MultiSurf-GPT: Unified Multimodal Sensing

• MultiSurf-GPT is a modality-agnostic framework that integrates radar, microscope, and multispectral data via GPT-4o to perform context-aware reasoning and analytics.
• It employs zero-shot and one-shot prompt engineering to achieve high accuracy, with improvements up to 51.67 percentage points on imaging tasks.
• The system streamlines multimodal surface sensing for applications in health diagnostics, manufacturing, and safety monitoring by unifying various sensor inputs.

MultiSurf-GPT is a modality-agnostic framework that leverages the advanced capabilities of GPT-4o to perform context-aware reasoning and low-level analytics for multimodal surface sensing tasks. Designed to integrate radar, microscope, and multispectral sensing data via prompt-based strategies, MultiSurf-GPT aims to expedite the development of context-aware applications across health diagnostics, manufacturing, and safety monitoring by providing a unified LLM interface without modality-specific model pipelines (Hu et al., 2024).

1. System Framework and Architecture

MultiSurf-GPT receives heterogeneous surface sensing inputs—including radar CSV files (Tangible Radar), high-resolution microscope images (MicroCam), multispectral images (SpeCam), and associated dataset papers (PDF/text)—and processes them through the GPT-4o interface. The system operates as follows:

• File modality: CSV inputs are handled via the GPT-4o Code Interpreter, utilizing Python and scikit-learn to fit classical machine learning classifiers (typically SVM or RF).
• Image modality: Images are ingested through GPT-4o’s vision encoder, with internal resizing and encoding.
• Document modality: Textual data is fed into the LLM’s text encoder to support high-level reasoning.
• Prompt Engineering Module: Provides both zero-shot and one-shot (few-shot) templates to facilitate task specification and supervision.
• GPT-4o Reasoning Engine: Synthesizes fused multi-modal input for both low-level label extraction and higher-order context-aware recommendations.

Informally, the feature “fusion” process treats the input as three feature vectors, $f_{radar} \in \mathbb{R}^{d_1}$, $f_{micro} \in \mathbb{R}^{d_2}$, and $f_{multi} \in \mathbb{R}^{d_3}$, which are implicitly concatenated:

$$F = [ f_{radar} \parallel f_{micro} \parallel f_{multi} ]$$

Subsequently, GPT-4o’s transformer self-attention computes a joint latent representation:

$H = Transformer(F)$

$H$ is then used for downstream classification and context-aware reasoning.

2. Prompting Methodologies

Task supervision in MultiSurf-GPT is governed solely by prompt engineering, with no model fine-tuning or parameter updates:

• Zero-Shot Templates: Specify the input modality and target ML model (e.g., SVM or RF for radar CSV; CNN via internal vision encoder for images). For radar:

  The provided CSV is <MOD> data. In the CSV file, columns [0:-1] contain the radar features, and the last column [-1] contains the labels. Build a model (defaulting to using <MODEL>) and return the accuracy. Do not output any other text.

  The system supports “Rotation: 100.00%, Orientation: 90.91%, …" style responses.
• One-Shot Templates: Augment the prompt with a single labeled example, enhancing performance in under-constrained classification settings:

  [Zero-Shot Prompt] Example: • Features: [0.12, –0.34, …] → Label: “Rotation” Now classify the next CSV and return only the accuracy.

• For imaging modalities:

  The provided picture is <MOD>. Identify the category of this picture from <CLASS> and return only one category. For example, <PIC1>, <PIC2>, …, <PICn> are sample images for each category in <CLASS>. Do not output any other text.

This prompt-based control enables flexible adaptation to a variety of sensing tasks across modalities.

3. Implementation and Data Handling

Data ingestion requires minimal preprocessing:

• Radar (CSV): Only necessitates that the last column is the class label; feature normalization/engineering is deferred to GPT-4o.
• Image Inputs: Resizing and encoding are managed internally by GPT-4o’s vision encoder.
• Documents: Supplied as raw text or PDF to the LLM’s text encoder.

