, windon on the front,
68x- chox betccbox---xccoooeeeeee 94ochoxe> and feo small
fest <boxs><box Socbox- 215xbbx chox
overall stracture 20==box-10xx<bxe>> which act aa its feet.
and maitiple centilation skita its sides.
10><box-- (5-<dbx-106><bboe and
the object!
choxs> chox 11xxx-1xxbxx-88000
11>=<bo--28x<ox: 106x<boxe> bottom.
Figure 10: Qualitative results for part-aware Q&A. Our model provides more accurate and de- scriptive answers, with precise part grounding indicated by bounding box tokens.
boxes. A face fm is considered candidate for B, if its centroid c. lies within the volume of By:
Cm E By
< (cm 2 Xmin.)) A (cm I Xmax.j)
where Xmin.j and Xmax.j are the minimum and maximum coordinates of box Bj. and the comparison element-wise.
3. Conflict Resolution. A face's centroid may lie within multiple overlapping bounding boxes, creating an ambiguity. We resolve this using two-tiered rule system:
Containment Rule: If face fm is a candidate for two boxes, B; and By. and one box is strictly contained within the other (e.g., B3 Bj), the face is assigned to the box with the smallest volume. This prioritizes more specific, fine-grained predictions.
If the boxes overlap but neither contains the other, the face is assigned to the box with the highest confidence score, Conf(B,). This leverages the model's ''s own uncertainty estimate to make the most likely assignment.
Confidence Rule:
This process results in a deterministic assignment of each face to a single bounding box, producing a high-quality, Ane-grained segmentation of the object, as shown in Figure 9
A.6 ANALYSIS OF SPECIAL TOKEN EMBEDDINGS
To better understand how our model interprets the specialized grammar, we visualize the embeddings of our newly added special tokens using t-SNE, as shown in Figure IT The visualization reveals highly structured and semantically meaningful latent space.
We observe three key phenomena. First, the tokens form distinct clusters based on their function: Point, Box, and Edit tokens occupy separate regions of the embedding space. Second, the 128 box tokens, which represent quantized coordinates, form continuous, ordered manifold. This demonstrates that the model has learned the ordinal nature of spatial coordinates rather than treating them as independent categorical variables. Third, tokens with similar functions, such as the start/end pairs for edits (e.g., <adds>/<<<> <aadd>e) are positioned closely together. This structured organization confirms that the model has successfully learned a robust and interpretable representation of our executable grammar, which is crucial for precise, language-driven 3D planning.
20

---

Published as a conference paper at ICLR 2026
t-SNE Visualization of Special Token Embeddings
Box tokens
10
cho--705
<tea-115p
t-SNE Dimension
<boxes sit maddase
400w555
Sppee the
padia
<mbdea: <dess
-10
<bow E5
15
t-SNE Dimension
Figure 11:
t-SNE visualization of special token embeddings. The tokens form distinct, well- structured clusters based their function, indicating meaningful learned representation.
Figure 12: Model-assisted labeling pipeline. Left: inputs (full-asset + per-part crops). Middle; structured tool schema drives the LMM to output object-level and part-level JSON. Right: validated ISON is stored and used by the data builder.
A.7 DATASET CONSTRUCTION AND LABELING
Scope. We build high-quality, part-centric dataset tailored for Part-X-MLLM. The corpus tains 85,771 unique 3D objects with an average of 23 parts per object. Each part annotated with axis-aligned bounding box (AABB) and levels of text: a coarse name (QI) and fine- grained description (Q2). At the object level, we include a concise overall caption and a small set of instruction answer pairs for part-aware Q&A. All annotations are serialized using the unified box-token grammar described in Section
A.7.1 MODEL-ASSISTER LABELING
To scale high-quality labels consistently, adopt model-assisted pipeline guided by a structured tool schema. Given a full-asset render and sequence of part close-ups, we collect:
Ql: short part name.
Q2: fine-grained natural description (S 15 words; avoid irrelevant rendering terms).
Q3: confidence flag (Yes/No).
Concretely, follow the schema implemented in labeling tool, which calls external LMM with a JSON response format and deterministic field ordering. For each object, we provide: (1) one full-asset image (front view); and (2) K part crops (one per part).
A.7.2 BUILDING INSTRUCTION-FOLLOWIGG SAMPLES
We convert raw labels into diverse mnstruction-folloiin pairs covering grounding, captioning, QA, and editing. A central convenience is box-token grammar with opening/closing tokens
21

