{"custom_id": "Lisa-242-3", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What device in the image can be utilized for amplifying the sound when we want to listen to music or other audio content at a higher volume?"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "Lisa-295-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: If we want to carefully examine the small details of an object, what tool in the picture can help us achieve that?"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "Lisa-610-5", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: As a smart home AI, what appliance would you turn on to adjust the humidity indoors and ensure comfort?"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCADAAQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD6TuWl+yrJCg3MeQRuArMjvbWUKbqaIbiQHjmVOR1/i6itKUpd2bSSP5ZSTggEZJGPasyxW4jkuEhaGQ4wwdslx/skycV7Sa5WfPzS5rsumB42RXu1mibJhmhjJXOckE7ue/T1q5cHyYQwt2aMNmMrkfn6VHbRGK2QMpUO2WV+dnH1OM0+6sheqDcyyLEMYVRx+IwaxvrqxS12K0l7chhFlRDtyMgliPT7vrWWSIZVlCyKNuJSYMhR6nge1bsFmIJEIVRa/wCrK7QCV7dvXkc96vWmn3U925y9vBuwMsHDLjG0dx1zQ60YXSR0U6HPZ3OI1HTUubmK6EuDHjClQwOO+cZ/Wi8HMJPUE1pXKCOeVA27a7DPrg1nXv8AyzP+1XzFSV9z7LnlKKv0Hxf6qM+1Pi/4+Ez/AHhUUJ/dx/Sno2LiP/fFOCvJCn8LKNrCf7fkGSP3kmMHHaqNzNILoMqgHIXaWHHvW5HFnXmxxmQ9vUVUursWtrJHJJbR8HPmEZyfTnivqIYZSaPj8wl70S1FbTW9ilyzqWZ9hHG4cdcfX0qf7QFRY7ZxCyqfnOQTnsfXv+dURdQQ2i/vxNNgMixjggjpUSTTNcRu9o0aouSWJBwe2Mc12rDqS9489t30LWk2DXkl3cMrRJAMhl6gn7oA9OMe1TwakbK1hginJWJ/MfMZXfx0P+fSrOkM8Vu6R+VbyKpLDJJmXrggjHTNc1d7luJxGXKsOgUKB6VlTw0XJp7Fyfuoksbq4uYdRnGVbeFC56Zz0rZs7qJZ0S6mjQIvDbsZ4rNtLZINEuZHdIjK6lmLYx+tXrbTmFrDLaXKXaTtsDrztb8TXTJQStJkSjdK3Y3be9V7ZIY5yyRuDuHOR6e9WbrWJX+SFRuQgxktjj0Irl7BJN+9hJH0Y/MQB+GK0UjS5nLbo1bAxgg5I/DrXLUw0E7oFUmlZM7DSJnwGkcNuXJNX7m4EMauAXDMBx2B71yFlJcRysglddp2yLgYzWpJcStaoscikZxtx+ledVoPmujvo4zkhytFfxmw3w7B8xXBx6VlXavFaxITjIrT1qNnuYI2/hUDmqXiXEcsMQ4IGDSxD5MPbuj1cJL2lVFFnGBVGyHm+IHP90KtT7gWAzUHhv8AfX80vXMh/LpXh0I81WKPZnpBtm74kl/0OOIH7zZrnJMY4rT8SSbp0jB+6tYrtgY6104uV6zMsPHlpoinYYNcR47svPgWYDOzqPauwmbNZOrRCSBlODxWMdHc0mro8kuNPjJYooBrOmsgo+ZPpXVX9uIZ2Q+vFUZIfMIQDcewHJNdKk2czpo+mbEJPY3FuUxmPLAbkPB69AaLF4rOIgSyOpHALE/qWqj4eeKS/URpGY5YmRghGB35AXHr3/Ol0RbuRQIrJoVAIbPHTofuCvefW58nJN2NmG8TAV38yPuS4OB75btTJriGIc/vH6NsAbvxgGqpSQS7CtzuP3SN23P5inRymJtmJEfkHe2QCfq/tWdl0Js+pHefa3Vv3YhAAZdy7vl4xn5e3tW1aa9HNZKsseXVSGfJXkdTggGsuY2d6jqbmNznHzOpx9OTUEFhFbS5igilQHcMKMZzkDAXp70p01UVpHRTr+yZRndWldlDAHkbupz61n35xED6SCtHVpXk1GaRwAXO7A6DisjUHH2dh6EH9a+ZrJqbR9hQlzU0yWFv3Q/3jT9wDqc96qwv8hH+1U4IJUe4/nSg7NFy2ZohdutAnoXX9QK5zxHE0jNKqW1tEXwrzS85+u9ePbGa6V2B1GNvdT+g/KuR1O7gE1zZ3j3UkiMf3e0gY3cDIIxx6+lfb4fv5Hx+N+JEtu3mQxxG+glYjBEZHp25NXBYBrhUNirrGP8AWls5/SsfzWjRHh8qAEAF5X3Z46Y31JBfw5MEuqLL8w+WMj+WTxXa02jgZtLK8d4XncRIUMcflxlyAe33f0zVPU7e4F4Y5YbqZo+G+cKP5irdlDb3JB/s9JFjBJZ1w2OuQNvPSjU2lluiEmdYUwmEQA/jgcVlDSdhX7k0LldBeSUCFGkGAzbu3+9jFWLLWZpo2hRYkVCCjQJgj3zk/nVYRo+ipGwkZWueryFSQO+c1Zt4mtEZRcwmJyNnc/nuqZRi07rqKUuW3oSqFK+X5IZu7P1P44qzFaqiCaCWNTnHC9fbGKxfEl+mjaDdak8cs0luhkEYbG8Dk44OeM1w0fxt8K2iLFdWl5ExXcQ0QLA/gKyqPTRhGnOesUeymeKJCqEyA59iDnv+lPs2dHjMgRfNP3Ca8nsvjl4DnI8+7u4nAOWNvw1WoPjV4CN1E8urgKGBwYzlQK5XFONkzRUat1eJ6xO0dzqyhfmCttyPbisPxPPu1UrkfKK5i2+Nfw0OpNKfEcMakk5MTgfyqhp3jjw/4p1e7Gi6gt15WCSARwT15FeXjr+ysj6PL42ndnRvPsjd88KpqbwUv7nzCc55/OsXUbkJp07AnONtb3hH5NP3dgP0ArgwEeau/JHp4p2pfMrazMHv5cHIBxWbK1SXEm+aRyerGq0hy1ZTfNNsuCsiGdscVRvDlG+lXJiKo3J6ikrA3c5HX4c5YLyK6/4JaDBdzXGqXcCyBPli3DgEd65/Uod+7ccDkGup+D3i3T4bKbQrpBbzW7krKDlZF9fUfyrtwfvTOeteMTc8XnXv7BuoPCs32bVlQ/YrmZlZC+OFKls89BkdT0xXingvVvjLrdsuq2Hi/wAO20kkrxywXkMUUsMqsQ0bKVzuB/nSeC/irr2o+MLPTUmjsrBZN0izXoRDt5w0sjYz9Pyr0b4XafD/AMLo8Umyu7WTRr0fbIxBMpQStt3Zw3Jznn3r3aijFNt3R86oNaWOQ8GeNfj9quoapp1rH4amvdKuBDdWtxAkbqSCVYFeqsBkGvRvBXjDXb3U28OePNLg0TxKsX2qEW0jPDdwcgsgGTuBGCM+4rP8U+GL2x+P2k3unSx+RrFh5FzIoJC+X93cCfYYIIPFV/j9o7aI3hbxObm2N1p2oeWf3mAY5AN3yn7w+UZ64GayjyScUuo5wbdrHpH2oYLbHOP72R/NuKhu5sFXnKxJnGdy8/Xk14F8TfjHrmneOBo+iXq2NtGIgp8hCpZhn59yqQMEcYyAa9e8OapNe+DYPEV/Hcyym1aeQRF9rkAk7BkDkDiqSRy1KM4xTfUlvp4hIZFZdhGcjGPrXG+IfHHhmwgnWfV4GZQRsiO85HJHy9DXD/FTx1BrHhuwlsbtLWS5k2yWgm/e4A5yMg43HGM9h9K8g8QXl5NaqDb3ICsF+6xAGMcAsw7CvGxOHi68uaVkfT4Su/YRsr6f10Ppvw74u0DV3a3sNTt5JxhvKLgPjHXB5xXTRur4I9Qa+MtM1Ca3uHQvL8zAnBKkcDnAPH5V7h8JPHMk0AsNYu2kZZkjhnlYfxZwpIAOPl6nPWuSWHcbOJ0xr82h7o+PtcZODkKeT9f8Kw7+CS41J2kUg+cwbHVuT149B61spqGj/LNcXYyoCthhgf5zUWo6Dp+p6ymoWmoWaF15jcqQzeufevqKGJppK76HzuLw1ST0RkX1vAgTEU7nt1wfpyMU610+OcbVRbZlUk+Yckn/AL6qPxW17oUUS24W/L8Rw2RUsRnsCMfr2riLjxtr1jv2+DNZc4xl22g/lmu516aj8R58cNWk9Ine3C2duT5dxJ5gGAUfBHrjrgYpILn94xjtY5GlG9mbjLdjwteVS/FXxBbgp/whkqqP70kmfzxVO4+MGpoN0vhiaIJyT9oYYH4pULE0e5TwWI/lPbby4kXR45JnVTvOSvzAcfQVSiuLSKENEJGYkEbmIz78msXwrrd54h8HWmo3Wmw25nlkMcYbflQe/wAowc5rSuWe5hjAUwhQAwC5/piumnHmi0cs/dkk+hyn7cGm31z4Y0DV7AE2Vu8n2gRj++F2ufYYI/4FXyYtwygHfn3zX1x4k+MPgjVPD8/gvxBpmsSPbqIWmtFX5tv3WXn09RXhusaX8PluDJZy+Itp5xNbJkfXFeB7N7N2t5nvUZNLY87+2H+8c0huXLfexXYS6f4PIOLq9X2Nqf6Yqjc6b4eU4gu5iPeBh/WsnTfdfebqeupzvnkEnNWdO1jUtNkM+n3s9pIRjMTkE1bl03Tyfkuzj3QiojpVqQQLxB9c1D1Vi07ao9K8H/FnVmsY7TWn+2RZGZMASD6+v86+gfA3iqwvtCLW84kDAhCD+lfG9tZrbZEd3ESfeuy+HmtXei3rk6hAlu2CV3ZG7sfr/OrXs4JyirMcZTk+WWx9Pq5I65oK5b3rn/CGtXmrrE/9nSLCw/1wOdx9h1rrzqNhp0e54pJHX70armQD1x3/AArxuSWqPW5lYdpGgNqAPmTmEj+Ejk1fHg+wx+/vHIPGRgCsO28ZaddzRrbSBoHYKJkG6ME5ADfxISR3FdBHf3FvJll+0RZ+YEgsv0PRhXbSoKUb2OapV5XYqP4P0eNm8xnkjJxlmxj2rIl+Gmh/bRf6fc3FtIm4FSMrk/qD7V0xuILgb7e5aJhyY37fT29jVjTpolkVGuAM5HP9Kr2MYNu9mSqkpdLiaJ8JPAemacth/YFpeRAg4ukEnI74PArnrL4QfD9/GV9HP4WsTbiEMsQUhQSeuAa+UrT4z/E+2XbF4m1IA9N0ob/0LNKfjf8AE37U1yfEF4JWUAvtTJA7cCto4iS5uZ3ujleG2aZ9T+JPgn8NZde0lIvDkECSNIJVjldQwwD61Y1r4P8AgPS9L8zRvD8Ed408YWVnZ2A3dAWJxXytH8dfiMJ455tUkuGjzsaaINjPWte2/aP8fRNGZYrScRsGAe37j6VUcRy8ru/dD6rJvdfiekftGfBy98R/Eldb02BEguYIllK9SyjBY++O9dT8T7XUPh/+zzYW+l+KLrQtSs2HlMi7vtLEMTEflOMjJB45FeV237VXjDJ+1aVp8g9BEV/WjxL+01ea/oM+k6p4ZspLeYAON7dj9Kj2qsoWs76u5UqErW00PKtM8P6rNptvrl9Be3D6g0pSQxiQyMrD+JvTrn6V0iNrNmm6LVplUjoYxW7L8c9Ln8NWug/8I1awW1tkxNF94Z981zVz4+0O5X5rMpWGKcKjstTuwNSVFPWxjeKLC+1m8hubi6DzJGULiILuG4nnH1NRaN4c1S3KG0uTEElST5QRkr90/hW1a+KfD5ALK2T15rRtfGHh6I8CRfpXNTqSiuW2hrOnTnLn6s3bS78aJL8uoCR2HJeMc1uwWnxHEaN9ljmjB3AbeST3rGtfHvhg+Vm4kBAAOUzXumjfGL4Xzadaq+vQwukQDLLCwIIH0rugqTSu9TirSnT+GN0Q/AnR9UuBd6pr9usdzDJtiCjC8rjOPXHevWILSPklFJ9xXxx8Wfjlr1n8QL6PwR4gEeihUERiQbZG2jJ5GeuRWLaftJfFGJeGhmHq0QP9KurNx22PPjQdSTe33/ofaV/o9rKSWtomz1GwVBa+GNFmV1uNNtXVhghoxg18fp+078S1/wBZaWzDviAf4VZtv2pvHina+m2IJBwZYcD9KlV21Yp4aaV7/me6T22oaJ4wstC03Td2k6rO9tcNhmNrIE3+auOgbnPqcUvjSwudAt2ledWiEDyNLtIxtz15ry/4M/HzxP4j+J1nouppYRW2rXm1jHHgxnadoBJ6fLj15r1z48XMFr4Z1Xzc7TpN60Z34w6hcDHfIYn8K9TDYmUanLfSx5Veha19/wDgnw1qxv5NGj8SiUMlxePA4PUMBuUg9SCM+uNtZTapdqxzkfjW/LMn/CqdOjz88uozMw+gUf5NVPBWnNqfiNbWO3Fy/kyOIyM7iqk4/OuKtG0/VL8j06crx+8y01i6/wBo/wDAjUy6rctjO/8A77Neg3Pha08lvP8ADl3DLu4IgOAPwNV28L6UrlWtLpQvRjG65+oxxR7F2ui76nEf2tOPvbsfWj+2mXko/wCdb2uaNYWux7WzuLlHByAWyh/LpWI1rAzY/sm8ycAct/hVxw85K6QOSuMh1XzpVjWF2dztCqOSfQV7l8Kvhwjxxax4hjWMdUikGFj9C3aq3wl+Gdpp94+tamAAXItVl6IPf37V6rZjUdb1VLLTlezt0wzFoxJb3qHhkB/hI7fhjHNckm5e7E6YQUdZE81zfCT+y9DtVjmKhWE4IglHbZIv3TW/oOh+KGCi4uS8AQBre5UNJH15WUDJ9eR9c11vh/RLHS7BIYECKP8Alnnco+npWqHGAAML6elaQoLeQ3Wd7I5dPDNx5nnXUkE8v8Uoj2M4x0OO9OSWzsiIJy6beBu7DsPcV0pdmPYdj9az9ZsYriHdIoZh0PpV8qhsxKXNuU2trW6AMUqsD371astGQIWLsD2OeRWTZskDFE3A9KuxX9yD8rcDispycuhpGPKz8/jIccdPSk34XBwSarb229selAfnpXEdFywZMDGMH2p6yBuBnIqqZO2AKTftPQc+9BUXYu5cAc8fWmSlvJkwcnacVEZD3yRSmQFDj0qEtRt3JUk9FGT1OBSnyieUX8qrQsdiseMipA/pQ1qJJD2ii/55J/3zSGCIj7i/ypvmdqXccdad5BaLGm3j6ruH/AqQxDH35P8AvqnFvU80hYYxRzSFyRKzwA9XY05FdRhZmH4VNkY6mlBHfFN1GSoIaktwv3bhh+FNuJLiVAslxvXrgjoakJQjsajfb6kUlPUbjoNsXns7mO6tplimjYOjjIKkdCK3v+E71tbbU49Yu7rVI7uwltlWW4ZhEzYw4B4J4xWCQDwKzdQ5gkyeQDj866KNSSnc5q9OPJZotw6kJvDtlpnkhTbTSyGQH74fHb2xXd/ACMt46a4KsRb2Ushx1HFeY2Tfu+PWvav2YrY3XiPVpwF3R2QT/vpx/ga6Jyb1ZyU0k9D2TS8yWEc16txcFxuDLHsOMdCB3FWLlYf7LlwGKhTgyDJFWhbX0RMkUoVfvFAc4pJ/tElo6y7J1cjBQY+vHrWE2rHalrcwLWG1ZFJ2LnrlOo/Kug8PaTZzEztCrxLz80eOfbiqCwXJcQxBDk4XNdFqtw2m+HZki2i68vCRk7S5PYZ4z6f061MJW0NJNWOP1y6m1HxGvhmwRYvNUSXEVxCTb3sPdoZBxvX069enb17wjotrommIkMXk7huZeq7+7fj/AJJrkfhN4Zt7O1a+khkgeaQyS27sWjV/7yZ+7nuBjqeK9ClfLY+77dq7KNPlV2cVSfM7Ie0hIxgFyN2Bx064p0PzDce/Tjr7U2Fc8ds9D1/Cp02iJdzEgnBY9Cff0PvWjfYSF64xnp/kVXvVZosL19PWrarhume2T3/+vWX4hmkjjHlHaR696lpJXNFuVZbZW5K7WI4IqJo5kYFuR0yO4/xrQE6SQoX27yB071B56Rkg89qzfvGx+dvJHBGKBmq483/nsuc/3akHnY+8hPuK8/U2Vib8M0pHz4wMVBun/wCmZ/E04PP1EaZ7fNSaZSaLARSOgpVVcgYAqASz45hT/vunebN2gPvhgamzKugTlfvdyMfjSjcPpUavNlv3EnB5xTjM4HzQyD8KonmQ/PP16UucVD5qkDKSKR6rQbhAMNkfgaeorokLA8nrTSV/Go/OiPcUnmQ9dy8e9LULkwY9DQWx61EHQgHcM/WkLjONw/Oi1xXJt/0pCwNQ7sd80bxRyhzE27HOaz5Nrq+R161bLKRkZqmx/csfp/KtqPxGOIfukFqoT5RXWeBfGes+Dbq4uNIeHNwgSRZU3AgHIrlI+WBqTJrqZxJ2PY7P49+IFwLrSLCb1KFk/wAatL8dJgRINBWNi2XCzfKw+mODXigO04pS+KjkTL9oz60+F2vnxXbPrj6U0MKsV5fOMcbj7Va1XVIdduJtKCbJEkVWt7jIjk54aNvXOCKpfD7WFtPhVp84s4YbyWACRACFmHTjHfHWqujMucTMV2Tblhu1YFecgBu4/wDrVCS5je7se76BCbbSYYwW4QL8/JGOxq4CM/XselR2ZAsYeSfkHXr+NOZsGu6WiOWOruW4QMY4x6H+lWNqn73PHIx/Md6z4pMHNTNNwOenT/8AXUXNEi1GeCP4R0z19v8A6x/OsfxB82CvPrz/AJxVyWYBB14ORj1/p/nNUriFrg7w+CO3rWNSdkbQiZFu0/nY3s0Pp6fQ1cYKT1BJq3Z6W5OWHHXArRTTIAMtkfjWXtka8p+Z5J/+tTw5PamKeNuDn1pCfauctaEobnmnluOST7VEGXg4PvShxu5BpMZKCnvTh04bAqENkdKMmlYolgLF3XoFb+manUEcg1UhkIlYHrwf8/lU6vkYzUyuVGxIWY9XOfqajO7j5s/jSknGcr70hPbFK7E7DSx9vyppbn7o/KlPHUZpuAexqk2ybIOP7q/lQQv/ADzQ/hS8UZHrVCaGYT/nkn4UbYu8Yp3A/ipCc9xRcVkNkEW1sIV4Pesye52Aptz3zmtGVv3TdOlYlzzMwzW1Hc58RsW7Zw4DYwKnUiqln/qsVa6V0HKOBpfvMqjGScUyprIZvrcHvIo/WkwPqa/hW18J6Vp58jDoo8qcN5coA5GR91vSokH2fTokVZEKYzHMS2F54yeo54q54lcJHZxtt2eSBh8lGPYED+fvVJWWO0t1jULsJ2IRkFfQYqX3OnyPcPCV/FqHh21mjxlV2MA2cEVfLfPXlfgHW20i4McjI9rKQHAyCAejAdOK9NgmhukE1tKksbDIKtmunmUkjO3K7FrfimvLioiW6U1gcc1MtEUkNnnIXI65qzp7lyGBwewrLupkVSM1b0qTKAiuCtJnVBaG/HIB9TTs9zVFpvLjZ+pUZ5rndX13UYLdprOITSKf9T/fHoPesUi3ofnqW4+70pQwxyOK6A6AuPluOnqtMPh85x9pX/vj/wCvWiasYe0Xcw947A0bgR3zW03h6fnbPGfTIIph8P3f8LQEj3NK8Q9ou5kq+OopWcYzkitRtBvcZxGfo/8AjTG0a9Cn9yOv98U9OhpzxMxGBkznJxVlXx3xU7aTeDnyW/Smf2Tff88JB/n61MrBzruRGTHGaPMJ71JJp94OTbyj/gNMNndAf6iT/vk0WQ+dDWcE9abv96U204PMbj6qaiaKUHJVgPpTsHMSFjilBBHNRFXHWk+cUWDmJSF96QkDoaj+akO6jlYcyEmb5DzWXcKQ5cDrV+ZiQRgimvbs8R+X6VvSOatrYhthhPrU+RUUQwvTFPrc52SZFOhk8ueOTurA/rUa80r498d8UgR9aaqPtFrY3CGQb7RTlMA9O/8Ae61VsyXs5IwSpRvMGyYAehYZ/UfWqvgu+/tn4c6Jf5V2ij8iUnkhl469qnglMN0jsGYnIYeQMsO/zdDxSWqNy9Y7xMgDsqbv+eoPU5Pb16etdFa6ldadNvt55RggsmQVOTzxXPxIYZdoy6jDKyhQCp6Ef4dc11Wlad9qdJm+ZGAwc9quEXKWg21Y6Cx1y4mI3Qnbnk5Favn71PFUbe2jgjwB2xnFWLbaX25raUGlqTGSbKdyrNuJqxoV0GQrnkHBHpUlxFhiKw7nfpuofalGbeQ/vQP4T2NedVg3sdUXY7B5FdSjcgjmuSXTdSstTlVpBd2L/NET96M+lbdreQSxhlkByPWllukUffrCLZe58WI2W2kEH0FO+YDBBxTANrEscevenK2ehAyODUnCOIDHKkgdTnvTWR3fIOBxzQN2OD+VOJIGKABWbnv65pNqsN2cev8AjSkEEkE4phLMQoYLkUAKz5faOcdKcJAUwQcj3qMZVtoAGO4NCkEnBGPrmgBznccY49TQWIJPH4015CWPyikDjqcUCHmQY5waYSNvQE881G7dkAxSKy5wTnjNNCYxlQvkqp9OBSSW8LDIhX/vkVKV5BwOKUjPOcfjTvYRW+yW5XBiTP0pkunWxXPlJ09KtEbZB6U+RsJ049qLsZktp1sRjyhSGziCbQoFaC8jkYBpSozjrVcwjk7/AE+aByygsh5yBVI8HkEfWu4wAMAZqGWCJz8yKfwraNZpWYjkFYL2pC4bjpXVtp1s/WFDTTpVp3hFV7dAemfsu61Bdx6p4NupQizj7Rbc4w3Qj8wp+hNd5d2b21xslQI4fEinKgdiSPr6V4X4aK6Drdnq9im2e3k3Dn7w7r9CMj8q+nS1p4q8ORa3prF5JIwZQrYzjuQO4xyT6VdOfPojWOxzmm7dkdpKFUAlY2C8qQc4PqCM/T8a9J0SAW9osfTuBXmFzuVhMDsLHnnpIP8AHAr0zw1dx32kQTx4DgfMp6g+hrrwz1YqjZqNyM1GrbJA/YGn8Fcg8Gon45Fbz1Ii7M0HVZodykZ7Gsu9U7SrKCelLFdtA2CcqamM0cy5JFcc4ux0xdzn2t/LcmJig9B0py/aG6uTWs8EXXjmkEUajrXNyrsXc+OWYvz+o/lT1CgKWwCB+VMKkkkkY+vFOC5wMHP05/GuM57CNIScAnGccjHFOVQE+Zjk9qVUDH5hjjBqQQgc5yPpSuIYA7DAPB754pSpEWDgAdfSnhW3ADHrg0OMDk5OelMCq6ykErikBKjn5fpUkuQMkHPXionOVG7/AOvQIZIx3AKabgnDdu9Ky4OMEnNKdw69+lVoAhHcdf50gBz8yke9KM4P5fjUqZwM9aQDVye2KeOQRjrSAkE8d6cTgYPGOaTAjYbuB1Ge1Kq44BySaUJ8wYHrUnlrnjrTuKxCyjAwDmo2Rs5zjFWwhxyCO/NMkQAN83amgsVWRsghvrTgnUHv0p6jLDAB96lxnA9adxESg4yAM0/lhyDxQ6EEKM4708YxwenWk3cdhsec4wMetdz8LPGcvhjU/IuHJ02dsSL18tj/ABY9PX6CuHCEnIPWpQj7aIycXdDTsfSXiHSob62Gq6afNt5VBmC/jz/n0qx4Whn0RUWVhmRtsyHjJA7D15615B8NvHl54cu0tbpzPpzZDBv4P8RXs+n3Wn628WpWMqvE6bdm4cMPf16/nXo0Kqlr1G+x0sc0btmNg3PIzSS52kjpXn/iCbUbHUEngMkYzywHyEdCT9MhvoT6Vqf8JDcQWAmnT5h1yePp/SupVVYOQ27kn8Kro754JFYP/CZafJtduFYAglT3OAfz4PoR7itbS76G/LtF91Tt+hHUGspVEzRJl0SS4wDUsfmMuDSxhAQOKergD3HP1rJtdCkn1PkPdkYGMcmpHy20ZIz7c/5FIOVDHGMcngAZ/wAaQkB8EM2fmH4eteYZMeAI87yQAep5JpRI5I24XPXv9KY4Ygg4yTn7vNEKMDufg5z7GpEPdevG7nkk4FRk7S2Txn/OKsPtyByxzznqagmIA44APHbApgRkBlzyTngHrTGiPHII9M1IgyhckgngZNJISke0fMxxnK+lFwsRBSxHIOOhppUbhhT06nmpSsuMHA9OajXcG25bPf2p3ECYySDz9KUpk5GSPU0o4B479qQ5PX7vpRcCSJeuQPT6/wCc0m3cBk4HtTtpPfFKSRjHGfQ0AIijIGTwfWnbBnOemARTUXB5JLE5HNKXznBGRQAEHgAk596bs3HvjGDTkIOAfy/KnNw24elO4iNUAyozgU3bjBU9DUoIUEYJpEAUNnnPSgBrJkgD04prDZx+IGOlSE8dxgUbQW5HPrmgZGjEx7sdOvrT42Jf72c8AH+dDZxtGOfSgDA+XAxT0AnjPzAkZHpXqfwmMkehagqOQWkBx2Ujp9K8qtic5IJrqfB/iNNBu5DPu+x3ACy46rjofwp05crKjueyRau3kILhFc4wwbrkd/1NZ/iXxTomn2UYvUCRSjYQV6VUbWNMFpHc+fG0TjKNnrXk3xU16DU7qKC2cOqHJweK6YzaNZ2ij0OTxR4akUulxHzyT0zkc/n3rt/B8dvNpiX1tnEyjk9wOn5V8qafCbq+gtowSZZAgA9zX114Zs10/RLS0QcJEox+FPm1FSbkakMOSCauQwr6VFAeRmrUfStYM0Z8fbT0UDvkY4pjI4IJAJJ+YnJ47GpiEVODvGOD36/0pJHUAD5Sw5HHU15VznYuFUNwvIGRjr7f4VGHbGS2RnIx296dvOEBzkngn/Pakm+XcqqMnBGe3fP40CGs5V2PCjoM98f4VAG35JAK/SluPmAbJyTzz/nn/wCvSshKopK5HoeMVTAaoAGFDe3pxSjp82c57H/P509kZcc9OMCmjaM5/h+b1H+fakIWTkYwc5GM96rzf608dTxmnszBsljkdz3/APr0uMkOAce1MZEme/HtRtYMoPIHPvUoxg4GSemaikYhuo9Pf3oQmSEAAFicdAc0qqpwWyKZHljwAfXvzUgBz83uRQwET7wyuMdxSnhMEcZ7U6QAnA5z1ApkoKgHeMZ9KADYwOcDPtTjyuTnkY6UA8FgMUmQQF3cjmmAmQwZV5YGl2kKMcmjClzxngUbiMDHFACE5OPWkYHgnp3pcoRkHBFISVAzgjvQCBjjop+XqaRZNrEsMc4FSD7wB5zUcgyPUmgZKjgMp7YpmrORalRjJ7iljXjBGMVBqD+bcwW6nO5hninFXYF3XBNF4YtrZZGUkbsA9K5KxkLbkdjuX175rtvEGCFiXH7tRxXDXi/Z7wSKeM59q2jK7FI734R6aNR8bWildyQ/vG/Cvp2JsKB09MeleKfs7abiC91Zhne3lp7D617NE3Sp5veOmkrRNCF6txtx1rPibpVpJAMDiuqmy2kf/9k="}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "Lisa-632-5", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In the image, which part can protect the eyes during high-speed cycling, given that helmets can safeguard the head, but speedy winds can be damaging to the eyes?"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "Lisa-756-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Some people enjoy having a view of the outside world while still being comfortable inside their homes. What part of the house in the picture can provide such an experience?"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "Lisa-839-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In a helicopter, what is the area in which the pilot is seated to control the machine in this picture?"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "Lisa-1173-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In the picture, there seems to be a symbiotic relationship between two species, where one provides protection for the other. What animal in the picture is known to seek refuge in the tentacles of another creature for safety?"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-1612-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which object, in relation to your current position, holds the nearest placement in the image? answer: apple"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-7398-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What is the spatial arrangement of paper and lamp in the image concerning each other? answer: The paper is below the lamp."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-8495-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What is the spatial arrangement of counter and bowl in the image concerning each other? answer: The counter is at the left side of the bowl."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-11682-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which object from the list is situated at the shortest distance from your point of view within the image? answer: plant"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-12955-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What is the spatial arrangement of clock and lamp in the image concerning each other? answer: The clock is right of the lamp."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-15924-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What is the spatial arrangement of tray and wardrobe in the image concerning each other? answer: The tray is beneath the wardrobe."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-17091-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which object from the list is situated at the greatest distance from your point of view within the image? answer: chair"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-17249-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which object from the list is situated at the greatest distance from your point of view within the image? answer: shower curtain."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-17382-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which object from the list is situated at the shortest distance from your point of view within the image? answer: cabinet."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-20852-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which object from the list is situated at the shortest distance from your point of view within the image? answer: stairs"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-21270-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What is the spatial arrangement of blinds and quilt in the image concerning each other? answer: The blinds is at the right side of the quilt."