Multimodal Chain of Thought
Combine visual and textual reasoning in explicit step-by-step chains — ensuring AI models ground their answers in what they actually see, not just what they assume from text alone.
Introduced: Multimodal Chain-of-Thought was introduced by Zhang et al. in 2023. The framework extends Chain-of-Thought prompting to multimodal inputs by generating rationales that incorporate both text and image information before producing a final answer. It operates as a two-stage framework: first, rationale generation from multimodal input that explicitly references visual elements; second, answer inference that uses the generated rationale alongside the original image and text to derive a grounded conclusion.
Modern LLM Status: The principle of multimodal reasoning has been absorbed into how frontier multimodal models process visual reasoning tasks. GPT-4V, Gemini, and Claude all incorporate visual understanding into their reasoning pipelines. However, explicit Multimodal CoT formatting remains valuable for complex visual-textual reasoning chains where you need the model to systematically describe what it observes in an image, connect those observations to domain knowledge, and produce answers that are visibly grounded in visual evidence rather than text-only inference.
Reasoning Across Modalities
Standard Chain-of-Thought works with text alone. When a model encounters a question paired with an image, it may generate a reasoning chain that relies entirely on textual knowledge while ignoring the visual evidence in front of it. This leads to answers that sound plausible but contradict what the image actually shows.
Multimodal CoT solves this with a two-stage approach. First, the model generates a rationale that explicitly references visual elements — describing what the diagram shows, what the chart indicates, or what spatial relationships exist in the image. Then, in the second stage, the model uses that visual rationale plus the original image to derive its final answer. By forcing the reasoning chain to pass through visual description before reaching a conclusion, the framework prevents the model from taking shortcuts through text-only inference.
The key insight is that multimodal reasoning is not simply “looking at an image and answering a question.” It requires the model to articulate what it sees, connect visual observations to relevant knowledge, and chain those connections into a coherent argument — just as a human expert would narrate their analysis of a complex diagram.
When models answer visual questions in a single pass, text-based priors often dominate — the model “knows” typical answers and may ignore visual evidence that contradicts expectations. The two-stage approach forces visual grounding first: the rationale must describe what is in the image before any reasoning begins. This explicit visual articulation creates a textual anchor that the second stage cannot bypass, ensuring the final answer reflects what was actually observed rather than what was merely expected.
The Multimodal CoT Process
Four stages from multimodal input to grounded answer
Present Multimodal Input
Provide both the image and the text question together as a unified input. The prompt should clearly establish that the model needs to consider both the visual content and the textual question. Frame the task so the model understands it must look at the image to answer correctly — not rely on background knowledge alone.
“Look at the physics diagram below showing forces acting on a block on an inclined plane. Using the information visible in the diagram, determine the net force on the block.”
Generate Visual Rationale
The model describes the relevant visual features it observes in the image and begins building a reasoning chain. This stage focuses on articulating what the image contains — labels, values, spatial relationships, visual patterns — translating visual information into explicit textual descriptions that can anchor subsequent reasoning steps.
“The diagram shows a block on a 30-degree incline. I can see three labeled force arrows: gravity (mg = 50N) pointing straight down, a normal force (N) perpendicular to the surface, and a friction force (f = 10N) pointing up the incline.”
Integrate Text and Vision
The rationale connects the visual observations from stage two to relevant textual knowledge. This is where the model applies domain expertise to the visual evidence — using physics formulas with the values read from the diagram, interpreting chart trends through statistical knowledge, or connecting map features to geographic concepts.
“The gravitational component along the incline is mg sin(30) = 50 × 0.5 = 25N pointing down the slope. The friction force opposing motion is 10N up the slope. Therefore the net force along the incline is 25N − 10N = 15N down the slope.”
Derive Final Answer
The answer is grounded in the explicit multimodal reasoning chain. Because every step is visible — from visual observation through domain knowledge application to final conclusion — the answer is auditable. If incorrect, you can trace exactly where the visual reading or reasoning went wrong.
“The net force on the block is 15N directed down the inclined plane. This means the block will accelerate down the slope, as the gravitational component exceeds the friction force shown in the diagram.”
See the Difference
How explicit visual reasoning prevents errors on diagram-based questions
Direct Answer
Look at this science diagram showing the water cycle. Which process moves water from the ocean to the atmosphere?
Precipitation moves water from the ocean to the atmosphere.
Multimodal CoT
Observation: The diagram shows arrows indicating different water cycle processes. I can see an upward arrow from the ocean surface labeled “Evaporation,” a horizontal arrow at cloud level labeled “Condensation,” and a downward arrow from clouds to land labeled “Precipitation.”
Reasoning: The question asks which process moves water from ocean to atmosphere. Looking at the arrow directions in the diagram, “Evaporation” is the upward arrow originating at the ocean surface and pointing toward the atmosphere. “Precipitation” moves in the opposite direction — downward from atmosphere to surface.
Evaporation moves water from the ocean to the atmosphere. The diagram clearly shows the evaporation arrow pointing upward from the ocean surface into the sky, while precipitation arrows point downward.
Multimodal CoT in Action
See how visual-textual reasoning chains work across different domains
“The diagram shows a pulley system with two masses connected by a rope. Mass A is on a horizontal table and Mass B hangs vertically off the edge. Using the values shown in the diagram, determine whether Mass A will slide toward the edge or remain stationary.”