---

Published as a conference paper at ICLR 2026
Algorithm Data building (simplified)
Load datas
2:
for each object o do
3: Serialize each part AABB to tokens; sort by (Zmins Ymin mmin)
4: for each template t {o..... .........)) 10} do
5: Instantiate natural-language prompt from a template pool
6: Emit the target sequence (boxes, text, or edit program)
7: Append conversation pair to the corpus
8: Shuffle and save shards; optionally balance per-template counts
<boxs> and <boxe> wrapping six quantized coordinates, and edit verbs <adds>/ <<ade>> <dels>/<dele>, and <mods>/ <mode>,
Quantization and serialization. Each coordinate E [-1, 1] is quantized into K 128 bins as
((x) = round
g(rr) i=
K-1
then serialized as six tokens inside ...oxs>...bxx>..... For reproducibility, parts in a list are deter- ministically ordered by (2mii))) ((ymin), ((Fmin))):
Task families. We instantiate eleven templates (Types 0-10):
Type O: pure box listing from point cloud ("detect all bounding boxes").
Type multi-part grounding with coarse text (AABBs QI per part).
Type multi-part grounding with fine text (overall description first, then AABBs + Q2).
Type single-part grounding from coarse text (locate all QI parts; return AABBs de- scription).
Type 4: single-part grounding from fine text (locate part by Q2; return a single AABB).
Type 5: box-to-text (given a box, answer Q1).
Type 6: box-to-text (given a box, answer Q2).
Type part-aware QA (replace textual part references <Part.i> with the corresponding box tokens in answers).
Type 8: deletion program (emit <dels> [boxes] <dele>).
Type 9: modification program (emit <mods> [box] new text <mode>).
Type 10: addition program (emit <adds> [box] text <adde>).
Train/test split and balancing. We partition the file list deterministically at 0.5% for test and 99.5% for train. Templates 1-2 are lightly duplicated to increase multi-part coverage; for templates 3-7 we downsample fixed budget; for edit templates (8-10) we cap the number per shard. See Table 10
A.8 DATASET STATISTICS
Task families and sizes. summarziss per-task counts before/after balancing. Counts follow our build scripts.
Our corpus spans everyday objects and scenes. Table 11 lists the main categories (top-12 by frequency).
Category distribution.
A.9 COMPREHENSIVE RESULTS ON SAUPART-BENCE
We report per-task results on UniPart-Bench. Note that QuiPart-Bench held-out subset of 85,771-object training dataset, ensuring identical data construction pipeline and distribution characteristics. Following our data construction, each ground-truth (GT) item may contain both BBox tokens and text. When both present, evaluate BBoxes with IoU and text with
22