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-21954-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which object from the list is situated at the greatest distance from your point of view within the image? answer: microwave"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCADAAQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwBvww8VWHhbxHqFy88MVyYh9kkuotpXOdw54GRgda91+Gnxh0PxJ9ps9WntdIv7Vd7+bMFilXONyMf1H86+U9Z1G2azC3NuVkDc7hy5x0/UmufcWzSvHgFCgwxP3T3Ge1FVKLstbneoqqry0Pq/4r/EOy1qAaPoEoubRJM3FyPuOw6Kp7gHqa8zkvp1A53D3ryrRvEl9b3UNtZSCSzjGxnc/dAPzH+dd7aajBexKQQrMM4zxWMlJu4SUYwsjWW9hYfvE2+61ZjlWWMhZFlXup5rDlBUg9sYqFTKh+9wT+VUqtjhlC+xPq3hrRb9mZrY2sx/jh459x/9auS1XwXqVuDJZMl5F228N+XQ11sepTIv7z5x0+YZx+NWoL6GRdwYxt9ciomqc9xxc4bHkk8E1vKYriKSJx1V1INCda9buYre+j2XUEN0nuvSuf1HwhYTEvp9w1qx6JJ8y/4iuaeH6xN4V1s0cnbPghlPNaNxqV9cwJavPPKi/cjLlgPoKivNB1TTzma2Zox/HH8w/Suj+FaWb+LLaC9k8pbh1hE2M7CWAOPQkcZrNKS0On2kZIxBoeoRqj3VtLbrMCEDrgkeuD2qjqWnzWUzRyAMOquOjD/PavsnUPhVoNyYDGjQtGMOzASFx9W6H6VxfxR+F2i6R4TvNSS7bZEV3JMBtIJxweoPPGK1lSfLsZxnFy3Pllxg1ExPWtC6gCOQM4B4JqqsLyyCKJGd24CqMk/hXC46m97F7wbeaZbeILc6vCsts52At0Rz0J/l+Ne3vKkMYMrpBHj5QO/0HevJNM8BajdxedqEq2EPUA8yH8Ogrs4kldFaWRnfjJY81tBunHVGMrTehuSath9tqm0njzH5P4DtVQu80rSyyE7erMelVpJ7S2XzLmWOMDgbiBuPXAz1NeUeMPiBPqjy2FgrQQojJcWsq7ZJAePlPcYxz19jSjCVRjSSO91jxvo1heXGnrKZLyDbmPH3twyMHvxXBTatq15qQtJr1nt9QmG+fz9nkR7sDOPuqO54FchpFtFrqtbzpez7XyDc/LJGR2Vh2/ziuhbw9qFv9ksoFlM92o2zLHubaThQZP7tdCoqOiKjNHdfGPwR4T8MaTp174e8Trr+pgqLn7DtkMWf4tyk4UnjDHJry2wsZJb2SQoqROW3REkhmHcDop47V7etjDoOgReGNG06O4nUBn8mL5ppW4Ab8ex6AVxt54e1HwnolxqTzWd5O5RZ4icrCSxxt9WJB/AGjmvqloVyNWV7s878SaTp5No8ly1nNIu8sy9Vz1A7nNeh/CeK0sNesJY97PdERK+TtYfeGAfpXluvXtxql9DeXkJnuI5RDHbxjChOuMDuc5r074QHW5fGUET2cFtBHt32+FLxoAMP6gdq2snC5ztPmaZ7nIfMyEBJHU44rI1q2XFsznJ3McEdziukmXC9OgrD14Fo4N2Qd/T8K46kVylRepjyxqYypH0FcFoPw6gvdUhv9XVjBBysJAG9snr7dP1r0Ro92Bk9R0NW0iLJ8prng3C6XU2bFtreOCIJEgjjHChRjFWIIjIVPTB5FLEgwoPQepq0gC5csAuKEu5B5Z8U7I2esX1rLbwyrFeSxCSFtpDBuu1hxXIR6PDNeQQQLPb3UhZWaNwwVh/eHbNfQv7VWi6Hb6zY6kkKJeXMTGdUGN+OAx9z0/CvJvAElmtxfNLBC0zkbpBy49ia9yclcyi3y3OI13Q7nT7h4YvJja6wCCFHmsByF7e+Kd4Tj+x6tbJcMQ8kgjSJ5CNpz6DqT6dMV6D4xtLF7Hz5kRxA3mDIyRjrj3xXn/gRI7zU7bURMu0O2BIAxYbwBjjg88mqjPmTTE720PUJ49y+9QmMmIg8H1q/MgyoQ4z1qF1X5Uxuz2rlZk0Y9/dWumwiW9uIoIt20M7YyetUo9Z0OSJUh1a0yzADEozuPasr43p/xRiLsck3SEbR0xn8q878J+GLnWJBBaTGKRkLkzxkqVHUZH+FTZPdlRjdHsUOpWryqLbUIXOTja47davrfTqPnVZRnGfb6iqHgz4VahFZR6jfwRzWquBNJbSLLJFHnn5eO2eKs/E3RtJ0bV0k8L6ndpZFRJJHcQPC+/GMHPBGMHpQou10U4Juxo2l/ETjLRE9m5X867z4SeGNO13xjazz2sRW1/0lmTjdtIwDjrzivDrHXJrcxDUDHKnl5a4jGMn/AHa92/Z11a3t/FojMi+XfW5jjPT5uGA+pwauMm3Zk+zaTaPb59eWPxpb6H8u2S2Z2Po2eB+QNSeONBg8S+Fr7Rp+BcR/I391xyp/MCuJ8RSPafGjSI2YYutrpn2Ugj9K9O3e+PrXQ9TJrlsz4nl8Fzm8kXUZRaojlSi8ucHB+lbem6dp+lR/6JbxW4x80z8u34mtHxvrEcniPUpLNYxG11JskPIYbj0rkZbiS6lZnd5Mdc9q45csHojf3p7m5c6tbBD5RM7kfePSpIIQIhI4ALqM8e1c6oLqSq7Rn866u3hJt4wSeVHFcs5ORpFWOB+MJgXSLNZbqCG1kl2zRSpkSLx+WK8z03SpriCKcT2ywwsXBuD5jxqBnCMOo9Aa9m+KNjazeG1+2W8c8AnG8suSoI/MdO1eLWMNvYQyRW8k1vFtkzFI+5H3dGU+2P8A69dlBL2epEruWh2GhajZxBo5LJLi2dCszchiDwcHt+FdJpCw3+o2/wDYqzz2tvjMZHlpCMnABBx/KvEdR1m5lX7NDJ9njH3iCQT613fwWk15I77y9E1CaKZ0KzsmVIA7E4HftWMoSlexteMFruekeI7y80R73VtOWwklFsI7eKKQySFieSSRjj6nvXm7a3c6ndsmqW/kTJnKHv6N79+a9Ifw94n1K6aJLaCCIDiV5QMZ9uv6VuaN8NdHQpPqcMF1erkNIqkjHYYbj9KagmtWJVmnc+dr3RbrWNbZ9MS4CQkb2hjLFjg8DHA+pr1H4UeHdVn8TWep3mm30CwBQXaTCEKMAHjkcZ69a9pstG06ziCW9qiADjA6VZ8xsMipyOgHTHrXRz2ioowd5ScmQTxTSuPmITvg4NZmrweVbKdx5bua3GUK3HV+ay9ZUtDEueCT0+lY1F7rLjuZdtFuTzCOvTNWHUrE20Y44qC4vLTTrQy3cyRRIOrGvH/iJ8a44TJpvhWL7RPjDXHVV+nrXNGm5aI0lpqeneJvFGjeGbA3erXiR8ZWPPzN+FeDeP8A4xa14h8yy0ENYWPRpCcMw+teea5e6jqd39u1u8kuppOQrNwtV9LYXeq28DgGHeAVXofauqnQjHV6mUps+0fGeqS+MdautX1WzCQSYjt7WXDGKIdM+561zsXhrS42JtIDbZ6lD1Fb/kqc7CGPXPWmtG6o3zjdWjbbuQnbQ47WfCl5e6fcRW2rMryIVTeMgfWuT8L+DfEWh6nZB/Ka3jbbcSIAwdQwIUDqO5z6gV6xsOzaATj3qM7kjO9Dgc+tCqOJd76MhmjbHy4IzyDSRwqWy2COvSneYSp3AqOmcU+HCp1OffvWDlcLHE/GmBpPCUUaKMPcqCTyo69a4vwNpOqSyvPZXgtAF8tWgcnOBjOD04r1Dxh4ftvFGmnS7y4nt1WTzFeLAII4/rWBpvgjUdHskh0rWEdkDIskq4YKTk9O/FHMkrDWhz/hzxLq/wAOvEMmma0xk0u++7Mx3DPJDDPPrxXY3Xi2x1l00tDLKs0Zkz5fyqF5IO7oDk9K878a+GPG9/d2y3OmnUIbWXcsgkByvXp/jVPV9TuNMltJhYXSzwuGf9wSUXoc8dKrlvZp6le0vui3rVpf6VfIrFRbTgFJXlQvFnnGRxx711XhvXL6w1O0v7W4WNIZFmjgA3PKFwe3PPrxXm+q6u18S7A3ILL5oK5OCc8j6Vs+G/EDbIUiiWKNEWNWYeVGSoA25GXc9TxxWyTtqZqakz6Y8WfFDT9d8U+HtbsNGvzLpbCSZXZFD7hkopPJwe/6V0l58e9Dv/DVyNL0y8Oqvuh+zTriOPI+8XHBHPQc/SvnO/LGzZmlmidYyytCDnOKk0kNDCsKkqoU4z97J5596Tr+7cp01t2Olkm3Q72H3R2HGarqyKYwnylmzIB9KhSc+WqoQ/zAMB1JFXE+cjfHtZ+pxx7VyTnctRAQqEd1TBJ6Gurs45SsUjfcAAIHYVzsIRY8Ox3jgqeua6myWUwxsTlSAAPUVmxnMfFPSNZ1bSba10GRUl+0q0qMuVkUdie3PeuQtPg/dvDcXF/qQ814yViBJ56gE10HjTxPq8FqzW629hDnCsdxmdsnCjoB05ql4d+I2oTRRtr9tbQMVIeOAEnPsScZrducIJMUY8z0OHhufCeg4YaVFc3RGHe4fcqup+bA9OO9aug/EzUbS2k2ESQxRbbcCNQoOQcMB1J557Cub1PQotQ1S6kTUnWGaRioEYLqCc8k9M561yuoCa3vxDC0yRrP8jBTuc7sFvc59OOKVL3+p0VIqC2Pa/A/xH8S28qx+ItKuru2uJMRzpDtlQseOP4l5+texq+6dZFU5HpkZ4rxu5vLfTtCsr1LSa4liTfOwh3PkEkYPc9OnpVJfj1bGBIdJ0K4uXj4Z52CqDW9ovWLOGTbdrH0EjiSPI55x1qFlKktvVFVvvEgfrXzdqnxg8bXEaxW7WWnxMpKi3TcQB7n61x1/wCI9Z1a5Meq63qEhIBCryDkZ7EAU7DUWfVmreK/DGnxv9t1uzUr1VZAzAfhXm3jv4x6BZ2JGkRz3kkWTvC8YrxCdIIEV3S4bzPus5+9+Q5qjfFhp0wRRseCQkqOvIxSspaFcvKWvEvinXPF1z5urXz29m7AJaxPy2egNcxdXtpGzWsEDQ7CVII5JHrUelTm11a1SVckSKNpGSMkc1Q1MhtWuSO87fzNbKn0MnUa2NW+gea3UqQDHB5hz3FVfDfmDxFZqcqfNGRiug8NWZ1TWU08hsTWyqdoBOM9s8V3t54H0GxsReWlhe21/byqwaeTcJORkDHHftTjsFT4jhbfxBrGmTbYdWv7duwMjL/Oty3+J3jWzUGPVUuEx0miV/zxzXOSaVkupUHA4qK1tNout6D91GpBA65z1/KsVJPY1ku6PQtO+NPiWNtt1o+n3JAGTGzRk5H4+tbdn8Z7GRMXuhX9sSeTGwkH5cV5dDa+XclD0wn57AayriB0i+0+a2GmdFVePu4zk/jTau2Q1ax9AWPxX8G3A2S30ts5/wCe0LL/ACzXQad4q8MX5Bs9dsJDjG3zQD+vNfKk7tIFRJH83dgq2CPwNBhAjjcwK5IZmJGOAepxS9kgvY+vTsnYNbyoxA/hbOaDA4IZ2bIHQCvksahLZLG9rc3Fu3Y2944x9Qa09P8AiF40s32W/iG7aNRwJsSfzFS6F9mHM0fURUZBHB71HKobO7BB4+YZrwO1+L3jC1fZdLpt2CAfni2E/iDW3ZfG3jGoeHjwPmaCbIx+NZOjJbFqXc9B1PwzoN63mTabAsuSRJENjE9+nWubuPA1jBmTSrl7V2HLsN7jkH5WPK+nFRWnxc8JXQCzxX1pn+/FuH5itO08aeFLwYg1u1UkY2ykoR+dCVSI7xZNcw4gEe4yFABk9SfWoINyMXlkBG3071oiW1uhvt7q3mUjgxyBv5VFFDiVshunGRWTTZd0kW7KMqkaLlQAPmPXmtSJWWMhT8+fSoYQoRMcuQPwFTXN7Z2dv9qvbqO2jX+JzjOPbuahlaWLkUYMP7wJlRyT1zUuseNPD2jWptptWtYb3ACxM2W7A8CuZX4h+DVl2TassZHeSJsH6cV5v8XNd8PeIrmybRp5Z7iJmDuLdhlSBjGeTyKuEG3qtCW9NDo/izqsK+DWu0ujPMzK0ZY5BOeCPfqa5y0uFuJoy6qoRBkYPDsBn8uBVXUBea54R0/SbLw1qzRaeVY3MhCKX9SMHjJ4qnb6N4rjypNtaKSSWZ97cnJ9a6MTyyitbFYaTpt3PUvhXctbardQRRwtHNBgyMuTGVPByRwOT+lc98QPE1tp3xc0ia+WK806xt22lGDBXfOfpggfzrlI/Dd7MxN7rVwwI2lY8gY/Ouz1z4Z6JpHhjSdcsZrq7W7yJvNIGx8ZHQVhSmoq17l1ffd9i9r/AMXtLu7EWtpYSMcEEx5z0+leHm2NtJLeN9oCzSE4KADBJP1r3PwR4d0i78P+KLybToGksdKeWFmXcVcso3DPcVyHjrw+kWqaiI08uKOYlFjAAUFlBGPxNddJupFHNOKhJ2OCstbtGjSFpGt3G4EuODyCOfwq6VRrt8OpRYeqkc8Vh6jp+C3y8K2PwzW7pemxxQSKi4AtCzYPfmtvZcyuiFUadmXdOtmlWFyScx5yf900XlsU8PSHbz9lk5x9K6XQ9MYaPbS7MK0C9fdKm1rSCNK1BAjDydJeX81Fc0Xp/XkbS0Z5GIQfFEKcf6+IfnitW/8AAvilYZtcfR5008ys4mf5QVyeRntUVjeyWXjqCVIwkrukZyquBuULkA8Z5/Cp/GHjO/1QSaZLGzxwuY1kuJmldQDjCgkKo47KK7GjjuTeGtSXTtZe/aKYrHbA5hIDIN3XJ6V2eq+OLjV00+ytLhYI5ZhG4XfI+D1JkYBM/wC6v41w9jmCG9lChsaZvAIyD84qr4avL+/8XaUJ1KqJhsXbhelENrF1PiTPV00G2uG4wpI6+tcvrWlraXGtxA8R20JyPU762IvEIiX73AHNY2q3wuU1maMZEsUK8+xb/GvLw3M5W8jsrWSKptM6qsW05Lwjj3RKzNStlj0iBzwpvLgfiNgrWe43a7AUPSS3zj/dXms/xFIF0G0boo1K5B/8cr0lBtS/rsckpq8TAiRZL6FQvJlUfrWla2wmj2YPNpKQPX5xVS22NqNvIBtYTx9e/wAwr1v4J+HfD2qWDanqwleaEm32iXascbMWLH8R+lOjTcloKrNJnjV7a7ZXjMZVx/CRg/lVOT5bq5XHC7j+Rr3r4waJ4UbSbzU7ZLmK/s4mME6yfJIm47dw79Tz64rwRdz3l0yxyMHVwuFJ6nilGLWjCUzadY/sZDKpLLECcdihOKqw2sfkssYzuBz9NwFVb64nZURA8aBUBBXB3Ku3/GpLK/EMTByu8IVXtnLA4/So5dSlJWGtaoJ3VWwqFuM56U3YZY12jJHr7Amoo7vM8ruAN4b7p4BNW9NdPKZTIgb5sZP+w1OwXuiCNZY/mj3I+eNjFfX/AAr0P4PXmoy6/JHcXt1LCluxETyllzuAzzXFWsbSOgOC3P8AJq7f4PRlvEc3H/Lmx+p3ipmrRGtz2N7hLWxaaRf9Wm7Cjk151r73OrXLz325g4wsZ+6i+grvtYcjRpsgAmMA4PuK5SRZWVeSR3J61wqNzVyMHw94f0NNJt5pbCJ5nTczNk85Pb8K3rOK0iidYIIowMfdQCuo8IeFXvfDthdxWsLRyxfeklbPBIPAHrmuqsPBKryPJjJ4wloW/UsP5V6f1aTitDjeJim9TkPBs8Tyz6dcH9xdoUz2Vux/z6Vl6hpV8bl4ltJnZSVJVCRn616svg7ywo+23aE5yIoUXj8Qa0bDw7oEShb2LVpz3Iuin6KBWNXL6jWqKp42lf4jxS28La/cN+706QD1Zgo/U16RbaRK3wzk0PUnt4roSK0RMgIX5uD+RruLfw/4IDBn0a/n9pbh2H866DTIvD1oQdP8I2gcdHeBSfzNY08uqp/CzWeOpW+JHlXhHwjY2ug67Hc65aQrc6c1vKyjOASCG69sVx3xL0uK1utRIZZAzn5x3AmUZr6Y1i6v7vQL63eC3t7Z7aQMiDHG0+nFfNXj25Fzqer23GIZCn/j4P8ASvQhhnT5U11/Q5lio1XJp/1c8R1O3USPleC+R+dbGnwFRdIR109m/wDHTUV9DvYnHY/zrWtIAss7YIDWD4H/AABq6KdLRkzqao7Dw5bRy+C7USZL/Zk/9BrW123h+w68Qox/wjT4PuFArn/CF4ZNK062J5a3zgdOENWNR1YP4evJ2P8ArtHnh+uCteXShaDfm/zR2TleSXoeIXMSr41tWYYHm25P5LWHrkSrrV2q9PPf/wBCNdFcPbf8JbbMHVl82DocnjbmsLXmifXLto2UK1w5AIOfvGuyfUwjY7nwLEs+uxRNHHIG09Rtddyn5x1HevTLxILwCC7toRc2rLLC6p0xjGO478V5h4JkvItftzZxQSSGyXAmlKIPmHJNbll4k1a98bHTHvtOtpJlZHeCMzBcDAUE8c+tOEW2gqyOIuLqTPllj9QaLCeQ6VqDSFmYOhGehGDiswXUbucMfvdMf1rT0wK1jfEkMA0Zx1PRq4qELOx01ZK1xtldzXWrxG2tp5GYxFQQFGVUDqfcVBdwand3EsJtJpESd38kKX2Mx5+734/StLwsitq9qTyu0cZ74Net/CSSz0yXxXc3MyWlui20xldtoT51JOe3f869BUnJOzOCVbkaujwoW9zDcLLJaypLGwYBo36j2rovCviO70ONY4mblTuDrIo3ZJzwOetWfHXibVX8catJZa1byaeblzbGO4jYbM8YJOelUrDxJ4rcr9k1SxSdDkyPcQjj2z3rKPMloaS5XuiXxb4kl1pXjhKRo6hXUvIw98Ar3rDsUWBxvzjy8fKjHJzn0rvPBnii5k10jxVrGn/ZvL5ZpYzl8j+77Zr0nSNY8AvD+/1jRseZ1eRelaUU5TszGtUUI3seWaDHpF9oAspRINRDzzMjK6L5SIzgg8DJxXB3+pWMjttspVyO7n/69e2fEf8AsoeJ1m0l4DayaYvlvCRsYvHMpOenOBXhdzBKJ2ia3fzFUZAI7Ae9a1aSirEUqjlqhloUvLtLeGFg8hwMsP8ACuj07w3DN4L1TxJuBS0uhaeWV53lS24EemOnvXO6ROlvqlu02IxHJ8xPYc16DpzsvwV8SRuwJGrxHA7Dynx/KsY042bNpTkrHGaMztexZ6AZ9v4q774LJI3iB8Hg2bZ/7+CuD0Y7Ghcc/L/8VXpvwWhWTX5RnG61bH/fYrKUbxN+azPT79A2l3MbDOAMkD3Fc7JZ4AYZCkDiuw1GP/iWTgqOUycdTgiudjVmJJwqE4Cnnt1rmVPQbmdv8MdVNr4K0y2NvK5jjYcLkffY11I1ybGfsU+PpS/DSy0j/hCNKaWUecYTvHmAc7j2rphbaWMHcf8AvsGvoqVRKCR8/VgnNu3U5eTWpJgiwWlxJIpyVxjsalh1W96PpN0T7VvH+zUlVo9z9V/HIGP1qzFJYkcBqr2qJ9lboYsGoaiSNujTn/ecCtS2vtYx+70eJD6vKKvRy2YIwrGrsN1bqeIQfepdRFezRmXl7rTaXdR3Fjb+W8LhmSTlcqea+bvEo3eJ/Evyni4XP5V9UahcwNpN2PKC5gfn0+U18v6yobxR4r9BcIR/3wawk7yj/XQ6qCtGR5zeQ7WYLj/JrVNuIp1GflfT88+6tmorpFLMOMDPP41oTIDdKwDFf7L/AJZp03ub1N0V/AVwhvdMj6qtu3b0jNUL2V28ECXpmCdf1Spvh7J/xUOmRhQR5EnbsImqnrAePwFajJy32kHH/AK4IRtTfz/NHW5XqL5fqcNdp5fja1dCAqzQEgDrytY/iSHb4ou3yCHunbA7Zc8V09zbNJ4tjjWKRzujZdozkAjNejeGvhr4U1fwxqmt65d3FrqiTXDRRNOI8hSSh2nk5rWVNyukZKajZs4TRLM3F+bOMgtLphXOM/xDt3rSs/Dd7puuWOoW8qyWqzokirEEKHcOvrTPCTrbeK7KZxkLY8jOM/NXs2oeHHtIBf3DxNb3SgiLPzBweD6dcVvh4XVzPEztKx8ni3uVzixfnuEarVh9rtkkjWynAfB5VgMjoelfpdqFjpcsGTb23y8gbQB/KuC8aaNpsljdSrbRlgjcbRjpXBGm2aSxPkfG/hWxuhLa3AhbYowSVPHb+terfDKwtNRXxfbX0CT2pgtt8Un3ZBvAINdl4js7SLwldiOFQ6x5GB7iuK+HrtC/ill+60cHPtvzXVh0+W/9bHPWkn/XmQeNvCHg+DV5I7Xwtp1jEjAfu5JGDD3DE/zqDR/DHg5Zw0thoyhVORIpbn1+9Vz41agmnLLeSFgm+Mnb1615WnjfTk3EpdsxBGQBiuVXep0O62PV28N+DLh1xpOhQoR/yzgJz78sasaH4H8GatcSQWdzbMYyDLBFaxnywenJBPODXkh8daaoUxpeq4AGcDj1Nem/AzUIby81W9gbfHIYwGIwT14NdWEV6lmcmKclC5peI/BHirW/F0mneBNLS6itdLg81PMjjWJd8gUfNx2PSsfU/gN8ZJQ0k2gWS8cn7dBn9K9v+C+qiw8e680hCifSrUrnviWYV6bc63DJASZCrDgDb1pV5tTcUOjpBM+HJvgv8SknEb6Xaq7Hbt+1p/QV1uk/s3fFO/sJLea50W2tp1+YSXrMAccHaq8kf1r6QuryZX3wK8hEmQzJWvZ62YrcqhkO4fN25/GsuV9zVVX2Pku8/Zp8X6QyQz6tpUiO4AKM/X8R05rt/hz8Gdc8KXsupXeo2NyskflqkIbIJOc8j2r2u9uxdSwho5CikbcnP/66uv8ALDsZgqYyo9+1HKhe0keaa7pj2Oj3PKs2zsvPUc9a49olQoXYY+nSvTPHbMmhTq67VEX3gP8AbXOK80kfdsjSInjILH+dRJLoaxba1PVvh1pVnL4I0t3j+dock4HPzGuij0OE4Crx/uiuX8NR3H/CsLGex1SGwufs/wAks6bokO89R+n40mj33jT7TbRzeIfDl5EGHn+VGVZhn+EeuK7+SfLocDcHJ3N6AW0M1kSgxbmWU5HXGX/wrRj0bbyrrjrVG7tQ9zcjfx9j2j2JCg/zp0N64t1zc7SBgjNRJSS0KjJPRmkmmlT99fyq1DaYGCy/lXOyao6Hi5GPrTE1yQHi4T86lKRfunS6rBjR7w7hxA54H+ya+ZNalCeJ/EwA+9LF/wCgkV7tea7I1ncI0ikGCXv/ALDV8/6pLv8AGGtAY+Z0B/DNKV42b/rQ0pJO9jmLuwnLMEi+QsSWL+9WdYe8jiia3iV99mIyRIFwMnpnNexaVoemS2EJmtYpC0QJ3KO4qjqfhfS5721U2NvtMyKRt/hLDjisaWJSvFo1nFtpo+cYIJNPuUltEeCWM5V0fBHbqK0dTvnm8KLaeU6PAJjkkEENj/Cvt22+GHgKZAZ/CekyHgZ8n3q7L8HPhpPFsbwhpqqwwwUMoI/A1NrNmqqJn5x22raimpJqQmTzV4AYfKPwrorT4ieJYDKy3Vgd8bRMHt1PysMHr396+2tR/Zw+Et5uVfDLWpI6293Iv8ya4zWv2UfAc05+wX2tWXt5ySAfmuf1pXn0Y+aHVHyZDrElpNDc2TxGQ2wjJb+Hnn8a6/Tfij4l2CG7urW4hEbIFkhzjPcHsR2r2ab9kTT5T/oXjGeM/wDTexDAfkwqlL+yP4it23WPifR7gA8eZFJGT/OnGc46JhL2ctWj3OS5lMPleUS4HOBnH5Vh+KpWbTLhfKKny3OSenymtLT79VsVMoDSknfggeuOO1Yd9qarpV6moxJJcMG+YrkYK8DA4roTiupwWk+h5x4gd30C7yoCiE5Oa5r4YWljdp4nWaTaxghKZfHO/HA/Or3jjVfL0K+ELw48k/IuAfyzXmPgjUri4vdSFrMIXDxbXZdwHJzlcjNXQkrWj/Wg6sHa8v61Or+KGnQeKY5bGO5MIcgiTZno2elefW3wqsywE2rzn12RAf1r0PUbW0jvUSDxPfXRYcsmnLFz7bmNSRabYQur6heam0ZGf+PlI8/4VxJNaNnTfTQ42z+Ffh7jzp9Sk+rqv9K73wXo+heFLOVLOUQJKwLme4B6fXp1rNk1Pwfp07STXGlbB0S6vDM34jOD+VR6S/hnxNeXH9m6gjrFt8yK0hEKDPvtz+tdWE0qXRz4nWFmd18Oddt7nx7qccM0apb6VDGHJyH/AH0pyDn/AGq75LuV1sJnnPnlx5sY5XHPTofSvlHxr4q1TwZ48uZNCnEAkgWGTK7sqDkfjXM6l8UfGt4GVteutpz935etZ4hP2rZpRgnTR9qalqrCOc3FxBBNkeSfMAwvfIJ69azbzxXoVulw0us2kUckW2MG4GVPOWGD9K+H77xP4gvG3XOr3jn/AK6EfyrMkubiRt0s0jn/AGmJrJykaqnFH25qfxT8G2WS/iKH7oXYnIT3Ge9ZN9+0F4Kg+VZZ7rHl4xH/AHevT1r443nHGBSh2JFTeXcpU4n2LpnxN0/x3oWqJY2c8RtEyWc53bnyBj2ArPlkLRgO2wk4P/1q8s+AN4LfSdeBIywj6n3Ndpd6mRscNuO44HQfhUzqJLUcabb0R6t4D1y0bwdp+mi5czeUR5ZU4++T1z6e1Zuot9l162nZ9irIXY+3mHn8hXmWja5LBb28tvKEKpgFTgjsa0LjxJfTK6vdyOHXadzZyK9uFSKgjyJUHzs9/a+t5LWSXcf3xXJ74xEcfrWHrNzZJqc0BWXAc/db/wCvWPomofaNJ8Ppvy92GB/4CqD+YriPF+oeb4mvmSRgBOw4b3qKk4wjzDpU3KXKekix0q4582dM+shqVPDmnS8pfSjP/TWvJINRu4+Y7qVfxzV+213U0I/fwyAf34lNcn12l1Ov6rNbM9MufDKWtrcXcd48hjglbaz5/wCWbD+teL3xMfjHWCwOC/8AjXaaZr9zJOUmt7QK0coLoCCP3be+K4TWroHxNqBUAb3YsfXGTSq1IzScf60NKMJRbTPVdNuVWytVP/PBSfrgVZWWB7i2kxlvPTBB6fMK53S72MQWmXAzCh5+grXt7uK41jT7SAx+Y06nqAOorjo01J3ua1Jcqse/WvlJGHUkHgn5j/KtBLg5UCRifc1nac9tNHLC4JeJyrkZGOeOfwpYnj+2SQKwZk6p1IHaumUTGLujYikdmP7zBxzxTJHKSMCwI4qhGdsm0/KSOB0pLmTDECQ9uamxdy7bSbWBwNvsavJOjZPI/CsSN3KKAwbPIyMGp7ed4zh0I9SvOKkZ+fep/HTxbOT5At4V7jzWP/oOK5i++KXiy4Bzqcalv7sOT+bZrgVMzthY2OegAq7aaBrt7j7LpN7JnoVhaobfU393obUXi3X76+jt7rV7qSJyNyEhVPPTA7V2XgSWJNRvJWYZlaMkZ6Ddiuc0X4WeOJlbUDo0kNtbASSySOBtX6da7Dwv4Ru7f7XcT3MaDYo2hDnhs98VrTqxirszqU5TTSRU+I2oNH4fma1uCkg2jdG2COR3FeQy3FzMf3s8smf7zk19A6/4K0/7K9vcyXFxE2wtjC5zg8YrEg8NeF7QnbpDM0Zw3mIzke5zxXIq0UrG6w8jxVEdmCohYngADNet/AK01C3utSkks7iNGRNpaMgE5PrXVRWNhE6RC1t4MfMv7sLjHf8A+vXR+GPsw+0OgdlXALPkZz0960o4lxmmkFXC3g02ctqXwX8afEfxldzaLDbLBGql5JJM4BJxwoJ7d67LR/2MtYOxtY8TW0AIywjTp7cn+ldh4E8W6p4av76OxgXbdRoCzg9QSfX371u3/wARfEUqMjX7W2eMxxKp+uRzWsqid5MwVJx91HiPxP8A2bLLwo9rDbeJWmkuAxDbAwTGOvA9e1cNpHwgvrXXEn1ee2vtJt0eedbSX97KFGRGFIyCxwue2a9n8QX+oanetPc6lc3LLyCzlsj6HkUzQ/3cnnSy+bGZVjkQgDCOGBOe+Otcirt1bdDqdC1O73PENR+IXiXTbhrKfw3o+n6UDgaVJpMflFP7pYrvJx/Fuz3rG+I2g2FlHpniDRNkek61B58Fv5m9rZwcPEe+A3Q+mK9/1TTsh4NQtIZI1OwFwpDHPQg1zd38MvDmspIVsZrUoDg2z4+bPZeRj8KPrSTcZIaw2icWec/CWUJp2sAkjIjGR+NdbcSnYuX7gjk56Vp+H/hkdFivlttVMqSlcLLFsK4z1Ocd+tS3GgxxhTNBcTMBncTx+Q/xrkrVPeZvCFkcHZXLLGAN3Ge/vWjDePgdaxrFGYnYrYDMOPqa2bW3cgcGvTWJUYpHE6DbbO18M+LGttR0CN3/AHdkjls9ASSf6D86yvt73V1JcOTukcsfxOayxAfPjBXqrf0rQgjxjisKuKco2uOGGs7o0YZsircUtZ8S8VZjHNefKeptyWLdxdGCzmlU8qh/w/rXJ+ILphq1zOgJ+Zs469629afy9FvH9IjXE+K5i0gCSFCd54OAeO9d+Hnen8/0MJR99vy/U9u8I+G2vbG1urvUEeHyl+S3cEjjoW7fhXoGh6TpdjdwvbWyJJuALsMseePmNfF9jez2MiG2vJ7cnnMTle/tXRWvxA8VWUZMevXU6IufLnbevHbnP6VpypO6G0ran37pqIkZIm2u5y3OBmn26MLya4yyuflJz1HrXy58Kfj+v7ix1xxCzDb+/fMZP+zJ1X6HIr6N8L+KdH1hUFtcrHO6g+VIwyfoejD6Vq5mXs2bUnmS3sFw0i/uu5PSptYZ7qx2Rsqt5inei88HmlWOJycqp49KeIFDExsy5GNu4lfyo5nYXKitJLMl9bxxxkRNu3k/w8cVOLgJfG0AZ3dN4C9MVOIkBB4yBSLEBOHAG4DGcc0KXcTieVnVPhZ4bgI0XwJavt6NJGiDjv3rzP4leK38TPCtvoen6dBbMXiFrD8x47nvVSWW64zKWJ5KbMkD/CqrK8fmpI6hSvHIGfUe1c1Ws5KyO2jQUXdmJb6nqawtBBcXJSTPmIG+Vvrk4p+lrPcGczKowMBjgKak/wBHVyXdASMFhkgev1p1vKNlzJaxhlhj8zy8kB/mAwfzrGErHRKBb8Vw263SKpKnyULP2HH68VlrEt7GLfzZU3naCw4K45wR0qDXb24uNSd5rW1gYKpdI8sR9SSaghvds4ly6BT8u3jA6n2FEkr3Q1F2Lc+jxaZJvNy8uRlcjOQOvXt9KemsyB9q2QMO4fMowT71Ztb2xum8qSNwjLwA/T3z71baxhKIVJIC5+YZwM+vTNQ5WHZ9SYXjPPEQrZli5JYep4qp51zNI+/cNozg8cfX0oitmml88LIoT5MscY9ai/s6UkbbiRMAgnI+YGl7QOQlcKkka/vEZzjduxkf4VdgbOnT/KCBIuCqZz1rPkvZbZvLupndG44AJHpWp4FmivPEtqktvL5MlwsYKtt2O2Are4BOaKfvTSXUKicYORP4hNu97IkRZJGI8xieH4HQVUtI5IpnlV2j4wrA7snn8R2/+vUmr2ku+byJCfJnKjcMscE5wfwH61Tk1RbPbc/Y7uS2MhiErNthZ/QHuffPFb1MPKVZpHLDERjRi2byxRPbsjtuyNu0g/MT1yf60RRzvGRCV27uQGJwfX2rc8OaBrWoaMtzJDo58074Sk5YBM8A4PXGc+5revfCktrD5s66XGrJlk85+D7HbzWzyqra7aMVmlG9keeXHh7TtRAaa1ikLcGRVCsT9RzVGfwCp5tJpIuOA+GGfrxj9a6TV7yLS7Brt4lW1E3lDy58/MQT0PPaqWmavHeqLm0cFGbHuCOxFcOIwtShq3c7aNeFbZannOqWU2n6mLS5QLJGGzg5BBxgj2pVI6Yrr/GOkPqE1pqMfzMqtHLjqOcrn9a5a50+5i6DNZzSVrE3d2mCnuKmU1mt9qjOGjYj2oS82nDZX6is3Bhcl8Svt8OX59ITXnfiW4yFIJyN9dv4juQ/h2/AOcwN3rzrX5SCoI4zJj867cMmo28zKW/yM5G3EE9gf50ssnyOp7q3H4CqySYVD7n+dLK/7zI/ut/KupGdtEVrWd4xjAdO6t0Ndn4T8b6t4dAexujJaggtaTkkf8BI6fhXFKu/cYhyOq1KHU20nYgVonfRkK62PsH4U/HeC+ZNPu5wk2MC2u5ME8dI5O/0Ne6aF4m0vVgq290I52H+ol+V/wD6/wCFfm2jCQAS846N0YV2Xhf4g+IPDyxxvL/aNipyEkb50/3W6j8alprYpJS+LQ/QtZMjirCO20dD75r54+FfxstNTgS3W7+0uow9pdMEmX/db+KvbdC1/S9WA+zThLgrkwS4Vx/j+FClcmVNxPmVFnfG+Zghz/EQTVaW2R2k8sBlYckn/PNTavNdJ+5gWASKPuuSF+uRyOKiadBbF55oonbjy4xuDfQ15ur0ueklbUqNaAxbZY2CKCd2eMVCsT2ySCIOyPjODx1zj+VT7j55iWdd2QcK3B+vrVuJJpGYCMhDnc23A/woV0Pmsc/qtw8kwuHtkhZjtwOQe2SalsbG3eJ5GnjbOMjqAe4Fak1oGtm81o1OPug53f0rLex3TDyy6RgZIRgGPvikp3Kewl5aJEHSPcQO46fpTrZrq0th5wkO/lAGyVFR3EUkd2rbZJtnIAbocd/WnhzMZC0bMzDnJwQccfSrlrsJNdS3Dq7uFR1K+rcA/kf6VJDqEfl7WA2lcfvGyTxXOXZSCAurBETgKoyc5+tQ2NlNfRv5kqHfyodgCoHP5+1TyWW47JmtcapDcyy2trEjtkFRv+6Pzx61v/D/AFJtJ12znn0sXp81drNny0UngHHXr3rn7y3s9JtVuESNpg3zcDI46j078Vc8Pat5joZYMxKCwIP3m7CnCXK7oVSKnFxOs12YXVyZI8WccXmLuGSo3E8+/pXB6nZeKG0R9JktFu7cTCSKSKYEDgjocHng12V4WR227mb1bBwfTk/lUYJwG2hSoxwSOw7UTxM1J+ZisNCSXkM8J6x4m02zt7OVpbW3VQANox+ddFf+INQmGLnUZWTOMM3FYiKGYMxIIGcNwM1PbYaTLDIcZ56Cs5Y6vLRyHHBUYu9jl/F1pf6nD5VoA7eZuy8gUYweal8D6NcaLo8kVxcrcTSytKdmQq9BgE9cY610T2wlkLW8IVeeB169fpUbRHdt2qy46A9PXisJVpNctzeFGEXexoaRcxtcLFPEk0L5yjkgE/Uf0qfV9OsZVIiso4W9VkY/zrIh2RyLKS6/MG69xj/9Vc7f+Lb628Ytod7A6RTv/odwjZVxjOGB6HtXRTbdK1tV+Ry14WqXWlzXudHYZOzp6VlXekRsPmj5+lbkWpSAbZASRSveRScMmDXK5LdMpRfU8/8AFejpB4fv5kZhsgY4/CvMPEJy2M/deUfrXufjQQP4W1T5sZtn7e1eDeIGG9vcyGu7Czco/MznGzM0AeUhHqf50kh5P0NLB81spHUE02ccqR6GuzqZNXFtfKZ9jt5Tj7ko6fRvb3q1c2wbd5i+VMByeoYevuKpqpPOOOK0YwfsQzzt4GfSr57EchDDMVk8uZdj9h6/Srg6cGlYR3SOtxF+7D7BIOgP9KrSR3Fif3gMsH98dR9fWjcLWK967wMtxAzRSocqyNjBr03wN8XtY0OWG18QRyX1qFCrPz5kY9QR1rzLU2SS28xCCpAwR9a0FXdEoZQy4HBoduoRbvofRqJcT+XFGjPhf3jlcDj29eaz5bexDOv2wRjGFCJjcfQ5/lXTWs0ioyRkYLFlCKFIHYc55rKjg8zUH88iXghdygscDtjgVwcmnmd/PrpsZEFoD5MkayTjcQhJwxx1z2xVq4tL0Xay72kRsBUX7o9z/hV9V+zorNHKyBc/NjLZz/L8O1X1jWKPOCqMPTBJPam4pIXM5PQyBb3A3S3DyRkfIEDDAweowMd+tQLZ3DSxwSRL5x3FX3ZAXtn061tyswfO2MoQCcg9Mc/Snp5KzG48sblKrIx4yOgz34FRypjvqZo0cbRHIW3Z3M7NwcewqH+ygn7xlglAIwq5UYJ79TmtqZ9smPNzG+MI+M8dcdO/rUd9FuUfZRCrbQd2zGWA/jA64+tSkm7FN2V2YmoaVb3EYVdsIxgnnOKwrnSZreVVDDC9AfT6V1XkXUjpAsymJC2XkcZJxkcim3FubmEOtvHJOjHywXO6ondbGlN3OIh/fzGw3MSxJctkD1PTpgV1Hh/TJIXjYQSKiKWkwcoq/wB0E9z/AFpv9iJFtxCsYUgkvySc8jPvW4NSFvD9kZViMY3YYkbh0OSevH51ScWrXB33KstvIhVm53OpZeMZ64x+VKAZk3E4YfMUPGT0yPxq3FLFsJKxp5uGRDJkYx0z3qvKkLERh13Nk7c8ken4VlJdCkupF5C8rLn5gCew9BU8SyW8BiMhxu5JX17f59KZL5xs/s8Uh38gEYO3pyc+lWrPfJEVkXLxqAy4PB9vX1rK1kV1IZY5MlnkJTd8vlEgMKSz+eTEAfBbaBICCo71oZgaMgoDL/CAxAbOKsQWqmSJwmyVR03bcnnmjl1uPm0K10qfafLA5xnJfaWPfBNcj428H2/iK8RWkcTRxb4nVsY555Hf3rufs4bEYeMF+BuPT2J7VnGaC3v1AQqAUCyiPKuHbB+bPqMYGcfjW1NveJhVXRnk0sHjvws+2OUataL/AMs7kfOB7OK0NI+IGkzyi11RJ9JuicbLhfkJ9mH9a9eubeGZCrqGrmPEPgbSdUiYNbR/MOyis3UhPSpH5on2co/AzC8RSw3Hhe/aKVJUe3fayMCDx2xXh3iD74I7mSvSdT8AavoguRolzJ5EqMrwMxaM59uxrzTXYbm1U2+oRvHco3O4EZz1x6114SMY/DK5jVcnurGfati3XB6k5/OluT84wONpqC2V0j+ZWHcZHY96kkZtwyTjB7e1dz3OdbEsH3jzxxmtJRmzP1rMszuVhjB4rVhAFvtPY5qJaFxFsC6TzsuCC5BU8hh71caMFD5ILIBlozyy+49R+oqOwjDNcH0lI/SpJ02xuQcHGQQelNTuS4mFqttHChngICsRmPs3NXLeZH2xsdkmOYz1FLriD+zY8dMp/OpLq0guPlPyyKMgjgirvdakW5Xof//Z"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-22416-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What is the spatial arrangement of clock and towel in the image concerning each other? answer: The clock is above the towel."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-24631-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which object from the list is situated at the greatest distance from your point of view within the image? answer: bed."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-26898-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which object from the list is situated at the greatest distance from your point of view within the image? answer: chair"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "EmbSpatial-28983-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What is the spatial arrangement of laptop and box in the image concerning each other? answer: The laptop is on the right side of the box."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-134-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where would one look to shield their eyes from the sun while engaging in an outdoor activity? answer: They would look under the visor of a hat, such as the visor of hat_1, which would help block the sunlight."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-171-3", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What object could provide information through its outward appearance about the genre or subject matter just by looking at its exterior part? answer: The book_1's cover could provide visual cues about the genre or subject matter of the book_1."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-300-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In the setting, which object provides illumination, and which part of it is specifically designed to filer and direct light? answer: lamp_1 provides illumination, and lamp_1's shade is specifically designed to filter and direct light."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-305-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where would you typically find the part of the container that is designed to seal its contents and how is it positioned in relation to the rest of the container? answer: You would typically find the part designed to seal the container's contents at the very top of the container, known as the cap. In this image, bottle_1's cap is positioned above bottle_1's neck."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-331-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Considering the slender feature of one of these pieces, which is the best suited for displaying a single stem flower, and what part would hold the stem in place? answer: Vase_3 is best suited for displaying a single stem flower and vase_3's neck would hold the stem in place."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-725-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What features of an object ensure its stability on a surface and storage of its main contents? answer: The features that ensure stability on a surface and storage of the main contents are the object's base, which provides support, paired with the body that holds the contents, as seen with soap_1's base and soap_1's body."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-1181-3", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where would you find the component that provides tension to the chain, contributing to the gearing mechanism? answer: You would find the component that provides tension to the chain on the bicycle_1's gear, which contributes to the gearing mechanism of bicycle_1."