Paper 2604.07413
FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios
- Published
- Apr 2026
- Research lab
- Independent
- Citations
- 2
- GitHub
- 13 stars
01 In brief
Summary
FORGE is a benchmark for evaluating Multimodal Large Language Models (MLLMs) in manufacturing scenarios.
It introduces a dataset combining real-world 2D images and 3D point clouds with fine-grained annotations like exact model numbers.
The benchmark includes three tasks: Workpiece Verification (WORKVERI), Structural Surface Inspection (SURFINSP), and Assembly Verification (ASSYVERI).
Evaluation of 18 state-of-the-art MLLMs reveals significant performance gaps, especially in microscopic surface analysis and fine-grained model-number recognition.
Bottleneck analysis shows that visual grounding is not the primary limiting factor; instead, insufficient domain-specific knowledge and morphology understanding are the key bottlenecks.
The dataset also serves as a training resource: supervised fine-tuning of a compact 3B-parameter model on FORGE data yields up to 90.8% relative improvement in accuracy on held-out manufacturing scenarios, demonstrating a practical pathway for domain adaptation.
02 From the paper
Abstract
The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution, yet current evaluations fail to reflect the rigorous demands of real-world manufacturing environments. Progress is hindered by data scarcity and a lack of fine-grained domain semantics in existing datasets. To bridge this gap, we introduce FORGE. Wefirst construct a high-quality multimodal dataset that combines real-world 2D images and 3D point clouds, annotated with fine-grained domain semantics (e.g., exact model numbers). We then evaluate 18 state-of-the-art MLLMs across three manufacturing tasks, namely workpiece verification, structural surface inspection, and assembly verification, revealing significant performance gaps. Counter to conventional understanding, the bottleneck analysis shows that visual grounding is not the primary limiting factor. Instead, insufficient domain-specific knowledge is the key bottleneck, setting a clear direction for future research. Beyond evaluation, we show that our structured annotations can serve as an actionable training resource: supervised fine-tuning of a compact 3B-parameter model on our data yields up to 90.8% relative improvement in accuracy on held-out manufacturing scenarios, providing preliminary evidence for a practical pathway toward domain-adapted manufacturing MLLMs. The code and datasets are available at https://ai4manufacturing.github.io/forge-web.