---

Published as a conference paper at ICLR 2026
Table 10: Task families and sizes. "Raw" denotes counts before optional balancing: "Final" denotes the target budget after balancing.
Name
Input
Output
Raw
Type
Final
Single-Part Grounding
point + coarse text
box fine text
506,755
7.30
T3
506,755
Single-Part Grounding
point + fine text
box
887,590
12.78
T4
506,755
Multi-Part Grounding
point + text
all boxes + Q1
85,771
1.24
T1
257,313
Multi-Part Grounding
point + text
all boxes + Q2
85,771
1.24
T2
257,313
Box-to-Texx (coarse)
point box text
Q1
887,590
12.78
T5
506,755
Box-to-Ti Text (fine)
point box text
Q2
887,590
12.78
T6
506,755
Part QA
point Text
text
577,369
8.31
T7
506,755
Edit Add
point text
program (box text)
247,998
3.57
T10
247,998
Edit Remove
point text
program (boxes)
1,394,345
20.08
T8
247,998
Edit Replace
poinl + text
program (box text)
883,941
12.73
T9
247,998
Pure box listing
point text
all boxes
500,000
7.20
TO
500,000
Total
6, 8,944,220
100.00
4,292.395
Table 11: Category distribution.
Rank
Category
Count
Share (%)
Human
20,426
23.74
2
Industrial goods
7,139
8.30
Home goods
7,010
8.15
4
Buildings
6,909
8.03
Personal items
6,730
7.82
6
Animals
6,582
7.65
Weapons
6,406
7.45
8
Vehicles
5,996
6.97
9
Cultural artifacts
5,995
6.97
10
Food
5,885
6.84
11
Technology electronics
5,183
6.02
12
Others
1,774
2.06
If a GT contains only BBoxes or only text, we evaluate the available modality and leave the other columns blank. Table 12 summarizes results for Tasks 0-10 while mapping each task to its template Type and name as in Table I0
Discussion.
Language-intenis intensive tasks (T7 Part QA, T10 Edit-Ade obtain the highest SBERT/SimCSE and strong lexical metrics, indicating robust alignment between our planned hox-conditioned answers/programs and textual GT. Among loU-based tasks, T0/T2/T10 show the strongest geometric alignment, reflecting reliable planning for pure detection, fine grounding, and edit addition respectively. Blank text or IoU entries arise by design when a task's lacks the corresponding modality.
A.10 PROMPT TEMPLATES FOR DATA CONSTRUCTION
To ensure the reproducibility of our dataset construction, this section provides the complete set of English prompt templates used generate the instruction-following samples for each of the 11 task types, as described in Section 7.2.2 These templates are presented in the tables below.
A.10.1
TYPE 0: PURE Box LISTING
ID Prompt Template
"Detect all boundi ng boxes in this point cloud"
"Show me all the bounding boxes" "Generate bounding boxes for all objects"
3
23

---

Published as a conference paper at ICLR 2026
ID
Prompt Template
"Find all object boundaries" "Extract all bounding ng boxes from this scene"
5
"Locate all object bounding boxes"
7 "Output all detected bounding boxes" "Provide bounding boxes for all components"
8
"Identify all object boundaries in this model"
10 "Return all bounding box coordinates" "Detect and output all object boxes"
12 "Find all rectangular boundaries"
"Gererate all object bounding boxes"
14 "Show all detection boxes"
15 "Output bounding box coordinates for all objects"
16
"Detect all objects and return their boxes"
17
"Find every bounding box in this point cloud"
18
"Extract object boundaries from this 3D data"
19
"Provide all object detection boxes"
20
"Return coordinates all detected objects"
A.10.2
TYPE 1: MULTI-PART GROUNDING (COARSE TEXT)
ID Prompt Template
"What di components does this contain? Please annotate with bounding boxes and provide short labels"
2 "What functional parts make up this object? First provide box-tokens then write the name"
3 "What structural elements can be decomposed? in the specified format"
Output
4 "What key components does this have? Please locate and name them"
"What identifiable parts are there? Mark with AABB tokens"
"What construction units can be distinguished? Please list them"
7
"What parts need to be annotated in this?" "What basic components does this contain? Please output bounding box label"
8
9 "What main parts is this composed of? Please enumerate using token format"
10 "what recogni zable sub-parts
there? Use the
specifise fied format for output" "Which distinct parts exist here? Provide box-tokens and short labels"
12
"Identify every component and prepend its quantized box tokens"
"List all separable elements; each line starts with takens"
"Locate and name each part of the object" "Enumerate all components with their bounding-bon box tokens"
15
16 "Break the shape into parts, output AABB tokens then a concise tag"
17 "Mark every structural unit. Format: tokens
followed by NAME"
24