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-2275-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What indicates the time in this room, and where could it be found in relation to the television? answer: The clock_1 indicates the time in this room and it is located to the right of the television_set_1, at a higher position on the wall."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-2468-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What decorative element on the wall provides both aesthetic and functional value? answer: The functional decorative element is clock_1, with clock_1's case contributing to the aesthetic aspect."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCADrAQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDyG8sYr5lW5dmt1GTCCQGP+1jqParMenad5XlCwtRHjG0RD/CkUnv1qZCc4oAoroslq5k0a+e0OcmJ/njb8DyP1qT7f4kh/dNokM79PMjuFCH8DyK0URzghG/AVZRHABOB9SBQBxlr4GuJrhru4mt7Qu27yIGJ2f8AAjWBPompr4gbTLVTeXRkxDbxLuaXPIO0c5I7da9XZ4oYnmlnjREUs5yTgDk9K5rwb4s8PW3xB/4SHU7We40uONxI6Ji4h+QIssRz8rqWDKfb1xQBS0jUvE0GoC38UW5u4NJgNnayXcSuNP2v90AjgKSQcjj8K7fw9Ztp4vJUu5Hhv5FndQ4MUrAHEgAAAPzMKraNPFfWUdxJew3sfOHhfCy88swPOT1KkcHNLBpVvZyl9Hv7zSwxy0cEu6Mn/cZSB+FAHRLeQ20T3E5byYkMkmwZO0DJx71xeo/EbVbyw8uAxW1q0hcQFAxBx1LHk8fT6VtyWtxdQmC88Qazcwt96Pzgin8FArK1HwdZyxs1hd3EEhHCynehPv3xQBu+CPFM3iK0nE8IW5t2UOUB2uDnBx2PB4rfaKZv+WT/AIjFcx4QENjp8lvFbG3ulfF2pkJLPjhsjGVI6f45rYacn+FPyz/OgC08Lj7xjUf7TgVEyJnm4hH0yf5CoDM46MB9FAqN5ZDn96//AH1QBYeKL/npIf8AdiP9ahkWADlZfxKr/WqzH1OfqahJU9hQBYke1A/1a/8AApc/yFV5ZYBjEcI/BmpjBuyk/hULq+Pu/nxQAS3KA/KqD6Qj+tVpbt+cGQfTav8AIU543z0H51C0EpOcEfgaAIZp3YHO8/WQ1TlYEfdT8QTVx7aQjBOPwqB7Yd3H4kUAU5C2f4Bj0UVBIX5/eH+VW5IUHWVfz/8ArVXdYBnMyj86AKkme5J+pqFlXHI5q2/2cfx/kKjYw9g5/wCA0AUmjHp+lRso5AXj2q47DtC5qJ9+OID+JoAqOnOQp/KonTtgVbZZz0iQUwx3H+wKAKbR89KjaEnPyn8quPDOSMyD3qNrdicmQ9aAOsS4XPCgf8B/xqwk7nkE/nj+VUUgkx3BqxHGwOTIo/GgC0JHJ7H9alSRuMMB9AKgQwr1mBqWOSAdN7fRCaAJnIliaKUl0YFWBPBB61yF14ahj1FdPtJMQ3kbu4cfdCFTjI+orsFcYyIJW98AU4QyPex3YtiGSNowGPZipJ/8dFAFXw1pMejWTwRMzl33sduBnAHA/Ctdd/pSIl43SKJffrU6QXp/5aqo9loAWJZSeFJq0sUvGePwqOOyuGOWuXP0wKsR6aCctLIf+BGgBBCc5LqPqRS7Il+9Mo/4F/8AWqYafaIPnIP+81TR21gPu+UT7UAUt1oDgzD8Mn+tJ5lt/CJG+iVqrHbjkKT9ENOPljpEx/DFAGNvXPyW0zfgBTS0x+7Ztj3f/wCvWySei27H6kUxhcEcW6j6sf8ACgDFZLxultCv1Of6U0wX57xKPZa2GW554jWoXS4P/LVR9F/+vQBjyWl63W4I+i1XawnPD3MhrXljk53XB/AD/Cq8kQ6meQ/jQBlPpoOd0kjf8CqFtNhx82T9Sa1HhT1lb8T/AI1A8Cf88mP1FAGa9laryVT8aieG1BODH6VoPAO0PP4VC8TDgKB+NAGe4gHTB+gNQsY+gjb/AL5NaDxN6LUEkTY6gfhQBQdh2jb8sVXffniL9a0Wjbn5/wBKgkjPOWY0AUHEh6RgfiaidZDx8tXXj9C351CydTg/nQBTZZeu5Rx6VCySc5k/IVdKjiomX5uBj8KAN9baLq8p/FqnjgtB12n9akigA+7Eoq1HE2eiigBkQtwAFRj9ENWoyvG2Bj+Qp8ULH+LH0FWo4T3ZqAI4zKOkAH1b/wCtU6ece0S/r/hUscKnsT+NWFhX+7QBCgk7yKPoKkVCeftB/DH+FWEjX+6B+FWY0weKAKsVuh/ilb8T/jU8drGRzCzfX/69XESrESe1AFOO2UYxAo/IVZWFuyxj8TVpIz6VKI6AKYgbvsH/AAGl8lh/H+QxVwR8UbOKAKJh9Xb86YbdPU/nV5o++KYU+lAFFoI+mP1NRPCn91fyzV9059BUDrzjrQBQeNQeFX8qryL1xV6VcDkj8aqymMD/AFiD/gQoApyL7iq8i8ZqzNLCP+Wsf/fQqtLNDj/WKfpzQBWkWq8ijtViSaPP8Z/4Af8ACqslzHk/e4Pfj+ZoAglGKgcfSpJblOw/Esv+NVJbxMdB/wB9f4A0AI478VXkFD3XXAX9f8KryXTDOB+S/wD16AFk71BJ1xTZZ5CAd2Fzg4QcfrRLA55M8p+mB/IUAUtY3f2fPsZgQhORwa4qLVL+L5Fu5MehOf513n2cZIJdiykfMxIrzm4Qpcsp7MQaAPdoxVmNeelRRrirUS89KAJYlqyi81FEvpVmMe1AEiJxU6JkdabGvtUp2IMuyr9TigB6KKnjT3qOBo5VzG6OM4yrA8/hVmNRmgCSNRViNaZGoqwg6UAPjAxUu2kRcVIFoAZikIFTBfamlRQBCRUZAqwVqNlwOlAFdxWDr91cQiZI3CDYQu1ctnFdC49q57XlLXRT3GPxAoAzPOlKDEYII4PlKP51C8lxnhT/AOOisDxt4km8OabYzxWguBMCpy2NuAP8a4mb4n6mwOzT7VfqzGgD02Zrg5+Zh/20x/KoJFmcYZhj3cmvLZviNrjg7YbRc/7BP9aqS+PPEL9J4U+kQoA9UkibozJj/dJqB4mYkl1H0T/69eUS+L/EMnW/I/3UAqtJ4j1187tTn59DigD1mWHnmVvwAqCSOMZzIx/EV5I+r6pIfn1C4P8A20NQNeXb/eupj9XNAHrUgtwPmf8AN6gIt5CQjBj/AL2a8z0y2ur6YAzSCMfebcfyHvXeaLZQWbbIV274wTzycf8A66ALlwirbkKoHQ8fWrR5jBJPIzUUqBrZh7Gnx/8AHunOcKBQBGeGU9K841qPydTuE9JT/OvRZB3rg/F0ezWJjz82G/SgD26MEmrUVVYuKtxigCeMdqsxjFV46sg4XrQBw3xPu7j7ZZ2cVxNFGI2dhHIU3EnHODzXEC3hkmbzELso53ktn866f4myI3iCNXYgLAvTPcn0rnIngjOVZFJxk0AegfCBVj+3JEAsYCnavAzk816PHzj3rz34SFWt750bPzIM4+pr0KMAEUAWoxyMCrMa5Yc1jtq9nBB51y5t4wcEylVAP1JxUKeM/DSvtOrWjN/dWUMf0zQB0yr61KFrmZPG/h6GHzXvYxEOrkNj+VT6X4y0fU3K2E6zkHBwrcUAdAVpCKytW8R6dpdv513cwA9RGjZf8s1Q8PeL7LX5riLT45g1uql/NjwMHOMcn0oA6Fh7VE4psDu0CtIwZyTyBjjPFQXt5DaKrTttDMB+dAEjYrB1pR9uB9Sv9K3JJEXq6/nWHq0sUl2NkisV25wenIoA8v8AjFEG8M2xH8DN/n9K8bNe3fFxN3hTP92Vx+jf4V4h+NAATxRk/jSHOKOlAC/1pDQc+tHNABVnT7NrmTn5YweT/Sq361v+F4xLC8ZzkEkY/CgDU06JU2RxJhV7DtXR2Yx5Teq7f51j6cu2VR6gj9K17Y4iiGejkGgCyFLRlfU/0NNtCTaID24P51ImRvHoR/OorXIikUE5Dkc0AJLnpXG+OExexPg/NHgn6V2MpPNcv45Um3tpABgMwNAHrUR96tR+oNVI1qzHwe9AFqM+9WFOcfMKqoPc1YjUAehoArX2j2F/IHurWCVx3dAT+tFvoWmxkeXZW6Y9Il/wq+nWrCetADrK3jhXbGoUegFXo/Sq8Rx3qZeFPPOKAOI+KgDeGI4u0l3Hn6AMa4qJtP03L3pECMwCZGRIePT8a7f4lpv0e0i7PdAHB/2WrhtT0qDULIwtcSWgRjL5ko3dASQB6YIH1BoA0PEMUeqWNvBApR0OUG07TkDrjp1H5GtbwnoE2mW5vZrpY2C7uG4Axkg/WiwVYIlnRfNAAUnbkDoAfxJrVS+jkgSKO3AZuQ2c5x60AVdRWwlulf7KrB1GcjpXS+A7K1t3upLcKGkRdwB7ZOK4jVftMdzlR8mPXpXUfC/eZdQkOduyMDJ92oA9AhH7hea4D413s1j4ee4iJBCnB96723/1Q/H+dcf8XtMfU/Bl6iKS8aFloA8rPxLlawhEjSNP5Y34H8WOay7D4hXkPiC3u5V/0UsFnXqSmeT9R1rg9xAx6UKryMEQFiewoA90+JzR3Pg2SWFg8bSsysOhBVsGvC69PSW4f4ZTWdznzIHG3J7EHj9a8v8AxoAPxpabgYPNBFADuSetIB15pKBx3OaAHfjXQ+CRm5dc9/6VzYBx1roPBLY1Fhn0/rQB0louJ1BI+9itKEYtyP7sgrPK7Ltsno5P61fUYWcA9Dkc0AXFHzsM/wAJ/lUduR506+hBp0Z/eof71RRjF4wz95AaAFl+vWsDxhHv0jd3SQGt+X059qyfEMe/SLhc8gBv1oA9FRvap4jg89arIe/NWY+negCwjc9KsRtVVOasIBxQBajOTn1qxGaqRdOe1TqwHHNAFtG/Cpyw8tvpVVDwKlY4jPHagDkPiQx8vSUUcG5J/wDHcf1rDgEHyiV1AjJyWHT1/nWj8SrhI7rR43YjMjtn2+WsuwC3NvsuAu9kXei5AxnjHftmgDY068ktWKmBMNEdhzjJGMcegJqKw+0yXGLl9wPzEZ53Z6/pVQCG8Elt5zs8GBJyVIOOPbnNaVu0A2wxOzSL98HAxyentxQA7XEDWwIUnnaQO9bfw9TYl4wQKCsYA/76rNltlvLVmztHByTxgf8A6zXR+Foba2t5RFceaTt3D064oA6G3b9yPqf51FdIk8TwyLuSRSGHtRA4MXtk/wAzQ5oA+XPGXhOfSvHU2jkFIp2Mts5HDIT2/UV1OheDY7ZVYxbmI5JHfmvXPFnh6w8QW0QuYwtxbtvt5gPmQ9x9D3FZ9tataR+XcRDzVTbuzwRzzQByHijTltPC2pKoAYNGcfXArw/jivoXxrg6Hqqn+KOI/wDj4r57I5oAQ9OlHHpSkY9aT/PWgBPwp6KCM4703HepYx8poAbtX0rY8JELqg4xkD+YrKx9a0/Dfy6oP90/zFAHW3h23UoA75q7H80kwwPmTP8AWqt+uLtuvIB/SrtoB56j+9D3+lADkYgRtjoBSTfJfJgD5lI/LNOYAQRHB6EE596dqiqhs5lY/OSCPTn/AOvQBHMR6VRvxvtJ0HOYz/Krs3t0qswBJBBwetAHaIx/CrKMc9apRsO1To9AFxGJ71OjfnVSNvWpkboaALqPU0bHNU1YmpkPPU0AXkbkVJIxMLVVjb3xUsrfusZ70AeefFe4dNV04Rn96sLFT6Zcc/pVK2cwRqQVE2VVx1z/AIf59aufEpkfWbdTIQy24OAccbm/wrD0ZmjvLqV5g32hkMYBychQG4oA6jTNslxI2QgcsXYng7eOnpToo4olWSHy2ZmaQhH7+4rBt5Lw6hNbSTP9kFvjCkbS5PPv0/DFW7Erbn9wDITxy4+XjgcUAbEzXslufLZ0LIV8skYLH1Na3w5ivbVNQivnBkDxkBTnAIPeuanvHbDNJJhVyckgDBHoOv610/gi8Nyl67D5lMals8twT/WgDsbeTMII7k/zNK7mq9s+IV9P/r0rydeaAHM5xWZrDnC5P8Jq6z1l6zJgR/Rv5igDm/GBJ0q/A7wJ/wChCvn98hyOOte++KGDaZde9rn9RXgs2BM49GP86AIjmkyakRS7BV6ngUjqyuUYbWB5BFADfwqSLODTDT4Twc0AO5Bq9oTFdTjx1IP8qp96s6U23UIfrj9KAO81YbLlCuPmiU0y71CCzjt7y4baiRBWPUkjIwBSX77xbNn/AJZAZ+lY3imKS5022RASRIfoOvWgCpceMpOEt7MbATzI3J/KrI8XwXSwR3EDwFHHzA7l7f4VzD6feKpbyCV65FVH3KdrAqR2oA9VMgdFkQhlYZBHQioGb5uR3rE8FXhk0trd2BMT4X/dPP8AjWwx/OgDsUb3qaNqpo3pU8bDvQBdR+PWpkcVSV8YqVXzigC+jngfyqVJOnSs9Jcd6mWXPSgDRjf9KkkkPljJ71QSX0pLq6WMJnuaAOB+JczDxCpDKFWBRgjPOSaw7V9wX5iwX+IY5P8ASvULj/hHrmQyXqWEjkYJl2k8fWlVPByc7NHU+uEoA85tZlWQknaWbAyDkCrUMgVYwvUtkk8Z4/z1rvxL4NGB5mjcf7tSq/g05G7RcHqMpQB5412xhmZnyWOPu4Arr/ho5a11F8Y/fKM5znC9a2VTwiwwP7HIP+1HzU0FxpNsrJYmzXeclYSvzH14oA2YG/crz2pHeqttOslvHIpyrKCD6g0rSDmgCQsfWszWCCY2xzgjPtkVaaQfT8aztTkBCfj/ADFAGF4iJ/s2bnraN+mK8MuuLmX2c/zr3LXyDpT9P+PZ/wD0GvDrzi8nBH/LRv50AJE8SriWHdz94MQRV5jbPEguFlZCPkYjDj/EVmo5RgygZHqMipZLhmjGWZpMksT+lAEl3BFuxbcqg5JPLe9QRdTTkcFsng1KAjZOCG9RQBHU1idt7C3+2Kb5M23eI2KnuBmnW8UhmQiNvlYE8Y70AdrO2bW3b/Zx/KqWpxtLYpgjasnKkZBBxT3vIPskSFiGUnjBqAalYyRSwAmQjG5dvBoAzL2QwxbDMAAeFUYArFkeMFmZNx/h9KuXFqrOzJkKxyqnsPSq6LH5pXHTpQBa0OOaJnkcFAwGB61sJezp0fcPQ1mwt8ig54FS7/TrQB6cjk1Oj1RQ56H61Mr8UAXA/tUglqkr9qkDehoAthzgVMklUQ545qVHHrQBoo5Pepkes6OTnrVhH96AL6kE8gGpAkZPMa/lVOOQ1MsnPWgCcQwHrFGf+AineTB/zyj/AO+RUQkyMU7ee/6UAShIh/yzj/75FODKvAVRj0FQb+/WgvngmgCfzfWmlzgk1AXz3pjMe2aAJmk4qjqUg+Rc8nPb3FSO5AqjqDHEfPQn+lAFDWHLaUw7+Sw/8cNeJ33/AB+T/wC+f517NqZJ03r/AMsyP/HTXjOocX02f75oAgoox+VBoAAaejt0php0f3qALS3kqRhOwpv2yUZ+ao+9Ic4oAt211lyspI4zg0aK+++lLAEOucH61a1q2V9LS9GAyEI3uD0/z71k6fcLbXIkf7uCDigDoJEjwewAwPaqJjjH+rTnHelW+tpTjzgo9+KbczxpC7pIrHHGDnmgCCwZ5J5RvAjXoD9asK4YkZ5B5rMspCjkZ+8KswsousMflbrQB3/iTVX0zTGuItplLBUDDIyf/rZrk28Va7J8yzIg/wBmIf1p/jO8kmaK3JJCux6fhWOp4A6UAaaeJNf6i6Y/9s1/wqePxVriY3SA/wC9EKyY2wQccfSpQ4zxx+FAHU6N4vuri8htbi2jbzHCbkyCM+1des2O9eS2N19m1RbpVVzE+7B6GvSba6Eiq3ZgCKANmKXPerKSDg1kxSqVAI5q5HIpFAGismO9SrJx1rOSWpkkGcHvQBeWT0NP8w/jVNXp/memKALYk9+KC/XkVT80Adab5w75oAulximl/eqvm+hpDJ70ATPJyTmqd+/KDPZv6VIXi2Dc5DE9MCqF5Jkx4z0Y/wAqAK+oN/xLSc/w149qJ/06bP8Aer1u+cHTnz/cryTUuL6X13f0oArFgO9JvGDzSt0phUCINvUliflGcjGOT9c/oaAJARjNKh+eo06Dinr97NAEpPHWkZuO1X/DsNjceIdNg1JJHspLqNJ1jk2MULAHDYODg9cV7cvw/wDAF5p94+l+B/Fk0keY1uEvjNBGxHBdlQdOu3HI70AeN3x3+Gzz90xt/SudLZrodatLjTI73TbwOssLhSpUrg8dj0rneKAG+1KuBzRilNAD4WxMp96syH98pz37VUjHX2q1J8yBhyR7UALfTNcXjyMT8zEgZ6ZpAeevSo7gFZScEfWkD49KALIbHejfz1qv5mOMfrS+Z7frQA+E5Lknk16Hpx/0WLn+EfyrzuDHzZr0KwwsCD0AoA1ITzxirccjZ6CqMLDvj61ZRwBnrQBbErDHAryvxBqtxf6rLcNK+wOREueFUdMV6UzjkA9a8jVc3AU/3+fzoA9AsPEuox2UEUhid1QAsyksfc81ZPie9UZYQAD/AGT/AI1ycR3Sje5VR1IPpU0sgkztGOvtQB06+KLphnZb4/3T/jTR4nuHO3ZDnHJCnj9a5feCQoOMDk05SuDnjjjmgDp18RXWAcQ9e4P+NczeeMtY/tT7RFcBYEbiAAbGHv8AX1pk021WIJwqnkHkcVzRIyc0AeyyXgljgkw6ZVZAAM9R0z+NLcSZER55Vv6VDYSj7HAucgRr/IUt4wwhGO4oAiuiGsJB/sGvK9S/4/pfqP5V6g7g25Hsa8v1Qf6fJ68fyFAFR/u1GOtSv92kATb2zQAR9+e1OXrUeMZwc0DpnJ/OgDT0OW3h17TZbsA2yXcTTZH8AcFv0zX3F4/g8P2VhNo/gfxBaW+j3dvLNd29tErLcTKU8tSzZKkqWG4ccc18GqRkZYn6mpzHalMrgcdyf/iaAOj+Jls1j4kvbR1EbGVW2hg2AVB6j6iuS4qWUrsREOQuf1qKgBMUEfyooPWgB0fB+tXreMMmc4qhn5hV6ybBK/1oAZJGr8lyce+ajjt2c8HB/wBo4q7jPHyH6rSbAT9yL9RQBSjgc4ZcDnjIp/2WbBPy/nVlxGoGYzn/AGWP+NNLrnCbx680ANtIGyN5wCw4HWu4tJkdQUbOOCM8iuJDFBlRgjua2fDSs1007ZJCY/OgDq45OMfpUyyEAHIqmhwBgVIG9qALDS8da8zA/wCJkw9JGP616DK2Aee1cEy41OfI4Dt/OgC4JiOpBpGnbON1NBQqev5U1toGBtoAkWQg43dRz61MZN3HIqtAm+VVPXrT2VhyVI5IBPrQBPcskkBwcHB4/CueHetokMGA64wPSsXGCR6UAegaBfzCNY3csqqMZ7VsXcuUQZ9cCuX0RsE/7orflYmCIn3/AJUALvPldfWvONU/4/5fqP5V6EDmPB46/wA68+1bH258eg/lQBUbpUfapD7UxvQCgBOMcUcUAUuOaAE7+1S+e4ULngDGM1GQAeacAvfmgBCSTmkNLQetACUUdqSgBR6VPan94pHrg1XPUfSrenIpZ2b+HGB7mgBouW9acLo9yD+FCwRSMQrkYBJJHoM1DgDnI+lAFpJfMPQce9KF6c/lUNn1Y/SrQoACgP8AKui0NVSDeBw3esFRvwgPLHGfSuntgscSKOAoxQBeRvxpwaq6tWVreoSxSpHayhXU5fgEH2oA2biVY42aRwqgckmuMJDXs8yglWbj6VbudWW5hEc9uzkc5VjgGs+eRASYgUTHfk5oAnZt3SkQ4fJNRQF2jLgqy98jBpWl/uAs+elAE8LqJtxJXHSpJHDL171UWVtxV0YH6VNuUrt3cZ9aAJVUBe+SPzrFfiRgfXFbIO1Ov51kzHN23+9QB1OjH7w68CtwtmCLnpurntIOAx+mTmtlGHkqM/xH+VAEwOYjgnJzz+NcFqoxdnnkqK7lSNnHXn+dcJqp/wBI6HvQBWJ4pjelOzTc5xQADpSgDFJxtxSr0NACYJbHH4mn+U/UBT9HH+NNP40wmgBSCrEEYI7UZpDR3oAXPHNBNAH8qSgBSenNT2jupKgfKxGT6VXqSLIB9CaALxxz298CpoJrYRtDJbRMT/Hsyf58VAnIJoXnmgAcRqx8pQFOOtCk96bknOT3pV60ATxTJHIjOcAMCQKvS62QMQwj6sf6VgsSXOT3pxJxQBeuNUupshpSo9E+WqOWklDfex2zTGpASMYOKALoRnGW4HoKGQZAKg+i96qPI+R8xq7EB5YbuR1oAVUxFsVsA9SOlSKAqhR6Ufwg98Uj/wCsRex60AOOCCPzqCSNxysh47HmrB44HAqOX7p+lACxShoxvPbk1RYZlZz65FPH3WHbJphP86ALFrfzWzhlYsM8qehrq4pRJaI4yA3P6VxD/ex7V11vxZRgdgP5UAXUf5Bz6/zri9Tx57fL0b/P8q66Mnyxz3NclqTN5ki7jjcDjPGeeaAKfFIfaloJ4oAbTgTg0dxToQGlRSMgsAfzoATPFMNdbqOj6dEjGO32n/fb/Gudu4Y4z8i4/E0AVKKX/EUh4oAVe/0ptOj5P4UlABUkf3RUdSx/doA//9k="}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-2935-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: If someone wanted to lift their small sack designed for holding valuables, through which part would they usually do this, and what is the primary part serving as the container? answer: Someone would typically lift their small sack designed for holding valuables through the handle], and the primary part serving as the container is the body]."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-3490-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: If I wanted to shelter the intricate mechanics of a timepiece from harm, which component would be responsible for that? answer: The watch_1's case serves the purpose of enclosing and protecting the delicate internal mechanisms of the timepiece from external damage."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-3919-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: If I were looking to find the title or cover design of a publication in this setting, where exactly would I direct my attention? answer: The book_1's cover would be where to find the title or cover design of the publication in this image."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-4067-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In observing the area where viewers can get information about the current channel or input source, where should they look on this device? answer: Viewers should look at the television_set_1's bottom, as this area often displays on-screen information such as channel numbers or input sources."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-4297-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What part of each utensil aids in maintaining a secure grip during usage? answer: For each utensil, it is the handle that aids in maintaining a secure grip, specifically knife_1's handle and knife_2's handle."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-5152-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: How can one describe the part of the first item that is primarily responsible for its containment capability? answer: The containment capability of the first item can be described by the handbag_1's body, which is the primary compartment for holding contents."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-5319-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What part of the resting structure would support someone's back when they take a break from an activity? answer: The backrest would support someone's back, which is bench_1's back."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-6139-3", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In a scenario where a person's handheld device needs to be charged while they are out, what item would they need that is visible in the picture? answer: The person would need a cellular_telephone_1 that likely contains a charging port compatible with common charging cables."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCACrAQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDs7P4xa5dTBUucDcBgL15rE1L4p+KZP3cWsG3SObZkkABQeleV2V1NJqUAhMjESLnaC2OR2rTng1x5AdP8OX04eb5ybdmB5PPArwYe0fdnS0juLjxzrt0YppdcvUgEnIyf3v8Asis648UalqGpWrG/ljtItxkVpDknHHux/wDr1mW+j+OLoxFPDVwoD8+dEUwvqN2KvDwj48e5gEGmxwwrkvmSJSeOv3siq5Kj2iToupdt9VWLUEuJRcS5jYPIWO1snoF6k/limahqGof2mz2VvIA0I2TY3MRnt2X6CnHwH8QriZZn1OwtIgpXBnPH/fINNf4fa4JN2oeOdNgG3BHmscc+4FVyVrWsg93uZdu2tHVLmSWO7mWQKG3AnIxzVyzfXYZ7iAB/K84OpdwBjHXmrKeDPD1u7vqPxUtFDAZVNpxj6vTH0v4ZW0Tpd/E6adWPKxhR+GQCce1NU5rXmX3i5okkV1qBRrVnVIFkYxyrMu7nuRkZHt2rMtYXgjIuPE1sincDtdmbn2A/rVu3v/ghpSuJPEmuX2W3bIwzDPt8lQp4z+BsCnytL1+5bcTl9wz/AOPCtI36zX4ku3RE1pf6W2nLavrBa3CHbI24tu/vf/WrUHiCyt9DvxpklpeagkAMEG7Z9oIH3QWGASfyANYa/E74UWkflWfga8mAGB5jnP6saa/xe8IBfLsPhhbOO3mqr5/AqapOytzfgO3kWY9WsLzQLW412Q6ZrDOxuLSIrII1BAU7lIzk9M/mcZMs+paPLtdIL+6kVgRIpwGPuB/OoLX4xamiqmjfCrT4wOgSzJ/9BQVcn+LXxdlh3WPgaG1Xs32CTA/MirTgtbsGm+g6ISX1xDJB4cnkKHGWLEjitVdN1ZJ4m07wte+ftYGX7PIVX6Z/nWBrvxB+PFpo8mpT6adJtUK7rk2ClUycD72eprhL34o/Fa4kVpPG17GWbH7m2gTH5JVyqU7WdyVCXQ9QPhbxZLfF/wDhGJVVk5cx47+5qUabrWhxznUrOG1HEoQSKSVxgnAJ9K8W1DxZ4/uXUXfjnxE+8nO27Kf+ggV0ng2+1CXQ5FvdSvr6SW4lXzbq4aVxmEYGWPAyDx7muOoqVvcvc0ipdR1lZa9Bo1vpbS2Oyzmufscv2pgDHLuCrIgj+YJvLLyMMOtem+G7tbl4iwBMdxIOPR1Uj/0A1422rSq5DkuvmcZOODXZfCvUTPfXdu5yQY5Rk+hZP/ZxSjWnOS5h8qjsfSXhqRBBxjJXg141a2kNn4ruR5YaKw8akFc4BgvUC4z2B84c16R4euSLcxxtg7CqjPfHFea6XcjUND8RXaoDNcaHYapGWOT5tqzo/wCObcV6KnZL1T/Qza1Og8VeM5PDtskAuE0+Bl3W9ta2/mzyLnG9YyyqiZ6PIWL4yEFcFefF7Wo5W8rTtYu0J4E13ZW5B9fltSce2a4P4t6zd3XxN8QNJkt9scB9xyUz8gHoAuK5nfEFVjEjFie5JH15pwwfs7qT1CVa70PWj8XvFjAtFpaxjt5uvzcfhF5dZkvxM8VKm1E0KIZJxLfX82M/71wR+lcLBMkMTM8SDjjgVmz3cjNnAHPYCtVRizNzaO2vfH/iGVT5t/4dBPULpPm5/wC/m6s+08c63pt0L6JrCaVQc/ZrD7E5HoJICn/jwYeoNcq11KTguQfY4qK5mdkOXb8zR7CL0sT7SR9JfDnx5pvivSxFrCvdWUh+zzRyqoltpOWCuqgLuwCVkUKrgEFVYYPo3hE3GgrJpP2lp7JDvtmJ+6rcj8Dn8DmvkL4L30ln4+FtuITUYHi2+sifvYz9dyD8z619VaFfHZBA5Dfu5bVT/uHKfoa863sa3L0OpPnhc7hbxb62mtJG+SaNo2HswIP86+Ib15bPV9Y06VSskF0xK+h6N/48Gr64068y4O/FfMnxutDpPxh1NVXEd/H56+h3Yb+Zeu5a3MZbF+w8Tax4b8PQTaHJHDdNYNMoZNys288sOM8VmXHxW+KVyMSeJYIQ3aHT4xj/AL6zRGA8NjGwGBobP+jVy+4b1T3rxnUlF8qOrlTZpXHi/wAf3THzPF+p88ny9kf/AKCorovAnhX4g+NBdXMPjO4t7e2KrLLf6uY1JYHgAtzwK5ezdPvsAc8ZPtUPjm1huvBUrFFfyrqNuR9R/WinK81GXUUlZXR6RJ8J4EmdvEHxh8M2eOGMurq5P4eZVWfwL8HbRdup/HLRZMdfs2ZT/wCO5r57+xRpHkRRg/7oFa+lWeky2VtLNaHe90LdyZyByM7sDp9K9CtRpUI80k3/AF8uxpgsNPGVHCMkna+vy8n3R7L/AGZ+zVY83PxN1a/IPIttNlOfx8uga9+y/YfdXxtqZH9y12A/mVrx230XTJIzcNfSiHbnHygp8xBJPTAwOPenaL4eF5pU15dWupIsTMDIIH2bCAQ+cdBnJ56YqVVw/K2k9LdO50vKMVGUYycVzJtXkrWXn+R67L8TP2e7RSLH4a+Krw9jPcRp/wCzn+VK3xl+GmlwxtYfBK0uy/Km61WN2HAPIWM4615HDaeHvs73YhmktIpNryfOSDvA+m0rz680yDWNIFpErWux7eR/K2wllQb1I6nkkA9+DQq8X/DpN28kb/2LyW9tiIRurrVu/wCC87Pq1bQ9e/4aIht9v9m/B7whZ5Hy+e7v/JRUj/tI+P8A7MbjT/B/guygAPzpp0jgY9y4ryprqa50+7uJ9NlmSWZ4rYCIeaoILAHPIAyT0NNsbvWLbTIYn0W7kjtlYMzLgDLKwPTsB+tH1l8l1BXTt8S/Db09TVZLSVS06kuVptNQl3sr6PRpOWnTsein9o/4w3cyx2V7otszsFUW+jp1PQAsTVDUfjn8bJZHhuPGs9syHDJFYW8ZU/8AfFc3Y/23JqMlmYSYmuBi5c4Ee7DABeCccDGfwqhfRz3mr3jrGEYysGAyRuBwcZJ+ta4TEuvVcLK1r6O5jmWUQweHVTmk25W1Vlone3fprtZ90zrfDHxE+IfifxCdJ8Q+M9X1GwmglEtrNKBE+VIGVUAHBIIqC5VFCll2nIYZHasvwDbS2vjW1aQnBRwfyz/StK4JmtGikX54hgNk+v8A9aoxa5KtrdDx4fCR3jxuF2r0fiui8GyKLKRXIXyruCTPsd4P9K57T7JLxnWWWZMKGXy495Y59MitSCFLVbyKFpgGthIC67Wysi9gT2zXE5LY0SMXUXMNxKh6q+PyOK6P4UakY/GItyeLiBx/3zh//Za53xECNXvCvRpWYfQnP9aXwVK9r4z0uXJUNOIyfZwU/wDZqdNaikfVehz+XKp9CDXn/hKdbG+ks34jhudS0qbPTy/tkcoH/fE712OlTFlR/wC8oPFcSNqeL/FtiwyBqlreqP8AZuLV0c/99Ildifu3XZ/5/oQ0eUfEqNo/GHmSA75rK2L/AO+sSo//AI8jVm21uspUEc59K7D4z2RTxFZ3WwbZfPjPbkSmX+Uy1y2w28Jk6ZBArtlNzs+9jNRsV9XlAAiBBUelZbMNvI5ouZHaQLjIHU1GMFGJboOnrWqVkZPVjWk/OljHmEk8Ioyx9qhk+Z1VGDEjAAHerscRjiWCF4yQcyZ61LlYFG5DouoLp/ivStZjchLW7jkYDjKhxu/TIr6vspfKtTeLKWW3lifaMYwpMbH8dor5L1aFIoGZ2VQeM19L/D3UP7X8LwbuWvbJckf3jGv/ALOktediVeSZ0Q0R2VvKI7qRM8Bzj6dq8c/att1h1Lw1roH3laCQ/wC6w/pIfyr0nT7tpIreY5LPEu76j5T/ACrjf2krRtT+HCy4B+x3SSbgOgYFD+rD8q2pzTVxSXQ87mkaNbXHB/sFR7fMP/r1ziRPIUYHkHB/pXT6lbv5lpFlfM/suGMgcj7o71QtrGSKflowMEMCe1ePLVnUtDKCsIwdxrQ1SLf4I1EYLHMbc/WrRs4Nu03kIGffirmqQWkXgbVNt0JvlQkqp+X519frV0kvax9V+ZEvhZ5x9iDw5Va6Lw3odmuntIro0s0TeaXjPydgFPTsfc/hVSBLb7KGFyAc4wUNdppA8c2vhWzl0i/02HTZZdsTs0QkBMhADbuQu9j19T2r6HH4P29LkUuXU1yvHfUa3teRS069PT8jL0vwze3OoQWtr9oSz8jyTbpHtff33IynqKYB8RbmwfTotIMVrMq2J32YTzgyKijL9SViQZHp710Ew8dtJi6+IGi2h+64/tOFQBn/AGR6+lULiO5vSP7U+KlsI45wwxLcSMCrEB1VVxnjIORwRUYHLFRbbkpX8vXXRavXc6MyzZ46EIuCjy32b6pKyvstNEttSofB+oaH4Qgl1DSb/wCwNGy3bKgJUuM7iv3sDHBHFbK+DNXVisn2EWL28HkvLcRhC4YfNgEtg5Xt+gzWJfW2iLFNM/xDv7+7EbBESznwx5BBZj0K5/MZrBtrCxdwwW+kJ7Lbj/4qrjw7Qm3Kcndu/Xd6+Xp6HUuKMZCEadNRSSSWnRK1t9V116nYaZpaSg3M+q6fYMl5KXg1K/VJo2ClSmwAkDuO1QyWOk6dZ36v4m028vdNhH2CVJGYXSysd4IGfmUDOMnqOlYL2NgjgzWl+D23hUz/ADpJf7PjI8vTmf0Mlx/QAV00+HMIlZK/9f0n3MqnE+YzafPa3ZL189b6p9Oh2cY8Gi5X7V4pinCSLKhj0uUhWUY3A55JHOCMVm+JLjQ59UiGgrJNa4LMxtSkhc8sW9Rnp6Vz0d3IuPKtbOP0+Uuf/HiaVryYgpJdSEHqkZ2jH4VcMpw+GkpQVmjixOb4zFRcKs20/T/L+vQtWPmxa9Zs0YiJZly3B5BH9a6i88Pqt7cf8TPTgrOTjzhlcnuK4W1GdasXC4UXKZPtuFekXHh2c3KXce7EyKxGMg5HOfxryszjaqmu36nJTfumO2kjT95OpWhLx7VEMmXOfQU+yht8xxvM7OlsYguzHyhcD+QrSm8PkKY3jZD16HAP07Ve0Dw9ILUfuiSqNzivL5G7s0ucde6dHcXzP8/zRoSu3OPlA6/hUMemmz1C2uQGzDKr5x6HNdxYaTIZgRCx+TH6mtS58K3UvyIgfgEED2qVzuWiG7WOz0V82cJ9Bt/Lj+lcrrIEXxRv0zhdQ8PRy/8AAre5Vj/47mug0PfFE9vICHhkKsPc4b/2asXxcmz4j+EJ+10t5pzf9tYuP1ruhul/X9akPYyvjdYomkabfSKSG8mYHHeWIqf1txXkep3xePy1B4HBr6D+P9qJ/hFo+pREpnTlYkescyj+Ur18vSyhs7pnOR6niu2nHlhG/YxnK7Jzuzkgiomyx2FgoJAJJxVZ/J2ZyzH3qSxjikkbdG/lgZYgdfatG7IizNMaeLIeYZ4XmcfulZsfjU0SRxKXbylcjLkdzSRxyykSfZmfH+rIjOQKa8F5NKI44Z0KkbsxnDCuWUrs1SsUrxzcKZY5UeFl4UIeD6817H8AtRP/AAjsSlZJGtZJIwo6na4YAf8AAZ3/ACrzBdLvZZ1srO1ee4ccRxoSx/Cu2+DMOp6Hrt9pOq2c9lLI0FxEsyFSVffCT/306flWFVOS5l0KWmh6rCPs97c2uCBDcPtHcK3zCrGvacuveE9V0huWubWRE/3tp2/rioNdcReJVdR8l5apKPqpwf0IrS0l2WVTgjmlF8t12A8VubSe4uoXWMlTY26Lx3KrTf8AhH50fzGUjqPzr2PR/CaSapp2lAYhF6sAfb2UHr+Vdb4x0HwZ4TsZbjXdTihQICFALSEdSdoBOAAa5PYyaumauSufN0fhu5YMducH3qTWdImg8D6uHjZQyIMkf7a13upfFPwLYB47Hw9eXlurAG4kk8se/G04/E1l+Jvil8PdW07+xHtBarOAZCJmIYZUgZ2+q0QTjOLb6rp5g9UeKWyCNQjAgD0PNT5UqAFLHsSa7Hxf4d0fVBDc+B3huisR+0263QeQdwVXr0zkVi3VjHY/YIFhnaaSP95iKT5mz6FQRX00MZSavzHLyMwmtsyf3WOegpFt2EuN7EYziu68OaFc3OuW8MukXE2ZyDE0DDIMO71HHfrXrWm+EbVlUDwPASowWa2z/OWt/rsHFpSFytbnzhb3eqwzQv8AaJIY1IYCOMYxn0/i/HrW1f6vruoWbQR3jiGQHdmGCHP4qMjketfTeleEPDsdsn2zwpZxybQSPsicHv1Y4/OrMujeDIVxN4f0gbeziJP5vXkzaerlctXPkhbPU4XRWullWQcxrMHPT0ya6OLSbVY4jNCzbgC2ZwMfLnoF6E8evrX0JqukeCblI400fw3bfNkt9riRiMdMrIDXgMVho8niDWbbUvFNvodvbXkiQfuDcGRNxxtIJyMd+c+tbYfE+zi1f7h2KMHh6S/uvsdleqZnJEcax8t16c+g71qad8MvEl1LthtZI0QlZZbtfIRSD23ct+ANWI9S8I+Hyb7QvF+rajrMXNqWstkG48YYOMFSCR071uRfGHU7eKb+0fDOi3EsUpiJSSRM4znhWK9u3FaTzC7tYHALD4VwWrJPqmsTSMpDeXY2cknI5+8QB+lfTHhHwroFz4Os7mew82SNCvL7cgMT/I183P8AF3w9cpt1LwPEhPJa3uc/oR/WvfPCXxG8P6b4N0/91IyzW6SqhIGFZBwf1rLEVLSTatuJLQr+NPDmmx3HmQaXJFzjKuCCD+NUPhjDGtvJBLaQsQrRnemcfp71e1L4r2N3aSWcGnIXA+V2fOB24xXNeGPiD/ZGo3k4ghYtMeG9CP8A61edKoufQ06HReEfDtpM84khDOFUg7fc5rtrHw/bAlo4IyF6jAyPUV5/4c8cJY3M1yETbOpIHYZOeKsn4gva3NxPBtR5zukGOCcAZ/ICqjVglqDuYnjW0XT/AIhazbxqFjcw3CgejRgH9UNcN8VXNpa+HdYU4Nhrls+fQMSh/wDQhXWa5rh1/wATx6i+PMltPKbHGTG5I/SUVzXxag+0fDLWHUZe2jS5XHYxurf0qJSu20Ulod74jtLC/wDhtptje263VrbXUsUsRbG+NmlXGe3Kp+lcTb6N8P7YYg8HaNkd5bjf/LdXTaLff2j4Qs24KSRmXn+8qwzH9d9eV6h+0dfadfXFjb+BNHje3laEtJcMclSQTgAelel7SSj7qOaUXc7dP7DQYtPCvh9P9y0d/wD2lVy2uJYxiz0S1i/646Ox/ntryx/2kfGc5KW2g+H4G9opH2+/LVm6h+0D8R2Z4YLnS7absqWK5/XNT7Spf/gIXJ1PW9YPiW6hMcFneIP9mwSMfq5rjbzwR4nu7pp2juNzf3vLB/TNeaXvxr+Jt2CJPFciA9fJt4k/ktZFz4/8eX6nd4o1eb1IuSo/TFYVKM57scG4u52mt6P4g8KePvD6Th4XvpQsZ87buBO3G4Djmu98Z6Pqto9vrU32e3mjie33LcyTSOTiRF+c4A3xqOB/FXz+/wDaepOs+q3t5O45R5p2fHPYk0+G+OmatY3kl1JIILiOQ7pC3AYZ6n0qXCSg4p9DTeV2fTniSWK4tND1SAnyvMaIEjHyOPl6/hV2ychkb1ArHa1C+AplDvJNp5EYYsSMQuVBx0BKqD+NaFjKGt42yeefz5/rXLf3r90WM+MPjyXwPpa3mkBI7xtQmMMm3cIxl+cdzhhivmO48Wazr2u/bdQ1G9uNRvWKCWSQkxx9eAe54/Ou9/aNvTLaaOzsd2+ZgC3AJxk+5rzPwtbPdah9tVgxXnH92uenO1FzfmdFOnz1VE6O/t7m7s/ssl9MqAYKj/Hqa5yfwfJM3y3RYDOMiurn3u5Kgj1pYV6L52CexrgpV6sfhZ731OhJWcTR+Gvwautb3XFp4nWwvI1JRFU5OB9alvvEvxF8La3eaPcXhhu9NVFknYK6MjkBHUsOh4/+titPwTPcQ6nC1teGKaN+OcBvap/2gMvPo9/c2kUl5NBJE7t1xGyuvQ/7RHPY13UMU3OKlrc4MZgoU4uUdLGbeeOvH1xpsi3figKwEwlZWjUnYMFQVHfoMda5yXV7t7ibZq94VMpwoupHwN3HJPPFMuL6eSIr5cEMDF3CJGNqmQDco4PA7A5xV3T7DWtQlkOj6RqupSyOpP2aGTb3P8KD2/KvWg17RNI8V7GYqX0mhrqR1OSUmfyfsxd2l6fexzxWgun6dLPe2/8Aa1xLFBB5iSLb43v/AHSpPA966a2+FHxQ1fSBAngTUoLg3Hm/aJ53jbb/AHSHYDHvXZ6R8HvGdpqk90uk+GNJWe2ERgur6N1TnlgMk56c0m6ijf8Ay8jWEad1f9Tx+x0/S59Os5oLyRbpnJuVlCrHCoJwQc5Pb8TgV2Xwq+HU3xJvtTtNP1C1hvLG3jmiW5gfbIocAgkN8pIGOc9e2K7ax+GPg7RtKRfFHj/R0gjkLbNNi+0OW7gORjp7GtaH4keD/h/pc+m/DfT5FluW/wBJ1K9BklkI9hx3OO3PShXi5Nu6E+Xlstzl/FHwVufAvgO68V+Jb62LRAQxWdvaeYzO0mQxctheBjODxx1NcPNZ6NfRyGHNuJX3xtGC0T+wH3l/Ddjvivd/C3xg0HxRo0/hf4iwpeWV2NvmrCU2jtlR3z0K81ymtfAfUJC198NfEWnazp0p3LbSSqrr1OCG+U9e+DWNe9SXNF2/rqKLsrM8p1rw6joW02OZpVRSUE0bjlQfUH8xXp/g7R5NT8F6W1w0sU0FvHCydxjI5/KsPWfhp8UI5U8/wNc3TLGqloTvBwoBHUjtXpvwl8Ha+/hg6dqOgz2F5AMtazBVZVLtg9PQg0YmNSpTit3/AMAuLgmznToQtAW3/MRnOea527jme9mgXcshCt14Ne2z/DjWZYcpZxq3PBZf8K5S5+H2sx+K1sntQZ5LfzFUEcgH/wCtXF9Wqx6Dc4HNQWs6adCjkiRANwz7/wD16sR2Mkgy0hxXbP4G11ldBZMuzbuyMYycD+VdLpXw21E22yeaKFWILKeTkUlQrSew+aCPK4rU2FxZSbtwklkT8WQN/wC060dZthqHhjVbAjInspo8fVDXX/FHwcugeFbbUVuPNeLUYAwC4AD7o/8A2euX0+QMwQjg8GtowlTSUtyW03oc78KdT8/4a6FO5yU8mOX6Msiv+hWvIvFXgiyHjfUri9nki825eZlLhQCxY4/PFekfC2Nx4E1DTk4NhfSxNnsRcFB/47iqfi/wxqus+LLnVNJ8Eahrwu4YZHmhti6IxTlSdpAP4jrW9SpNU7wvfT9SYxTdmee6Hpegab5U99NZgu0YkD36lkzk9uoOB+fbFZ/ixNBb7XNphs5Lp7fIMRdiCTgYIyAQM/n6V6Yvws+I90A1n8OpLZd27EsscX55dabN8IPiSvN1a+FdNDDBe91OLI9P4m6VhGVZvmszRqFuW6PMY7PSrbTFSCFprpUw7JaMQT5YXgsP7wJq7JJpsbRCOyn2lixJSNNuT0GSOnFdvN8Ldbt/32pfFH4caWR/Cl2rkevCpXO674Z8OadE0lz8aNMvrj/nlYWE7k46fNwO5raMaj+L9TF8i2Zw0t7MsY+fKGR3UHHyfMeAazdXvNLlLlort5D3eYAZ/Bal1ZYYrpo7a6N3bqTsmZCm/POcGse7iM0gSKNmZuw5zXeopxMup9afDh4NU8HqU27NSslnfByDI0YV8fV43P1Jo8OSu+k26t95U2N9VJU/yFcp+z9fyReC7O3my8lm89vtj+YkAiRQPXiR8fSup08G3ur+DBTy7pjg9QHG7+deVLR27No3toeTftB6dfPpujXcVu0ihplbBGRnBBx6cHmue8A2TReE5NQ2DmbZjucDmu1+Pckj6vZ3UFrlZbfYrbCfLjRnLAAf736VjWF3YQaZHa6aGNnIDNGG7k9TXnTm1R9nbqe5h8LGEoVVLdHJa/q9+Q6LHNbW6/eaOMlj+Nc3Yz39zqaJa3N7JFkbmkPT6V3s90rSlEhVfqaozzRW0hkkZUGCWYjpV0qyhHlUdTplhJTkpOWiKviuLVNFg0q7hvrxLW7D7riJfuOrYK/XBB/Gum1jULm68H2EPiCDUL67jknj0+6kbysRkISzqV+bnGORV/4b+NPD8UQ0W9T7Sk0vmxy7T8jgYPB6DH9K0v2n9Zt7uw8MRQRpH5TT7GTjchEfX8a6sLFSqQhLR/8ADnJmFP3Jzi7rT9DK8P389hbWK6W0LXlu0kqeYqyKHYFclWyPuheua6W5+IHxMVBbT+M7uzjwCI7bEYA9tijFeGW+ozw5ZJD+NbeneIXk2pO+QOMk5r6SGFhT1R885XO2vtd8SXnm2974t1W7WU5O+aR+h92qe0jufNFwNY1Iy7PLLxwtnb1xk1k3VtJbQo0F0GMhB3IORnsDVmw03U7nyiJAWkYIAex9x615kqE/6saJ2JYLGKMNFMk+1uQGIXP5CteLTdDnt1dZSrEZKuSQPbp1qe18JazDtBi3BnxgdzisEJqFneXEDLEPKkbzNzDMYGc7hnIrswkVG92TK5bn07SzKyxq6qpK7lIGffBFJa2SwzZtdQvI8DnYMY/EEVBarNPcyLCfPOFY+WcjB/8A1Vradp9/MD5C+Z8211jOSpPYgcitZ+y6is+g+W51NY/Li17WePS4Zf613HgP41weAfB8ultHda7rjXUrKtxMSsaME2l364ypwo5+lcD40+0+FvCs+rXoEchYQwK3VnPT8gM/hXksGpEIZZ5GLOC2FOGYnqSew/U1zV6kZK0Coq2573q/7Q/xJuJC/wDaOn2UTEnyoLZflHpliSfzqld/HbxTatZ30s0VzckFEnkiVV56qdvPf1rw86lDOP37iGDuEJyfp3/nV64udOl8OSW8GnXBkIJVyhA6jByT/KuS+vvMtK60R9j+Efinca/oceoBijSQqrRhs7GXORn6mtl/iLd7CyysSRXyb8EdeWDWBZtMywXalShP3ZF/+tn8q9ttImiUOsvzbcetcs6s4TcblJJq503jPxhd6z4U1GznlLp5PnKP9qNhIP8A0CsTTJlabKtuG6qgtg8UsJyRJGyfgwI/rUegT+Zp9rN3eFGP1KgmpU3LVjtYyfh+PL8XfEPQscFnvI1Husb8fm35VkfE/XvEOi6do8mjSX5iuPOjljgvngUsrfKSFPPykfgK2fB86W37R89tJgR6rpSA+/yuh/pWT8UMxeCPOdGLWV+nTqQ8ZT9WjNddHVW9f0f+ZM1oeXy+MPEt+WZ4I5MEgma5nkwR16tjtVO61bVpCm0aZCSM8Qhie+csTilEVnNdGK42NckFmQPsVfXp1+pPNTJpdqoGILIc5yxLE10qmmtImFzKuNQ1J4sPfoGYE/uoUU8duKj/ALPaZlee4mcuMncea1NXtrSx083ErQZZ8LHHAOc++BWRBqcUfIhZePu7uM1ooW6CIrWy84YkYgRswYnv6VO8lrYxMY0DHHJbqx/w9qom8dZDLncxyD6VBcyeYGYnqOaHBtlqVj0/4CazPLe6lFLkL5kE8Y6ADLRNj/v4v5V6rrDbNdunXpc2azj3KNz+hr5/+FevSw+I7bSSkaLJb3EUTDglipdQf+BKK95v5457vRbo/wCrlZ4GI/uupx/IV5OL9ycl6M2h7yMfxVax3qaTZXUrxx3ZlUyIBuGVJ4zXFz6Da6H5lsLlpo03eWzAKR8x4wK73xNDea3rXhvSdG8hr8CSeUuT5dvFtAMr4/hBPTueK4vx1p2m2dzeaZpuuXurX8Q+03V3MAELHClY1X7qDggeh6mvPnQl7P2l7L/g7no4TEOM+TdHJhd1038WTwfas3UprqUzQW9m7xDhpDhR+ZqS21NoeLlQuOG9qtXeo+HnsS890XDDmMN/MVmoyjL4bnp+3hOPxW/Ap+DDcaZdzanZWdldOI2SaKC8EkjL3zGeo9cV6/L8ObX4i6bFLPe3FrPZ25FrEqKQxbDEEnBzwB198GvL/AGteBdO1F3nthGTk+eHIKDH+eK9F+H+s3I1CHXYWlFpK4yPNIEat9wbTxnGCT9a9PDxk8QpSTVlv6nmYqrD2PLBrV7LyPBdb0y90m6lgmVj5crQyKfvxSAn5HA6NxkdiOR7d14dt/h/b+FrK51GG2uNVexnnmSW+dSJkmASIop43KSfcCuq+NHg86N8RpdaVWl0zWY1kukdidhPDNu5IAIBBHSuIvvBsumzfaIdP/tS13FBLbOX+Yeqk5/HGK9pt1IpNtHjxaTPWtSvvAdpYGXSktJ2iG+EOHZuCChQkHIGDwSPfNW08X6Ml3HdW0S3cT6kZ52NmqN5ZA2jJ6kNznviuGtvDOtzWlm7+E76KPyz/rV8sj0zuxgc0600rW7Aqs0ul2CY+bz9SgBB+gfP6Vzql1/M1cj0/UPFUN3cRG1DOYtuZZI1VnP97AyB2FYjaXp11fzXxsJTdXEjM7ZI2seSV96wdH1yytnZNX8TaGMABTBJJIxGc4O1CK6BfGfhVHymqy3HoY7Odz05/hFQ246BvqS26/ZnK2tisbMu15GA3tgn7x4qnpeqT6fezRNHbxq8m+ZkwGkI6Z5561JdeJfC9380cHiKZgMAx6ZsB9zvkFZKDw7dXZd9H8UXDscnM9pD+eWaslKN/eaKs7bHn37Sfi6XVdS0zR422wWytM4B4LNwOnsP1rA8PeFrqXwwfEOoowtuBbwHgzMThc+xP6VhfEmaO+8b37WsEsUAlKxxyyiRlVeMFgACeOwr2D4safqkenaQdPgebSookkW3hfaSdow3vXPjKzp8lOLtzX1/rudODoqblOSvbp/XY5rQ/D0dtAJZ40kkzwSP5DsK3UtvtETwGJdrLgnHSub8IPrV7qBE0dxBaZxsnO4j6elR3uvahZa48ExuooFYhBbxbi+D64rllh3Uk1zpvc9WFanTpL3GlscnqMt54e8XsbeQoySCRCP0NfSfhXxdFqvh+1u0UCRowJF/utjmvmv4hmY+JEmlWSN3iVismNw+td38GtWleafShy68oPVeo/LLD8RXViablRjPqkeMrKrKPS57dDrDs+VVjS6DMEsRF3heSP8ABXYD9AKxvInkucITGpHC+lXdKJjlu4mPzJcZP4orfzJripSb3NKsEkY+uXf9mfGrwZqrHasu+2dunG9T/Imup+Memt/wjvim0RTvgga7Qf8AXC4Rz/47MfyrgfjK/lQ6BqUZw1rqIXI7b1IH6gV7L4gSLXNRsxgGLXdKeMn1M9rKP/Q0irvw7u1f+t0YSWjR8sWXli4kvWhRguRkg9z39a1pZbqzsftt/YrFEGARWTqe2Ce9ReDotTksLx7fUFtILZBJOWbGPl5PT2Nc3rOs3+rlFurmV4Yydiu5bHv9a9JQuYXSRFq17Nqd61zMVQZAVc8KB2qqwTk7kH0zTWZQD8wz2qMyjPB6VqtiB5CdC2fotOby9p+9z6YFMZtsXmMevQevvUIulBOO9F0AukXY03xPpuoIMfZ7uNzk9gwz+lfRAM6eG/ME6ldNlX5dvOY5Cuc56bR0xXzRqEisdwPvX0P4GvG1HSJ0lO5bq0SQ/VoUz+u415WYw95P1RvRe5ueJNngvQZtCtT5mr6hB5upX4485+gUHsichV/GvKPE+oyaN4h0a4lt2is543inyu3csnG7HfBXr7VzWk694llSOXxD/aN1EZN4kmf58AdMHkgk9fQdKd4g1VdW/wBDkuJZY35GcEr3Xd34AFXVoud1LVfoVTq8jTiaXiqwHzC12kdFYelcBqOlXEMv7x0LN0ArstAv5pbb7JdgkqMKSP0qe0sbeSSW7u1Jii5PB5PYcV51CtPDNxev6npVaNOuudaHC6ZoolvAbuQR2qZM0jHAUDv/AErs7HxJqFjZPZ6be29pbSY/f3R2KwAwpVWPOOecH6VzXiDUkF+bDSVR33Ydxhl3/wCz2OMkAnjv71Bp6zQt9qisxqFwz4+1ToXj3eij+L6n8q9qN5JSZ48rJtI9O0LV/EWrzW11qHiA6np9opR5pInMKIeSDLLtP4KDW7oni3Sb3UfsWnLI6RsWecAKnBHQZz/Lp0FeU3Fj4k1C3hu9aS/NiSTG0yGODAxnYoAXjIzjpkV1Nlq+m6Vp76RFYWqvNiSO7gZvOGMcLHyD0I54INaqbTMmjvfFer6f/Zn76whvrlm2W8MigqZD0JPXHc+1UrfQ7U6WFkhtPtIOWk+zpGp9q53T76O512J7nfGIAERHT5/Mbr8ueuPfitHXvElnBMtoitK7DcsSEZwP43PYfzpTpwqr3kVGTjsai6BKgARoVJ5G3A4qyuiXSf624jAzxhqwbG5ubbRd01zN5jkPtL8qD0GOwxzj3q/HfySodsszYAP368OtCrTk43/A9CnNS2Rsy6FJHA7C7XpnjNWrnSLPTNCn1Ka+IZYyQMHrj3rNSZ51IBbkd2JrF+I2pC38P/ZGYBpAGY99oHP59KxhKpOSimaSVk2zx61s31fxK+26igmJLwiXgStniPPYtyBnjOB3r1U/EbQXsLKwvrq6he1tkglSS3YYZBtI4z6V5Cv2cyzzzOCSdqr+HNXNaP8AaOjW+sOu65E7WlxJ3l2qrIx/2sEgnvtB65r2sRgqeIjHm6djlw2MqYaTcLa9z17wl4r8IXd9LBcarBbebxbBshSRnO4nG329aSLxL4VttUun/tK0eNXzEwcYPr+ua8HAHcdvSpYIQXUyEKueBWCyymru7u/Q7VnFS2sV+Jt/EfUk1fxa95BgwFAsbDo2M5IrS+FWpJpfj2yklYBJ1MOT0yen6gD8a5rUj5mn2kkQGxZ54unQjYR+hH61JBDNNPbG2VjIHAXA6GuqVCKo+z6Wsec6rlV9o973PrLcDcxsDwfT0qAnytZuVJ+/FFIP/Hgf5CofCJuZtEtnvkPmIq89CQQDzU2uFF1u1eLAD2zqcHPKsuP0Jrw4QaTPQq/Ccx8Y4mm8A3c8a7ntJIrnA9EcZ/Qmu68D62NR8FeCtT3ZNoqw5/643ER/9AV6x7mOG6tZba4QSQTI0ciH+JSMEflWZ8OrGTR/CF54defz/wCy9TdYnB5MU8RCH2OZBn3FdVF6WOVayPN/ESTaTc+M9GjUq8Mzowx1VJyD+lefLdSyvtiR3OOigk4r6Y8XaTBf+JLnWEtXMlyzbZoxlnR8PjaeHxu7HI9Kj+GXg7whoWpa1e+LrC8v1vHj+wR2Vg+YRk7gwIwM5X1Fd8cWnHz89DL2WurPnK3sNYunhEWmXjibJjPlMA4xk4PeraaHrLoXXSyAsnlnc6g7s46E5696+yte0/wT5sf9gaRFZQRR7WWSwRcOD1DNj6H6VlS3Wlx3UObm1jgRGWSFLiNNxIwCNpJGDzTc6r7FqFFbyZ8tweCvFl2NsdmvPcEv/wCgg1u2Hw41lvL83QVeTgHPnfOfYYWvo1dQsWe1ey0xZ5LfG1gs1x5ny4O8BSGOeR6dKzrqKXV9RSfTvD1yfs0kiF7a1kjET7slDuZeRnv0FZSda26Naf1Xm9+9vLX/ACPAB8J9Ul1GRbuX7KAS8kaRriFffLkgfXk16lZ2cGh+Gbm9QFI4bYJCGGCRtVIx9SBu/wCBCus1K1+xwtd+IJbXS7UYYi6eNQcdMRITvPpuP4GuOAg8danNdwibT/COnMG1DU5B89ywyQi5+9I2TtXtnccACsqlOpO3Pr2RnKdPmfs1ZeZ85+M9JfRtU8uWVrmY/MZWyc/j3NZNrdTJNliSw+638QH17j2NbSSrqmnLFKS2BlcnlWrnZ43jkIYbWU4I9K9No5Edx4S1SKTUUtpihjmHGR3+tb/xG1T+w/C62VoQlxqJKkjqsY+8fxyB+def+C5I18Q2ccwVo3mXr0zmut+Of2O9+I6aZpUccdva2kUeFJIDkbnOT9f0ry6uDUsTGXTd/LY76eMcMPKHXp+pwFgAqTsB8/kNt9ugP6Zrr5vHbadYQ6Npdja3NnHEgZ5lb5nAGWTBBXnvWRaWUKpqd0jEwwQbFPYlvlA+uf5Vlx2TkRSNwmfmPoK9Q887vwnqs2sR3H9r39z+4UtBAJGKvxzkk5AAA4GM/hXU+FodBkgTUPJhtbtI2WeNUwGA6OPT3FeURyRQalayW7t5fm7STwSDgH+ddlrF2LDw1d7WwzoIgR/tHB/TNS1YZknxCrXt/fJvN3dSt5RPCxxn075wAPpXQ+B7XT542vb6f5CS88s3Abb/AAjPUe3fiuK0nT2nJllztA9OT7AelbE8wMUaS5McX+rhB+UH146k0XSEdy18t1LcyO6x28w228eNzuw56Dtjqe1XNLlthaPIziMKoJDHr/jXOeG7QaXE+o6gpkuJgI0iHVEY8/QkVzXiPVpItd8mfctrCDCVXrkZyT+NZV6CrKzNaVT2buejweLNLiU28JaS5PRSMD8TXDeJ7y51e8f5mnDHaWUEjj0x2FYkM6XyOygcfeBPUd6f/pb2EhsL4BIzloAcbQSct/L86ilg4U5cy3LnXlNWZiXsZjLqY2Ac5x6MOtW7IM/hTU4snEFzbzpz6h0b+a/lVoS3l5BtuTa7gRgsDk+/FbOj6HpU/hm9tLbVGOs3gVY7aQBEZlcMgRs8lgGHOMNtHfNdZgcOJZBwFwfXFO3Mfc9qW4WW3neGeOWKWNisiOuGUjqCD0NdP8N/Ctr4pmu/tGopbeRtxHvw7ghjwMEnoBwOrDJpOSirsErmVGBD4au3lAxc30Qtx7ojF2H0DoPx9q0fDM17axPNYS7J4sPnAPHfrTPFtvDp+tQaLHMs8OlqYGlHAeQuWc/gWC/8Apul3cdn5gJ+aTIUj/PY805IEzu9J+JHiSzhEk0tveRZAKSxBSO3VcV6Po2u6f4k0jTdVscpIty8FzExyY3MbHH0OBg14GGkGm3PnHa+e/pmtn4X+I7fRtTlgvp2jtbmaM5VSf3itwfxBYfjXJicNGULxWpvTrSWknoe7EkHmqekTfZvFGtQD/lvp9veKPVoZGJ/RVq5qMU1lci3uoXhleFZlV1xuRujD2rIRtvjvRiPu3dvcWb5/wBoKw/ka8uKlGVmaxaumXtQvfiBZau3/CJWthqtjHCkd1pl06ZdlJQMquRnhMcHPHSrtt4o8eSYjPwPuTMPSORY/wDD9axNe8TQaFPBdXH2UR3UKDM8xjG/G4jKqxJyx4xVe7+JNnFCoe50uEMOABdS5/8AISD9a7YVZLS35CnSbba2R1cV38TPPiuovhZ4U0po1dU+1XUKLhiCSytKATx1IyMn1qabXfi2owdb8AaIvpFPExH/AHwj15TrHxS06EBjdsR/0w0nP6vOP5ViSfE+3vDstrXXrthxiGO3izwT/ckPQH8jXRGpPfl/L/IwcUet32s+M3JXVPjTYQDuthYTyH8MIg/WsK//ALOuSPt3xS8bX5zllttPWJW/F5j/ACrzNvHM94Ga28JazcgAktJfOQMDJz5USdBgmmtrXiya1S5tfh8q28mzy5ZReSKwcgJgtLg5JGPrVe1dtV97/wCCTY9IsNN8AQz/AGq407xDr86nKjVtQWOH8ViUMf8AvqtbWNfm1qKG18yzt7O0XFvY2arHDbg/3UX19Tye5ry+TS/iY84itfDvhuPekTK6W1q4PmYEYzJuPzMSo7EqwGcVgaN4o8S2njiLR9WvoQiXJtriG3iiSMsMrj92oBw38qynJzi+RpadClpucno96LW5USNtjf8AiP8ACfWtXxJpryWw1OMKdwzIFHGOxrmvvQH2NbPhrU3+zzaZO5aMoWiB7EdR9CM12mZkW8rRSLIjYZGBU+4rp7ZJtV1G91nUSqROAGlkzt6cn3+neuatIBJcurEiOPLSEdQB6e56fjXRXWnapIltc3EbLaxBdkI+7ED0z7+p71PmBHf3n2qFLO1QxWUZDBSAGkYDG5sd/Qdsn1NV9STZa2zrgg5Y4P5fyqxdWk0l4LK3Ta8rc8fcX1qvrwS3vkaFf3ZQLjswHH8qoRRnYCOJx2lz/KvQmudMmhvbS8G4SQhYyCMq5IIb8Mc+1eaXkg3rsBAAzgmui0XRNe1KyOorbXCWT8NdtGxTA7Agc1E7dRmvfeRaj7BaXMaR5AMgYSSyn/ZVM4H1NdDpGk2FjArtBJ/aA5czsGMfpwOFP5ketcsJF0TY2nIrSMMG7fl1PfaOi/Xk+9JZeITaTDzQ0qE/vOeTnqfrQhHTR6hcpqSm1txc4b7sgzu9643xsR/wlGo20yeWZHEgGfullBI/MmvRfDWueF7iSOOGYxzE4USjGTXn3xaiKeM53XjdEjfpj+lXsM52wmls73Yc+49a23iZ4nNs4BdeQe9c9I/mIHyNw4PvWrp119wFsY70IAsRPE21xnB9KnuQAvHH0qxMNy748Huas6f4Y8S6vpE2r6dol9d2MJYSTxRllXaMt+Q6021Fai3Ot+HMGneLoL238SWaS3VrHGtpqUiF2lbJCWrZIBd+Qrn7u3n5RxS1a31PTrOPUtJtbrRtNuHkgG6yW3mjlTAeN5FUMT8w5yM56ZBrK1PxT4l0/wAMeH9GstZvbdpY5Lx1imMaqHbZGvGAAFj3f9tDWJd+MPFyRyWk/iHUZImzvja5MiuGGCDycgjtVe0ul3Dldrk82nsQmMc9SP8APtVsW1klt5EqKR94HuDVrxX4X8ReF9HsNUuJbbUdIvokkhv7JzJErMoby3yAUcehGDg4JrAike4G9nHPY1MZxmrx1HKEoO0lY0Lye3ktZtq4ZQPlJ6isK8EIdVDkqDlip6Gp9WwqiVTywKsPpWcpBix702I+3NF16x+Jf7POjaxEP+J74ZiWG7yPmeJQqOc9xjy3+oNebatcC2uNLvcAm31GI7u4DZQ/+hCof2KNUL63qWhXALWMyFblfWKRShz+f54q3430ufT21XRbhSbixmMZ92jcYP44B/GuPG04tRmvRm1K+xyPxXlEmgXJDAPaXRKnr5YLMoJ9un51yuq65CNd0/UrIPqCQJaZt44cgmIKDnAweAVz3yPeusvbgp4pWRsFHeOQg9DkJnj6k12cl66DanygdMDArgnLltpc76eJlGLh6/j/AF/w+lvNdS8QXeorC1p8PbueaGRXF1c2y75ccZf931xx1xjBIJ5pJtZ8cTQQpa+D7OwSKeK5HIRd8fT5cgYPzZXGMux4rv7vUHPBcmsq8nyeWzVwqJ/ZX4nHJeZzc/iL4nXMUkTf2TZrI6SEgZJZSGHc9x0/DpxWM6+OZ7GKxm8Q28UEQAVYowDwoXJIUEtgKM5z8q+grqLiTLkhqpyZJPf6Ct427L7jNmSbTxZdsPtnjfUgBwFiZlABGMABgAMdqgtfBVnHfpfTaneTzrKJSzYBZs5yTyetbijB6VMrHuKJSa2Gl3PFoyfLb6c0WTFLtHU4IOf0ojH7mQ+4H86LXjzD3213GR2Pw00yPUtSVZUDx+YZpQRwQvCD8WJP4V3finxunhXR7/w5ZSCaTUV23NnhWjUcYLkglTwOFIP0rD+FkaJ4Uu51UCXON30ya82DtNcNNKxeR5NzMepJzzRdbCtqdDHdSpbTeWheaVyJpQMnb6D0FV9fjD2cUq9Y/lP0punSOtxMFYgYpdUYizGD61fQRk6NAl7r1jayAbJbiNGB6YLAGvvLwnrvhK+0K38P+EtasLxre3CeQvGQBzhSBkfTIr4P8NAN4k09WGQblAR/wKuysrifTZ7a80+aS1uIWBikiYqyEHsazUFN2ZV7HuPxJ+Cr6ssmpeHbWK3uWO6axX5Y2PqnZT7dK8tHwy8Ti6S0uPDmoxuxIANqxz9CBivq34Ja/q3iDwH9v1i7+13MZCrI0ag9O+AMn3NXPEV3ci42CZ9uM4zRKi6btcFrqfLVz8O9V0Sz88+BNVvSBkyGMsB+CnNea+PJnuNaiknjaOU26qyMCCpBPGDzX26tzPHJCUldd2M4PWvmj9r5ET4k6dIiKrvpilyFALESuMn1qU3deY7HibcMR606GVkxg44p9yBwcc1W/iNWI0ra+kiBX7wPrXV+H/iP4l0jRv8AhH9MYfZ5SyxovG5nPf15PtnODmuFyeOe9DMQNwJBByCO1D1GtD3bxboSeGrDR5JPBkerXR06GO5lM6yyCRECsmwEgAY/h7cnnNcifE2g3dzFFdeDoI0Aw0bwYB+hGKt3d/ep4UhdbubctxGVO8kgmAE4/EA1uaLdT3WmWdzcOJJXZlZyoy2AOvqffrXz03ZOcld+rX6s+lp3TVOD084xf36HbfC/UPBVzbLpwvdR0UyJ5Bs7kC5s50P8G1xkD6k4ryLxHoui+D/Emr6Zei5u/sV08aqqFgEzleFZeNpHJfn+7XqsOn2U2mmeS2jaSORSjY5Brz/41syfGe5kU4aewtXlP95vKUZP5VeVVeatJd/zMc4j+7jfdaGDHd+EtVkFqtna2kjYEa36SxIx7DzUlbYT6spX1NZOueGmhurq1s4bu21CzybnS7vBnRQMl42UYlTHOQAcc4I5qr4igh/tQKI1AYDIHFdl8SLm4Pwn+FniDznGqpb3tut2DiTy4LjEK5/2QSB3xxXvuG9j59Mtfso64NK+MWm2c0gS31qOTTXOcAM4zEfwkVPzr3/476cf7bs9ejGF1ez2T+1xENj5+o2GvlPXj/ZvxPgl0/Fq6XdrcJ5Q2hJGWNyQBwPmJOBwO1fbXxxjRvBmoMVGbfxAnlH+5vjbdj64FTL95Ra8v+CVH3ZHyv4gn8wWV4uRmNM891LKf6V2hkDRLID95QR+IrgNU/5BMI9J5wPoJVx/OuxsXY6Xbkn/AJYr/KvKrRtFfM6luOl2kk8/SqV0V4IAyKsMxx1qjKSTzUUyZEExFV2Y881LJ1P1qtJx0roizNi7ueaUP82KjNMmJUgg4ODRK4I//9k="}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-6793-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: During a winter sport known for its high speeds and chilly conditions, what kind of equipment would you find securing the top of the participant’s head? answer: In a high-speed winter sport, a participant’s head would likely be secured with a helmet for protection from impacts."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-9581-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What detail on the basket aids in preventing the tennis balls from rolling out when being carried across the court? answer: The basket's rim provides a barrier to keep the balls contained, as seen with basket_1's rim."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-10764-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Based on the posture of the dog, how can one deduce which side of the dog is likely sensing the wind while in motion? answer: The orientation of dog_1's ear that faces upwards and towards the front indicates the side of the head that is likely exposed to the wind. Given that the ear is catching the wind, it suggests that this side of the dog is oriented in the direction of motion."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCAEAAQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDxOd9l5Mr7mdHZSVOcYODz0Iq1bwtctcNJPFFHDEHy5IY8jCjjrz9KyVdin3flAwSOc+xrQge5u02yvKYgOGbLbR0Ge+O1dFjjlE3NI0X/AISKa4t9LL3FzBA88wG2MBVyWkLMcBQMZA5yeBS2vgHVryDTLqwRJodSj8yFNx3qA20lhjpnnI47dayNGv7/AESQ31pFEyzo0LGaEsqg8EE9MkZGPTPFbXgXxbq/h8vc2H+keUu4ROWCRtkkMApGQME7enAznAos+gttjKkZ7fTYYLm4n2wTMsEf8IDfeOCeOQBkDnnPQUzSmBnuLdILiSSaIrAiANtbIznjoFDcgZzitXxNfXHiK6jvL+wtdLmBCJ5Nv5MLhsEEk9zydxJGAa56y843qzWolYr83yH5uBzUh6no76qkkdtDJp8aJBbKtqYiyFHz97JHJYEZHAyDirIGpXEMFpIrQ2xlbc4YbkyRkqfzzisjwzrOjQ3RvbvVZ5pbdVFugg/dlgc7gCRg446HlufWugtvFc97dJqd+Fa0WQslquFJyST5bFeAM9Cc1zzvcymupr6JpMM9/DYRs8Uc48o3Fy5KzEkhX3ngc8Z7V6P8O/C8ui6vNuvHsLuCVkSaVfkkHAA6Yw2ehPPBHSuY+G1ha6y0Zsb24hulieZIy4KqwIO3HOOct17+3PuuhRPLYR3N7aFLiSNVkDNu3hfuse2eM1MZO1jShSi3zCzgweJ7LzmDG5tJIgwHRkIc/mM/lV69sZpYw1tII5kO6M4yCfQjuDXM/EHV5NFOlaklu03lXJBAUnhlK9QDjqO1JaeIf7X0h5Eb7PdxMJBCy43hRuPXoCM9cVWu51c0btGj4cRLrw3Ek+396sgmX0yzBh+ZIrzqPxjY6f4YuvDwuftV8WuInc8EszkA+nQiuev/ABL4i0v+3bbS0gnKzuvlRYfcrgElck8AsOhzk+1eWwarM9yP9Dnjcyb3AB5bPfv1H6Vqk9TirYhq3Kj6ql1e5KQskUUq4G4K+AgA5bdwOxxzXMRa3Y6hdz3zODPOUhtvOBHkQseZGJ4AfrkHptHeuOGvz3ump4YtBcQrLAst9LF8xWHHzhTj5RjAHYEnJ7Vpz+HNLXwLda1At1BJCqSWWM7lUFSGbjBznp0AA6GpTXU25nPY6XxN4k0fQ47aCJ1eCMH7QXtgxfkE5Y9Cck1wHiKXTvEWpahf2BZxBB9pW6iTyiqhgCp9SQR1HPpXfeLNKsIvDl0kOmz2k/DZknyZSf4jksM+pPIryua/1CwmfUVv7j7NIBFMjWw8tsHgEhSPxH9KpWMK8ne0tilqimy05X1HU7xLm4IaWyMOxirZAO853E8dhxnFdb8PLDSbZbubV9N1GRLaUyNJIvkGHy8ZJUckZ7H0PeuG8FXl3Zajei0t7PUo7iL54JXbaoJ6jCkDjjn1711/i42+kPJHqlzrA+2xL9pt0wAzEElYpW425J7cA4FKSujKnyx96xR8R3Nz4q06bxNrEsttBDeAQqsRWEQhcA8fNk/KP61wFlrF5aXz3ENyyEgjz1ALbG4JUY4b0PWi71FbWWWCNZkZVKyhpWOD3Byf59ar65rEreF9Piiu2wjOiwrGUVxkHJOcNyPTinCD6kN878xLj7HdXWyK6dbB33ebduAwJXnJA5Oc8AVnaVv+0TRoXkYxsseE3EnscZ4rPurm5v5ZPkKu4XciZwSOBn+VNtruW3WaONgrSRmNjk5A3A9iOePfvW6SSGoHoC6pYppWkPbQsLiFPLnWZepLEnow4PB6fjXoHwz1S40iH7bJq1q2mSljIrn5yyrkqoPT88/XpXz3Dcss53yuvqQT+damnajAJo4JropBJxLIsRYqp6nA6mlYlxlGV0fSOlfE+zuowtxDHFI023dv+REzyzc5GBj65r0BXV0DowZWGQQeCK+ZdItUvtXjttGH2vDxqk3KiRSOrdlbJ+6cHgnpXvOreJtK0PQxPc3tmXijC+Wtwg3EDoMmplHsb0aknfnOgJFNryjw/wDFWe+1VLW5tLaG287Y9w0v3Vz1wM7iAMcdzXdXvirSLW+Fo9yhcKHb5wNq46kZyMd8+tDi1uaRrQkjbJpCfSq5v7IxJKLqExyHCOHG0nGevToKfDNDNGskMqSowyrIwII9sUi7pnwdY2l5cQR+UDLAJNzRbtoJAz34yQDjnPH0r1/wN4G0zxhN/alvpEmh2VvGIpLC5kkY3pC5Zo3Y5yMHgZ6CuDutMTRfFi2+hapa6nmFUt2WNiszyDG3bjGcNnB4+tel+APDWp6ZY3Ok67fS6XeWM0N1BZopdgzEeY3y7jjYcHHox7Ypt6Dd2eS+KNSP9py22mRXNjp1uxEdqZjIqkEjdyB1PPIyM47Vm2l1N9m+zA/ud24kLzx3r6Gl+E+lavDqUenoZ7mS5V4L5WdVaHcpIOQFZiAxySRkgD38cudIn0G61bTvNga5sLua2mTyQzbEcYfcRxzj0oUkD0jqUNNtjqGk3Ns3mpGx3RnzCAWXJAC9D1I9uelVNPtZjO8iTbfK43OmVOOxHrXYNLYaj4f1S6vp/MvogskcaRKkeSRmTcMEEE42gc59uee0ZbRnCPeiFWBZzJEWUsOmQPfFZxcncy5nYiNgt3cKFuVjnKmRdwUIzensK19KaSQTwSXMnl27483YPLJwOmMjGfQ9+1WLaSU6gkKw2s0czZQvAuJCOvy9eR+WRVmN41hZEkUpENyRjaWKgkjAPB4JrCpdrUhybVmbnhy6v7S5EtmZUmMpjt5Y9wjaQMDkNnBwcZzwO9fQ3w81S/8AFekpdX5vIfJ4iuo5AvmdmHAwTkHnmvAvCWq6Nb6G1xdyIt9G4lhtkfATfxuAOQeCWOCMYAIPNfSek6poVhoUS2l3BHZW0KgFfu427uMdeOeKrksrGtD3XvoZXxP1fTj4a1TTLyO7hkih3Q3DxnZ5i4K4b34Gfc15xoOuWN8klvHfq8Qj3pHMQJJD3UqSORk8jr2xXTeINe8D+INRuQL2W5uYo2TZ9oKJLx0TIIzz2HP41wfh3wPY6trt7pCvNpUjx/abOOT5nKZw0Zb1Ug/r6VairahUm5SvHUpzWOmajrOo2dpcXNm0xElvcTS7FjXBJUq3JXd0ORxzXE2sV/8AbLqTT8tHbrJJI5bI8teNx9jwfx4zXf8AxQ006F4tttNge81Vhp6mNAn7zzBu2K5H3l278hcHB/PzbTdQS51O6vJld5dpmWGNCUkl3DaGA4CjrzjoAK1UdLmModGaBv54kgu7hYUmmkPnQqCCV4xlcYwfXuSe9blz4i8QSaFPZwX8g0yR47aa1QgJlssAFxlQNnPufeuZxqFzbm5ujPJeSE+UnlndtByz5xjbnI68UyLU1NtGkszzQ+YstxHGeG25x0BxwTzUcupmm0z2vwxPrYf7Lqmu22oWkyBIRbym5YMqnjaPvDAIOQen0rzvx1qOpafruqadPqFzcWskmHMWYR6gCPpjpxisrwz4v1Cz1mK9hTUjBb+YtsI3DCNSCGxleQAxIB6ZzxXZxeGtBTRbfUdYfUf7Q1dGayne9il3AsAqn5lIcjIJ6DPtTa5dzVxdSNkQfC3UGjtdO0qwgjGoalqA3XDjbsSMFsc8HPpwak+INzcN4nTV9fuLG5FovlC1uZNpPy5IURA/LluDnPByOa4vxEviDwmLHRdZtglhNdpdxOAGlwvBKuOV49DUHiPVoNS0qFbW+uLlYZJmjjuJWkmVcglm/hUEHqP7pz1p26kO6jy2MvUI7aWd7mKWUCRiWQvngn14qneqixw3LzJHEPlVU557/TrVea6ZV8xhFtLEkJ246f1qA3QmidXZDE3OGPK/ShJomMZJ3F1aF7eRh5bRs22RByAUIyDzzyCCOarxTsVVDtjy3VVBbB681c8Qazdapc/ablgztGsSgIFUKvAAA4GPasXzJPMD5BA9RWu5vFaGlBLGv8eGUYzjP55q1CltPKEZyXZjhsdSazYFjmiZptyuCMkDgj/GrMmERY40OIzk7R95cZ5osQ0e9fBPxdoOi6RN4fu/t0t5czY8vMYiXg427iME8dTya4Tx1YhvFd7p2lWU94JJG2xiyAcfN1wvTnIznnHauPsr2TzYpFYlkGMjgge/vWrDf6jdO0NvPMnm8FfM3b1+n4n9ahXizOUtLS6E+laje6DDqFjBus2