No local hardware acceleration is necessary; all computations and model execution occur on OpenAI’s back-end. The framework makes use of the GPT-4o API, including the Code Interpreter plugin (Python 3.x and scikit-learn), and all tokenization and non-text encoding are managed server-side.

No instruction fine-tuning or weight updates are performed; the system is prompt-driven throughout.

4. Evaluation and Results

MultiSurf-GPT was empirically validated on multiple datasets and tasks, yielding the following results:

Task	Model	Zero-Shot Accuracy	One-Shot Accuracy
Rotation	SVM	100.00%	(not tested)
Orientation	SVM	90.91%
Identification	SVM	90.45%
Count	RF	72.50%
Order	RF	68.00%
Distance	RF	70.30%
MicroCam-Obj	CNN (GPT-4o)	60.00%	83.33% (+23.33%)
MicroCam-Mat	CNN (GPT-4o)	45.00%	56.11% (+11.11%)
SpeCam	CNN (GPT-4o)	30.00%	81.67% (+51.67%)

Precision, recall, and F1 scores were not separately reported. Tasks included low-level classification (e.g., object/material type, material class), as well as high-level context extraction (e.g., inferring original sensing method from dataset papers). A qualitative scenario demonstrated that MultiSurf-GPT produced more context-aware, scenario-appropriate sensor recommendations compared to baseline GPT-4o prompting.

5. Comparative Performance

MultiSurf-GPT improves upon unimodal and non-integrated baselines in several respects:

• Radar-Only Baselines: Off-the-shelf SVM models performed well (≥ 90%) on shape-based tasks but underperformed for count/order tasks.
• Vision-Only Baselines: Image classification, using zero-shot or one-shot prompting, increased from ~30–60% to ~80% in accuracy for MicroCam and SpeCam datasets.
• Against GPT-4o Without Contextual Integration: The context integration module in MultiSurf-GPT yielded recommendations more aligned with scenario requirements, with relative improvements of up to 51.67 percentage points noted, though statistical significance was not tested.

A plausible implication is that prompt engineering and cross-modal reasoning together offer more utility for integrated, context-aware multimodal sensing than unimodal pipelines (Hu et al., 2024).

6. Limitations and Prospective Directions

Several limitations are inherent in the current MultiSurf-GPT framework:

• Exclusive reliance on prompt engineering without instruction fine-tuning restricts accuracy, especially with complex or noisy spectral data.
• No end-to-end fine-tuning or chain-of-thought prompting implemented.
• User-level and in situ studies have not been conducted to quantify application-level gains.
• Precision, recall, and F1 were not analyzed, limiting insight into class imbalance and error modes.

Identified future work encompasses:

• Incorporation of instruction-level fine-tuning on sensor data to yield more robust, modality-agnostic embeddings.
• Large-scale user trials to rigorously quantify real-world context-aware benefits.
• Expansion to other modalities such as acoustic or haptic inputs, and exploration of true feature-level (as opposed to attention-mediated) fusion.

7. Application Domains and Significance

Demonstrated use cases for MultiSurf-GPT include:

• Health diagnostics (e.g., material identification for wound dressings via MicroCam).
• Manufacturing quality control (e.g., radar-based verification of thickness/orientation).
• Safety monitoring (e.g., detecting slippery surfaces using multispectral data).

The system’s use of GPT-4o as a “universal front end” enables rapid prototyping for diverse sensing scenarios. The approach avoids the cost and latency of building custom models for each sensor modality and permits extensibility through prompt engineering for novel hardware platforms or sensing channels.

In summary, MultiSurf-GPT establishes a unified, prompt-driven paradigm for context-aware multimodal surface sensing, enabling both fine-grained classification and higher-order analytic synthesis, while offering a scalable, infrastructure-efficient pathway for real-world deployment and research (Hu et al., 2024).