---

Published as a conference paper at ICLR 2026
ID Prompt Template
"Point out all functional pieces and give their tokeni zed boxes" "Provide the set of parts and their six token indices"
19
20
"Give every recognized section together with its AABB tokens"
21
"List all structural elements using 6 box-tokens name format" "Return the quantized bounding box and short name
22
for each part"
23 "Please enumerate in the format of tokens followed
by NAME"
24
"Output part AABB (tokens) and their names" "cive the 1ist of components together with their quantized boxes"
26
"Return each element as six tokens followed by short label"
27
"Provide AABB tokens plus name for every distinguishable component"
28
"Enumerate all parts with their bounding-box tokens and brief tag"
29
"Please identify all parts and output bounding box tokens short name"
30
"After completion, only return the parts list without extra explanation"
31
"Output strictly according to the speci fied format, no additional text"
32
"No extra description the end, only list the parts"
33
"List the token AABB and name for each part" "Cive tokens and labels in order of appearance"
34
"Use six tokens followed by space and name"
36
"Example line: tokens label, please output according to this example"
37 "Return all components and their quantized coordinate indices"
A.10.3 TYPE 2: MULTI-PART GROUNDING (FINE TEXT)
ID Prompt Template
"Please describe the overall appearance of this point cloud in detail, then introduce each part one by one (with AABB tokens)
2 "First give an overall impression, then explain each part in turn with bounding box tokens"
3 "Please provide an overview of this model, and describe each component with tokens"
"What the overall shape like? What are the materials and functions of each part?"
5 "Please first introduce the complete structure, then
list parts with tokens detailed explanations" "From this point cloud, give an overall description then detail each part with its bounding box"
7 "Describe the complete object, followed by part-wise details using quantized tokens"
25

---

Published as a conference paper at ICLR 2026
ID Prompt Template
"Provide a holistic view and then list all elements with 6 box tokens and properties"
9 "Summarize the scene, then output each component in the required token format"
10 "Give full description first, then annotate every part with its box tokens and long caption"
11 "Please first present the overall features, then elaborate on each functional component"
12 "After summarizing the appearance, list each part item by item (format: tokens description)
13 "Give the global appearance, then each part line starts with 6 tokens"
14 "Present the overall structure and afterwards the detailed attributes of all components"
15
"Explain the general design; afterwards specify each element with its tokens and features"
16 "First output an overall description, then write detailed explanation for each part with tokens"
17 "Describe holistically, then provide component-wist explanations with bounding-box indices"
"Begin with the object overview; subsequently list parts and their detailed properties"
"Offer a complete summary and then enumerate parts with tokenized boxes"
"Return the overall description and AAB3 + detailed explanation for each part"
21
"Finally, please list all componentss and their features in the specified format"
22 "Please output in the format of 'overall description tokens description'"
23 "Provide each part in turn (including token bounding box and funetion/naterial description)"
24
"Provide the overall description followed by every
part in the required tokenized box format"
25
"Please finish by listing each component's six box tokens and an informative sentence"
26
"Return first the global description, then each element as tokens LONG.DESCRIPTION" "Include holistic summary, then annotate each part with its quantized AABB and details"
27
28
"Conclude with the part-wise list using bounding- tokens plus their detailed attributes"
29 "Output the parts list, each line starting with tokens"
"Please output the description of this object or scene and its parts' BBox information, overall first then parts, format and order cannot be changed" "End by outputting all parts and their respective detailed features"
31
32 "Summary first, then component lines with tokens and descriptions"
33 "Output strictly in two sections: overview + per-part details"
34 "After the overview, enumerate every part with its quantized box tokens"
"Overall parts format example: tokens The left handle is ..."
26