mBQrJFtlAyDng8H8+M+tU7fWZku0ZFaaVZg+zbyw75PfPoeKztSYWryW72fk3IckhR29PpWRJc3DyHysLznCr932HtWqu9RqHNqeqaz4qil+z6Sl/vE8aPJJvKRwyH73G0DGOOMj37DTTxzcw2VklpMkX2L9zCkUwUzyZ3ZYMR8hz1xx6mvFI5ZRcGUnfgcBuh+oq9aYWaF5SJzgb1YH5Tn7uD14/nRsDpW1uTeC7qG1uWtr9TbW1zJF5l4EYvAFcEtGwPyt1+YAmvs/TdS8Pz6x5dtr0V3cW9ssTx+cr5x0Zj0LY9PWvizRNW1HS7OW3glDW8w+eOb54wfXaeM9a9H8K3/g0R31zNJDDNb2qmKN7f5Lklfn+TJCspyeGA4HXFc81c6+ezPevGXi3RPD0VnC0jraTeYQ1qy7UIBYk4IJBOBgEcsM18veK9W0vVdK1rUXsEhur3WpbrT7hXJYpwJI2BOe6MCR/erc8f+M470SW+k29qdMliCrFtbMQwuVUZ4+YE5ryufJl3LgZPXNOEbIFPnNHT7hkiZZYhcIPm2ZAyf61XSSQ3RaJREBkqC2Nvtk1FbRsyPJFJgAcjtzxSSZjhI3Bjnk5zmrsFkP8yS4u42Z3G7AyAWIx7V1fh4xGJp1gWW1jjbdvHzAAE44BIJ7Z4z3riZJkO3Y3r0Fa1hqsn2sOtzcRSDLs/wB75s5zgD2H41M1dCnC6N+K/wBPeSW2XT2hmLFonzuB56HPbFev/BvxPbvDd6fqN+SHMZto3QMsYU5ds4IHAAz3JHTrXhMuqoZI2imnjdoyJJHAJx/COB6cY5xUserxQW6pETG+RntmhR0MORxd0j6x8RfD/wAP6xpIutJtlhuRHiGSJEXdk554AznqTk8VkRaHqHh/xP4Y8o7LebzrTMgCyDK7ypIHPzICvHqc81U+HviNtC+FumXIgu2uLqRyGuGyoRV3F88lUxxz71p6/fWvxB8DXer6ZeXdpLpkbXMAib/lqilgxHUYwcHg81nr1N7ReqWp538QNbvYfE0l498EuLbVpl3yR4xst1VTx1z8wwOAa8us754tMfToU8kXLjzXDkB1B6e3rmuq8Ya5p+p+BrOGWWA3li8Twus2+RzKC8zOcfeLHJ9DxXFQXSzW8MRijhZdz+cp/evkcLx2/wDr1stjKSua+hSw3euQLqFy1jaSMFkl3FdqA87W59xzketdubGPXdeurfQIoZ2iimcTWwUREKRGgIwN4KgduS2fevP9L1Ke1eCRr2aOOMbIhG2GjxnABIOM5/xre8H+JLjQb+ZLULcmdfKZy7LkF8k/Ltz360tiU0jqrnTrrU7HTL/RL6a0tL23Iv8ATlJEYVG+YKp7+o+9xxR8PY/C17I2k6sLYywK8y3AuZM+SeqDnA+bHy4B5PUcnm9K0vU9R1VdPaX7GrzMmJn2BW6fdLDJHGcc100Xhjw9ZteadrEjW+ospWTUos7AQDsHlMBgEAFiRj6HBKuhxu3exxPxQ1u71Lxhcy6nDCoij8i1S1IMaqB8hU9xzn8awCuqWWmvfwJbvBLGLe48tQWi3fwsCOCdudw/PtVjXb6CXXJJrGwgtYtLGQ0MmfMfd94k9TnOAOgp6SHVNVK6Ba3z/aCvnwSlJiXbhmzgYG5uDjjPWqSsGq3ObOWZACyqTyMZx61ensLHzp5LTUEKqwCKwwzDnnjIOMetei6n4G0u0juII9SnbUEtd4srV1Myy9Nj8hSv3gceg9cV5JNLKtzPlILVxnMa5Cgjggdefxp7lqLew+9YnYd7bmOTnjPrn8aiimZT8wyemcc1A7u2WY5z1+tND8+metC0NVHQv/a2O0EsoHHXrQbidiFXduPBAPWqKE4PP0NWY7ibfksCAAPu9RTJcLFqK5nVDGFdcnHpg1estUuEVHRhHIMlG6H61nzTSqqlth+XAwc8Z7VEjoXO8nOOx/Sp3M3BS3R08WsWE8k8+r2jXt05VkffsQ4X7pAGcE4zj9Kwpp3OCqIoxn5Gxiql+zOIyjZVRgBTxVaOU7VZhnnnJqkxxpqxZlkXezbtxPBz61Ok8kMaTSlW3g7RuBPXHPpUEeLiRFwqgN94DAH1q/dWUYjgS1hDuQfMbzOPyPShyWw3yrRmP5j7AvmZHpVxZmEahYyvy5LFt2RxWepA+bIyOntUqMCucn25qTZxJ2IDBhIxceh5FPJ81SswB/2hw3/16jgWP/lo5Cd8DmpFkTjYCx680yGSWFlGxbZKWTGCrHaetJJAEZsyBMHgNxTJJXZWUqCCRk461NbXTvtjmTzVGNqvzj6HrQhPm3KsseDvCgZ7gUz5ky+SGzwRxWmbW3nkIhfycceXJ0J+v+NUrm2mtnaOaMjdyD/9em0EZ3BZhIN0pyc81YjO9VOQwXnDDvVJFwwBf9OlW7YK7fOwJ7iqQpJHaeA9buLCaSynunWzuIyjROcxNnBKkYPp19cV6FF4tg8H6dONLmxNfRsWtOGt1DHAYEksOAeOnPSvGbNZEfEJyCCdpPQVKZnMu2R8qPl+ntzUSitzG7T0I44UmUwPvMYdiAW+UHocfgBz7CqcMBQmSIbxngNjA/8Ar1cP+sLJJtXuMYqZopUs0mEbvCeN4T+L0zRe4+ZlGyA84+YTxkDLYAJrofBms3Wha+2o2sVrNHHHjbMocYJGQM9yM/rXX6H8MtZ1WwtRLDYaZK6h5jdOXlwenyAYXjtnPrWFc6dNquu3mgaQsM17ZF0UxRGI3Mad9jdGA9ahvozT2ba5kyM+J9a1LxjFqVvB505ujdQq7Blg9guMADA5zzgZr6M8M2+lW3gBtc1bR7eG6WylW6lnXMki853tzuBI9+1fMthYyIxM0SAxMUYMQG3D1HX6/lXqN9481C9+EOqQXUiQsyiC2kj3s7twSnQ4wATnpilON7EUqqTaZxdp4fv9H+H8fiu31iC3Op3DiC3iXcWGT8xPYKFXGQcEjnmtLwr4U162trrU9djXSvKnjLX8hBlZpZFyWbIzjBPzdGAHqKh+D2uHU7mHS9XuI5baG3jtY/NnEQSAPvaNSSNzMcZOeFXjJAr3vxFd6ZYQ2s8r20cEk6q8DMgSQqjumCTjduUc57n60NtaGqinqfO3xQTU9V1W6vrPxBpFxFZWjRT3Q1RQ0i7mIjCth92GxtGQcHmvJl3LgD14OeldR8TL9rrxRelNOjsFWTBiSVXG4jkgrxz1wOBXJgNxtbH1q0XBaErgluwoZlHHcVEUdVL7sjPNDqVC4bJIyRjBFOxViWM+YdpC5qZAAjZUnPAIPT61TXIJ5watQzHAVlQDPXGCfxpCki9CyBdg2s4Pylhx6c5pLuxnFms8sLqGchJFHB9apxSnJ2yZJGCpNC312oSB53MMZyqOSQM+goI5Xe6LVnaTskNzaMXYMdwIxtI/mDXQjStJuvDEjPfvb6lbMMWf2cBShc87wSWIyeoGARya5dbuVEKxEqGPYYHvUtvMFjYxSGMgHIIDZz160rtEyUie3hWCPYYi7ButX5nKwxrDIrtJne2fuAc8/wCe1Z9st0WE0rB0A+UE8t+H41OTBHAyJlmcbySCFPt9KhvUzktdTnwVOcn8acjhUwGBJPSkSMoSGGfYc1JHbsd7ZHyDP1rQ7G0OR22gsQfTvUygsNwIGR271HArlcEkcY5qSUGPblgcdT059KdzNkzOHiCNgc8H6VAzFXVwx3A00SJkHPHtTyFYYB5z3oC1ibziwwWP4VNDdTKhj3eYp/gcZU1SX5WOB07U8SlcMx9xzVEuJazaSMRtFvIRj5vmTPseoqF4JbcgsMqfuuvKn8ahLBpCwHU9PSrULzWrBwjCN+drchh+NCFaxe0bUJ7WYlcZYYDEDK1bYtHbDf5LbzkE4LDBxVVZreYbTH5RxlSBlD/hRIHjQySoGVuFZDkVLZjLViNJuIGTtB6Y61teCopZNRju/KWWOxZJnRoy68HIBA5wSPyzWDE6k4ZRjtzXSaGfsXhq7u22gXD4iWOfbJhQR93IyCTj8KxqycI6bm+FoqpUUZbHp2vfF7T7fRLe80qzN7qF0P3oZCiRMOvu3OcY/GuMtfi9rtp4hbWU0nTY3ki8mQGD5nHH3pPvH6Zx7VwEX2qCMW8lsCByB6VXuRdzyiJmcykkqoPy7QMmt1CNhtSudr8QdXs9evbbW7CNIheRA3CID+7mX7w59RtOe+a5XUdVvpbaK0+0O0UWdidlz9KqxTmGJVbORksvofWoZp3lk2pwG601ZbGCjqbOi38em6ebxJYzftcIoR4dxSJQDkMeAScDpnArZ8V+OrnXdEh02685zbsrQNwFX5WDZ/vMd2M+3vXH/Y7raThSmcZDZqKRWERDBiB6c0rJlWTZTlfDYH3fTNRbTjPX0qRkAOM/rSOFAAUk0mdCERlUgOMj0zTzcOsEkPyMkuCdyAkYPYnkfhTGB44OKYysRyPxoQxCTtzmnrISAvB9PY1G3pmmigdiZwydRz2NImCQP50itgk856UmTnAoESMpJxngflUsX+oIG3lse/Soon25U8j3qbHygFV2g9AMEZpCZLb3BiXAOD7cZqwJUZxvkfaR1LZqtOilRkqAechhmkaIeQGiILLwRjlqmxnZMgWb1/CljlO70wMGoBzke9SKMHB6VVjaxZ+aRQckdMc9KbOW4VsYHTFKi7SDk1HK4ZuQPTA4oJHLGOGJJHrUxTIKgjPaoIwRx056VbhV5HEaDluNoHJNAnoQxsAfm6deamJjfGAPTr1o1Cyu7WUrcW80eP76EUyJdwCqFUnuTiqJdtyZEjR/3gVeOOM1oxyC4tjCxDqemBjFZSI6krKhyeMkVu+G/D2o6n+8jxDbgEmRmABA64BIzjv6UuW5lNLdlIwzKm2JP3efXrTDvidiGKcd2zk/Suql8ONBDuc6mFA5kWAMn144/WsldCM97CqXsDxvKokLfu3VSeTzwcDPQ1XJIiM09Db0bwReTeAm8W3kTwLNPHb2MSj/AF25tvmEdlzwPUnPQc9BqHhzVrtrLT/B9vNqyLbKsmo3Cp9lRh94QbhjGcjccn09a9I0DTpfFWmR6hq63FjpqgJo+nwymI28C/Ksr46yMOn90dOTmtn4f6hb3HhqGzRFjm0wmxuIgMeW8fy/kQAwPfNU0rao0Taeh5Ha/BrxqIvtct3p00hI3W5mbJHf5tuAa4rxbp1/oPiGfTNQtHtLmOMKgOCGQ87gRwc/496+sjMoAUYYnoCazNctNG1e1+x65pEV3Gpyu7llPqvcfgahu5bk7WufIFxG8koPlCIH+8vPHbFM2wBhIrlHU8gcmvfPEPw18CaaJdb1JtRTT0+by1kIX2XoGB7cda434p+CrPSre01jw7aTJp0UQF0hUs65JIkJPbt7cVkqiUuVi9nLluecn7TK2zDFT0IHOO1VJvMLnaCSB8zAk1vW+p+FZY1W4TUUYDlllUkfgVH86i1A+GJIJHtdVv4gF4iNovzH6hq2SZnFyT2OYmUL83TPBqPYwXd27c1JMS2f7pqIEg9sUM6UKVcLkfzpjE54JHqDUivuPACk1GR83PHrUjE560g9SKdjHenbcjn1pgN7U5MLgtSNw5x0oYEY5pASLs64PJqVW2yY/L6VAoJINTKN+FPX19KTEyQEbwnJU/rQ0UschByAehJ4qKUYfuCDyTTriX5xwWUD5TQTYq7fm6/lUkWQ2COa2o/DN0zYXeGxn5gB/Wp4vDF2DyzHjghf/r1POgdWJjgApgk4z170SRRjDhic/St1vDl8FBjHQc8dab/wjl+Ttxxnn5cA0udE+0XcwQCCRndjmr2kPbR36Nei4WIZyYMFs+ozxVu+0G4hilmZCqouTwRkAVqaPqGg21pHErJHPgq0lzYLIAT3ByeR0HaqTuHOpLQ2dG1vQhazM154nR48+XHDKhRwBxkZ6+oFdLDP4XubCDbFPc3DlC4uoN7AcbhkxADqec9qyEv9ISGH+y9UsXkjA/1sXl8+/GDWtpUt7cKGa1027PUhCnP4gA1enc522ug6bRPCY1izBsLYw3BZNsUmXDbcqcK5DdD8uBk4GRmrer+GkS1ltoLxfJuUWIzOwJ2hgxQKQiqeACuflGRjnNQXt9HFfaWl34ZuLbF1hmtFA83MbgDJyepz1Peul0Gy07SNOjiluNJsbgoPNmWSVpXPPJC7MHtxScuXcte8cfb+Gb60EMWiXIhlScXDzJli20YQbYQ/AyxOTzkccVS8eaBLaX89/HFDDZTyAou8K6swyyiNsPtB3AHHQCuo1/S9B1BSLrxdqsowQqrkqM/7zEmuXfwB4eaTK65d+qlUUH8cmqVVLqLkTN74ReJLvTZV0i/nL2krERM54ic9s9gf51f+IUuq+EPFSeMtNhlFpOFi1OBc4fBwGPoccBuxHvXDar4Vu9MsJ7rS9ea5ESF/JkiAZgOuCCefasPTvHOsW2YnuTJbuNskMg3wyqRyrRnjB9Rg1pGUXqhKMj3uy8U21/paapaSC8sZh2OGRh2Pow7j8elaNjrttqDizlmEiMB5crYBB9D714NazXvhxD4h8OeZLol1gXVpvJNux7E9x/dfv0NVdQ8ZPDdJdabISJCGkjbj8Pr70+VWF719D6SkhSawm06+hWa3kwdpTO1h0YfQ14D4wvNQ8P6v4ktdVjkEeowNBEAWZM/eVwM4z0+ldRp/xb0+18PGeUGa+VcQwHOd/Yt/sg1zmleP4b63lttfigvPMYuTOu4Fic/h7VzVY21tc6qF5aPQ8utLKe+vY7aIDzZZAgLcDcemakh0TU5ry5tILR3ltd3nY6Jt65NetaY/hiVZnXSo9HuHdXikcmWIuuQrMucjqeRiqM8WueGoLqeHRptRhvJGka8V0lG9sdTGM7OMgEDqRTjUi/IqanHZXPLsSQ28cjJGyOPlznJ96YI2dvlR+nYdK2ta8N6/aaPFqmpadexwO22KYx/JyehHVfbI5rs/2ddS0238YR6b4gt4bmy1BDawvJJxBKfmBxngEAjPvVuz2E7pXPLXCgnrn0qNiT3zXX/EiXQpPGGqW+kZktY53SGYLgtgnr6jI69xXKFGAzg+tJocXdajBnqKdnrxTmjZTjHvRjBIOQKQxpJJ560rcn1pzfM+MYzTduXVQepxmgB8YJcA9DTzny885zwKjBYONy/d7VZhwx5BHPNJiZCx3xgnPHT3pAVKhXByO9SSWkg5B4J/KkEMgBDc5HFAro9Si0VFfzJNSlYn5fL8oDnp1Bp6afGV81dSmXnaY/KAz265+lYUWr3O9QSFJA+bGcc8VObuTkINpPODnjvXme+cLn5HQx6LK8AlTVJFLD7rQDB/HNLFooaJs6pMCCTnyh/jWVDqzSKkABBUYOcVYttRAkOWzlj97tScmJ1EuhW8S2DR6LI3237QTgbDCVON2Oea4i9RbKdrea3OdoYGN/X616Bqn2a6t2haTeHGOD90e3+NeaXvnvKzuwkI4z7CuvCybudmGlSlSkmveurdrdSUSWpI5df95f8ACrME4Q4iuQMdCHK/zrKSRscAj8aUEuMYOR6DmuwrlOhl1bVIYofIv7g7JAyYkyAw7/rVibxdrm7M0sExxj97CrEfpWZYrZziBP3e8lVYEYJOai1oQrezLbH90rsqjOeAeKz503ax3zy1wpe1Uk1/wLnSX/iuZ7eB7aLTmkOfMAt2UD053cn8BVVPFd1txJp9k/0aRf8A2auXAlAyPlB9aV3mLZLg/wDAavkRwKKO2tfFYk0+TzLBcxPmNBdEAZxnAIOc/WuLvGjlupXhQRRuxITdnbz0+lMjdgrliDtH61EpJqopISjY9p+HNndWXh6ITQRX9myt5jwDcUVuSrD+Nf8AIrm/E/g+O68VG10xYreK8s2uLMpzG5T76nPI/pXLaN4m1XSIkhsmiGwkhnGeD1UjoR7Go7/xBqWoXAnvruR5I92wDChN3UAD1wK1ck0YKlNS5jGZsDBPTrWv4X0y11fWIra71i30uF/v3E6kqn5f1xWNsOOCOtT2Uj2tylwojcpyA6Bl/I8Gszpa00Op8c+HI/DcdrNZ69a6vaTxq8UkQdGIPRipGADg4wTWVpfirVtMZXsr2WMr2DGqM96ks0ktzGZnkILs0jEt+ZqleCHeGgyFI6GplFPcItne3fxQ1a7sWikjt5C5xJFLGHjkGO4OR1rj74Qxob3TrlFikba0DECWEnnGP4l9GH0OKyuMc07dkc5IHTNKMYxVoot3ludB4F0X+2NSkeSOSWGAZkROp3ZA/CuzuvDekByv9m3SlVU8PgEf1rnvh+ZLO0urmJkXzSF3HOQBnI9Mc/pWnPqEkjeb9/5wOp7fyrirTnz2Rw1qj52kSjQtGE2fKuUcfLtZuvtzSt4OsJcqLLUM5P3RWfeXgRizZKKdoG7vznH/AOuk0/U5hKFWRiXOSPT6VHPUtczU5mkfB2kgbvJuyR3HSoT4b0qFfnjm4OVJX9DVnewiVVm3q3bd071ACwbzGMm0EjaTkcd6j2s31J9rLuQXPhixlcNi4Tc3BVeKfN4QtM7I2uAAMkbSScd8irCOCv8ArwOc43cirAkuUHmAZ38cHNHtZ9wdaXcz18KwzS+XHPOq/ew8eAB9TVs+B5Giys6lVGeCO/41L5zLHsyW3NimW7YG5sbx90Ecmmq0hOqyhLZxyy/6zBVRkKMYJ/mMUxESIINrMBknvweB1qxHMsauEEjSZxt4OOABmmxuhkLSqzE/fLkkkegqLsq2gRKskLEIoVHAVO5B9DTotm+SU7geDsBB4/yKsxRxLDiPYXJDFQPu8dOn40SPGFB3IoB498+lFyWuw+ExFXLIR5b4Ldvp9ao31hpMjj9yEZskmM4z7+n6VdW7MshMiI0SKVAGOT3JHr3/AP1VXVhKgQP8m4gndz/n/Cmm1swV1sY83h+Fwxjuc4IwrR889sism702e0dg9o2B/F1H1r07w/olhLGZtQ1KJHYAxQo43D/eBHX6Vi+KhoOiOIrmTUdRllziJJUjA6csdpP6V20nPqdNOVTqcCYo9u54nA9RnFPsLFbuZo1kVMKWy7gDge9XFthcW6mFkyedhYZGPbqazrzCzlIjuYL83zdDmt2ro7KNSKmnNXXbYttbRxmFHmT98gZWD/d5xz6VJe6Slusshu4m8t1UqJBzkZ49axRuzkuB2zmp44oSPnvYE+quf5LU2l3OlVaCUv3e+2r00/HXUdIUwEXkHk0+1+zeeDcRzSxDqsbhGP4kH+VVWdQSN27HAYDr+dT20NzMcQwyuT02oTWl0cbRJfPZu6mztpoEA+YSTCQsfXIVcVXkwCSHznBrSTRNYdcjT7rHvHj+dVr/AE69tU33Fq8a5xuYcZ9KLgmUNxX/APX1pHkzwOlI6kc03aTgKCSTgY70tigBwfWpAVETbh16fWotpXIYEEHkHtSk/JjrRcZLp7rHeRtKqlM4bIyMVvaPokup67bWdtab/tUg8sL90DoMk8AZxk1zinjFdJaE3VnbQWtvLLPbsP3achlbO7j04Wi10ZzudM+l3WmXUumahbpDNasQ6RuGXP8AwHt9Kc0MD4hdUGBu3DK80lu6i0nvoYnijjxHPEwz5DN90Z64ODg/gfeVLqJUDFlweSMYrzakZRZ504tMjFlayxBTGoDZKhf60XOnxlB5W2MOAAWPI47/AFps+rW0eAY0I/uq2Oar/wBrJMis0iqq5UJnBHTmoSmSlIsWektDICZnkJOc9j2xViSEgFZkYsDyv90VQj1KKM/vZPM3Pn6VpxajaMpuPPCtjDKR1NS+bqg5ZPcY0ELKMR7H7kDoOwoAKscHbEG6sMHFSJqFjNKoyE68g8Z61ka5ePbtt3bx1B55P+FNRcgVOTNlWRVG5m3EcYHWkVdrHzPu7TnArkP7UupJFaNZGYcHaDg1umXV7qxYW9heMwwQoiJPPeqdKSK9hIgiTK7t7ZfqQfTr+GKe80ZjcqxU+rNn6fjUZeRkjVLaQsowrIhPWnnSb+4jxJYX/mFvkzE3PvjFPl7nQqbfQhluVSBRHud88hep/wA8U5Y5JrZSAykHIx+vPp71OPCniiMFho9ztPKYX5hU9v4d8Vbl36RKgXpuZVGPzqmkuo3RktkUW/dgxpOMdcLwRU2mrBNcx/u5ZVi5k2nHy55rQHhDxJcSHzotPiGOA03b8BVye5ttFj+w32ixKyxiGSbTmUjPqVYBic9cN+FXCnzr3XqbYalSjWg8Rfkvrbe3kdV5llrhktoLRnWDapWdEG7K7vlGTnArynW9MubrW7m5ls2tI1l2RQMfLbaBwce/Wq1xcXa3U9/b6pbJJuyY3kaOQAcDAOO3uaZbDVNdLyJDJM6/edWByPxrrcWlo9TrpzwtPESlyt09ba2flcz71oRMsUK+Ts+ViWLA+/Sq1/CsUjAsrk9CowD74PSuz05tLs7ZbTVdItzMp+UzEpKxPowHPsM1m6F/wjMpvU8SWt7Dcg/6O1vIFUNnpLuycDHbnrn1F9DndRSk3FWRz1tbllBZSAecVt6Np2lzSk38yRQJy5Y7Wx3AyOTXS6ToelajuNlqAdl+8vBxWH4uMNm/9kW7h/LfdOwH3n7L9AP1NS30I57uyLMf9gQc2F1pkPPDSwyO/wCO5SPyFaOk2Taw0mzxfplvFFgM89zJCmTnAGVGTweBXCMy9MH60+G5aKFITEkkaytIVbPzEgDnHpjj6mjlG4noDeEbViT/AMJt4XfPc3r/APxFYOsWun2Ms1k+vWF58uC9sryIeOMNtHPSshNRRQNmnxgAd55D/JhTLSD+19bWBmW2iYmW4dB8sMSjc5H0UH6nHrTsHKZ08RjMfKsjjcCueecVNoVwbbVopdxidWIDr1TIIyD2Poe3UUaldLeX1xcxRpbxFv3EAHCJ0AHuABk9zk1Vm3pIGYY4z15pl7qwycL5rEEnLN1Oe9M2/KeO1TYkmkCbMuwLZHfjP9Kj4I4JOaQyJTg811vgPUDplxNeyaZBfWjxmG5M8QdIA/CyZIIVg2CCeuMd65Qxt6Vp6eZDYTRo52nZvjyQHG7vjrz60ClsdxBqd1quj3Vi+qTzRbwgENtHFG2CCGwM5Hp0NVhoNq1tl7+93/8AXNRz+dU/h8l3JJf28NxBbwKwIMzHahyeOOTx/KuubSpZZFV9etgq8lYLaRv5gVy1p2lb9H/kJQW5ydjosUt28V288kakEMmEbHr0Na1v4d0OKYiSK7uIj0Qy4wfqMVq/2DamQySatfE42kpbBe/u39KlTR7AEbn1OX2Myp+gBqOaUtk/uC0Vu195Tt9O0W1yItJWVQcgTSl9v0rS82wUDGk6UmPW3Un9afFpmloSw055j/01umbn6ACpRb2aHdb6dp0RHQ+Wz5+u41PJOXR/f/wQ54LqiuNWt4cqF06IZ6JBGMfpT215nAWOfzCOD5cWf5CrUTPGAI47YMx6rbJ+nFSm6uiuz7bKn+4238OMUKjLt+JPt4d/wMiOW4M5lgtL5nPUx255z+FXA2tMmPsN/jAxvOwfqaskSSKQ9xcOAR1kPP5mkWGEtuHy46Djmq9hJ72/En6xFdGSnxJft8iN5ZA6KMVC+v6o6gfapOP1rFGk+KplLtYG3A7uCMficCmHS74lWude06DPY3EeQfwJrn5aX8y/P/M6Pf6I07nV9Q5Zrhz365xWfc6pdFCrSyOB3Lc0sWl2PP2jxKsvr5Mbvj8QoFRNp2iKMyXWsS4PQQY/m1XGMHtr8mDclv8AmVLrWrnT9PmkDqFKBWRwSrDIwDzuAzjlSDWdHrek3zAX9nNZhAd01nMJ4x77Hw3/AI9WxLY6AVLJpl7JgElprhUGPpg1mSy+GZrAXEWh2sqBivzXLq24DO3hVx1zzkEfQ46qUpQVlF/h/mZSUZ7tHONpA1vXPs+k6it28pOPOj8nYo7sSSAPx616Vp2mR+F7K10u7heC4nPylhkSn2I7V5xoOrW1l5qpfRWU0nEiy25MbgdBlQx/MD610GveMzPZ2F1MlrJfWKNHEbeVXjdG7kbtysPx7dK3abREk3oZ/wARtS3a7HbLDFMlqMOGJwzHr0wfSsx1GokT3NrexiUbt8RWXJ9SDgn8Sazjr80o2XtrbXceSf3iYfk5+8pDfma3NN8RaIXQXEd5ZhQBlEEyjHtlT/OiOiLlGSWiHadb2umpLNZ+I1glZcKrwSxup9wAR+RNJJpujQwLPqPiWV55hvxBZu5Oe5L7aab7w9e6mk9/rkpsoyC0SWjeY4zyoOcAn17VJf6x4av7p7y6m1CWSTH7uK3SNIxjARSWPygYA47U7iSkZrx+G1ckahq847AWqR/qXP8AKmC48Ox8jTtUnI7SXaKD9cR5/WrUl/4bXmDR7iZsf8vF7x+SKv8AOom121hAFr4d0hD/AHpFklP/AI+5H6UrlpDTrWlxLmHwzYj3muJ3/k4FWNQ1IxaGqR6fp1lJqKEukEHzeSCMEsxLfMwzjPRQe9RW/iS88wMBp9oq9Wt9PgR/oG2ZH1rNvbu81e/Et1cz3EzkKDI5dvYZNO4NFZQcZ7dBWjoFpbXqT20oInkVVtpGkVIo23DJkJ/h256Y5xT10e9EKmK0uJpHHyBEOPz78UjWuo2ECG5sXT59wWVDhvUN+lNMlu60JdL05rXxXb2t0VVbWbfdyK4dEjXlzlcg8Z6E5OAKwVxn5cj0zVrbNdSuqRFS5J2Rg4xnp9BWxa6LFawCfV7q3sFIyqsfMlf6IP60FXtuYJfA54NXbaCaJPNJAjK889hzUV39ka732qytH38zGT+VTLJJeSRWVtCqmRgoA6sfc+lAnc7b4aQPHpU92wObickH1A/+vmusJ+X5Su7OfSs3SoobHTobKN12xKAWI6nufz5q6JgOwIPtipOKTu2Sjn52zjFKem4k7gOOKgaZckZHB5OetNEmWyRigmxbWTjaSM9j6UbkJwCSTxnHSqvnAZ5yOv0oaXsSQG55FK4yyeuASMd6aXIHXnPBFUWvGZvLCggHlu1Oe4+YgljxkmjUNC+r8YJI56+tKJVVON2Tx16VmPKS64HH1ppZmyCcDsRwRTsAxrezZSXRXyeQ+WJ9Cc0RxWyqdkYTPXAx/KufvWvI0TyLm4cOR0ZTkn2A9KWw1S4jz9qyQeEby8sOe/OP0HSp5bbGri3rc6WJo0/1fycckk/hxSyMcklz+PP61iHUIyCsbO2eD70i6jCY3XzCZRjAHp707EcrNWZFmhljc8SqQ3HYjGa4z/hFrtMoLyHy93BOSfyrf/tEN98ge4GKia/ixvZiByCR1FMqLcdjGk8IHG7+0sbvvDy+P1NZXiDw62n2qTwyPJl9r5AH06V1T6mP4mJAHy47VDd3tve2z2ruNrrncOx7U7Fqc0zidMsY7mV4Z3eKQIzpyAr4GSuT0YgHHbOBxnNO1PTDb3Jjt5BPHjhx9SP6Z/GtNbWYNuSLLLkZ3YqOW2vSSSjEexp8pv7TUxPsUoOOn1pDbkYBcA1pSQ3Sj50kA/3aqyKxPIzSaLU7kAhQEkyj8KTcI1JJL+ma07JIoI2d1DSN6j7tOlkhYYYcHtRYObUox6kY41QWttkdWMeSfzq2niXVY7aS1hmWOCQYdI0Chh74FVZLaA/dyOajNtjlccUrB7rLkur6ncqglnuCFGFG84Ueg5pomlcgSXWzPckmqhWYepoKSkcoePamDSN/TpdPspRMszyvgjlsfjxVLVPs0s5mgfeHOSpySp+tZbI+eVNOijfnDEfSgShre5ZihlY4ROe2a6Hw35FjKZ50Y3BGNxxhR7Vg28kygYc8HvzWrb3sqn/VKRj6UWJqXasdXHqSyDK5LAZGKsC7nK7ljPHOCa5aK8CsAYypGfqavWmpI5wXYKOORSsc7ib5uJSytsjGO/XJp8crbmYyN1+6B0qhDexO2C8ecdxzVqO5hYj5UPb2FIhxLSy/IF3D05P86dlHPzDPbkVApjb5ypVffvTwxA4IJFFxWLBGSR/CeBmjGMhlABH51X3Og+YA55JFI0u4hSMHvTJsy0ypwcDHr6/SoW28nO0YzgGoQwCkBye/TFN/dhuNpwfzpgkdVfeAvD1yB/xLkjYdDEzRkf8AfJFc5qXwpsX3Na6nqVofQyeav64P616Sl2o5zkCn+ejLkcj+VZ6m6k11PErz4WeIYWJstdhmA6By8bf1rPPgjxlbSN9oSW4XsYbsZ/UV7yxjbngn0NROsedxHPp6U02P2sjwSTRvFcJJFnqnHo8bVRuofE0BLy2Wojjq9sCP0r36eJGH3VYfSqrwgYxvHtnii7BVl1R8+vf38Shpcqw6o0LKTUM2sNJw1tEq5yVQkV9Az28MgxIsb+0iA1nXehaPdAi40u0kz3CAGjnL9pHseHJqqqCFEqgnJG7NKdVB6SOK9auvAnhmbP8AoBizxlGNY958MtJb/j3u7iInpnnFNVCueDPPk1Z1PEhqVdVBHzdfXGa6S8+GF2n/AB7ahG/sy4rGvfAniC2JxAkoHdWp85X7tlY38Lodyo31XFNNzaHAZV/A1UudE1m1z5thcKPULkVQcSocOjKfcYp8xShHobkT2bHClgfqDVpIoTwPMB9Cua5yO4KKV2KT65ORU0F3EDmUT89SkoH8xT5kJwfQ6RbeILggk464P+FM8kbwVZR3x0rJjvLcN8l9eRf76Bv5Gn/anGfKvYZSx6klD+vFPQjlkjYSJCQrbCT7g1KLGBn2lF554rG+23cedyhwO45H5inDVgcB42X/AHTRZC5ZGwNNiXO4bcDnmpI9NBYgSNx096x11AOxInkRf981ML6faCt3LtHqoIosS1I1TaSByAMkcg5qB7eQbjt+92HFUv7Tu2P/AB8QuO4YYzVldW1DH7uK29OTRYVpD0hJXB+Rz04qxCbmBQFlbrnrnFQx3+os3z2CybRzscVdTVNOk+S6ie1kPVXUj8c0hO49L69jwN6sp45HWrI1UqV8yNuB0BwCaaLeynUNDcgjPBHODUd1prRL5sREg6EdTSFozTj1S2ZAH3IT2I4HvmrUdzCchJIx7k4JPf8ACuZkt5NwkVSwwCpGT/8AWppR2lDMrZHIJHGfelYXKmdWVRgyrtQ+xzUcgBIG5kx1ABOf8K57zZ0kEoklPzYIx2rf02WSe3JeVfTBbH40rCasdJpOuW2oQrcWs6SRtySp6Vpx3mGyrc59a8Yjjktbj7Zo0wtpurRZxHJ/hXS6D4tjuZBa3yta3a8FH6H6VVipQ7HpiXSSDIIDDqPWn+eHGCOR74IrloLwEgg5Psa0oLxZMFm5HelaxibKqAvUc1XkVwfl5HemxTHHLD1xT2ckcHOBRYTKxkG4q34ZqIkAkkkA96nlCknIz7kVVlj3KfLO0/pRZDRLGcdPmWpAQwGc/U1Vhdgdjgjt9aso4Jx39KLDuxGgRyML+HSmPadMFvXk5qyqckYJoKuoyhyO4NJoLmdLZKQQ4DVSudDs58rJaoR7qCK2vtJDYkjxilWSByCQcYoLTZxl94L0aXlrGME/3flrAvPh9Ycskk8GemRkV6oyxMRyDxwKjMSHkfnQUqkkzxS78BXik/ZbuKbHY8Gsi78La3b5zaM4HdOa97uNPimzvVG+qg1VfRoiP3MkkXsrcfkc0FrEPqfPU1tdQHEkMqH3U02CeaFt0bFTXu17od0VOPs84z0kjwfzFc/qXh+2bIu9CHTl7fB/wNMtV09zzEajIf8AWxQyfVMfyxUq31uQQYXiJ7o2R+Rrqbzwro7sVhupbaT+7MCv86y7zwdfRLvt5Y517bTTuy+aDMnzI3PEyn2YFT/hTxhRnLAeqMGH6VHdaNqNsf3ls+PUDIqk6SIcMrKfcUrlpJ7GtF5jj9zchj6Hg1Y+23cC7Zi+D6/Mp/OsOOaSMEKeD1BGRU4vn24KD/gJI/8ArVXMS4GnDeYy8LbH6jacVo2XiG9hVVchjjGGrnBcQP8AeG0/7v8AUYpysh+7Jn2Df0NFxOCZ2dp4hUEJNbKkf+x298Us2qXEL5EcNzbynCsDt49D2Brj1kkTndx23AjNSx3koQqFJB7A5FaU5Ri9VdGUqXY9CZQtkks1lIg8suwHBDdcYPWm2U1skAeNyqyKZCHByADg8/Wq/gL4iS6RCNL1yyW/01uF81NzRD2z1HtXfQax8PtUtGMMaQuuTiFShx3wOO3pVT9lvZ2MOWpe1jxtLk7QDxg1PL5N5EFuF3EfcdeGQ+xrMVzgCnRzFGwO9ZHXym3p2tXmkMsd6Tc2nRZl6r9RXYafqcNxEksEodCODmvP4JztK8FSMEHofwptv59nJ9o0tyD1e3Y8N9KRlOnzHrVvd/ONzHaQMYrRW5ycAgjHavP9A8Q216oTmK4XhkPBzXQ294mchsA+3Smc8otOx0fn7k4703f3rMW4BAIOR1qaOcnJHp6UrEl7O7C8HNOVFGOB+IqrHMC2TVtGBGDSaAnhk3HDcenHWrSAMDn8hVKMYPqBU8J6FW4HrQwRO0CyKQykf0qF7HDHADA9+9TrJyM5pzupBG4ZqSjOe2VTxuU9KiaKZchJASfWtIkc9D65oMSkZwCMUxGfmUALg54+lHmSAnI6Vca32kMDgeoqORCB1U/UUDIRMf4h+dJvRiRwDQyv1IZfpzTCpP8Adz+VAr3Iri1tpsh4UYEYPFZNz4c01zlITC3XdESp/Str5hncp6dqjLgj2zTHc5e58P3ajbb35YD+GaMN+owax77RboL/AKTpMNwP70DDP5N/jXfnHUc/Q0yQBh0A9xQUptHk13o+in5Jo57KTP8Ay1UqP8KpT+E9432d2kqk8HIr16WGKRcSIjA8HcOtY954a0uclltxA5/jhYof0osaRrs8lu/D2p2xO6AuB3Xms2SCaM4eNlx6ivXLnQNRgH+h6szjslwgb9RWXeQahF8t9o8dwgH37chs/geaVjZVjzdJpY/uSMvsDS+cxbc3Pr2rsZbTw9dMVctZy/3ZFKH9arXPhDeu+yukkXqOaDRTRz63akBS0ij65FW9Ov5rK7iubZ4JHQ5CyDKn2Ibg0288P6lbAloCw9V5rNlhmiOJI2U+4pqbjsFkzTRs96fnHSs6CYqcE1dVgy5HNCY2idXwcVMkp3bgSCKqpT89RTuS0XJIY7sh8tFcDlJl/r61bs9aubJxa6mGB/glU8N+NZsMhDgA1c/dzRGOdQ8Z7Ht/9en6ESS2Z2emagJYVfggjir8N6Q5VhlTwMda85ge80g+Zblrm06spHzJXR6XrEF5HvjfJ/unqKb1MJU7ao7CGfHzdc+n+eavwy54DY4z71zFvdZcAHFadrdqRuB6dRSaMmjo4JQw4xnuDUzXMSY3MQSKxo7lZFAyAR09qp3Vy0Mu595B6k0rCsdUJgwwGH1FSARvgOBk1yttdyMF+YkdTitK3u5AAQW/LilYLM3FiUDC8Y4wTmgoycdx6GqMV4Wz0471aScevOKQxzFl655pAykY4P1p4mU45wPfrRhGPHr2oERGNevOfaopIsnOV+hqdkKjhxVaUy5AAK+ppisMdCvXK8VG5QclQfoKlUSBsE8HPWmso53cc/WgZAI0Yg8imujKeDkVaKrg4bA9aaAqkgHI96BXZQbGcnr6Uwr1KMSSKuOFbIClf1qMxKByOlMLlV0bGQAR7VSdFPUEH3rW8sDuB6ZqCWAY5YEihMehzWq2sEsZWaNHH+0M1x+oWEdvIZLQvbn/AKZuQPyr0W9tSw5H5VzWracXBPJGPSqSNYSs9DCs/EZto1hukkl28GQHk/hV5NT0G+ASUxZPUOu01h6hp0iNxyKyprdlJBU1nKJ1JRaP/9k="}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-10966-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In a dining setup, where would one expect to find the rims of dishware that provide support and stabilizing boundaries? answer: Rims of dishware like plate_4's rim and plate_5's rim provide support and stabilizing boundaries and are found surrounding the upper part of the plates, often used to contain food and prevent spills."