Visual Chain of Thought
Guide multimodal models to reason through images region by region in explicit visual steps — decomposing complex scenes into spatial observations that build toward a comprehensive, grounded analysis.
Introduced: Visual Chain of Thought emerged from research on visual spatial reasoning in 2023, building on earlier work in multimodal chain-of-thought prompting. Unlike standard multimodal CoT, which generates text-only rationales about an image, Visual CoT explicitly focuses on spatial regions, bounding boxes, or visual features as reasoning steps. The model is guided to attend to specific parts of an image sequentially, building understanding incrementally rather than attempting to describe everything at once. This spatial decomposition prevents the model from fixating on the most salient feature while missing subtle but important details elsewhere in the scene.
Modern LLM Status: Frontier multimodal models — including GPT-4o, Claude, and Gemini — increasingly support region-based visual reasoning, making Visual CoT patterns significantly more practical than when first proposed. These models can follow instructions to examine specific quadrants, describe spatial relationships, and compare distinct areas of an image. Visual CoT remains a valuable technique for complex scene analysis where thoroughness matters more than speed. For simple image descriptions or single-object identification, direct prompting typically suffices, but for multi-element scenes, spatial reasoning tasks, and professional image analysis workflows, Visual CoT delivers measurably more complete and accurate results.
Treat Images as Structured Space, Not Flat Input
When a model receives an image with a simple prompt like “What’s in this image?” it tends to produce a general summary dominated by the most visually prominent feature. A bright red car in the foreground captures all the model’s attention while the partially obscured street sign, the pedestrian in the background, and the weather conditions go unmentioned. The model sees the image as a single undifferentiated input and responds with the first thing that “pops.”
Visual CoT treats the image as a structured space. Instead of asking for a single holistic description, it guides the model through a deliberate scan: first look at region A, then analyze region B, then compare the two regions, then synthesize findings into a conclusion. This mirrors how expert analysts actually examine images — a radiologist scans systematically rather than glancing at the whole image, and a satellite imagery analyst works quadrant by quadrant rather than trying to absorb everything simultaneously.
The result is a more thorough, spatially grounded analysis. By forcing sequential attention to different parts of the image, Visual CoT ensures that no region is overlooked and that the relationships between regions are explicitly considered rather than left to implicit association.
Human visual attention is naturally sequential — our eyes fixate on different regions and our brain integrates those observations into understanding. Visual CoT replicates this process for AI models by making the sequential scanning explicit in the prompt. Rather than relying on the model to internally attend to all relevant regions (which it may not do), the prompt structure guarantees that each specified region receives dedicated analysis. This turns implicit, unreliable attention into explicit, verifiable coverage.
The Visual CoT Process
Four stages from spatial decomposition to visual synthesis
Identify Visual Regions
Divide the image into semantically meaningful areas. These can be spatial quadrants (top-left, top-right, bottom-left, bottom-right), depth layers (foreground, midground, background), functional zones (navigation area, content area, sidebar), or any other partition that matches the analysis task. The key is that the divisions should be meaningful for the question being asked, not arbitrary.
For a cityscape photograph: “Divide this image into three depth layers — foreground (street level), midground (building facades), and background (skyline and sky).”
Sequential Region Analysis
Examine each region independently, describing what is present, noting details, and recording observations without yet trying to form a holistic conclusion. This step prevents premature synthesis and ensures each area gets dedicated analytical attention. The model should describe objects, colors, textures, spatial relationships within the region, and any anomalies or notable features.
“For each layer, describe: what objects or elements are present, their condition or state, any text or symbols visible, and the lighting conditions in that region.”
Cross-Region Reasoning
Connect observations across regions. This is where Visual CoT produces insights that flat image description misses. Compare elements between regions, identify patterns that span multiple areas, note contradictions or inconsistencies, and trace relationships that cross regional boundaries. This step transforms isolated observations into integrated understanding.