---

Published as a conference paper at ICLR 2026
Table 12: All-task results the 400-case unseen benchmark. "Type/Name" follows the template definitions in Table [0] Blank entries indicate that the GT for that task does not contain the corre- sponding modality.
Task
Type
Name
loU
SBERT
SimCSE
BLEU-1
ROUGE-I.
METEOR
0
TO
Pure box listing
0.755
T1
Multi-Part Grounding (Q1)
0.728
55.60
54.19
35.55
35.58
18.09
2
T2
Multi-Part Grounding (Q2)
0.736
63.68
60.68
31.01
33.68
27.72
3
T3
Single-Part Grounding (Q1)
0.528
73.28
71.70
36.29
38.94
33.21
4
T4
Single-Part Grounding (Q2)
0.443
5
T5
Box-to-Text (Q1)
57.35
56.49
38.12
38.14
19.49
6
T6
Box-to-Text (Q2)
64.64
61.96
31.35
33.73
28.13
7
T7
Part QA
0.554
78.98
84.25
40.54
42.26
34.24
8
T8
Edit Remove (program)
0)473
9
T9
Edit Replace (program)
0.409
10
T10
Edit Add (program)
0.700
80.38
79.71
47.62
51.66
46.63
A.10.4
TYPE 3: SINGLE-PARE GROUNDING (FROM COARSE TEXT)
ID
Prompt Template
"Find the (part_name) in this model"
2
"Locate the (part.name] in this model"
3 "Point out the (part.name) in this point cloud"
4 "Mark the (part.name) in this object" "Where is the (part.name) in this 3D model?"
6 "Identify the {part [aaet in this point cloud"
"Please show all (part.name) 8 "Where is the position of (part_name} in this scene?"
this object"
"Locate the (part.name} in this model"
10 "Find the (part_name) in this point cloud"
"Point out the [part.name} in this object"
12
"Where is the (part.name) in this 3D shape?"
13 "Mark the [part name) in this model"
14 "Show all (part name) in this object."
15 "Identify the (part_name) in this point cloud"
16 "Highlight the position of (part.name)."
A.10.5
TYPE 4: SINGLE- PART GROUNDING (FROM FINE TEXT)
ID Prompt Template
"Where the part corresponding to this description: (part.descriptionn"
"Help me locate this part: (part.descriptionn]
3 "Find the corresponding part based on this description: (part.description]
4 "In this point cloud, which part does (part.deschl dettriptsriioo refer to?"
5 "Mark the position of this part: (part.desrription)"
6 "Please provide the bounding box for the part corresponding (part.description)
this description:
7 "Find the part that matches this description:
(part.descrias description]"
"Locate the component described as: (part_description)
27

---

Published as a conference paper at ICLR 2026
ID Prompt Template
"Which part is this referring to:
(part. description]"
10
"Mark the boundary of: (part_descriptionn"
"Show the box coordinates for: [part_@escristion)"
12 "Provide the bounding box for this described element: fpart.decriptionn"
13 "Where exactly is: deortdeescription
"Given this description, locate the corresponding part: (part_description]
"Locate the part based on this text and provide its AABB: (part.descriptinn""
16 "Which specific part does this description correspond to? (part.descriptionn"
A.10.6
TYPE 5: BOX-TO-TEXT (COARSE)
ID Prompt Template
"What is this part?"
2 "What is this marked area?"
"What contained in this box?"
4 "What is this marked portion called?"
"What part is inside this bounding box?"
6 "What is this part called?"
7 "Name this highlighted component"
8 "What is contained in this bounding box?"
9 "Identify this marked region"
10 "Cive the name of this part"
11
"What is inside this AABB box?"
12
"Name this area with one word"
13 "What's the simple label for this bounded area?"
14
"What would you call this boxed element?"
15
"What part does this bounding box point to? Please answer briefly"
16 "What is this outlined section?"
17 "Provide the name for this demarcated part"
1.1
TYPE 6: BOX-TO-TEXT (FINE)
ID Prompt Template
"Describe this part in detail"
2
"What does this area contain? Please explain in detail"
3 "please describe the part within this bounding box, including appearance, material and function" "What in this box? Please provide detailed information"
"What the marked portion? Please provide complete description"
"Describe this part in detail"
7 "What can you tell me about this highlighted component?"
8 "Provide comprehensive description of what's in this box"
28

---

Published as a conference paper at ICLR 2026
ID Prompt Template
"Explain the appearance, material and function of this marked area"
10 "Give details about this bounded region"
"What are the characteristics of this marked area? Please describe comprehensively"
12 "Elaborate on the appearance and purpose of this part"
"What contained in this bounding box? Elaborate its features"
"Tell me everything about this outlined element" "What is the material, shape and function of the object in this box?"
15
16 "Please characterize this demarcated component
thoroughly"
17 "What's inside this box? Include all relevant details"
A.10.8 TYPE 7: PART-AWARE Q&A
This task reuses the questions from the 'QA" field in the annotations and replaces textual part references with box tokens the answer. No new templates are generated for the questions them selves.
10.9.9
TYPE 8: DELETION PROGRAM
ID Prompt Template
By part name "Please remove the (part name] from this object"
2 "Get rid of every (part.name)"
3 "I want to delete the (part_name} here"
"Can you erase all instances of the (part.name)?"
5 "Show me this model but without the (part.name)"
"Take out the (part.name)"
7 "The (part.name) needs to be removed"
8
"Omit the [part name) from this scene" "I don't want to
the {part name) anymore" 10 "Could you proceed with deleting the (part_name]?"
9
"Let's see what it looks like if remove the (part.name)"
12 "Exclude the [part.name] from the final output"
13 "The task is to get rid of the "Wipe out the (part.name) from the 3D model"
15 "Please filter out the (part_name)"
16 "Delete the component identified as (part.name)"
17 "I require the removal of the (part.name)"
18 "Make the (part.name) disappear"
19 "This model would be better without the (part.name)"
20 "Execute the deletion of the (part_name)"
By part description
21 "Please remove this specific part: "I "part description
" don't want the component described as part.description
"Delete the part that is part.description!
29