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCACqAQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDyuLQPEsMQee90OGQnpHqCMP16fnVa81TW9PDWl9Is8DDDKpjmicfhkVXXxr4bDfL4QuG+t0B/7LVuH4g6Ug2w+B4iR0LXX/2FfE2xd7ypX/8AAV+rP0WpDK3G0ajT/wC3n+iMGfS/CGpJh7WbTZzn97avlSfdGyPyxWHqPgu9TdJpV5balF2CN5cv4q3H5E12eo+LrLUYykngCwyejid1cfioBrCAvnuPMtdMlt4+yCQvj8SK9ChicRFXlp5Np/inc8XEYTDN/u3f0TX4PQ5M6lq+msLSSJrVlGNpi2N9eeTUF1q9/cx+VPOWQHIHf8+telrPqZthFf6SL237pPEHH4cHH4Vl3Xh3wxqjHykudHlPUITNHn/dbBH4GuuGYU38cbemv/BOKeCqr4Xf8Dz5ZXjyY2Oc9DzmrcEf2qNnSMhlwDjmt/UPAutWoM2nfZ9UiXndbufMH1Q4b8s1c+Ha6XNf6hp3iZby1lKJ5TwxLuRgSDvRhluo9DXTLEU5Qc6bvbtv9x5eOnPC0nUlFu3S39fgcc9sc8rz64oS3bdtMQ56HitXxBZNp3iG8t7G6ju7eOX93MBsVwQDnax461q+FrzQZL6KPWUeB94+bf8Auj9SAcfyraM7pMKbVSmqkdmk/vOemhiXTFh/svF0spdrnzTymOE2YwOec5qnqFhd2U4gvLKW3l2htkibTgjIOD2I5r6q8LeCYJ/s9/ZeGdOv7d5A0QlPmxyLg9+M1c+J3wQ0+60eDUtB0XS7a5C4nEU7iNzgHIU/dPOPT6da0u97DUlsz5HtYy8u0RZ4J6D0qML1+U16+PhXqwlyNMZDtIzHK39O1P0/4O39y0Svayx5bDETH9Plo5i1a542d2eCQauyaVqsF09rcWN1DOib2ikhZWAxkEgjOMc19BaF+zZf6sHMdy0ZDYGZQePf5a6uD9kPWby3Ey+IYklY4IuN54HuFq1dmba3PkeQOjlWwD6ccVZ01UeYrIOo4IH+FfQ3iX9kT4iWbTyabNpF9FGMri5KM34Mo/nXmur/AAW+I2iSEXmgZC5y0VzGw/nSmnaxVOUVK559eoizOI9xUHA4NMVS7AAMT+Oa6SfQ9WsbmYXek6ml2ij7M0EgHlyAjDNwSRj0IOcc0Q6ZdrPHbLbzzavcHdtYg4yeWcnoPc1jKqoRVzXl5pOxnSQxWsUYBlaZzhYxuyx9hXW+FvCYE8N9ritNMSDDaD5gnu3qfat/QvBT2+LszQ3F2gzPezNtii/2UHt69/0pbzxhZ6EZoNHjhv71R81y5yFPqK8Wtjalb93htX1f/B6L8WdsKEYe9V0Xb+t/yOsFlpVjD/aHiGdolUZSBePz5/nXHeKfiE14jWWhxGK2BwGYnb+Wef5Vweta5qes3Jm1C6eU7gQp4UfQd6oCZlJaNS2MdelPDZRGLU6z5n26ImrjHLSGi/EkuZpJbt5rpjcy9zIx/p29hVW4vTtYLFAB/sxD+ZyaVBHLNJJqMk8aFGZPIjVsv/CCCRhc9TyfY1SZ8r8x5r24xS2OBu+5ejuppyqH5VC8gUrYRSzDoOKrQOQpdeCaGWSTn5m+tDjqNeY9XjUHd8xNSRJeS28s0MErwQ482RULKmem49B+NRJbSN1IFWYhLDE0SvcGKT/WIrYVsdMjPP40nyopeRScs5+YljV3T9HurpgVhYJ3ZvlH61Na+c0gjtoFDf3FGWP5cmt1fDutSwK7w3kY9LgCBB9C5FY1a/IrXsbU4cz2uegWvg20AAbVtOZu5W3lP+FaEHg/T1wX1i0T3+xN/WQVyFl4Phv1VrfR9WOR1M7kH8kNW1+HBaMuuj37+h/ekH8fLr5ufKvirfgv8z34xn0S+/8A4B2UGh+HrYf6R4nslx2Nui/zlNWLP/hC7aR2u/F1kFU9EaIfnya88uPh9dwoWPh2QADOXL5/9BFSWfgC+aMF9Fs41cbv3r7cD8WGKzdPDtXlWf8A5L/mUnWWyX4/5Hoaa78OTvabxeqAHagSSEED1OUNZl7qPwpmOJ/FM83/AG8oP/QYqy9N+Ft1LCsr6TpSK3I3zoOO3BlzVi/+Ha2Ns8ht9EG1c7fPjOf/AB81gvqcZWjUl/5L/kDjXnu4/dIrapefClogLDxPdWkq/wAW9pgfqNg/Qiueu/EejWhwNRstahHAElnIHx+K8fga6CHwgEjEj6t4btRt3ENMjEfkpp8PhyyECSXHjDw7BvAON4yPbiKuqnUw8PtSl6v/ACjcznh5zTUmvuf6s4jWv+EN1Ym8S+vtPuygHlx2jzxtgYHBAI/76Ncxb2F5LOBFY3M0W/G5bdvmGfpXr82naFBGwHjnTJHUZAjDnP4iMUsdn4TeWJ7rxogcRkl1t5n2nj5e3v8AlXbTzHkVkm/W7/8AbUczwELaSt8v+CzjPDPj3xJ4B1qX/hGb68sITjfZXaZRj3zG3Q+4wfeuruv2kfiBM7JGujLE6FPKe2Z0GRjoW6+9Xr6DwZdwiG58bT3yAEJFPorzD8CTkfga4rU/COjz3DJpx1ZA2SskFlI8R+qPhh+ZrroZlGekk18nb8bHHVwL3Vma2hfEvxv4g102NxqNtaxta3LhktgqgpC7jH4qPasfWfHPjfTb6Qad4hu7iyglYW1wkCpvGeHxjK596PBWh6to2uxyarbOlijN5ZnyiyYU5wDyMjI+pxVzUfiPpus3sd7qvhtJNQ8sC5An8pJZNzEsqquFGT0rthU9pP3XdI450/ZpNrcyoPi38TrSVGi8Y6zGpO4qk+0H67cVUvfiv8S7gHzvHfihjnr/AGpKP5GvS9N12xOnQXVr4Q0YmZPniutSKSRHpyP7p7Hvg8cc7Wma1eyS+bb+Bfhvjklr3VWKqCRyfnH+c1vGonqmYyjbRo+f9Q8Y+LLtVN14n1y43ZyJdRmb+bVWspLm8LzXt1P5cfBLOzNI5B2qM55OPpxXtHxHh8SGaaaX4YfDtlmBbzdNUyLggHKlZR+lcHp+jrc2z3t5b2GlWkTlmkWRkSPI6LuZgWPHIyewz2iriacIXvf8R06UpS0RW8OWGp3EqQ2iwiZEKyStCCIQTnGcZZjngf0r0WL/AIRDwfpaR60873MnzvAjg3Fy+PvSEfdX0XtXC6v42hsLZNM8O2Ys0UEG7YAykHrsX+HPqSWNcW0sk07SMZJpG53HLMx9/wBa8qWDq4vWo+WPZbv17en6noKtGnpHV/l6HU+NvHV5rrCCGCLTNNTiO2hJOR/tN3rmYLmdJGIByyAAkdAehqa+S5vo7YeQkMcMYhznJJHJbA55J9KSLSpytvL5F1JHIzLkJtDEDOFJ616VClSowUIKxzVXOTbeoy7nQzHb85UgKwPGBnIpB50oHkAEuxyiqSV+vGP/ANVbOn2MJJaexnRtwAaQgkn6VfutO3SMqTxoO4dcEc+ma2sjG5y9xYz+WskjKqn1bP8AKoGg8li6SvvUEgrxiuh1XSpY0jjknIAGd2CPoPT/APXWXcaesCSvvLMFx9ORVXsTe5a0rS3ksoZkjX51JyRnuasHSZWVQxA/Cuh0JYT4ZtYpIv8AllnfnBBznNVb7Urniwto4XmZDmcKcooPLY6Z9K5JV5uTUTpjQjyqTMpdDY7ibgRheWyelWrfw356h475Ze+B/hVPxEtlp2mwzgC7up3BWR+FAHP3R1H1p1tavHptlqNwsYW9Egh+ysElDKQDuA428gjvUSdSUeZSt8jRRpxlyuPnv/X6nSWNxrlpD/ZtnqcMKZ2lYoEVvxIGa9u+Cvh15IJLzXPE1lYzlcW8MFnaNdM3Y8oW/M14Hply9u8X2yU3MI+QpbnEq/UYBOPbHWvU/h2uj+KWn0qNrPR7WNfOuLubbHLgcBQDyTnB614WZc8Kba0XV2/pnqYelTmtXZ9v60/rYo3WqaRHKbe38ZXUSFeHbX4di/7JZARk1zniXxJpljErP4kutRTO3Zb668rfkEGBXD2t893ePaXUsFraXUvmTJFGQARyOCfqBk96w9cija/nS2ZpERsKWQKx/KvQoZXTU7Sb/D87GFStiFQ9srWvbzva+172PSfDXijwNdSTf2zPfQ4wV+0Xs7qw742ITn61p3WsfCue6VILssuDkC2upB+rLXi0Vqyjc8iD5enfmrNiiW8wluAXjZHClGyVbB2nGexwcHrXTLKqTk3Gcl8/+Acn17EW1ivuPUYdZ+H0d7IsVqbsOP3YOmzHBHXgzj+tXr3xV4JsbCG9h0GF0kz5Z/swcnpg5nPT0968q06RXu4jPcfZh5wJl2ltgxzwOozimXt0ot44HZ7h4mlVpN+UdT93aCMjueeuR0qXlVNtXk382V9fqqN7W+R6V/wnXhkXKvFodqWkC4Q28a44znB3ACj/AIWhYKQy+HrZUjIwogtwSeRnPlHjj+VeRRuRJC/90DqPQ1NPJujLKqjC4OB1+Yn8PTireT4a+qf3v/My/tOu+p69c/E9IZYWGh2rO6bdwlhUDOOywimWPxD1m8vI4bawhMm4bUjuASxzkcKByPT2ryBgXjTAJ2nGB1rp9KvFXyoNJjuItW8wGJwOXkJGcEdBjIx6VlVyrD043jG7+dl667GtHG16s0nLTt1fktNz0lfipfWV68OtXOrWN3A+xUtWLYbHzBi0i46jp71VvPjH4lt7m3VNZhzJHlprq5vCEy2No8uQnsD0/pXlevT366/dXF3Istz9pd5XAyrMeeh7VBPFcXtvbl2XZGm1AAAcEk/jyTVUspwySlKK1Xy26GdXFVuaVNbp2133PSh8WvG2u6hb6cblWM8oRPJM0jncQuE812xn2Gefwri9T1y5mgi02TS9LRIQy+abf962SW+Zs9RuxxjpVTSJ59P1qxvYrhvMt5o3RgTkFSMEH2xTWglmuZJSFEcrnbI54B/iP612U8LQpfBFIxbqN8smdTLqzQ3sWtW9vp6PHpqmCEMroCNyMJA/3twV8r1O4VVuviP4jlsWthp/h22jnUp+50aBHA9QduR9RXKvOWlSOImYgYUn5R16HNWri+klvZzc2Cxq+NqW4BSL6DBHr+JzVRw0L6xMp1b7vU7vXfiV4psbHS4ra9sGS9sRJcL/AGDaIYySylUIUlhgfeOD7V59LqOp6tewx398zxrhFLAFY1PovTNNmVLmbyJBNBIkZ8reeuMkA5x1ru/hS2j6HHrGpeItOivYbOLZHHIV5YlgxTPVwwAz2GaicaeGpOpCF35Wvr/w5VGDq1eSUrL/ACOM1Wyihkj8m6klg3bdzoAw/AVsCbRrSzjdXv1aZeHVRjAZgzcfQDGKw9RuQmqsEaSOMTkoGALBOq57GrlpqtqJDBcQRLKzrJ9rC/MhA5XGdu0n2zVSpycFe7N4Vaam+V29f+HOv0Dxpc+H1ilsrjVYRLL8z26JGxQEhhuKcHpnrTL/AMbaj4lns7W/1DVbryJpJd9zOpU5QgBVA4Puc1yfijxNf69qElzPs8tW+QIgRVyckgDABY5J96j8OzAXVuoUBgGJbPJ4NY08HGNqjj739eRVXEqd4xenoW7iSW5lbEzSBTIUibqCOpOMD1q/psZS4heOHedvIA3DGc8+oPPPoetY5uJrae7hUZEkuA+Oiknp+JqX+1ryzmDRQxxKnygvlmPGN36Cuo5raXOv8Sy7lswsAT930GTs/wAa5LUWZvNdgMsP/ZhUV3rN/IFvHlEjB/LClQF5Ge1VEvmuzIzRqhAwdpPPNDi9yYzjax29jKsGjWJCMf3ADZGFORwOfeqJh261dQQqWeXyRGMjkFSevpnJqzBNG+jxW4I3JGhGfUYNN0i5g1LxfayW6ENawt9oBwVwDgKD35OK5VHdnV7TlSsCeC9d8Ryx6fp1hdX1yMkx2kXmNgk5x8wHBx1I612ekfBHx34f0i41nV/DWq21nCuA940alATziNHZgPU4qXT/AB1J4e+KUd9a7YY5IlDoi4Ctgj8uBXqnjz4161b6Nrml+IIpoBaRRW8ZYYM7zgyA9BuwikA8cDuTWFWtWinT5d0mvv1T/wCAaQpQlNVIy0vZ36HgOpLcK0cca/Z5XKkAyHJDdOCMnv0rMvW1+zhlWHXhbgOcxbAWyDjriuj8b6yz6FpOo3ljbxXNxZIrOcZkK9GY+oGBXm91qYdsvMXJJ6c100qcZQV4L7kzCrVmpu0n97RUiWQzNtRywGSNpJGauWSXEEwuDYGaIjDB046jpnofegtI213kUTIfldc06CeeGCaFJsLPgSARg5Gc4GenPpW0ptq2hrHDSTvr+Baa08xY4109liB2hmwMc85Oe1X5vD+mJaHy79mmHJAdSM/Qc1mQvc3UcNq8sxtgxUADgZ5PSuw0/T9A8kwiONZlTjJwcd65Ks5wSd38v1uddGjGpJxcV8/0scV/ZN35DynyQik7juP59KYukvPA0n2iEIpxkZz69KvXEE0eqyWkJBLnZk8jb61vxaFYzaf9lDhbl2UoznIye+O3+FVPFOCTb38iI4KMnZL8Tlv7A8gKWvIuQenOMYPPPHUUf2VE6lmuhjoQB157c81d1zTptNuwDchxJwHJ2kkdRSaZYNJqc1vdPsis2P2td/QKcFePfiq9rJw5+bT0GsLSU/Z2V/Udp1idMmt73yYpEkyEa5thJC3rndwSKu6eIbNGu4Y1NwWyWGOx+6AOle0affWFx4ct8pbrHHbBfKmfP7v0IHAU9K8c1KGC01DMUJSzuJHaJM5CLn/HI59K8yhip4hyVSLX67nqVcHTwaVSm7v8ivr+n2Mmq+a0jeXMok3MQdoI6Yxk1nrHYo8SrcSxBfvcbgPbGBWjqRtrnTJLtD81vEzcODkhlHI6gYJqDRotOnu7druJWhex89wXIG7zCD07YHSvTo06nKrtnl4ith9W1eT1K14uPDWpyRxMPIv7cRuy4JVkmz+oWobPQNUubVJI4Lm5iBJJRwFUnGePwrq9P1XTJpDb3lun2RJ7ZgjABcLvB49PmFa3he4jh0IfZJWkRpWEhxnaM4/r+lOvXlTiuUwwWFjiJtT6I8h1Sye1uCmGI/UexpljO9sxZJpIXIIzGxVsEdM+lex/DnxZpOg3V7b3UcMsdzcs0krSKMjkYIPGP8a86j0Wz1bWb4trWm6TD5rtGbnfsK7uAvlq3b2reliG01NWt17/AIHDXoKErwd7/wBdzBa4lllEstwXcdCSSa9PsNCNzogF3MXizISyjDHe245zXJz+EtLhidh430OeQKSscMV0zOeyj90Bk12HhtgdJtxdMQ0aDekjEEN0zg/hXNjKnNBezdvlb80jvyqnF1Je1Sene/r1OI8UWH9jax5FvK5EMoMMrfeZOoarUnh27eN7qKc3MxjIjiSMl93oPXv+dSeNdG1iW8XVzbSz2WwLujQnygpIw+Onrn3p+neI4Lj/AEeaJYkWHO7dkls/5NaqVR04yjq7anLOEIVpReivoT+F/A+o6jZTzPCkO1irpM5RwynkbcVgXEI07U54H327wAxttBbB6ZGO3Oa9D0i+sbaOSCaeQ7GLXSedgAZznH5mqmqTeH59U+x30vkCZjIhZMJIhwMs33lPyccgYNc9PEVHUbkro762GoqhHlaT/r9Tk5mto4YbtAJTOceW/JXnAPHOcirdhoV9qVwsKLFJLKeELgPn0ANUvF0dtZeJZItIt4kggCFdshkDkqCSSxPr06URajq/kx6jEIE8iYMSMllOODXQ4ScU4O1+5zwxCi3Fq/oWbI6Do0dzF4gspdSeOZ0jt4ZvLCSjaCXPUgDPTjNYEDwPPcPbxNFEzAqjNu2jnjNOmtpr+S71BnUnfvcsTkljTbK3cRTSqRtUgHn61sopJu7u/u+45qspNpWSV301+/ct3WpSwI+zOT8gz0FavgTVrHTYbpr3cryBFXYmeAcnNZi6Tf6jPE9rD5kTzeWmTj5tpY++MA816DqPw5iFrDLE1zaziICdJITjcBlnzxxmsq1elC0JPcvDYarV5px6fqcDqOo/aNXub+ORgGkyp9McD6dK3PF2snVBYM1+1zDcFZpDLKzSRyBFRo2BOAq4JXA+63XqBmDwtqUN27SRq8B4yrEbvTGRVFPsum6jbLdWcwkjdWJd1KNz6BeR7VsnTm007tHM/aU009EzsfjDrum6lpGi2+n3XmvaRCJlJU4XaOm3tnJ5wefxrzLcc1PK0bOdzd/4VppeADCxMfq3+FXSpqnHlRFaTnK7Z0kFm51GexuWMUkcUrjAzuKoWA+hx+tU7UectwG++sW6MZxzuXr+Ga3vt8Gp69FJZxF8AAt0yM4Oc9ODWlouhw2myZY1mlUB2ct0A7Ae5rijV5YJzWvY9iopSm1GWnczNBtriOAefHgbi5HdR0z9K37tsKGRVlVF284wcj/69W5LDKh4gf3Q35CknnjHX/OfesjWvtMduqoPKMyZVivXI4K/jxjsawa9o7o6KWI9iuWRzWq3Lx6zcmFiI1UIoBwMjg13/gP7N/Y9vfOJp5RMzZR8YAGCDwccV5DPdSlhGSAqccd/XmvVbHUbSDw0Ly1hRZdmMDIGO+QOvHNa4yly00l6GOW1lVrSk+mtv8g1LSrHU9ZkilkE3lyIyNO+NzbNzJx7Dp61kataXA8RT39jbyE3jEOvlHazHlsdv8K7nwpZaPc+GJr57tQkW9riGODbKrqMspLM2SfXPINe1/CptF1PwHBq8elpFb3RMgeSJRIoBKgnHQYAPFeZVxM8PDWLfTXRf1odapwq1OZNLr5/10PKNI1SSPwnA073NnIiBmVUDZAx8pz+WCDXmWp6J4nvY7vV10e6awMp8tkKkRrlmOcHgnI64619I/EnRrWDSJfENlpZ1QW+A1pESBcfMEwCvfJ4Iz0xit/wR4YS08Dxk2yN9viElwjNwgYBtoHtnHNclDGqgnUUdW7a9Ov9fcduOSxKjTctEunXofFznUotOXS309YprhT+8fd5hAOSF5wB68VFbuDaQox5NjIn4BmNeg+MPhR42tdTvrnTLGW6tLOWSSCZZFG2PGR1OScA5FedrqOmRWyxrb3Er+Q0edwUKxXr7/Mc9unfNfT0qsasbxd/Q+XqwcJNSuvXr939akEVrGyBiuMnGD3Neh+GBpc+hrFbx+U8j+SIkkwPMxnB74OC2fqKytN8LSXXhuHVTepEhRnYNEWwPYj+tbfgPwBd6/aS3lvrMNiUdowoLFmIIHOO3J/KuavUpVabblaz89z1qFN4efw7pfczjPE1tbw67cQRCORYyqblAwxAGenvmgx6OlkrpcTNdZAeIwBVHrhs/wBKhcQ2t+0MqrIIpysijIDBWwea6vxRo2ny5vLIhPuosSKNqgkAFj2HP8q3qT9mox/E5HSjU5pq3oQeHbCBbRLuNMPJnBOMgZxxW/ptrFBdBpV8xTk4IyRjnjvVi40ePRtOs1W4gk8tVSREk3MHyR0wPTP41IrI6IBBIrA5BxzWUmpx73MYNwndaWNO2vri0jEjtHBCc4UA/Lk15H8S2tj4slkt7eOAtGjOIxgF+5wOh6V6lrlvcR6fb3M6siMw3jzBhTnjjPevGvEcj6h4iuniUvmTYoUZyFGP6U8JTjdzROLqyceVnYfBm0h1E6qt1CJWh8ucMy7uu4H8c4rnviPdrP411AoCI4HFuoPYIAD+oNd/+zvam2n177ZE0TKsIKuCvAL5/UV5Jq87Xep3ly5JeWd3J9yxNTQfNjKnZJfj/wAMaYh8uApLq2/w/wCHJNNMEmqWkN0zpbyTosxT7wQsM498Zr6Am8OeGG0U6doa6rPpglD+XOFk3EkbgSApOOuCTj0r5xBO0HvXW6X8RfFGn2ywR3UEkSkkCWBT+owa2xGFdWUWpWSOXDYn2Slpds9D+JVroOi+D4riLwnp4uGZI3kSOSANkHnAk68HjFee2nizSrbT3sE8J2Rgc7n3Tuzk8fxnkDjkDANbHjTXrvXfAGj3V6sSTXN8+REpC4TIHBJ9a4+c844qaeHjCPLK717v/MueIlJ3jZfJGhceLmF1aS2Gl21klrKZVijdipJUg5zz3P5165ate+JdAlOmx+dPNbCd1kO5wOPkGPpXM/DPwLazWY17xLbvLEw3WWnn5fPH/PSTuI/T+99Ovp/hX+ykjB8v7DN5zAR26bExtAUAdh1NcWLqUIzjCK95HqYDD4idOU5P3H8jzTRnltbYw60l0Yg5AVoyMc9Oe9cr48Nq9w9qkBSMMTDJjJwemTXuPiyCwfRZVG+eRcmQK3yqRjjg4P8Anivn7XVeAeZcRbpJdxG4ncBngkdv/rV24WanrazPPxtB0Wle6ZyxRu45o2N6VYLDOaQkV28zOL2Me53fhPT44rWNwY23ZeQFyvy/yyegrqmJSMO6EkgSyKeuP4QD3HPT0Ptms63xEgKo3kbMlNyuMLkAbQfX69aju7+aKFRFcEHcHZiMhj2wfbn8c+1eZNObuehCSgrGhbXLi4dciZ+fXGT1zj3IP4fSi/tNsQJK/LhmXkfxYI/mfwrPs7iw8kM9w+/yiWWKRlGS+M8e1bAkF0sqxSbo8MoUyDcMgDkH6msqkXB3RtSkpqzOD8YabpH9mrJpuhata3scmJ53lD28o6EgYypzj9a1NGn0bRdJtrXxG+oPLICXhs41JjwSCGLEYbGOMdhzzWf8QrPUIraPUo7if7LK3kTRiQ7VbGRkDjkZ/wC+a1PDngfxL4y1Pw54f06CSa8uIsz3DgtHboz4VnYdguPc9BXbNRq0VzOy/rqc0aqw9aXsl73nZrz0X6+dzvIb7wXofhO5v9PgvXjv4FMZeUkyHZtUEHgHA5I9K9J+Aev283wldDLaJLYq8KQeaTI6oic7eOpzgD9a426/ZT+INhL5s+saZf2UCYiW3aQu2T02sBtHJOQTVWDxR4T8AXF/4cutDv11WykAbZO0cbH5Sd6H7/fGR0x1615WJwidNw5nJ3v3PSpYp1Z88YpdHa33nafEj4ga/o/iyDRLyxH9gmWBjIqF24kDE5XPJ2g7cdCK674b6npniPRriRZHkS1uJrRSSUKIDlDgjIyu3r71zF4+jeLfDOm69cWRiWS3W5U5w6Mvcnp2x9K7P4R6PZ2K6tFc2cl6z3CyqoZWZiRkgdMtzgkngYrxZqFvZ2tP8DrqSknzpe6vvPPk8Z3N1F4rkjt3nOnPJBBbxRSO85yY4mUYO7Oct6YJ6GvKfhJ8H49e1i3h8S2WvWts8mHdYVjTGCQCS28ZOBwh61932+j6HdIr/YJ7NdoTyWkKA9+gJBPGKwviJ4X1XU7Cz0/w3qi6TbKXN1h2zJx+7AI6Ybn+XNenRrqknCkrXt+R5NSr7aS5+hzmhfDuy07QBp0Ojadp6RQeQu5AyshII34Yh2GPvHBzzxXkPif4Xr8OtAutTj1+6mhEZuolJEbJJwrMSSAV3FflJHTO7qD9JeH7eS10Oyj164+2ajFCqXEu7CSuBgvtGBz1wemab4insNT0q70u8jWa1uoXhlj7MrAgj8jWFGXsW4rVN6/ebylVqO58O+HvhfpfiG9t7TS/Ecl9fzn5lh8lkSTkld3mbnOATlVx7119l4P/ALL8fQ+CdQ8y5t7KKG4mSbaRPIzBfmU5AUKcgeuCeleu+BPhZ4M8E63D4gsre7vtSt1ZUubuXew3E8gAAKcHbkAcE+prnLmwu9V+K3iLxLc280ES3sFjbt93cggbJGeoDoh+lerOuqkW0/TQ540pU2la1/M5Dxx8LJ7zVJ7RtUe1KTBtiWzuWJB28gbeM+o65rn5Pg/cWc0EVz42igedgscZt33tn2D+9fUlugu4EutxIlUOCepyKp6p4S03WbiSW8smnkkhSEsWOQiyCRQPT5gDkVksTUirX09Cnh4yd3v6ni8PgC51C3XT7+5lvRbLjdATC0zDKjIZTz+mSDXnCfDC7sfF5hXQ9WtLdG2SPeyRXO1m5U4iA6EZPtX2XDod0wLBcEcjIzzS3OiSRkyy6eZXCMZWjI3vgcYA5zWccW6aduo50lK13t9588+E3tNN8Pi3mjLvnEvmW3zSZbfkAjOCGridW/Zt8TNp11qtrqmnOWV547dWO7pu2AY6/wAP4V9RWng+91tTatosCxxqGLXEkkO8FFIQYBOBuIyR1XOK534lf8Jdo2h32n6Pp6vqcsbQWvlzbnVnGA5YgAADJznrj3xnDEVoTvT+09b/ANepU4UaseWb2R8J6po2r6Wo/tHSr+zB/wCfi3eP/wBCApqEf2eCPQ5+ua911b4cftA69pjWt7Jq2pWbjDQPqaOpHoV3Vy118CfiNafu73QTaMRlUkkGW9gFzk19BDEK3vtfeeNKj73u3PPZtRnvNP03TWlQQ2nmGMBMEFjk59elaXhiyWbURcXLpPBbkM0RXiRuyn2459hjvVXxF4U17RNeTRNT0q8s9Qkx5cM0JQyAnAZc9VPrXXRaPaWNn/Zlux8yIhriUPy8uOSD2UdB+PrVaNqzNYxb1seg+B7rWdWuJ4VYNavIHup3iBdc8BVJ6ZxwOcAHjiug1WRbTWIbuGzklso4isqxDDKw+6fckE5/+tUHhOzn8OeEbWCZHa6mJnnJGWLFcr09AyceoNa2uQRQ6UtnE4E8shL5BJxknaPfGMk+9LD5ZTxNSXKrX6pHNm3EVTLKUG9XeyTemz19Ec3e6t/aiNF9jMFoxDEF8Mw7A+me9ebfFeyhntEmtkzdIwD4yAsYBwAOnfNekTRQQIEB3MD8xzkD+lY/iDT7S8ieSDc6n76t1HuPavbjlNOhSahG77vf5Hxr4oxOMxKnVlaP8q2+b3Z4CAacFYjnFbXifQLnTL9VUo0UwLxkduen8vzrJa3nUfMyD9a8macXyvc+1oTjVgpwTaZ6ZZ3SzQy+VEdwhAbKYwQVz0OOxqm0EaakyBnwHdyDKFC57DHrxVIXxtDG11C3mhj50TDBAIAb/Oe9XWnDGKe4eMJ91VVVZmI7cde3J9a40rPQ7Grx1N20iEMZCGRiI1QD5X+YtuxwPqPwqe/vFG/GcF5CMxAdsA/maxkuoNgPmIzLk5ORlz3444/n9ahi1XQ7C/gm1pbmXT1PKWuN8u3nb8xGATyT1A+tYypX1tc1jW5dFod54d8A+IPE+mrdAta6RPIHcBlVrgKegU/wnnmvrDTdW0qw0+0tLHS4rO3iiCJEjKBDgDaBgHPPU/zr538O/FddXtrddE8KXskO5YYIpblIl9FAABwOwrvbK68dXcCfZfAsKFmyRLqhQjHqfKry/aY1uyirfLT8T0JRwjjdy19Hr+B6ufErGLbHFb+Y3QGd2AP1wM18i/GDwpqHjf4w6zJo8ZuLiGWGHUJ4YcQCdgoCDLZyFxnrzXut3f8AxGubGS0sNI8OW966FVdbt7kwn+8F8oBiPTNZ2gfD/wAQWFium6TbXdsdzyXVzPMvmXErn5pcA5B6n8fat41aqbdR69Ev+ATCiv8Al1ourbt+f+RB4K8J6c/gT/hF9T1FVvptPForwNseAg5faHyCwIXnv14Fdf4T8OHwvb33m6rq+rzXbxO8tyiBRhl4HlooAwOeta9tofiDRbCFrbTrfU5UP71pZAGxgljk45J7/pV3RPFWj6o0llct9kuPlR4pGyCW6AMOMnnAOCe1ckqSvtv5fl1LfM4817pb2f5r/gFtNQupyPMXbhvlUHdghgOv1rjvih8Qbjw5rNvomm2TX9+1u17Ou/aEgQ8jocs2CAPp68dHptkbfxGbe41WOe3thmCJpf3juRzv7MQCOOT0JrbaCzM7TNY28sjDBaSMMcfjV0KCT98ynPS8EYGm3MWr6Xa6lAX8q5iWUKx+ZcjO0+46H3FWEtNxx0AGSfateKyXAWKFY0B4VBgCrUOnMeCOp5rdQjEl1O5zqXENsZILi3lkAxJtC5Xr0PHtnFQ3Gi2dzdJqF/MzGKUSRrnqQTgbfX069TXSXFoLO/F1LHL5J6GIMzbzgYKgHI757c1dFpbyzxS3EKLcHG1fLJ2DoBnHH+NRKT+FaGDnFPmMm10pNwJHTtW5Z2EYx8g/KrsenlTnirUUOwVEo3ZlUxKtaLGRWsaDlRVG7tI7cmaXzZYVIGAwHlDqWJ4yP5CrslzaEZa5Q87cK4OT6YHesY3DDMWnWc6uxxI725KbeeAcjr60vZp7nPBVJao04Ps0lu2oBJSgjKoy/MWXOcqBnOf14r56/aEk+JK215pXh2z1j+yrmPfNcxje7qwyEwozGABzgdzz1z7vb67JYWirqdnMNpKloo+AO3GT+Y44qtqE2m6jLHLb39zbyPyYgAvmEcYZWUnB9q3jywira2NadOpG7cdO+5+ftj4u8e6AzJZeKdbsZehVtRZP/HXPTr2rb0P4vfGPSoRDpviG6uEUcBraG4JHudpJ/OvtGe08to5G08OAQGSaMbWHfkZA/Spp/CmjanHOqabYIirw2wYJPr8mP51VDFwrJ+0puL81f8R1Ycmqdz4e8WfFj4i+KG06HxDZ6fdXFtJttLmbSxHKhfgqHGMA8ZHsKk8O+FNQvPFslxqktkLSznU3wiuBySN3ljGeO2eMD3r6K8e/D7wVYa3ptvHb29zNeTqjRxxIjICRllKj0J5x2qv4q8IfC7Qp9Qisrm6k1QsivbpNuQuCAQw2/eAJJAOeO1da9rJ8mFW67ef+QlisJhqXtMW2knte19G9PNsxYLi203Sv7UnBkupg32c45+Y9FHpjHPpXDapdsZnJfdK2QwU8AZ+77Cuh8bTsZPLhk2xqNoDHlvXAHArjZ5kiTAAyOpr7bLsKqFFK931Z+QZrjp5hi5VpKyey7Lov633K10XYdcnt6CoUikRdzsdp7Z61Bd3pL4HPrWV4p1abT9DuJ1DIyphC3948DH8/wrrqTUU5PoZYfD1Kso047t2Of+IckLzQwRMGliJZ8nG0Ht9eK41huGMgbjjrxUcU884eWWR2Zzkk0BeUwfy/xr4/EVHVqubP1zAYdYTDU6Mbu343N7xR9pJTUGdZFK4KqoGBUuga5ai0S3ZlSZRtVygbP1znpUEF2t5YvAxCvt5U+vtXM3FtJAeVOw/dbtXNCHPFwkdPtVTlGSV0enxXFrOqlraEvjHmxjA98qK0LCyt726VbuGJYYz8qbQSffPpWD8H3tb2a50y6B8xl82E57j7w/kfwNa/j2G70cpfQNN5Ua7XZVBx6ZriUnCr7Ft+R21406lNVoq3c9R8N2MN3CbeBEUEiOPaAAG2sV/UCvQ9C8UyXtlLoWuK8sbxZt58/Mu4EA+5Bz15yD1ryL4C6zNqmgy380eGh1JFDf3gFBP869M12xVDczWXzPGWuYEAwW6GWEg9GyAwHrj1NdcMLF0+SS2OKWJkpc0Wdb4MXUdGndrLToJVkRUZ4rjOVHI4fBX6frXXPr1/aQ+dcWFvaW4zuknu0Tj1znqK5jwjNHc2+lXUT7o7m25I7hcjP5EV0ej2lrN9r1SWyglvIMRSSsmXIDEDnqMD0rWGGjBWgjOrip1Zc1R6mf8A8JBqWqxmxefEMoP7+OAqrLnoHYLn6qDUtp/Y/h2ARwaha2KzSfOI4Gkknb0zzk/QGtG4Oj32Gu4ySp4O8gj86t6L4T8PS3D6jboZ53UgyTSMzKvdV5+VfYYrOWHk3dpBGukrJuxyng21t9S8RXNx/wAI1b6dCHLQ7zuuZGB/1jgHCA9gSW6Zx0rmNc+MGh6B401K8vLu5u9KtYWjjt7NDI0rrwXA4ULuyN5OOK9f8Q6Bf3mgS6Tor2NjHc/u7iYlt6xH75TH8eMgEnAJzz0rz3xP8PvEeleJ7TUNF0fTb7w6ssEk1g0O9k8lNiIFByycKcdiMkN0qaWHvL31oVOv7uj1Lfw28beM/G13LqNj4OfStCeJGtH1FT5kxwckEEAA8Y4b19q67TNP+JMsMs97qHhu3cSMIrZbKR96A/KWfzQFJHoGxUz/ABC8Ow+XHqANjOF+eORHXZjsPlGfyp6/ETw/IqNaTfbGYcRwoxkJ+mMD8TTnSfM+WKS+b/M5nUmzh/Gnj/xb4WtrJfEmmGzae8aFBprAtIu1SCPMDK3VgQGU5XOCK9C8E+MtE8Ra1eaFYzS3V3p1nbXM87xhRiYMVXj+IBctwBk1z/i3SfFPj+OzsoraLQNNguPPea4TzpXZeEKKwxjBY4I6461p+E/CieFvHSrpGkJFpcmjrDPeAKGknWUsN2OScM3sOAK0jCCafLr17CdnHV6nelR6Vy+n+JY5fGWo+GrowW9xbxrLbpKWSS4Q9XTI2sozgkHII5ArppFYtuV9pxVK8t7iY5ZLGQAEBpYySAevetKkOZq3Qyi0r3MrVNIUX32xIEj3YDCMgEt6n1z09atvrFtaxhZ1kiA6gxkY/SktNFVJiz3k0lqyFXszhoDnuA2WXHoCB7VZWKOwgEAkmlTJx5rlyPbJ5x9a5FhuWbktLm7qc6UZa2M698TaPEpxf2w9S8gAH4dTXE+PdROvNZR6Ak17Iu7zJgGWJTxjLHC8c9811l1eRGeURwrmMZztGa8P+M3xfTwpqCW84aTzEZwI2xsUMFAzgnJJP5VdTDQcHzPR9jfBVJ06ydJe8r7nsOl37aRCIJtVutXlaMEidUAjYD5sMAGI68HP1pus6jc32m7eFDsuFXgY3V8p3H7T0cBlGm+GVLvH5Ye4lZ8ep/hyTXKeIP2kvHV7bm2sZLfT48EfuYVDfmQx/I1Ps6aVop3No4Wo5c02l/Xkj0XxppVz4x1fUNZ1fUo9P0KK6aOJ5JjEJI42wvPGRxn0J/KuF8YfEbw54XW7t/CUktzdSuSlzMqlIhzwmRuY44zwO9eOa54q1vV2Vr3UJ5di7UDSM2wdgCSSB9Kwik0yyShXdU5kf057mnhqVenNydRpdlovmPHwwNWmoyoqTXV6/crntfgrW9U1XQXur+4llmllZjJNIWYr6D0HU4FXLyQsQkWW3DiqXhmzNpodnBLJ5UZQMQGPJYZ64rXEEcKbQoGScv1P4k9q+6w0OWlGPkfjWPqxliZ1F1bt2/rToVbTy7ZsuN0mD2zg+1effEjUhd30OlKx2K+6Uju3T9Oa9D1BY7a0kvZGARFLMT6CvFL26a6vbm/c/MxJHsTwP61xZpW9nTVNdfyPd4Ywir15YmWvLt6v/JEBwNxRcKOF+lPDfOoPp9BUSjCDAB5704n95yckDoK+cauffp2aX9fkRretxvTkdxwavaTc+ZG0cnzYPQ85FZmdqndGxbPLdQafb3EcVwrKCoJw30qrLojFu27NjTb2TQtejvbYbWjcSoAOCvRl/mK3/Gl+NYRniOUfG5sAEj69fT1rnL1BLaiQYLRHP/AT1qfT/wDS7FrdSBLHx9VPT8qykle50L3oJb2Z2fwFvLy18YroMN44sbwSNLAWBRnWNirA9jxjI/GvpuyFp4h0CK9e8Sy1G1GyWZ32qxTj5j0/Ppmvkbw/ZXWmX8OowXssN1CwaJ4jgoc56/pj0JrW1rUNT1CWSXUL+a4SSQyFC2EDHqdo4H5U41kvMKlBvVKyPqv4MX9rNYmwgvba8XS9TmtvMgkEieXIpkUBhwQM7fwrzX49/E3xt8N/ictvoerPBpt1arL5GxHjY7mDblZSCenPB965b9nDxDd6R8R7PSo5R9h1Rtk8TdN6qxRh6HqvuG9hU/7aOkEeI9H1qA747qFo+WGQVPT9K0bvFGUE4zLeh/tJXZJTWdHsbgSD5niLwEe+PnH8q9K8A/tFeDbKQtqEd/GjqQVieKTBP1ZT+lfFIyjlXQqw7EYNSKeetZOU11O+NGhNXcfuuj9DrL49/Dm5jymp6hGr9d1hIce3yg1vp8b/AIaG0VT4gmDgd9Nuf/jdfm1E3zDhTj2qyjtgAEVLxFRdjeOV0J9X/XyP0Uuvjx8N1QBtXupR2/4lk5B/NKzbr9o/4cWCny5r6T/ZW3SPP/fbrXwLvBTDNwO2KikWPZ8o+b6VP1qb6Gv9jUEr3f3n3/4H/aD8OeNfHNl4Y0eyuYpLvcBLLPHxhSfuqWB6ete0RrKAMuGH0xX5O6TqmqeH71dX0e+nsb63Vmingcq8ZIxkEdDg16d4V/at+L+hxJHd6pp+uRKcY1C0BfH+/GVP55ropTclqePj6EKM0oKysfohdTiMhSkpOM5RcgVVWSSUiMSk5PPFfCuv/tjfEW/s/K07R9A0qc/8vCRySuPoHbH5g1iaR+1j8XrG6E1xqGkagmcmO405FB/FNpH51outziP0UjGFCjtWfrLhBGzf3scV8ueB/wBtLQ51SHxl4UvLGbo0+mSCaM++xyrD82r0WP8AaJ+EXiK1Edt4uispyylY7+3kgOc+rLt/WsasW9UaUrc+p1s9wI7u+5+7k/hXwz+09qkd58Q57eB2ZLWGOI57sQXb9XA/CvsrUdStplub23uYp7aS33eZE4dW4J4I4Pb86/PzxNqdxrnie6ukkbzL69kcFSR998D9MVU3dJHXhVaUpeRhwRXE77YIZZT6Ihb+Vatp4W1+7I/0FoF/vTkJ+h5/SvWbi5FnbJbJ8qxqFwox0rFub12bA4rBTb2R0Nu1mzlrPwfa27b9SuvP28mOLKr+fU/pUt6lrPcWOkxRrDbz3UaFUHRd2Sf061cu5y7EEn61l6VNDJ4yskllWNYVZwT03EYH0rpw8eepFSfU83H1HTw85RWtn+R6hqhtkVYVP0weCOmazDNcQkBJCu3oDyB/n2qO6eQcOdxxkHvWbPqMcJzLKnHbOSfwr66pUUd9D8sw+HnPSOv4mb8QNXvRpS20siZmfHyqRwPxNedq24rEpxltxJOPpWt4u1GW/wBTZ3yqRjCpnp/9esm2GMsSeemBn86+Zxlb21RyWy0P0jKsH9Uw0aclZvV9CZ+qhieTkc9aQ/f6bRil5MnyjPHJAxSYPmEnJ46A4x/jXLbQ9Fu89fz/AFFmMZUjaq89Sear3EQ8vcDj2NTO2FK4SNT9DTSh8v5TtGMdc04qwV5pve/ysO06+2Dyp+UxjPt6VY0+cWmoLIGzGG2sR3X/ADzWXGwimViAw/iUitG7too4EuLZy0TnBB/hNE0mFCTWh2qsCnDfjRkkYYZBrK8OXH2mxVG5kiO089R2rcjhZh8wwffiuOUbM7VO6I9GvZdH16w1W3BzaXMcw9trAkflXb/tN+P9B8X6DY2ekvLILWbcrGAjqOSzHGPoAfUntXE3L2lsm6e4ReO9c34h1exmsHtbaMuT/GRjvW9OTtaxzVYLdG/4KuoLvQY7a6ghnEJKYkQNx1HX61uP4e8N3OGbTxEcc+VIy/pnFcP4DnImmt8j5lDDJxyK7eCQletE1qVCVhq+CNAkGY7q+iPpvUj9VqxB8PdKeRR/al0oPU7FNTRSkY5NaFpcNnG4+1ZSidUMROOzK9t8OtHaPMmqXWQM4ESirD/DXw9HG0rXeoy4HTeqjOPZa1YpThWBxnrz2FaFrKXtplY/wE/ielZ8ttUbPE1JKzZ8+3sjR6hc2aRqkaysnPJIBOM5qrcWMe/9/wDulPQDqfoKveJkEHiPUUxjbMx/PmvZdE8KXN9FDJcNDDE6K+5sOcEA8Af4itnXhRV5HLVw08Q48up4XZabbzTCI+blzhSRgfjW0PC0C8zM5HovFdJ8ZNOs9Ca0bT7sPMS2792EPGOeOo5rA8PeJopQttegKemf8P8ACtI1XUgpw2OSdGNKo6ctyxa6baWo/cQKp/vYyfzNWBbNMwXyw+f9kGumttJs7qDzob7zPZVxihbSO2yEU7jwWY81zSxa2W51xwTtd7HOb73QrC8ms9QuLJjGQVtpWjDZ452kZ61heA7dbrxdYIBlYSZWz/sAn+eK2PiFJ5WlxRqQGmlAIz1A5/nioPhYgiu9R1F8ARQiJD7scn9F/WtYScoczJko03yROu1WfMj855rIkkwCar6zrVlA5D3CZHOAcmufm1q5vT9m0y1klc9WI4FVGDsROoi9qN7FbwtJI4A7Dua5my1S5gvp7uILul+XDDt6Vb13Sp7WyW7vrwSXJbHlDooPvVCyuLcQCOVGyOQ0bDP4g1p9nQx1clzG1D4ouVAV4VK46K5H6VK3iG1kiKtZ4JHTAxWI5tpJE/esiN97Kcr+PPFJcQxLGZIrhDzgLuzn+tS5N2TN4UI8rlFJfcv8ivOWmkJ/idiTTokcDkM3oQe30qNAxmjCttPOCauBECjd5iH+I5J/XpVt2Rkoc83Zr5kSBmkOAQB0LDkUuz94x27yR37fSli3nJAxzwxXqKI0G522+bnv3B/Gm9iIr397fkOMbBTsjChvXHH4UkcWE+XcuOme59abGS8koc7gBxnnFJp/PmA8gEYHpRHTQdXmklJ9blWeJ1Bdip56g0kU8iRsgPyN1H9au6sAsKBQFG7tVGQAWsZAGTnn8aowStdroaWh6g+n3vmxleVI+arV94hu5mYrJsB/u/41hr1X6VGeWOeankTdzZ1XFbE8128jFpHZ2PcnNRbnc7eF470+x+857hSRT4eQSeT61TSiZxlOq7XLnhi4MOqw5ON2VP416LayhlHI6V5OjMr7gSCDkEGu58PySNbIWdmOO5qZxLpTvodVHKOOauW0o3CsaEnJ5PWrluTnqaxaOhM6WGTKKB/kVoafKC7Dsck/QCsO3J8ocn7v9Kv2hPnvyegrOxqmeQfEJTD4uvUIxv2t+aivbPD2pg+E9OuPMYMbOI5J77BmvHviuAPGkmB/ywj/AJGuz0tmXwNpm1iP9EXofassVTU4xubYWq4Sf9dTz/4hat/a3iK+laTcsa+VHzxwef1zXKtkYI71bXm2nJ5O3OT9arx8jHbFd8EoRUV0PLqpym5N6vU3PDXie70uVQzs0eevcf416TpGtWWr26HcquR1B4J9PavFB96t3wi7rqJUMwBQnAPuKzrUIz16lYfEzh7vQ2fiVOP7Wt7VTkQxbj9Sf8AKreHPC97qmnLefbPIt5WPy884OM+lU/GZJ8Qzkkn93H1/3Frs9KZk8J2ARio8gcA4oS5YJI1b9pNtmfH4W0iy5upXuGHYtgH8qh1HWLLTYmhtUSIdkQcmk8Qu62shV2B2dQa4Ykkkkkk9SacY31ZFSfLsi3qeoT3zs0pwucqvpT0tLfaofJZlyuOP1qi33a1rb/j0T/dFbpHLPUpxWzMrMsjLjjaOSDTZYZkl8suhz0z39qtXaqELAANjrjmoLH5ndm+Y+ppcruX7SPJotfUjTO8NkgDqQM4zV0bwG8ufIA6sQQap25PnEZ9akulUdFA/Coab2NVUjG/NG5LFKWBK5OT83PQ0kWAW86Rw/Y5wDT4gAmB09KbbdG+tDQU5crva5//Z"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-11897-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In the context of usability, which part of the second tray features a boundary to contain items within it? answer: The tray_2's rim features a boundary that helps contain items within tray_2."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-12199-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: During a bright sunny day, which parts of a vehicle would be most important for shielding the driver's vision from direct sunlight? answer: The car's windshield] would be crucial in protecting the driver's vision from direct sunlight, along with the sun visor if available, which is not listed in the provided information but generally serves this function."