Visual observation: The diagram shows Mass A = 5 kg on a table surface with a friction coefficient label of 0.3. Mass B = 3 kg hangs vertically, connected to A by a rope running over a frictionless pulley at the table’s edge.
Reasoning from visual evidence: The tension pulling A toward the edge equals the weight of B: T = 3 kg × 9.8 m/s² = 29.4 N. The maximum static friction on A is: f = 0.3 × 5 kg × 9.8 m/s² = 14.7 N. Since the tension (29.4 N) exceeds the maximum friction force (14.7 N), the system will accelerate.
Answer: Mass A will slide toward the edge. The hanging weight of Mass B produces a force nearly double the maximum friction that the table surface can exert on Mass A.
“This dual-axis chart shows monthly revenue (bar chart, left axis) and customer satisfaction scores (line chart, right axis) for an e-commerce company over 12 months. In which months did revenue increase while satisfaction decreased, and what might explain this pattern?”
Visual observation: Reading the bar heights against the left axis and line positions against the right axis for each month. The bars show revenue rising in March, July, November, and December. The satisfaction line dips in July, November, and December but rises in March.
Integration: Revenue increased while satisfaction decreased in three months: July, November, and December. These correspond to summer sales, Black Friday, and holiday shopping seasons. High-volume sales periods often strain fulfillment capacity, leading to shipping delays and overwhelmed support teams.
Answer: Revenue rose while satisfaction fell in July, November, and December. The pattern suggests that peak shopping seasons drive higher sales volume but degrade the customer experience through slower shipping, inventory issues, or reduced support quality under high demand.
“This topographic map shows a region with contour lines, a river, and three potential sites marked A, B, and C for building a new water treatment facility. Based on the map features, which site is most suitable and why?”
Visual observation: Site A is located on tightly packed contour lines upstream on a steep hillside. Site B sits on a flat area adjacent to the river with widely spaced contour lines and road access visible. Site C is in a low-lying area downstream where the contour lines indicate a flood plain — the river bends around this area and the elevation markings are below the flood line.
Spatial reasoning: Site A is impractical due to the steep terrain visible from the dense contour lines — construction costs would be prohibitive and access would be difficult. Site C is in a flood plain as indicated by its low elevation relative to the river bend, creating serious risk of flood damage. Site B has flat terrain, sits above the flood plain, has visible road infrastructure, and maintains proximity to the river for water intake.
Answer: Site B is the most suitable location. It offers flat buildable terrain, elevation above flood risk, direct river access for water intake, and existing road infrastructure for construction and operations.
When to Use Multimodal CoT
Best for tasks that require reasoning through visual evidence
Perfect For
Physics force diagrams, chemistry molecular structures, biology system diagrams, and geometry figures where visual information carries quantitative data essential to solving the problem.
Multi-axis charts, scatter plots with trend lines, stacked bar charts, and complex visualizations where extracting and cross-referencing data from visual elements is necessary for accurate analysis.
Spatial relationship questions, visual comparison problems, pattern recognition in images, and any task where the answer depends on understanding the geometry or layout of visual elements.
Textbook problems that pair questions with illustrations, figures, or diagrams — where students and educators need the AI to demonstrate how visual evidence supports each reasoning step.
Skip It When
If the image is a screenshot of text, a scanned document, or a text-heavy slide, use OCR-focused approaches instead — no visual reasoning chain is needed when the task is simply reading.
When the question can be answered entirely from the text without referencing the image, adding a multimodal reasoning chain introduces unnecessary complexity and latency.
When the task is straightforward captioning or object identification — “What animal is in this photo?” — a full reasoning chain adds overhead without improving accuracy.
Use Cases
Where Multimodal CoT delivers the most value
STEM Education
Help students work through physics diagrams, chemistry structures, and biology system charts by generating reasoning chains that explicitly reference visual elements alongside domain formulas.
Financial Chart Analysis
Extract data from complex financial charts — candlestick patterns, multi-indicator overlays, and comparative dashboards — and reason through trends, correlations, and anomalies step by step.
Medical Imaging Reports
Generate structured observations from medical images, connecting visible features to clinical knowledge to produce reasoning chains that support diagnostic hypotheses with visual evidence.
Technical Diagram Interpretation
Analyze engineering schematics, circuit diagrams, and architectural blueprints by reading labeled components from the image and tracing signal flows or structural relationships.
Geographic Analysis
Reason through topographic maps, satellite imagery, and geographic information systems by identifying spatial features, measuring distances, and applying geographic knowledge to visual observations.
Visual Data Science
Interpret data visualizations — heatmaps, network graphs, dimensionality reduction plots — by describing visual patterns first and then applying statistical or analytical reasoning to those observations.
Where Multimodal CoT Fits
Extending reasoning from text-only to visual-textual domains
Multimodal CoT occupies a critical position in the evolution of AI reasoning. Before this framework, Chain-of-Thought was limited to text-only problems — if an image was involved, the model either described it superficially or ignored visual details in favor of text-based assumptions. By formalizing the two-stage approach of visual rationale generation followed by grounded answer inference, Multimodal CoT established the pattern that modern vision-language models now build upon. The framework’s core principle — that models must articulate what they see before reasoning about it — remains essential for getting reliable results from any multimodal AI system.
Related Techniques
Explore techniques connected to Multimodal CoT
Reason Through Images
Apply Multimodal CoT to your own visual reasoning tasks or build structured multimodal prompts with our interactive tools.