GReFEM: Multimodal LLMs as Zero-Shot Semantic Assistants for Physics-Guided 3D Mesh Refinement

• URL: http://arxiv.org/abs/2607.08798v1
• Date: Wed, 08 Jul 2026 14:46:54 GMT
• Title: GReFEM: Multimodal LLMs as Zero-Shot Semantic Assistants for Physics-Guided 3D Mesh Refinement
• Abstract summary: This study explores a question: can the high-level semantic understanding of Multimodal Large Language Models serve as a viable, zero-shot geometric proxy for finite element mesh refinement?<n>To bridge the gap between 2D MLLM pre-training and 3D geometries, we introduce orthoViews, a view-selection module that maximizes the observability of key features.<n>Our findings reveal that MLLMs demonstrate robust zero-shot capacity to accurately follow complex spatial-physical instructions, isolating stress-relevant features with higher precision than blind assistants.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adaptive volumetric finite element meshing is a critical step in computer-aided engineering and analysis that dictates the computational budget of a given problem. It traditionally requires iterative PDE solvers or heavily supervised, data-driven surrogates trained on large-scale simulation data. While Multimodal Large Language Models (MLLMs) excel in 2D visual tasks, their zero-shot capability to semantically ground regions based on geometric understanding and physics remains an open question. Overall, this study explores a significant question: can the high-level semantic understanding of off-the-shelf MLLMs serve as a viable, zero-shot geometric proxy for finite element mesh refinement? To investigate this, we introduce GReFEM (Geometric Reasoning Enhanced Multimodal LLMs for Finite Element Meshing), a framework that utilizes MLLMs to visually localize stress-critical regions based on physics-guided textual prompts. To bridge the gap between 2D MLLM pre-training and 3D geometries, we introduce orthoViews, a view-selection module that maximizes the observability of key geometric features. We conduct an in-depth empirical evaluation across diverse CAD geometries, loading cases, and SOTA MLLMs, comparing them against a tuned geometric heuristic under a strict, matched refinement budget. Our findings reveal that MLLMs demonstrate robust zero-shot capacity to accurately follow complex spatial-physical instructions, isolating stress-relevant features with higher precision than blind heuristics. By mapping both the successes and current limitations of MLLMs in physical grounding, this study defines the frontier of foundation models as semantic assistants in automated simulation workflows.

Related papers

• Dual-Pathway Geometry-Aware MLLM for Spatial Intelligence [63.11502936651325]
  We propose GAMSI, a dual-pathway Geometry-Aware MLLM for Spatial Intelligence.<n>It takes only RGB images as input while internalizing both forms of geometric prior within a unified autoregressive backbone.<n>GAMSI achieves state-of-the-art performance on seven spatial intelligence benchmarks.
  arXiv  Detail & Related papers  (2026-05-25T01:33:19Z)
• GeoAlign: Geometric Feature Realignment for MLLM Spatial Reasoning [12.641199394796436]
  Multimodal large language models (MLLMs) have exhibited remarkable performance in various visual tasks, yet still struggle with spatial reasoning.<n>Recent efforts mitigate this by injecting geometric features from 3D foundation models, but rely on static single-layer extractions.<n>We propose GeoAlign, a novel framework that dynamically aggregates multi-layer geometric features to realign with the actual demands.
  arXiv  Detail & Related papers  (2026-04-14T11:58:02Z)
• Let Geometry GUIDE: Layer-wise Unrolling of Geometric Priors in Multimodal LLMs [13.627465963609936]
  GUIDE (Geometric Unrolling Inside MLLM Early-layers) is a progressive geometric priors injection framework.<n>We introduce a context-aware gating that enables the model to fetch requisite spatial cues based on current semantics.
  arXiv  Detail & Related papers  (2026-04-07T10:45:28Z)
• GAP-MLLM: Geometry-Aligned Pre-training for Activating 3D Spatial Perception in Multimodal Large Language Models [70.61152292499737]
  We argue that this gap does not arise from insufficient geometric priors, but from a misalignment in the training paradigm.<n>Existing approaches typically resort to naive feature concatenation and optimize directly for downstream tasks without geometry-specific supervision.<n>We propose GAP-MLLM, a Geometry-Aligned Pre-training paradigm that explicitly activates structural perception before downstream adaptation.
  arXiv  Detail & Related papers  (2026-03-17T12:43:48Z)
• Concise Geometric Description as a Bridge: Unleashing the Potential of LLM for Plane Geometry Problem Solving [50.05273675575345]
  PlaneThought Problem Solving (PGPS) aims to solve a plane geometric problem based on a geometric diagram and problem textual descriptions.<n>Large Language Models (LLMs) possess strong reasoning skills, their direct application to PGPS is hindered by their inability to process visual diagrams.<n>We train a MLLM Interpreter to generate geometric descriptions for the visual diagram, and an off-the-shelf LLM is utilized to perform reasoning.
  arXiv  Detail & Related papers  (2026-01-29T02:03:33Z)
• ORIGAMISPACE: Benchmarking Multimodal LLMs in Multi-Step Spatial Reasoning with Mathematical Constraints [42.713620384054146]
  This paper introduces ORIGAMISPACE, a new dataset and benchmark designed to evaluate the multi-step spatial reasoning ability.<n>We propose four evaluation tasks: Pattern Prediction, Multi-step Spatial Reasoning, Spatial Relationship Prediction, and End-to-End CP Code Generation.
  arXiv  Detail & Related papers  (2025-11-23T13:42:22Z)
• Actial: Activate Spatial Reasoning Ability of Multimodal Large Language Models [75.45940282834327]
  We introduce Viewpoint Learning, a task designed to evaluate and improve the spatial reasoning capabilities of MLLMs.<n>We present the Viewpoint-100K dataset, consisting of 100K object-centric image pairs with diverse viewpoints and corresponding question-answer pairs.<n>Our approach employs a two-stage fine-tuning strategy, resulting in significant improvements across multiple tasks.
  arXiv  Detail & Related papers  (2025-11-03T14:27:00Z)
• Large Language-Geometry Model: When LLM meets Equivariance [53.8505081745406]
  We propose EquiLLM, a novel framework for representing 3D physical systems.<n>We show that EquiLLM delivers significant improvements over previous methods across molecular dynamics simulation, human motion simulation, and antibody design.
  arXiv  Detail & Related papers  (2025-02-16T14:50:49Z)
• MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science [62.96434290874878]
  Current Multi-Modal Large Language Models (MLLM) have shown strong capabilities in general visual reasoning tasks.<n>We develop a new framework, named Multi-Modal Scientific Reasoning with Physics Perception and Simulation (MAPS) based on an MLLM.<n>MAPS decomposes expert-level multi-modal reasoning task into physical diagram understanding via a Physical Perception Model (PPM) and reasoning with physical knowledge via a simulator.
  arXiv  Detail & Related papers  (2025-01-18T13:54:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.