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-12378-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which part of the personal carrying item might contain the bulk of items stored within, as opposed to its carrying mechanism? answer: The majority of items would be contained within handbag_1's body, as it is the primary storage compartment, while handbag_1's handle serves as the carrying mechanism."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-13621-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In the event of this item inadvertently coming into contact with a rough surface, which part is likely to make the first contact? answer: The watch_1's case is likely to make the first contact since it usually protrudes slightly from watch_1."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-14582-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: During a shopping trip, what item is usually carried to hold purchases and can be identified by its larger area for storing items? answer: The plastic_bag_1 is carried to hold purchases, and items are stored within its body."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-14908-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: For proper disposal of small wrappers or napkins, which item found on the lower side of this image should be used? answer: The trash_can_1 should be used to dispose of small items in an appropriate manner."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-15221-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which part of the attire helps in keeping the wearer warm and is located on the upper body, covering the arms and torso? answer: The sweater_1’s sleeve and sweater_1’s body help in keeping the wearer warm by covering the arms and torso respectively."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-16040-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: At a social occasion, which personal item is typically checked or used for communication, and what is its outermost part? answer: The personal item typically checked or used for communication is the cellular_telephone_1, and its outermost part is the cellular_telephone_1's back_cover."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-18168-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: How would one prevent the spread of crumbs and spills on this table while dining? answer: To protect the table surface from crumbs and spills, one might use napkin_1 or napkin_2 which are placed within reach on the table."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-20306-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where would you typically look to find the time, notifications, and control various functions on a modern handheld communication device? answer: You would look at the cellular_telephone_1's screen to find the time, notifications, and control various functions."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-21718-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where would one typically place their lips to sip the beverage in the upper part of this image? answer: One would typically place their lips on the rim of a glass to sip a beverage, which in this case is glass_(drink_container)_1's rim."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-22581-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What part of a timekeeping accessory secures it to a person's wrist and is typically made of leather, metal, or fabric? answer: The watch_1's strap is designed to secure the timekeeping accessory to a person's wrist, and it can be made of leather, metal, or fabric, depending on the style and design."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-23390-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where can you find an object that provides protection from the sun and is typically worn on the head? answer: You can find hat_1 which provides protection from the sun and is typically worn on the head, designed with hat_1's rim."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-23603-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: If one were to look at their reflection, which object would they most likely use in this setting? answer: They would most likely use mirror_1 to look at their reflection."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCAEAAMADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD5LsIgVHGTWnHEMDzEJA6Mv3l/xHsf0osIflBx1FaUMXtXkVa2pvFDBbowVmClMgeYOmc9COx/zzVu+0kGNZAvBWpIbchg8LFH6ex9q1LaUhBFKirjoP4fw9P5fSuSVTrE2SRw9vDJb6rcQrlGGJIz3wetdZompJ8sNwArHgeh+n+H5elR63FBBewv5TMGQ7mC5ZBkdf8AZ561Vv7ZRZyTKCQPmx9DV1JqrbmW44x5TtoIonTcgBFWI4k9K4/SNZmtJlhn3PGcYY9R9fX+f16V2dlPHdRCSM8H0Oa4qkHA0STHGFT2FW9OdI2MUq5jb9KasZPapBbk44rJMHEbe2awy+bDjA5FRXR80LJswSMGrbO8cJDg4WtfQvA3jLxD4XuPEulaCZdMg3lna5jV2CjLFVJycVpCnKo/dQaI5N4Qf4QKgkt09M1bt5BNHuCsvswwRTZRzUbMqxnSQLnhRUEsK+grRdRVeRaaY7GXNCuelVZYF9K1ZE5xVd09RVpk2MiWAegqrND7VsyRE9qqzQ/StIzE4mBcQD0qhcQDniugnhPOaoTw+xrsp1bGUonPTw9eKpSx4zW/cQj0rOuYutehSq3MZROj06P92pHQVqRwggMO9VdE2AhH+63Fa6J5TlCOOxryKr1ZcUNgj7YrQhjHRwMe9Qx46irCsduAa52zSxe0HSTqmrwaarQhZyUVppliUEj++3C598D1qK78OSWa3+nXmIpoi8Z3EYDDIxkcH6iuj8EeJPDWjaFqGmav4H0vWp7ssUvLh3WaLIwACOgB54wawLdWC7HLMB90tzxWrcVFWeoJu9jAurSPyYHCfO8Stlh14qtZ381g26Fyu5i2xuA3+B7V0V6ZwmHUSR9mC8r+FVU0sT2rvGyyAdFI5HzdfrSU01qXbsa+ia3FfR7S2yVfvKTyD/nv/KtdC7NgZJrhWtnjj81QVmikwwXg8+lXdE8VNv8AssylipChhxlvQ56H9PpWbpXu4lX7naiBdpLnJ/Srw1O/tNGbTbO+uoIJs+bHHOyo4PqoODXNDWI2wSFA/wB7J/QUyfWGZhsjJH0xWSclsOyZeSEKtQzAA9DWXPe30hPlKV+rf4AVmNqd1b3rxTMTnnBYkdO2TxQotlG87DngVWkbniqsd8sqhwwwfWjz8ng5pJDJH5NQOKUyH+8BTC+7+DPuapCI3AHpUEgBHANWSu7uV+lRPAG6sx+pNWmJozrhfoPrWfcKD/EPwraktgOirVeWD2rSM0iGjnp489m/Ks+5hJB+Wulmg9qz7mJQDnA+tddKsZyiS6eTsFbkL+fAMn51rn7B/kFaNtcFJAw/EVhVTuTE1I2+UNn2NTowzxVLfhgyglW9qlQvngfrXMzQvIVz0q0Jht2Z+YdDWaokPenlWOM9frU3HYW8uLouPKfgn7uMY/Lk0yCW6iffx+AxVmzj8yRvlDED6Vbe1IKswyAfugdaq4WIopIriCV7gbGcYDgg4PbI7fjWLdWiJrYCqoLbSxXoSa7HxNe22pWlottodhp7QptZoCd0nu1YiRq09km1Qwcg853dTTjIrcbawSDAAUj8q0YIGPWI/gRU1vauCQT09q0IIRgc1DZpYqLbnAPlP+Ark/E8IGrzIAcYXr9K9GgtwQMr+dcJ4wVo/EVwpQ7SF5HUcUQeomY1lIwYox+Ydf8AaHrWlEx3dc1zUqal5pNlLE6n5g7sTt6ZGOxBzxSrbXkj4vdVm2nqsOEGK6Z4dXu5JERn5HUmaGJczSRoPVmAoS9sngaaKUyorBCY1L8ntxXPxafpEYDSKZveRi1Xbe80yL5YVhXH91AKzUIro3+BV2ahlndC0VsCP9uVFP5ZJ/SnnT9dbRn1v+zp00pZBG14trK8IbOMb8BevHWs5tThZcDcfpir/wDwmOuR6Kmix6pfLpqneLT7Q3lA5znZ0681cFHrElvzKM8N7Iv7m+Ce/kqP6ms+fTb5/wDW6zcf8AAX+lWm1dz9+FG9wvP9Kq3Gok/diYZpx51sl+AnZlCfQ0Y5l1G9k+slVJdC09Qc+c59Werkt85z8hqrNdSnsPzrphKt/MZtR7E1gyJEDIQox1Y1M+q6bFwbqMn0U7j+lZ15ZWdtPGrwtMSgJaRixzk+tTwXdtCnyRJH7BQKJU4y95XZCbNCHXLcjZHBcyZGRiPH86tQ6neyL+60iZvQvIq1DoPibU9GvWvNGvrmyuHjMTSQttYoeqk+h9KhgupcYjixWU6UUtI6+bLTfc0kudaZht0+0j93uM/yFWEOqbv37WaD0jyf51mi4uyeo/CrV3b3ypFJMSiSLlTkc1zvTSyX9epR03h6KN7uQRTyTYhUvujC7WJ5AxnI9/0Fbstm7RDavPvWP8L7Kae7vMBnIiX+ZruJ9PuVhI2hPccmok9R3OWmsvLjywyx/SsuWF11ewZJDt3kNFjg/Lw2fUf1rq72DEYDZ/Gsa4ixq1g4IMXmEEA8529aUWNHX+FNOtLqbbcEAY71JqOmWdveFIiBk8elQaZIolURvzXRWNh9qlBkw2fWstbmpgx2gDDaik+1eb+PImHim5XBGAnH/ARX0RZaHaKmGROfavDfivCsHju9ijAC4TH/AHyKaYjjWQxxyHYWG0nGO+K5exnuJri6xmSPIdUDAtHn1rufLAQnHauU0mLHiTW0VRwQAMcdTXZhprkm2trfmZ1E7xG2qCRcgZ9auRWauwyg/Ko7e7t7Z54LlY40Zo3aUgEoeR0HJGDyB6Ct7ToxJGr4ZVPOSuKqtzRs1sxws9CrbaVExB2AfpW1oulaZ/acD6pHM9kGHmrCwDFfY1padZqwBBzWxFYEDIHI965nJ9WXY47XdMsf7SnbTYpY7QufKWU5YL2yayLmxdQML+Yrvb+zJyWBJ9etYN9ZA4ytUpCaOSltXHWM1EbTPVMVvzWeDwSKia1kHQqfqMVftWiHEwtWhBu194x/M1mywcHGB06/Wug1GP8Afx/KTmP+prPuIsIxYYHH866KVSySMbENlbAupJLEevQVvWNmrkcE/pVLT48sOMfWun0e1aXbk7R7VzYiq2zSMSs1nGiDIAoFsSRhMY7nmugks40U7ME1UELF+u0e1c8Xcpo674QRiC5v2Zc5iQdPc13VwFlQhikY/WuQ+HsAgFyzKpDKuMjPc+tdYJFAwiL+CgUS3EYms6fAY+HD+2axf7P23Nq4QgCX04+6a66/aV4wC5UH0NU44f8AS7WL5mRpTkE5/gNOLHFak+kWIMys0JYf7tdRHEkYwtqPxK/41lW0KRSgJlfoa08srfez9ak1ZPEZFb5Ywo95P8M14f8AFWLf49vXOScRg4Of4RXtiSkEZFePfERfM8aXre6/+gimhHNeWFt3OP4TWa2jpZ6hd3yOX+1DJUjkHk9a3LlT9jkOOcYNJdRsbONsZzHn36U4yaTS6jaTObtLa3knE0luPNHAZkzXQ6fAm8ZbrUNjbvgZib8q39NsSSHkifA/2apXe4tjS0rTvMAwoxW/HpSLHy+Diq1ipVAscTD6CtEx3ITgP+Ipid2Y+qaawUlG3Vyep2pDndkEccHFdpevcAsrIOP9oCsW/i85fnRc/wC8PX60xXZx01vIG4cn/eGaRYGPUD8q2J7R1c9Mf7w/xqAxOqnhf++x/jUyeg7HJ36ZkhIPBj/qaoXUYEbfh3962b9AqwljjCEfrWbcJG1uzowZSAQwPUZFXCWxlYfpkfzryPzrpdLBAALr9BXP6cFBUCt7TidowMVjUd2aRNpcY++B9BToLdGcMSxx7VXjZc8tk+lWoXckALtHqaURSOn8MsA8o+YDYOp9zW4sylwFy2fSud8PRg72f5sgda3kkRWAyB6CqZGxJeFiiZ4FV4226zZLyVLN9PuGpL+Ryi7Fx7mqiE/2xZK5bO4kEDj7h/WnFFR3Ogj/AOPhcEirdw0i8qFb26VnRGRbpcMGHvV6abA+ZWAwOcZqbGgG52kb43X3xkfpXlnjYl/Fl5IvKkr/AOgivUY5VY5VgfxrzbxYu7xLesBn5x/IUxoyktTcW7oDgsODUus2ipaoF6BNp/AVYthsx6d/ai+uFeWW1MMu1bfzPMx8pzkYHvxRFAzJ020woJJrbtFcEBSCPpWXavFgKsm0+h4rb0mF5JFxhhmqvYLHRaFZvMVyh/CuobSQLfJU59xS+E7QKql4z+HNdZKsXk46cdCKxlJ3NFHQ8k1yzEbHKg1zkzIvyk4xxivRvFdoh3sAK871FPLbG3P/AOutISuRKJSmCMOB+VZ1yhUEg8VYlVCxwSp9jiqsokC4Emf96nLYk5zxJbtcWcSoSrcsCCeoIPasy1tDaaQLY5YgZJI77s10WspiG1PI+9/SsmcKYJNvp/UVcaj5VHpcxS6jLFWDLkHr0rZsfMbAJ2j0B5/Osmz2ggZya2bLIA4rGe5aNS3BUBVTHuSKtwh2Izg/8CFUYicdcVestjSqCwP40IZ1WmWzfZYzuZAc52kc1R1rxHbaLqX2L+zdVu5PLVy9rErgZJwCfXg1u6ZGptYx6A1ZsbtdKm1a8Th1gQ5HoAxxVRkkrtEyWpymkeMrPWNVXTI7DUoJ8Fv9ICrgAZ6DmtCea4j8U6cFl3QOWR4DjIOxiHBx2xj8a868CXMs/wASIjI7Nm0L4z6qTXpUyI2v2LMo3CQ4Pf7hrZxUZfImLuaGvaudHt4roWFzes0qxrFAfnJIJyOPasW++IDWMImu/C2swJv8vfI4Vd3Py5x146V2uhTtBrEUoyrKOG9K8n+N99Pd6PfXm5kaTXzjnnaBs5/75zUQipy5TV6K56bo88Gr6Naal5Dxi5jEgRnyV9siuK8RRP8A8JJdgMdgZRg8n7orr/h2m/wDojE5JtFz+tcr4lZU8U3sZODuXGf90VD6lIz7gPFZzvDzKkTMueeQDikVpW0aGWU4ke3BcEY+bbzVoDDZFN1DH2Nzg/cJ6exoT6DZnWiBwBuU+xzXVeHdNViGHlo3sxH8hXO6WiOyjIz6HrXb6JGIkBFEnYaOs0ZLi3jwJoD7M7f/ABNabXU5Qhhb/hI3/wATXOR3RQcGplveBk1kUJryieJuYAR6u3/xNed67Ayt96Ppn7x9fpXf3cySRHPNcVr8anIIHSqjuJnJyocnJj/P/wCtVSdXCnDp9N1XLpAHOCRWfcBgD836Vo9iGVNeANraEj+Jv5Csi4GbeQY429q29WG6wtyOz/0rJnQeS+W52n+VJdDFbFeyEnACqoz6ZNalvJtxubH05rNtlUAEk/nWlBgHqTUy3KRcidT0BY+5rZ0ogMPuLWTCWzgfzrc0suuDvP50lsUdrociyrs3AlEzwfesPxdPLDe3FnHjFxbAAd84atbw67yyOobOE5y3vWL8QdN8QR3o1XTzZ/Y4oFDiVssHyeQPoRVRV9CHueffDseV8TYQQflswp/79mvUp42PiOwnEjbQzKYyvX5Tz6+v51wvgTRNTh8R22v3UcKxvGQ2xjnGMDivSJoX/tiymwQBIQR1z8pronK8tOxMUT6refZI4ZkJyZADx2wa8u+J5mk8NXMZQMialHM0gORl2JC/kea9K8a/bRY2/wBgtBcTLKGMTHaGXBB5rzjU/wC2dbtrzQotGY77tLlhG24gqowFHXHGTWdN8suY1e1j1j4bRf8AFCaH2zaLXH+LlU+LL5H5G5f/AEEV3Xgq2ubPwjpNvcRTRTRWyq8bxNlT6dK4PxcwPi++4/iX/wBBFQ3uUijdzSWlq8sarKRhYw7BfmJwoJPbJFTQTG+0GK7ZAjyREkA5AbBBx+NYPj2SWLwpMyyrGjyxgkqDjDZz+lWfAUV4fC2+bUoLuLYdsajlTxyT2yOcf7Wa0VP93z+Yub3uU29KiGBvUH8K6KARrGApKH2OKxrLYqjdGR9Ca0FdCnG8f8CH+FYssviWVBxJv/3qct2wHzLg+xzWaWAPDTe2GX/4mmiQd5ZB/wABFIZtNcK0eAwz9aw9cKMMMuflHP4VIxUp/r/ziz/7NVTUeRgzdAP+Wft/vUIDmL9E3Ha5X8ayp94BxJn6itq/TJOHQ/gRWFeIBnDLn2J/wrR7EMfqQB0uFs4+df5Gs11UxyfLklG5/CtS+50RCBnDristicHJySpwO3ShbGKKtv0HGPwq9C5DcL3qjaq5A+YD6VeRRnksfxrN7lF63bnJYCtqxZQoOGNZFpuPITP1Aq99q8mGYIsTTxwmRYyDlvTp0/H0NVFXKvY7Pwg5a7mH/TPp+Ire12Iz6LdRZ6xmuO+Gd7LqBaaW3+zuYiCDnnBHOO2fSu4u126fOxYn5Dx+FDVnYlvQ5LRmjGkW4LD5Rit8pv1KxliK7RKQxIPTaayNIthFYbdvRq6mKBWvrNigOJOvp8pq1uStyK+2NdR7WBGysvwvbCPxbLIg4Zd35Ait7U7ZGvkATnYOg+tVPCUGdRuZSql9pAJ4OM1JqdKXbPOa8k8Y8+Mb89/MH/oIr1mViCAVxXkfi5m/4S/UCF3fvB39hRYZh+L7SK68OTLKjOiOjvtHO0Hn+dbegaNaaZ4bieK18u4a3AYkkkAkHHPTt+VRQs5ikBjx8p7+1dBdh/7Pk2jPyD+Yoc3y8vQfKr3KUJAXnIqYMMVUWVo0+eNseoFHnRNna4z6d6QyeR2GdrD8aiMxB+ZT9RzUZc9iDUbS7T8wwKLAWRPG4+Vh+FR30oLtniq7ujjJAOajupTvbOCMmiwFC+2nJ4NVdItkNxLfzDMVsMqp6M/b8uv5U++dOcgr9RSXRMFpbW2fvpvb6sc/yxVPYTM2dv8AiQNk42lf51lIJJTiKN29SBmtizZXsWVlDAgHBq5CdgBEa8eozTMEctbxzxgB7ebj/YNW3lf7M/khRLg7d/ABroZpZEw0fyA9AKcl5O8eyVY5FPUMgNR1uUcpolxrEEIikgjuRu5lLdB9B1ro9HB+xyGZSjOWLhsZb6/4dq1rCXTY1IbRrGbaOcEgj8q6jStHbxNamz8LeBrySQ/K139obykz3JPyj8T+FaNuT2BaGR4LIguSsK7j5XTPuK624le4s5YHzFlcH5CSK0IPhtY+G7kXHinxGvnCJXTTrFMu3qPMOR146H1rpbTxLp+naWZPDHhlbCSP/l5uj5src4yCc8fTFWqXVslyvsc14d8E+IdRsFuX097O1fDC4vG8lD9Afmb8K0msdPintEstSa9kWUibERRVIBGOTmszw58QodVmvNN1zUrqS7uLtfJiaNiqysxUAcYALAe3WujttDvrvWM6fasyK5SVjwvTI5PGecevNEktbD5WrNkN1p7MFnjkI2JyCM5ArB0FJ7fU1WWIqWt1ZsDOM9D+PNd9Loep2lu7S2MuEiLMYpNwAx1wDWFDYm2muJEhuI0EUZciPGCR1JIrKzLTQ513cgg/SvG/FZJ8X6hj/nr/AEFeqT3UTsQLiUtg9l/wFeWa1EZPEl86vx5mMnqeBSKQyWS8Ok3cMMkaM8LBSYUZgcdQSMj86fp1jrVlbSC81KOeBtnylSSFGBgc4Hbt2qW1jWULaqdssx2bj0UHua3mt01SR7TQ3XUlIIyjFZUZeoaNgCORjNLnaViuXqZMIyDtZlx3BpJTKOrbvrVu60fVdLUNqFhPaq/3WZflJ9MjjNUpXcdRn6UgIWf1C590FRSFskgR/QoKkZ0Y4z+dRSLx6UwImkUEb7dOvVSf8axJNdhk1RbP7HN80zxeYrEAFQD3z15rZIbp1rE1C3vVlJtZcfOJwhPyOwBHI+jEZ960hy63JlfoWkjiu72O3JlTe2DlhwOp7elVtcmEt/CsWQrTKvrhR/8AqpsN6JB58OYpVO10Ycoe6kVLbRh7CS6kT51lCIfTgk/0qZaJgcvdatLpmmLdwiMuqjhuR2z+lO0fxjfXzKv9jxzDjcYwQB9SMCur8MfDvVruya81nTo7WwRAfM1A7Bj/AK5/eP4jFb9la+Dba7xes80yMF+yuwgRVAHzBFJyO4y3Ira9Plta7MFGV9zLs7+G9QW8fh4TzYwvlSPx7nFJqGhXmm2R1DWpWt7cn/U2SiRwD2LZwv4nPtXW65e2qqmm2MNlbRFDIFjVeU4/hxhiSR1561g8i0XSr5rgreRACSUj93xnB9v1qIpblsh+GHibRn8SDR/+EWVhJnyZriYykMBnLLgKc/Tiva7LWNb+0WcUk8kcMhCKsXyKO4GB24wfoK+d/D2qWPh3UnvXtWnugpSIu2BGQcH9MivpjwNp994r0Gzv7C1e2WRVlSedSqqevHdvwrSUPeskJS0uzI8aaNdZivrJ2W4Xem4jdjcDknP0/Wqur6fetJqFsutXNnBbaMkxZ7PzYrfDA+ZgHLk8qV/EV7tZ+FrIxL/aRS+cAEhl2pkY7DryO9XtQ0HRrxLr7Tp0UgubcW84UlfMiGcIcEccmuhYKo4p81v8iViIRb0ufM3hP4V6rD400+/Op2l99puYr2K3EJV5YPNDiUknao2Ett+9k4r6hNkkUjvbsIsqFVNuVUD0HaqkVjaWssEkEMVvFaQiGNjkbIwMbQT0AAHPNcfF8X/C158SrPwLpNwNRv3jmnuZYjmK3SNCxy3cngcetbUKcI3UtW3+H9X/AAM6tSU7NdDi/jJ8XbnQfFLaF4esrOeSx+W6uLhS/wA5wSigEYxxk+vHavNvHHxa8R6t9ih01F0y3uysVy0TFi/I3IM/dU9cdeTzXU+HbSI2txqN3Ak17qcr3Nw7qGJ3sW289gDXDeMND0+x1VIbWZGW6/em3CkfZ5Afl56EHBPHTkVw1JKcmzeEbI0tPumYqS+7K965LUZc+ILvJx+9/pV7Qbl2XaJMMpwQeayNQWRtduSAOZPXjpWLRoi7pF1NJ4y0vT4Syi4chmHUd/6V6bcXUuiQBbcBY4SRK5wDz3PqSa8uufC/im4uLXU9HeOGVAdrbwCM/X1rP1TWvEcHiRNG8SXlxNdSRruEYXylGflLEdc1Cg3sVzJbnqtvrOu+Jojp8UFnHpUmUnurpsHI7pz1BqjP4JumfZbatpszYyAJTn9Aa56TIVIy7BQBgelRFRnqaSC5tXHgTxAMhYraX/dnX+uKzrjwf4oh+7pdww/2MP8AyNRRz3Mf+ruZ0+jmrEWtazAcw6ndKR0/eGnqIyrjRtdtxmfR75BnAPkNj+VZN7Dc211Cs0TqSCMOpBrtn8a+KSqh9Wkk2njeqn+lVNb8Za3cyWF9NJFJPYzb4yU65GCD7Gq1uB51rFrul89TJDJjBZDww9G9fr2rS0+KWDwlGs0nmu8pYtjGR0H8q7TV/Gw1OwlWfQtKkyo+byVDAg59KwtRurdYw7WkaJJysSDCp7CiU24WaEkuYtW2nwfEbVLTSfFmv3Wn6ZCjySPbyRp57BSF5f72GGMYPWuK8L+HfD1xq19p1jreoNcQ3c1qkMYAadUcqpxwOcA4wcZr17wX8NbkXVtb6rdwy+ZAWdYWYRw7WBGGIGeSfu45PXiqfir4XfDPVPGF9byeM9bPiB8T3aix84AtyGZ+AM57tXXTnHk5Uzld73IvDXw81D7cWYG2jYjZAAJplA7F+FHXr+ldZ/ZXhLT5BY6hZ3hDqBLI1u8sgbPI3qp24x/Dik8E+DPGXhOS2i07WdavNNeYENcGAWyRZAZpNzvjA6BSCc13eua/o4uEEEDahNGSVIdliB6Z64J98VyVFLmupfcdEGrao5zTfhj4RstXk1S3tWmuJHEga5InWP3VSMA+5BNei6dc3EEIjWaVwO+7NcdceIL+RtkVtbwjAAO0sRjr19alg1XVWdpGkjO7gKYx8v07/rSd5bgrI75NXv8AftEZ29ix/wAmrsesEnBcZ6D3PpivPY9V12AhjJHIDztdB0/nW5pOuw3ciwX1ubeU8K6t8ufr1Bp8847MLRfQs/ErxB4N03w5NF41uoTZSLzbNIyvL7AKQa+X/hDrHgk/tKX2oeFNLfSNMOhXEbQPcNKQ5ZQT8xOCVxxmq/7TPhC+8H6/J4m1/U7vXtLuZQLBZekbHnZL/exzjGAQOfSvL/Cmt3Vl8WDfySbppbMqTjAI4IAHYYXpXoUuZ0pO97p/oYS5eZLzPqhLq1lt0k06R5LR1zA7LtLJ2JHb6Vz3iK0W88oIE88SKQ3fAzxn8TV+CSEafALUBIPLBRR0AIzxVSycS6/aQA5LSDP5iuC50nnt95+l+Lbu2ETFGYOuCOjAN0znua53xBqlzaa1cyRMfklDAEd+DzXefHqzGleOLWWFMCa1BwPVXYfyxXlniW48y8uCPvM3Azz0FW1exN7Hb2HxZ1eCLddaRG0EXG8Q8fhgita6+Ifh3VljbWfD8Ekkf3JMEMn0YZIrzga1plo8UN/IIJzGGIYEgdutPS/064fZFqNp5JO5V3AMDzxz2qnT62FzHoo8SeC7s5eW8tgf7rqwH/fWDUy3vhWY/udbkXP/AD0tyf1UmvN5PIlQyIsUhUE7VIOR3rHvRbI8QiG1WbBAJ3JkcZH4VKpp7A52PZRDpcg/c67pzf77mM/qKfHo7zt+7vbArj74uVI/TmvFJ0lhaMJczR7/AJR85xk9K1fB13dIb95ZWYxRLwfUk0OlpdMand2PVZ/Dt9txFLaS/wC5MP61mX2h6oIXU2rNkcFWDfyNcLFfX21XS6kBbqQx4pRrurwkLHfzE+m7pU8j7lXRrzWd9BKqzWs8W5gpLIQOam15s+Wq9ADVCw17VbvU7ayubkyIQ0rAjsq/4kUa3qTWsqQ7Vb5ASeuTSnFpaiT1PuLUNE0+5sU81jaeSrASRsEwD1zxivm74leGPCHhC81Hxda+KtXmYlXvZpcSIRkKqpgDPYAYIrs9S1/W/EsgfUbjZBnK20Xyxr/j9TS6pY6bqGlmxvbCC4hKjzElXesgyCMqeOMUUpciS6dTHlvqVvCUgn05lSWSS2uVRhknByCQcduKsi3COVAxg0/SkEEEkKABVKlQOwHQflWrb2Et3cr5SMwbjgVN7GhXs7R5ir4OM44robTR2RQ0iYJ6A10eg6IllF5s6BnA+UehrTkjVusaDHTApO6KRyyaYe+36Ypk+l7mVEXLE4GK6nyMngVi+K9c0/w5p9xdXFxHE8SFpJG6QjH6sewqUmxnlP7VM2l3vhVNH1H95GbiFFVWwWaM72OfT7qn6mvjC81Rz43hvcoiiTyl2DACHKj+fU16v8TfG9x4v8S3kUKSLFaxAxxnnYrfcB/2jncfqPSvGfEtrIuqvDCuWEpVQPUGvXwULXhLqvzOas+qPqDwTqz3vhG1xueSEGFsDJyvT9MVveArK8v/ABXDIsLttnVOnfPNeXfArVvtGoJpckoUXy7VGcDz17e2eR+VfUHw+8HtCP7VupZoVBzEImKM7euRzt9+/wCFefyuMnFm7d1c8h/azjey8UaGkq+W7W0x54yPMGP614RqdxvuHKkMd3b6V+hF3o2kX2pDULzSLC7ugoTzriBZHVeuAWBIHU4HrXM+KrX4W3cos9Tt/B80hHzJIIA4P14INP2kEhcsj4B8ZSLJrcBwcGJBgjOeTkVX8mycf6sRn0DnP88V9jeKPgF8MfEbfbdPhuLGbblZdOvi6Y7fK25cfTFeP+Nf2bvEWm77jw3q1vrEanIglTyJ8egOSpP4ivTw+Kocqi3Z+Zy1Kc072PHvCiM2uNEkhTAZSQecZ/nXWan5clk+IwQhDKSvccn+Vc3pdhq+jeJbvTNW0+4sbiMMzQTx7W+8MHnr7GugUuyhS+9WGTkDg+1Y4nSpdGlN+6UpZ1ktNoCtEyAxsD+VWdBkK2eqTrNuDrGoPU5y2az7eNbZNhLOwJXnovPQVWN1d2huPsvlMkoB2uM/Muf/AK9ZWveKKvbVnRW7ssWN2CPUU6JVXdLJLFEo6O54J9K5m38SX/leZ9mtplCbmG05B/A0upeII9Y0lbN7c2TvtcOPnUgHn37U1h531K9pE6fS7iOHxAl7dzRLE0DQIyHIDEg5P4LTNevkvNWl8gDyQg2Z6nAHP05rhneCVo7db18KwJeIEH049etXdPS/g1a7t55iZIE2Fic55x/SnOh7rbfQSnrZH2Tpcf7sYq7M8cY+dgOO9dPB8PdXgTbHe2co/vfMp/LFaOmfD2JZRNqUyzEc7FPH51wQhJrRApI4WzttRureW6tbSaWJAA7IpOB612nw41JJA1h9ineRQSbny8Ko/u81L4q03xPM0Fj4d8RaToOnxrh2VC8xPsAMUvhTTNb0OWWbVfFeo6/G6YEUtssUaH+9vY59atxsr3GnrY7EMuMBcCmTNFCm+aRUX1Y9fp61w3i34k+H/D0LyalrVla7R/q4D5sh/E8D8jXgfj39om+1CWS08HWbRs2VN7Mdz49ien4YpRi5eZdrHvvxI+I+h+EdOeW9vBbMR8iDBnk9lX+H6nn2r4/+KvxcuPENy0t8rw6bEWa0sIySJZOxkPcZ5J69fw5bxNqFxJdC+8QalNcXc3LEnc/PRVHqf/11heIvC2v2UkWo6zBLY6ZMAUkBEqx8EqrbejdsnAz3rvwuHjKSc3p+foZVanKrI9f/AGcfgh4h8YXk/i3xrJdaZol0PMHRLi7bOQVBHyqD3I9gPTlfit4HtPCHxKm0+HUzqFpt863nCgNhsja4/vA5zjrwfamfBX4oeLdDuL3Qxqd1Po88bCOO6mLCBh0ZSenuBwc/Ss3xdqk+sX096HclFcKzcscc5Puc060qvtpKVvkEFDkTRkeE7XX9I1BjbQSSxrIHRo+SrA8Fa+3/AId+OvEwntD8RreLSINciQ6UHj8oROnylHGflL5DDJ/LIFfC2h+MZ7G5jkmi+aOQOrxnBBByOK970r4wS+IvDf8AYvilINa0+UAqLg7ZV91kHIP1zUYqNS95rfsOFraM9l/akTxY3gBB4WE7wxzltSS2B8wxbeDgclQeuPbtmvkWx+03P7wsjfNg7gM5+nWvbPDfxI8S+FUEekamNb0ZMbLLVSfNhH91Zl7fUY+lbUvxB+EviDdP4q8IXGkXzH551tw6E+vmxEE/iK5ItxVrFtX6niVhNqdhL59pJLbyLzvgkaNh+IrvPDHxd8TaYyRapL/altnBFz/rAPaQf+zZrtIYPgncsZLDxPFDuBG03eOD2w4NUbyx+C1h+/uNdt7rBzsa5LZ+oXGaTnGW6f3DUZI6LXNL8PfEfwvb3N1aZWaMm2nZAJrduhwfqOR0NfNF5ZyaXqdzpshy1tK8bEHglTj8uK9e8XfGnQbCwGm+D7IzSKgihcx+XFEOgwvfFeX3lnpd7PJdW/iRZJ5WLP8AaYTGxYnnJyR1q6Skl72xM7X0OYvWZZZDt4LZwapyybs4HfqOxrd1HQ9RcmW3+z3SEY/czg8/Q4rn7201C2LGaynQZ6lDj8+ldMEmZt2M+yke0ungkZdpBKkD35z/AJ70kIZZpIXwYlY7COq5OabdTKJVZiMr1/HrVb7Sq3W3kMSRz7V2qLlrYyvYtRYhvoJHUFY5FdyB95QemK3ZJRNPcXHeV92fUdq5vzSzHksCABW6pxHiufEJ2VzSn1Psm2/aH+HRtwZERTjoO1Z+q/tK+BYEJtbCORh0+TNfDtrcBiA7kA+lTw3JimV2iSUBgdkgOG9jgg4/GlHLlDS7D21+h9Sa/wDtTXBVo9E0wR56EIBXl/ir40+OPEDMsmotbxt2VjmvLpJ5p3dztjVmLFUG1R7AelSW1xFFk+bkjqVHT8atYSENlcftJM2S9xf3Am1C5llZj96Vv6VLqPiDT9FgMVmq3F50XI+Vfc/4Vnxyl41ePI3rnOefzrntchVJzIpOCQMY9qujRjUnaf3Ezm4xui5pt/8Ab9TM2ou81w5+VmPAz6CvZPCvie0sfDrWeqbpoEQqqsm4Op/gIP8AWvFIra+vZ0utmxMqF6DaBwMAV013dyXE6HoqnCIOg4H60sZTjKS5X/wB0ZNR1Lplt5NVuprC0WztywWOAHIQAZxn8aWxJeWVSeTI3P1//VVLT5T5Zcj77s368fpiqkmoS21xK628kqIw3FO3X/Guf2cpNpF3SSNc2OlyMwubaNm7YTBz+FPjjjhCxxJsReFx2FZtrr1nczhGjZCBkl8Y/OtCGeGcBomBU1M41I6SuNOL2Nawu5oOEmkGfQ/0q6t3K4OXRgfUVjkBFAzk96bNKiAMHP51iWXbgRvktBETVI2/myiOCENIxwFQEk0aPqWkreltWS7mhX7qQ4AJ/wBrkHH0rs9K1XwndXbSWdxBY7EEaLkxksfvE54PYc+9XZx6E6MoaX4QVbN5r8yrM3RYyPkHucH/AD3qleeG41DGC+C/7MseP1BP8q625t3YboLoFOuWAI/NcVm3UEzkggO3+y/9Dj+dSpPuDSONutLv4B+6MbjqDFLgn88GqDXurWIZWe5j68OCa6a8glLlXWQAddyHA/EZFc80jpfXJjkxghflb0Gf61tHVaoh6bGdPqe8E3dvb3O4870B4rNmm0uWds2AiYfxRueBWteN52fNWN/99Af161mTxWuzIg2Y5wjf0Oa6qTiu5lJDkh05riMRXMy5cHa6g559a1ZBDg7Zcc9xWBZQxNqcLLK+Ac7WXrx61p3OOcNU1o+8lcuD0Zxyucdak811biVgPQGoBnOB1rT0+2jDqZcM7duwH+NerNqKuzkjd6IhSRnO3OSTjNTOWWNoUHzHAJ9KisztuMEYC5q1bqskvmE/ebd+VZSdmaLU2LceXAkYOdq7c1C0cVxJvMSuFIK5PGee1Mjmbgq6sCC3+FSwSAqo+6Tyc9s1w2cbs2umWI94O0jHXJp07hYGYdUBYUNKFKL70XKxtEFAwXdVyPTPP9axW6uVfQkt1McMaAj5Qo/Gq+uTNHGunafOZxOqu4CbSHI+ZffHrVzyz/CSSeg6kmoPGWg6h4daMXpMN9NliiuD5akfdPoeufTpVUbOeopOyOQnguI3ZDG3y9cc06K4uocBXbHsa6XSYitss0pEkjndnHarE+k213lyBGxGcrXXLFxT5ZIyVJ7pmPZeI7yEbJQJV/2uo/GrDawbluTtHpTbjQ7mHJCLMnbb1/KqawRjKlGiYUuWhP3oofNNaMvvc7ULAKSeF56k9KehRFVQeQOc9/WsuNXMxCMGCDPPHP8A+r+dXI8Mu5g6Ef7OR+lTKmkgUjXstWvLF/8ARLh1HdCcqfwrrtH8RJqMRhYmO4AyYyevup/pXARhZBlZN2OoBqRHeOZJICwdCCpHXNc86UX6minY9Miuy24szIQcVjQGOey8+aKNxK7Pl1B+8SR+mKfc3GfDjXZA86SEt9DjGPzqvxbQxWwbhFCjn0FcyVkW2Ubuxs33BVZCO6sf5dKxbyzCg+XKG9d3B/Mf4Vs3shVucA1jXU2SQpGe+a3puRnKxTs4ZI77ccEBTghs0+4kIzUMcwWdz7Yqvc3HXmunlcpCTsj/2Q=="}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-24700-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: During an online meeting, which object and part of the object would most likely remain stationary on the table? answer: During an online meeting, the mouse_(computer_equipment)_1 and particularly the mouse_(computer_equipment)_1's body would most likely remain stationary on the table."