---

Published as a conference paper at ICLR 2026
ID Prompt Template
"Get rid of this particular element:
part description"
"Find the part matching jart.descriptionand remove it"
26 "The element characterized by pert.descriptioonshuuld
be deleted"
27 "Erase the component with this description:
part description"
28
"I want to exclude the part that is part.descript
"Locate and then delete this item: part.description
30 "Take out the part that looks like this:
part.description
31 "The target for deletion is the part described
fart.descripting
32 "Can you remove the part with these features:
part.description!
33
"Please cmit this from the model: part.description"
34 "Based on the description part.description, remove the corresponding part"
35
"I've identified part to remove: part dart.desctiption "Wipe the following item from the scene: part.description"
"The part to be erased is: part.description" "Remove the object that fits this profile: part.description"
38
39 "please execute deletion on the component identified as part descriptions
40 "Let's remove one specific part: fart.description"
A.10.10 TYPE 9: MODIFICATION PROGRAM
ID Prompt Template
"Please edit the {part name] to be [new description]" "Change the (part name] into {new description]" "Replace the (part_name) with this: (new.description]"
3
4 "I want the (part.name) to look like this:
(new.descristion)"
5 "Modify the (part.name} to become (new.description)"
6 "Update the (part_name) so it is now (new description)"
7 "Let's s alter the (part_name). It should be (pew.description]"
8 "Transform the {part name] into new.desccrptionn"" "Could you make the [part.name} to be (new.description)"
"My instruction is to change the (part.name) to tnewdeeccription"'
"The [part_name} needs an update. Here are the new details: /hewddescription""
12 "Let's swap the current [part name] with a new one: (new.desee description)"
13 "The [part_name] should be revised to be (pew.description)"
30

---

Published as a conference paper at ICLR 2026
ID
Prompt Template
"Please perform an edit on the {part name]. It should be {new description] 15 "Adjust the (part.name} to match this description: (new.description)"
A.10.11
TYPE 10: ADDITION PROGRAM
ID Prompt Template
"Add the [part name] to this 3D asset. [part.name]
2 "please add a (part_name) to the model."
3 "Insert the {part_name} component." "Attach the (part.name} to this object."
5 "Place the (part.name) on this model." "Include the [part.name] in this design.
7 "Incorporate the {part pant_name).... into this structure."
8 "This model is missing its (part name]. Please add it."
9 "Complete this 3D model by adding the [part.name)." "
10 "The [part.name} is missing, Add it back." "
11 "Restore the [part.name] to this object."
"Fill the missing (part.name)."
13 "This asset needs {part_name)) "Enhance this model with a (part_name)
Add it."
15 "Improve this design by adding the (part_name)."
16 "Augment this object with the [part.name)."
17
"Extend this model to include the (part-name)."
18 "Could you add the (part. to complete this model?"
19
"I need you to add the {part_name} to this 3D object."
20
"Nould you please attach the (part.name)?"
21
"Can you help me add the (part.name) component?"
22 "Mount the (part name] in the appropriate position."
"Install the (part name] where belongs."
24 "Position the (part_name) correctly on this model."
25 "Generate and add the (part_name) to this asset."
26 "Create the (part.name} component for this model."
"Design and attach the (part.name)."
28 "This looks incomplete without the {part.name). Add it."
29
"To make this functional, add the (part name) (part.name)..
30 "The model requi (part_name) to be comples lete.""
Part-specific templates
31 "Add the head section to complete this figure."
32 "This model needs its head. Please attach it."
"The top part is missing. Add the head."
34
"Install the wheels to make this vehicle complete."
"Add wheels for mobility."
36
"Mount the wheels
this vehicle."
37
"Install the door to complete the entre
38
"Add door for access."
39
"Place the door in the opening."
40
"Attach the handle for better grip."
41
"Add the handle component.
42
"Install the handle mechanism."
31