“Now compare your observations across layers. How does the lighting in the foreground relate to the sky conditions? Do the building styles suggest a particular era or location? Are there visual elements that connect across layers?”
Synthesize Visual Conclusion
Combine all regional analyses and cross-region observations into a coherent, grounded answer to the original question. The synthesis should reference specific regional observations as evidence, creating a conclusion that is traceable back to concrete visual features rather than vague impressions. This final step produces an answer that is both comprehensive and verifiable.
“Based on your regional analysis and cross-layer comparisons, provide a complete assessment of this urban scene, citing specific observations from each layer as supporting evidence.”
See the Difference
How spatial decomposition transforms image analysis quality
Direct Image Prompt
“Describe this satellite image.”
The model focuses on the most visually dominant feature — perhaps a large body of water or a dense urban area — and produces a brief, surface-level description. Smaller land use patterns, transition zones between urban and agricultural areas, infrastructure networks, and environmental features in less prominent regions are overlooked entirely.
Visual Chain of Thought
Systematically scan quadrants, identify land use patterns in each, note transitions between zones, compare infrastructure density across regions, and synthesize findings into a comprehensive spatial analysis.
The model examines each quadrant independently, identifying agricultural plots in the northwest, suburban development in the northeast, a river system running through the center, and industrial zones in the south. Cross-region analysis reveals the urban-rural gradient and infrastructure corridors connecting zones. The final synthesis maps the complete land use picture.
Visual CoT in Action
Practical applications of region-based visual reasoning
Step 1 — Region Identification: “Divide this floor plan into functional zones: entrance and circulation areas, living spaces, private rooms, and utility areas.”
Step 2 — Sequential Analysis: “For each zone, describe: room dimensions and proportions, door and window placements, traffic flow paths, and any structural elements like load-bearing walls.”
Step 3 — Cross-Region Reasoning: “How do the circulation paths connect the zones? Are there any bottlenecks? Does the private zone have adequate separation from living spaces?”
The model produces a comprehensive assessment that identifies the main entry flowing into a central hallway (circulation zone), an open-concept kitchen and living area to the west (living zone), three bedrooms clustered in the northeast (private zone), and laundry and mechanical rooms along the south wall (utility zone). Cross-region analysis reveals that the hallway creates a natural buffer between living and private zones, but notes that the utility area requires passing through the living space — a potential design concern for noise and traffic flow.
Step 1 — Depth Layers: “Examine this wildlife photograph in three layers: background (habitat and sky), midground (vegetation and terrain), and foreground (primary subjects).”
Step 2 — Layer-by-Layer Analysis: “Background: What biome does the habitat suggest? Midground: What plant species are visible, and what do they indicate about season and climate? Foreground: Describe the animal’s physical features, posture, and behavior.”
Step 3 — Cross-Layer Synthesis: “How do the habitat clues from the background and midground help narrow the species identification? Does the animal’s behavior correlate with the environmental conditions?”
Background analysis reveals a temperate deciduous forest with autumn coloration, placing the scene in a northern hemisphere location during October or November. Midground vegetation shows mixed oak and maple understory with dried grasses, suggesting forest-edge habitat. The foreground subject displays a compact body, reddish-brown fur with white underside, and a bushy tail held low — consistent with a red fox. Cross-layer synthesis confirms the identification: red foxes frequently hunt along forest edges during autumn when small mammal activity peaks in preparation for winter, matching both the species characteristics and the observed posture of alert, ground-focused attention.
Step 1 — Instrument Groups: “Identify the instrument clusters on this industrial control panel: pressure gauges, temperature indicators, flow meters, and status lights.”
Step 2 — Individual Readings: “For each instrument group, read the current values, note whether each reading is within normal operating range, and flag any instruments showing warning or critical levels.”
Step 3 — Cross-Instrument Correlation: “Do the readings across instrument groups tell a coherent story? For example, does a high pressure reading correlate with an expected temperature change? Are there contradictory readings that might indicate sensor failure?”
Pressure gauges show primary system at 142 PSI (normal range 120–160) and secondary at 87 PSI (below normal range of 100–140, flagged). Temperature indicators read 285 degrees on the main line (normal) but the secondary loop shows 195 degrees (below expected 240–270). Flow meters confirm reduced throughput in the secondary loop. Cross-instrument correlation reveals a consistent pattern: the secondary loop’s low pressure, low temperature, and reduced flow all point to a partial blockage or valve restriction in that circuit, rather than individual sensor failures. The status lights confirm one amber warning on the secondary system. Recommended action: inspect the secondary loop isolation valve and check for flow restriction.
When to Use Visual CoT
Best for complex scenes where thorough spatial analysis matters
Perfect For
Images containing many objects, people, or features spread across different regions benefit most from systematic spatial decomposition. Visual CoT ensures every element receives attention rather than just the most prominent ones.
Questions about relative positions, distances, arrangements, or spatial relationships between objects require the model to explicitly map out where things are — exactly what Visual CoT forces.
When important information is in the periphery, background, or less visually salient areas, region-by-region scanning prevents the model from overlooking what matters.
Tasks requiring before-and-after analysis, left-versus-right comparison, or identifying differences between areas of the same image are natural fits for cross-region reasoning.
Skip It When
A photo of one product, one face, or one clearly defined object does not benefit from spatial decomposition. Direct prompting is faster and equally effective for single-subject images.
Visual CoT works at a semantic region level, not at pixel-level precision. For tasks requiring exact measurements, pixel counts, or sub-pixel analysis, specialized computer vision tools are more appropriate.
The multi-step nature of Visual CoT increases token usage and response time. For applications requiring instant image classification or real-time visual processing, the overhead of sequential region analysis is impractical.
Use Cases
Where Visual CoT delivers the most value
Satellite Image Analysis
Systematically scan quadrants of satellite imagery to identify land use patterns, infrastructure networks, environmental changes, and spatial relationships between urban, agricultural, and natural zones that a single-pass description would miss.
Medical Scan Reading
Guide the model through anatomical regions of an X-ray, MRI, or CT scan, examining each area for abnormalities before synthesizing findings into a differential assessment that accounts for the full image rather than just obvious features.
Security Camera Review
Analyze surveillance footage frames by dividing the scene into zones — entry points, high-traffic areas, restricted zones, and peripheral areas — to ensure comprehensive coverage of all activity and detect anomalies that occur outside the primary focus area.
Manufacturing Quality Control
Inspect product images by examining each surface, seam, and component area independently, then cross-referencing observations to identify defects, misalignments, or surface imperfections that might escape a holistic visual check.
Urban Planning Assessment
Evaluate aerial or street-level images of urban environments by analyzing infrastructure condition, green space distribution, traffic patterns, and building density across different zones to inform planning decisions with spatially grounded evidence.
Archaeological Site Documentation
Analyze excavation photographs by systematically examining grid squares, stratigraphic layers, and artifact clusters to build a spatially accurate record of findings and their contextual relationships within the site.
Where Visual CoT Fits
Visual CoT bridges basic image prompting and advanced spatial reasoning
Visual CoT and text-based Chain-of-Thought are natural partners. Use Visual CoT to extract spatially grounded observations from the image, then apply standard CoT to reason about those observations toward a final answer. For example, Visual CoT might identify that a bridge in quadrant B shows visible corrosion on its support beams and that the water level in quadrant C is unusually high. Standard CoT can then reason about whether these observations together indicate a flood risk, drawing on both the visual evidence and domain knowledge about structural engineering.
Related Techniques
Explore complementary visual and reasoning techniques
Reason Through Visual Space
Apply Visual CoT principles to decompose complex images into structured spatial analyses, or explore our tools to build stronger multimodal prompts from the ground up.