---

Published as a conference paper at ICLR 2026
ID
Prompt Template
43
"Add the legs to support this stzucture."
44
"Attach the leg components." "
45
" "Install the supporting legs."
46
"Add wings to complete this model." "
47
"Attach the wing components."
48
"Install the wings on both sides."
32


Reviews:
[{"review_id": "nirU1Dk83I", "reviewer": "Reviewer_4VTq", "summary": "Novel, but paper writing could be improved", "questions": "n/a", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper presents Part-X-MLLM, a 3D large language model for diverse 3D tasks by formulating them as programs in an executable grammar. Overall, the work is decent, includes a large curated dataset, and is generalizable across 11 tasks. \n\n+ The two-stage training process is interesting to help the model learn the underlying 3D structure and associate the pretrained language knowledge with it.\n\n+ Semantic Granularity Control is an interesting part of the work. The part-aware synthesis is useful for many practical analyses.", "weaknesses": "- Small typo in line 192 'boxe'\n\n- The writing is a bit hard to follow in places. For example, in line 35, I am not sure why Part-X-MLLM is 'native'. Similarly, the mention of 'structural opaqueness' in line 53 is not clear.\n\n- The distinction with past works is not clear enough. I would love to see a table comparing past works and Part-X-MLLM.\n\n- For the qualitative analysis, it would have been great to see a small-scale study with real participants and evaluate the performance of Part-X-MLLM qualitatively.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QpcwR0QXxN", "reviewer": "Reviewer_ZDc2", "summary": "This paper builds a multimodal framework and benchmark focused on 3D part-level understanding based on Qwen2.5-VL. The authors propose a dual-encoder architecture and a multi-stage training pipeline to jointly model geometric structures and semantic appearance for 3D objects.", "questions": "1.Evaluation metrics are primarily limited to standard natural-language measures. It would strengthen the evaluation to incorporate LLM-based automatic assessment frameworks (e.g., GPT-based judging protocols) to better capture semantic correctness and reasoning quality.\n\n2.Baseline comparisons appear insufficient. Please include strong contemporary 3D foundation models (2025 SOTA) for part-level QA and grounding tasks, such as Mini-GPT-3D, to more convincingly demonstrate the advantages of the proposed approach.\n\n3.Benchmark evaluation is largely conducted on the authors’ own dataset. To better demonstrate generalization and fairness, we recommend evaluating on established public 3D multimodal benchmarks (e.g., the Point-LLM test suite) and additionally reporting performance under varying point-cloud resolutions as well as part-level generation quality metrics.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1.Addressing part-level 3D multimodal modeling is timely and necessary.\n\n2.The proposed dual-encoder design effectively encodes complementary attributes of 3D objects.\n\n3.The use of task-specific prompts and special tokens enables diverse part-centric tasks within a unified framework.", "weaknesses": "1.Evaluation metrics rely mainly on traditional natural-language metrics; consider including LLM-based scoring (e.g., GPT-judge) for more robust assessment.\n\n2.Baselines: comparison is limited; please include strong 2025-era SOTA 3D multimodal models on QA and grounding tasks (e.g., Mini-GPT-3D).\n\n3.Benchmark: experiments are primarily on the authors' dataset; please evaluate on established public 3D benchmarks, such as the Point-LLM test suite, and include metrics for point resolution sensitivity and generative quality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TTVeBOwMno", "reviewer": "Reviewer_jEq5", "summary": "This paper introduces Part-X-MLLM, a novel part-aware 3D Multimodal Large Language Model. Its central contribution is the unification of diverse 3D tasks—including generation, editing, and question-answering—under a single framework by formulating them as programs within a structured, executable grammar of parts. The model takes an RGB point cloud and a natural language prompt as input and autoregressively generates a unified token sequence that encodes part-level bounding boxes, semantic descriptions, and edit commands, forming an executable \"plan\". This approach effectively decouples symbolic planning from geometric synthesis, allowing any compatible geometry engine to be controlled through this single, language-native interface. The authors employ a dual-encoder architecture to disentangle structural from semantic information and instruction-tune the model on a large-scale, part-centric dataset. Evaluated on a comprehensive benchmark (UniPart-Bench) spanning 11 task families, the model demonstrates a strong capability to produce high-quality structured plans, enabling state-of-the-art performance in grounded tasks.", "questions": "First, part-based 3D is not my primary research area, so I am not fully aware of the most cutting-edge advancements in this field. I would like to see other reviewers' opinions on whether the comparative methods used in this paper are sufficiently state-of-the-art across various tasks. I believe the paper presents extensive work, and the unified framework for processing 3D patches as input is highly valuable. I am currently leaning toward a borderline accept score and am inclined to recommend acceptance. My final score may be adjusted based on other reviewers' comments and the authors' rebuttal.\n\nAdditionally, part-based 3D generation could potentially be generalized to 3D scene generation. It would be beneficial if the authors could briefly analyze this possibility. The following papers on scene compositional generation may also be considered for citation:\n[1] GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting \\\n[2] Layoutdreamer: Physics-guided layout for text-to-3d compositional scene generation \\\n[3] Semantic score distillation sampling for compositional text-to-3d generation \\\n[4] CompGS: Unleashing 2D Compositionality for Compositional Text-to-3D via Dynamically Optimizing 3D Gaussians", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The research on part-based 3D generation is highly practical, and the authors have designed a unified framework that integrates 3D generation, understanding, and editing, which is very valuable.\n- The paper not only proposes a large model, Part-X-MLLM, but also introduces a new benchmark and includes extensive experimental comparisons in both 3D generation and understanding, demonstrating substantial effort.\n- The writing is clear and easy to follow, and the figures are professionally designed and visually appealing.", "weaknesses": "See the \"Questions\" section.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Q8DjJH3Cls", "reviewer": "Reviewer_HyBn", "summary": "The paper proposes Part-X-MLLM, a native 3D multimodal LLM designed to operate not on whole 3D objects as monolithic shapes, but at part granularity. The key idea is to cast all 3D tasks (part captioning, grounding, Q&A, generation, and edit control) as autoregressive program generation in a grammar that interleaves tokens for bounding boxes, part semantics, and edit operators.", "questions": "1. Can the model sustain the same part-aware behavior when the input is not a clean synthetic asset but a noisy or incomplete real-world scan? Are there any stress-tests quantifying degradation under distribution shift?\n2. Given that the benchmark shares annotation grammar and task templates with the training corpus and the test slice is very small, how do we exclude the possibility that the model is primarily exploiting training-time format priors rather than demonstrating generalizable planning ability?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. This paper is well-written and very easy to follow.\n2. The paper identifies a real gap: existing 3D LLMs “understand scenes” or “generate geometry” but do not provide a unified, language-native, executable control surface over 3D parts. Casting all tasks as program generation is clean and technically coherent.\n3. Unlike prior 3D MLLM benchmarks that focus on scene captioning or open QA, UniPart-Bench directly tests structured part-aware program generation (BBox alignment, part-level grounding, edit planning). That makes the evaluation target exactly match the claimed contribution, instead of proxy tasks.\n4. The model shows consistent improvements over OmniPart and PartField baselines in structural planning metrics, not marginally but substantially (+2-5 IoU), which is not trivial in box-level matching.", "weaknesses": "1. All experiments are conducted on clean, part-annotated synthetic assets with well-formed AABBs. There is no evidence that the model works under realistic conditions (partial scans, occlusion, noise, incomplete parts, inconsistent normals).\n2. UniPart-Bench adopts the same grammar, annotation style, and task templates used in training, and the test split is extremely small relative to the training volume. Thus the results may reflect memorization of format rather than robust capability.\n3. The claimed downstream benefits (editing / generation quality) are entirely mediated by third-party geometry backends; the paper does not isolate how much of the improvement is attributable to the proposed planning interface itself versus the backend strength.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)