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-27112-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What signifies the simultaneous usage of different transportation modes in this area? answer: The presence of car_automobile_1 and car_automobile_2 suggests the utilization of roads, while car_automobile_3 indicates additional vehicles on nearby paths, denoting a multi-modal transport network."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-27435-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In considering the space where the contents of this item would be placed, what part of the vessel primarily contains it? answer: The space where the contents would be placed is primarily contained within bowl_1's inner_body."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-27983-4", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: On what part of the bike would someone adjust gears to change the difficulty of pedaling, and what part would they press to halt motion? answer: To adjust gears, they would use the bicycle_1’s gear, and to halt motion, they would interact with the bicycle_1’s handlebar, which typically houses the brake levers."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-29200-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: If someone were organizing the kitchen utensils, in which area might they place smaller bowls that could be used for holding spices or condiments? answer: They might place smaller bowls for holding spices or condiments in the area that corresponds to bowl_3."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-29279-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In the scenario of organizing a room, if one needed to know which parts to adjust to change the height of a chair and to rest one's book, which parts would they need to look at? answer: To adjust the height of a chair, they would need to look at chair_2's spindle, and to rest a book, they would refer to chair_2's seat."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-30032-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: If the person were to move their arms, which part of the clothing is specifically designed to accommodate this movement? answer: The sweater_1's sleeve is designed to accommodate the movement of the arms as part of sweater_1."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-31548-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which of the objects has its volume-holding capacity primarily in the upper section? answer: The glass_(drink_container)_2's body has its volume-holding capacity primarily in the upper section, as it is designed to contain liquid above its base."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-32992-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: How might the infrastructure in this scene, which seems to be for transporting fluids, maintain its structural integrity? answer: The tubular components, such as pipe_1 and pipe_2, appear to be part of a larger system likely connected to the much larger pipe_3 ensuring stability and continuity for the conveyance of liquids or gases."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-33740-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where would you find the part that adds a decorative and functional element to the upper portion of a sweater, usually around the base of the neck? answer: The decorative and functional element is the sweater_1's neckband, which is located around the base of the neck on the sweater."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-33998-3", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Considering the first car, which part contributes to the vehicle's aerodynamics by guiding airflow over the vehicle's body? answer: The car_(automobile)_1’s roof contributes to the aerodynamics of car_(automobile)_1 by guiding airflow."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-34992-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In case of any spillage, which part of this item is most likely to catch the drops before they reach the table? answer: Spillage would most likely be caught by bowl_1's rim before reaching the table."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-37312-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: How is the helmet_1 secured to ensure it remains in place during movement? answer: The helmet_1 is secured with a helmet_1's strap to ensure it remains in place during movement."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-37405-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What type of protective gear might the rider in this image be using for head safety? answer: The rider is using a helmet_1 for head safety."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-37799-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which item would be used to ensure a snug fit around the midsection and secure clothing, possibly found on one of the onlookers? answer: A belt is typically used for this purpose, such as belt_2 and belt_2's strap."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-38631-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: How could another vehicle maintain a safe distance while following the first car during nighttime? answer: The following vehicle could maintain a safe distance by observing the illuminated car_(automobile)_1's taillight."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-39225-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: How can one tell the time accurately in this decorative setting? answer: The hands of clock_1 allow one to determine the time by pointing to the numbers on the face which is protected by clock_1's case."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-39575-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What are the protective components of the timekeeping devices visible in this image? answer: The protective components are the clock_1's case surrounding clock_1 and the clock_2's case that encompasses clock_2, both serving to safeguard the clock mechanisms."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-39635-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which components would be essential to support the riders on these parked two-wheelers when they decide to sit and control the direction they are traveling? answer: The essential components would be the saddles and handlebars, which are parts like bicycle_3's saddle and bicycle_10's handlebar."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-41248-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: To ensure safety, what part of the containers should one inspect to make sure that they are properly sealed before putting them into the refrigerator? answer: One should inspect the can_1's lid and other similar parts on the containers to ensure they are properly sealed."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-41383-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: How could one adjust the accessory worn by the individual to ensure it displays the correct time? answer: One could interact with the watch_1 to set the appropriate time displayed on its face, which typically involves using the dial or buttons that are part of the watch's design."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-41541-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where would someone most likely place their hands to carry this decorated item to a table for serving? answer: Someone would probably place their hands on the horizontal edges of tray_1 in order to carry the item securely and present it on a table."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCADMAQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDzXVpIlBAasK0sDfXeRnGakvneSQ8k5NdN4T08hQ5FfPSdkexbmZa03SHghXBNXGluYFwCcCtWOJhT2txICGUGsrmtjm5tWaNvmyKdB4mEJGQp9av6joiyg4FcjrOjXFuWZQSK1jZmcm0bGq+KA8J8tsfjXI3+u3cxK7mwfes+6aaMkHPHrVNrjH3lraMEjJzbNS1uW3Bnb65rTXWhDFhWrlvtS47imNOrHlqpwTJ5jphq8krE7q6z4iXK2dzYQjhIbSOMe7bFdv1c15gt0iA/NXcfF+XyIvD6s2ZJLETyk92kw36AqK3pwXs5r0/MxnL34/P8jPGrFlwG4qKbVAB97tXKm9x0NRSXUjdM1h7NG3OdBcarxw1dv4CmebwBrV0T8sM5C+7siqv5Av8AnXkf75j0Nel+FTJD8H9U25B+0yTyH1O2NEH5lj+FXGKR25dJ+2bXSMvy/r7zT+K+pmKUOvEIupo4vTCbU4/74rzKbVZXclSa774uwPMmmWMalltRhyB1dkR2z+LGuNtNDuJCP3ZA+lYw5VHUyxif1iaXf/IpJf3JPBNWVju7ofcJFdJpfhnLAyLn8K6iz0iCGMAqBUupFbGKg3uT6xYSQ/s96Hp4HzSXbSuP9ppHIH4Kg/OuAtPD7tywxXtni6COLwB4csyMfKZyP97IH6D9a4smKIcACuTDTlaT7tm04LT0Oah0BVxlauxaVEg5AFX5roDgYqlcXZweeK3u2RZEMttDH6VSuGiXpTbq5JzzWfcSEjrVpCbsSS3CqOKpS3Ge9QXEhHeqbzHPWtVEm5ZmkzzmqryUjSZ71CSzNheTVWJbHs/vT4UknbagNWtP0uWdgXBArqNO0mOFQSoFJySGk2RRReZdqPevQdDhEVqvA5ri9JQPe++a72zXbCoHpXHUZ0wRdQDHSrEQTPNV4zU6HFZll+G0SQYxUeoeHo7iI4Xk1JYykMBmt22k3J+FNMhnj3iXwhIm5kSuGvdEnjcqYjkdxX0dq6RPEwZQa467sLV5SSg/Kto1WjN00zxSTSpgeY2/Kom0uUnhD+Vewy6Vak/dFRjR7XOdoq1XZPsjx6TSp9pyh/Ku5+LVq93DodwVIM1sXUeiAIi/omfxrp5NHtcEYFXvE1lHfTQOygQwRrbw/REUEj/gRb8q6aVZunN+n5mFSnapH5/keKJpMhH3D+VTQ6NMTxGfxr0/+yrZew/KlFpbJ/CK53WZt7NHCWXh+VyNyY/CvQ7HS47T4dJp+0b7u6eV/wDcTaB/48aahgjHCitW8ZU8LwTE8zuyIPREJJ/Nm/SiM27+h24OKTm/7rM7WbeOZIHuB+9mL3LZ7BmIX/x1RVSKK0j6BaueJ3DvHOmQjKI0H+yihf8A0LdXMXE8qk4JrFJsWMf76T9PyR0XnRIPl2iopbtcdea5SW/mXuaryalIFPzc4p8jOXmPW/iPKYPD/hxCeZLUP+AVVX+RP4157Pccnmur+OV19kbw/bA4VbEBV9FUKg/MqT+NeXy6iT3rHBwvST9fzZdWXvGvPcDrms+5ufes+S8J71VluuetdigY8xeaXJ5NQyOD3FZ73XXmoGujnqa0USbl+VQRxVC4XBJpBcs3AyavWNhLdEFgcelPYNzPt4JJ3CqK6TR9E6M65NaOmaVHCASBWm0iRLhazlPsVGPcbDbw26DgVHPc8EDp7VDNMSetV2OetQWdVDoT2t2X2kc10MQwij0Fa2qNCxJUCszv0rnqbm0SVDxUgaoRwKcDUFFqGTaRzWtZ3HAGawlODVqGXaBzTRLL+pz/ALs81zVxLhzzWne3GYzk1zV7PiTrV2J2LLy+9N873rPafpzTGuPenYLmhJcAD71aHiORY4LCOM/JFbopPq7De3/oYql4b0DWvErXK6PaGf7NH5kp3YAH+NXPFVjcxeHtOG3E0KBrhD98vIxCgDvgKBXTRX7ufovzOeq/fj8/yMGW696hZ5nQyJG5UAksBxgdalu9G1PT9YsbDXbSfTBdMuGnTb8hON1dhY/CxLy/0zRvEfiS4DSzytaR2wLRyQDnG4fdJrkqVIw3N4pyVzzqW8yMo4I9Qa3dcuGj8B6XctwGbyo/cAs7H82A/wCA13lv8NfD0nh++sn03VNNbT5phFPKwZ5V6huOq5rjPEeh6pfeEtLsbGAyDT4lEmTt5IZ3PPu6j8K1oVIzTaO3C0puNSyv7v6oq+IElnhsfJwVhtYlY9i7Lvb/ANCFczdOEYqx5716Lp/h46hp1tazX8dm8Vn9rm3DJcsdqqPwWsPXPh5fTW32vS9QhuCeTG/yk01KCWrDEYWtVqNwV7Jfkv8AgnEyqr9OaqS2zsyqoyWIA+taDaNr9rKYZdNuS4/uoWH4Yrc0fwn4qlvrFptDuo1llRkEoCM67hyATkiqcoxV7nA6VS9nF39GaP7SLD/hLLGBT8sFkI1/4CSv8wa8kmbBPNet/tDRO/jto1UmOG0SMNjgkFt3P1zXjFzKyysnPBxUYFP2Eb/1v/w3yJr/ABskeX3qCSXPemfO54qaG2JPOa67GNyDLseKmhtmY+taNtZFiOK2LDTgCCVpOSQ0myhpelF2UsPzrrbCxjgiBIFJa26xjpirMjYWsZScjRKxDO+DgcVUkJPU0+VstUTGkURt9ajapDzTG6U0I9K+0eZJ1zUmTWPpTtJIMn9a1x61yS3OiI9elOBIpFxQaSGx4bNO3kCoN2P/ANdNkfAqkJjL6bCHmubvJiJOtat/MMEZrmdRuAH61rFGUnqWGn461BJdAfxVnS3YC9a9Xsvin8NbDQbTT/8AhXtvdSwwqkssyIWdscnPXk1SiRc6z4Da14VPk6XpFlcrqogMt/cyy4V8HoB6c8Cul0vwdB4l8eXGo31y1lLZXYvliRQysgOxVOe3yk/jV34Yf8IfrHg5vEdn4QsNMZlbEaRgOqjuzY4z6U/4Y6pa32ua7qwKQQPAsVusjYULGeSfbLV1ULKMk3pb9TKtCV07dbfejrfFumaVrulve6z4Xiup7ZG+zrcoJM+4xnrWF4VY3EEMlxZJayCHJRYiioAcDGenFXLnxxYaZprXL3yXksx2oU4UY4+QDk1nWHilNasZZVmM2VKuehA9K8jHYqlPRO7+X59zspYepBPTQfe3BvXddOv7dY2JiuJB84A7jjoa881NEj8QX+n2hRo4Y9m4jhnYlmP5FavfAaDw3AuujRpNTcyXheYXqEc8/dz1HWsTVGt7fX7mKa/FpcGQkt1BZyWI/AFBUUJckJp9LfiezllRR5nLZL9d/wCu5n6LbTXE7yvaxSySEIFkOAqRgL/6ETXUWtvdJbzSfYoCY4ywjhBY8Cucvr2HSrxZb+eZ4I1S3WWHozNl2Y+3K/lXpPgbQ55NZg16PVFn0gQBrdY2P71z94t7DoKcpaJp7/1c7qmMhThJ2tZdtH2V+9v1KfgrwtdeJ/Cw/trTpdGMkwkzE22WSP0bPK/hXVax4X8P21rb3B01JptPjAt5ZCSybeRz1611Rljt7SS5uZEiiRS8ju2FRQMkk9hXOJ4p0PXtGkvNMneeBRuy0TJuUEjIyOQccU5QhGDu9bdT5yrmNetV91tK7dk3pfR+fkcd4q0jS7rwDK2oWyTyJNI7M6jORw2D7n9a+dfEvwz026L3mi6h5UjnIt5umfQGvffFKa3b+Eb+71u3LaGjo8Zs8NOQXzJvB6LuyM9cVz2t6Gg1G0mt4mj028tkubWTAw2R0PofapwtRwprldtFoerhsNSqR5KyupN2aezte197+R8z6l4f1DR7r7NqNq8En8ORww9Qe9SWlkT2r6R1nRbPxDpxs75E4GEkxyrj3rxrUtKk0nUJbKcDfGcBh0YdiK9CFfnXmedj8ueFd46xZl2lkFAJFX40VAMCgdKDVXOAlDVHM/y0gao5TxQMgZvmprH3pCeaaT6UAGc03qcdTTlUs21Rk1uaRo7OPNdSaqKbYmy94en8zGK6IfrXJeElkwCwrrFrim9TpjsSqKRx3pyjpSPwKSAryNjvVa4mAGM0l7ME5zxWHqF+FU/NzWiRLY3Urv73IrltRuv3n3qn1C7ZztXJLHAA5Jrr/AvwZ8U+LHju74f2Tp7c75V/eOPZe3410RSS1MGnJ6Hm73LyusUSs7scKqjJJ+leufDvwJFp9quq6/bpJeHDRwvysI7Eju1elw/CvSfCMFpFpGkSXNxJzJfzjJJ7KD2/CrvxU0S38PRadZQXxnubg/6QmB8mACen1xzWVacmmkrJbnt5XhaEZwlVd5SvZb7dX/wRg1vVdT0WPw/p0KrFzxGmN3uarnwTrA02KCNoUjjh2mJpT87FixJwOnTiuu8A6SlnpqzyLmeYAv6gHkD8q6a5UI+ET5e59K+QzDiZ4WcqVNX6O5viqsPapU1bld/Vnj/hy38UaDq86v4dhmkli8i0kH7yOPP8Qx0P1xXpvhXQ7bQ7IKsXn3snM079z6AdhV5/KyGVdrDuOM02a7PArxcVxTKdLkhHlfff+mc1XmrS5n1LN1K8ETllj2gZICivIZ/B58S6Xe3EM4i1lJTJtkGUbcocofcAgZ9q9K1GZ/sjyyNtjjUux9h1rm/APnMweTImnne4kH90MBgfgMD8K68ozWv9WnUbbvOEVfz5rm9Kly0Jvbb8DivCmgDVNX0zw9qVgt3FFGTdFHKiMszfMD6gKPzr6A0+xtbaCGxtIVht4VCRoo4CivL/AAzPHpPjm9jg2yNLHM8hHoHVVH6NT/jZ4xuNH8IWsWkau1hq1zOCVix5ghAbcc/wgnHPU19bTqRd5M4c3rSSjH7KSfq31/T0Knxh8STa94ytfhxbO9vpjsG1OZOGmUDcYx/sgDn1P0rrjb2mleG4Io7cJ57q0uOkUYHyr+CjGPc15l8LfCV9qep2XizWL+SeedS/lsfuwYwGJ6ksR+XNej+KNa0+8to9PguIPnnEeS4y2OWCjvwDWfPKSlOW9tP6/rqefhqUtPPX5dP8zO8c61f6V8LITb20k11ffuoY0h8z5nYnkc8Yz174rJ+I32q08I6VZXN2o1KKKFHdFCjzMZKgDjA9KfoGmeIX8Rjxbr+qLDp1rD5Wl2UEhxIrDgsOmeee+fpWL4yuJdU1hPk3iLcwPX5j1P0HSrhK9vRH0GFormhThZ2bk2u76fJdur8ipoE73kKSTI3m/MX8sdNmckj2weawtd8O6br3ieOxN+YJZz+6mCbhkjhSPTIzmrVxGI9LlidgIZHznJDAnrzUnhQxXfiqNp1mSB1ZU8kZc7VPIFdMHZ3R6GNjzUpp7WKlj8H7m/02CK01Dfqy3Yiu4QuUijJwJB34HNYHxF+GviDwfqcsJgm1GxjQSfbYIG2Af7XoRX1F4S022tT9ojSRZZEwJZF/ebT/AHvpWf4q0rxZrEdzY6V4p0yNJYzE8M9tlsEYzwf6V6EYXhfqz4pytK3Q+Nc8Z4qOU8V0/wAQ/Bes+CNYXTtXWJjIm+GWJspIvTj0PtXKymp1W5d7kJPNJ1OO9B69an02Lz7tIx1JwKAN3wxo8lzKrMhI9cV6FY6fFCFXyxz7Vp+ENFig02NSoLsBzitu+0oAptUjFdUY8qM27nl1tYR2nC/hVpelVppyZiM96sIeAa8qR2olXimXDYU04VHcH5TSiDOZ1y6ZC2KwrC11HW9Ui03TLaS5upjhUQdPc+g966DVbN7qVYYULyyMERR3JOAK+k/hL8PdM8H6NEfIWXVJkBubgjJJ/uj0Arpgr6Iyau9Tkvhn8KNK8LJDqmvbb7Vm+4gXcqH0Re5969e0zY0iq8bW8e0EtIANp9ODWB/bcd1r13Z2FrM3l/uzqDRM8CgY3Im3lm3Eg8gcdeMVPdatcaOY4bWA3rOoht7V8Kqvnc0szfwIBjjk9OMkV2Uqatzb/wBfj8i5xlblSt/X9bnSm50vSLVnuJ7m9M82be3dQzu/pGvHHueB1JFcj4512/uLvUtG07Q9NvoYbZbm9klkVWjZs4GOpYAA/Sse81ya9udQaK5ltY4j5Wo69MvlhWyAYLVG5A7ZPQ/3jXGah4nt7+2Om6AXtLWGQmWIvmWc5++5ySxPXknPeoxFRRp8r/D+n8+nRdzqwGCdSsnu99em3a3yW/V22PRPBd95+kwSOyl41EcoB+6y8VuyCUnzCcg15h4Oku01mC8t7yC0hm+W5imbbG+OQd38J9/wNem2s0cl0lvZGSXehYZifyumSBJjb+vPavzzMuHq2JqOpQTfkjsx0Fh6rv11G+Wx5weelQy2/PUVeF1GpAmjeI/7YxUrwxthgwINfJ1MucNJb+ehzqq1uYepIn9nSpcAeUUIcnptxz+lc34Y1E2Og6jrs0TMAHkSNVyz5JKqB6nKgCun8WRx/wBkyQSOFSQEOT/c6t+gNZ/hZ9K03R7bVfEN3b2sZIuIIHYc5OVYjtjtX2PDuA5sJb+/e/a0bfqdE6qjhnLu/wDg/wBeR4jpOu6/a+GbzxDLazW93eTSWzO45tzuLHI9cseK1Z4dP8a6pFZ6ZHKdK0a3El3dSn95dyN1BJ5y2Mc9AD7Vlapa6j4t8b/8I/oN59ugubqW7VI8+XapI5Ysx7tg/wAhXqngrwR4bj0nXvDlvqV9p0lgUfULmbAYkg4JJwFXCk49DXsfV5V6kXFbN27erv1t+fzPIr4l4urzTVoqyfyOCfxn4p1fT7vT/DenRaXEsq28TzZXfGBjI7gDtXUrommW9rpDXU0NzrttEbdp4CRGFbO8gdM4J5qn4X+GGq3MZ1W61iSysfMYRGSVS8qA4Eirj7p6jJ5FaEvgPULLVTqNnLqWqRxRlRNKCiLkEEgE88dwK6ZYao42toevTq03LlptR/Dys3+hx154p1rWZJ9N0t2RInMRvH4jt16ERj+J8d+1btvIxREVpYoEjVUWTG9sKMk44xmpZbSK2Bhjjj3ITkgYVT3x6msbVL5Ig0FrJvuWH7yU8hB7VcIWVj2qGGhTV0Z3iC5iuL9LOHmGI75mz19BWv8ABvWba+8WXs8QBazg2xEj5Tk4OB+FeZeJtQly+nWRYiU5eQHJb2Fer/A7wReaFaS6rqkfl3F0qiOE9UXrk+hPpXVFWOLG104OC6ntWj6obcNPLGrPJwSOoFRa1/ZV+k/nb5Gdcwug2Swtjsw96526uL23eNvIO17jyRFsOSvOXB9uOO+asGY5IbOfet/aPl5WfPexhKXMjw7416FrcZh1W+1SfUo4x5WZDzGD0/8Ar15VKeehr6u8T2EWp6VPbyKGDoRjFeNv4Ztbhm2xYOcHinDUivHld0eXhWY46Zrf8B6ebzX41J+VT3rtbbwJDLKDjA+ldPpHgq3sMSw/LJ3Nbxicrk2dDp0QhZEXGAK2XQSIMgCqVhbJCg3yZYVl+ML+5g02X7ISXA4A71tzEPTU8M8Q6obCfapwSeTVabxFLFbh/MLcZxisn4h3CCYbWGc1S0O6W5gCTdPQCvVw2X0PYRnUW5jVxM/aOMTXTxpITglh+FS/8JTJIvV/yNUbizgWAzfKncDvXLz+IoLeVozIBg4xirlh8FFX5TP21bZs90/Z/UeIfiTbLMpaO0ie5O4cZGAP1Ofwr6g16G6bQb5LGRY7poGWJy23aSMZz2PvXyP+yT4lt5/i0loJQftFjKoB9QVNfZqL/fAIPavLrxpuq/ZqyO6jOSgmzitRhggit7axiuWjhRYrXTrRwGbjq7jhF9WJ/Xg8xbSQ/wBuzWrapHdalI3kXTWrER2uwbjbwjtgHLSHnJHIY/L6rrIaLRrx7CxjluRA5hQDG59p2j88VgeCfC2gaVoOnyCwiiuUtVW4nePZI8hwz78/eJfJ5zzS0v8A1/X+X4nTGr7jb9Onr/Xf8DItoBNd2JktbZbK2tbmJ7eNT5R37fKOxiQSoHJz/EzcDG7mv+ELEXh3SLWKHTI9Zs3laaZYhG8w37V5x1O5flb5ua9b8P6PpYsJjC73LZKCaV8uh9iOhyc+ueTzzXP+IP7N0C7tYYCbu+mVltElI2JIqkvM5HIVQRluwOBgty504TinLZ/1+hWHxkqdW0N1/wAHf7zgX12DSbywtda0ZZBYib9zZQGWaS4AGwugHCjkkngHb7V1Fne395ppkvdVaR0TzGttJjMZYH+/cOB6/wAJQD3rMstMi0aIWt1dtbXGv+a39qhyb6aUKXVgpBXaTwIl5AYc5zitoel62sE0/i+9vL6W2RpoILbUGjjWVB8sb42hm5JY7iqYGeaVKDhZb/p6/I6a7pVLyjp97b727K97632Ok8DLbWd1qp1BJIkmeOWbzLppLeE/dWMNI27eQdzEhc5HAGM9BLd6Cb6O1tY57iVwCzWh3JCCSAznOADg+p4PFefeINNvdUn0Nbi/i0jR7GyjfWY4Yvl+1Sj5EVMHc4YgFck/OD1NVRoniK1OveHtHa0Fwyp9rnkjb7PblRujlV1Od+08xkHsTgdcsTh6deSjVpKS80n528v+HM40ISg5+0tLqultr+fp6dz0/wAX6Ol94fNnY2zPNOVDufvCPOWAPqQMfjVSfTvC+NIt9U0azv8AUhHHEYpIhI0S45LDBGB715/r9xbQeB9FiW+sw0crxx7bvz/PH/PUOQDyeoxwTxxipNH8eaxptgdO0mx00wIMC853Mc8kr/EfxrDnoYd+yhBRWmyVu+3fzZ008sxFbDLlfMuaWm3ldv7tF6dz0pPCHh23vbm50nTV0ee4Cia4tYEjD46DBHv2Fa0mi6cJLmY20TTXYT7RIyZMxQYUt2OBXlkvjq88QaPH4ae2urW5klQS38jARsgfJbjkZ4GPrW38RvFK+CLXRL/TtRW8t7Vxb3lirqTPGy48zPUMpUEduSDWsKtD3pJK2l/X08jzqmWYmnKNJ6Sd7L06383tf8Dunt/Ptti3cayLwJVjVip/HivJfilf694bKpq2qzSWV2SsJiwiyYHIIHQ+3evPvGvjgjxbc+J/Duo32m3d0UY26t/rQFA+aMEjPHU8VJrWofE/4laLaQPocMNvHOJUuOY2fAIPJO3v2rnq141U0k7/AJnp4XAVMFOE6ji4vdO10ZV3qt1fu0durwxngAAl2/Dt+NUr3RNRurGWOMNANpIHUk+/rXt3hLwLp9jpNp9vjU3KgGVARgt7+tdTNa2SpGkNvCqx9AFHWueNGVrmuJzaVR8kFZHjvw1+FdrpX2fWdbJvL7AdYnHywn1x616gd3GB05yBitJ0jb1+tMEBc4C5FbcnQ8+VVy1ZmS3NwF2ruIGaquGbnGPcVrXFlNzkAfUVQe2ERZj8p6kjvUyT6lRcehj6hOtvC7uRtAJNcJpRjlt1mXGXJP6mtT4j6ylraPaxOC78Ng9BXM+Gr2zeyhiS7jEqrhlY4wadN2ZnifhR1EUgg5wC3YUwX10Zju4Wi1tDL8zNn0IqWO1HmbCSxPStOaVzkshFvjNJ5cakkdTWjb6RLfRNlMjHNNt7OGz5KgMTXQ6HehIXhC5J6VrDV6kS8j4K8Yat9o1R03fKDVC08RpZHY0hHuBWLfSmS9nmJ6tWTMrtIW2mvcjNqmos86XxNnYX3jB3iMcW5+MAmuUuLmSeUsepNPtLV5P4TXU+H/C09+4WGAuT3xwKylKMVeT0BNt2R0H7O7ajpfxK0PVxHJHbmVozI3AIZSMD8cV+ivhzUodTsUlUjeB86+hr4OSz16zktLKzsJDJCyGIxjgkEEY/GvqPwlq13ZeRcbWiLqC8Z/hPcH8a8T64pVOfS3kfVYnLaFGjCNKfM2rv10/A9izSSxRTLiZFdVO4BhxkVS0fVbXUYVKSL5hHK5rSgwr5YA+xr0I2mtNUeFJSg9dGQwW9vZ6VHZ2Kx2dsBiMAds8gfrzXIy+GLCXxHql8bk3Md7Zx2oBLfIBu347ANlc4x90da6jW7C31SCC0Lyokb+YTGxGD2B9evSotO0iHTrdord3YsxZmYnJJrGanKastF1/SxpRmoRb5tX0/4Jz+s/2PpsMmueKtYt7W6Eb28F2QIktFcfdhBzhuMk8k49Bgc6J1vrAav4S1K28S2dqqJcrbuv2iNVDMdi8KrcKFBHBLNgtzXG/tcLqsUeiXMStJYwrKJABkJIxGGP4DH/66479lVtZbx9d3tpHJ9h+ysl25OE6goD6tkcD0zWf1mUZ8rWn4n0FLLqcsB9Z5/e7aWstLd1/n3PV9VtLzVxFeLr15oFhY2f2j/R12Jbb87pcOpd5MbkVmwS29gBgbsa28LS2et6Zd+Gbm40zULSdp9s/+kTt5gHmG5Z2+aR0/gBUICGZgcCvarS1tmu5r1oAJp0RJTk/OEyV46cZPvzWTr3g+21GSaeyvrnT5plVZVi27JQJPMIdSOQxJDcjcOCTgY7fdmr9TxqeK5Hyt2X4fPuefv4T0PV9R02a90uERuHe7lt7l4o9vzEzorE7IQQBuJG7+EY5Or4e8J+F9UtjerZ3dvYM5NgUumzLDgBZCMcbjlgP7pGeeKzvJ1i8tp9FudQtbu6e4eC/klsnUFQSrzMpbavGRGCWH3SqgZNdl4TF5LPJcL5Ueioiw2NsY+cIAA+fQ449sVzTp0v5Vd/1/Xmd9TGYmEbKo9PN/1r+SMu/+GmkTW7Pp+pajbSEHAd1YE9ucZArndH+CtvJLcf23f3RjYDb5LqhY++ATivWwqMSeV/Gg4J4cn6Vk8PTvexz/ANq4zlcfaP8AX7zltH+HnhHSNPFvFo9pIxXa8rxgu/vn1+lWtJ0Kx0gyrYPMkUhyInkLKh/2c9K3XBIAPI7VC0YDA5PPam4RWyOb21SV+aTdzhfir4yt/BekQXIt/tl3dSFIIC+1eBksxHYZHA65rm/hD8VE8W69caDq2nwWl4kRmhkgctHIoIBGDyCMjuc89K3/AI1fD2bx3pVsljfx2d5aMxi80ExuGxkEjkdBzzXK/Bj4Q6h4L1m41nWL62muniMMcdvuZVUkEkswBJ4HGK53GanfoezTlgfqFn/F+fy8rWPZVWI/dCH+tRuiYPGDUQZUHzEYqhfaxaW6n95uPoK0c0lqeTGDb0LVzIygfNuHv2rjvGGuxWduyQuDIe47VX8QeJZGjZIzsUjtXnHiHUjJks3rXPKfNsdcKfLqznvFOoPczsWYkk1yMm9LhiGZWDduDWxKfPvIwxOGkAJHpmu2+MvgrT9HtYNX0xmAbas6k/eJH3q5q0W43XQyqSvKxw2k+Jda09gLe8cr/dc5FejeFPiha22xNZ0vzPWSLH8q8mtlBbJqxj6VhDE1KT91kOCktT6U0rxT4I8RYVL+KCQj7k3ymuv0Pw3p+PtEUqyKehVsivjvpz0rZ0Lxd4j0NwdM1e5hUfwF9y/ka9Gjmsb/AL2Cfmjnlh3b3WfMuwOOvU5NbekaS17tighaRz2UV0Hhb4b3cpWfWJjDH1EKn5j9T2r0vSdJs9NgENnbrGo7gcn8a6sZnVGnpT95/gc1LAznrLRHI+H/AAFGm2XUCPXy1/rXc2Fnb2cQit4ljUD+EVMqkVOi57V8xisbWxDvUfy6Hq0aEKS91HQ/Dywu7jxJZXMNjLc29tMrTMq5VB6mvTtaijbUbloSNjyF1x2zz/OpfgdeQjwbPZW1p5svnsZ2UcjpjP4VZ1W3D3MkkQ4BwfY134WmoUU0731HTm3Ud+hj2t5cWkmVdlOeoNdTpHjOWNVjuSsnuetcxLGrnaeDVWe0kU5Xkex6V1wnKGsWdEoQqK0keqWfiewnUEgofrVxNZ09+kwBrx2KeaE4yePzq7DfSuM5HFdKxk+pzSwFN7HqN5/ZGp25gu0trmFuqSoGU/gafp9hpNjAsFhb2trCvSOFAij8BxXmiajtwUdg47noaspqtyRgOcnqR0p/Wlu4ieDlayk7Ho17d21lamVm4HRUG5m+gHJNc/qMsut2iXmganLE6ybGDyFApBwwI7EEcjFcJ40urqXw9K8d7dQSxsCj25/er6kA9eKufDm2XRvDkdm949wspMhkcnJz6570RrxxN4dPxM/q0qTvudtqEt1BpaPqNrZ3+1f3xVzg++MVo6Nf2ep2aTWrEKeNvQqRwRXD+JPE+naJpc5acSKUIVCQSeOlcD4E8U3pljeGSRVklL7QeNvTP6VFSq6NVJO6tr/w5ccN7SL6P+uh9CCMCjYoOQK89/4SW5IwZj9N1I/iK4Ix5v4dK0+tw7GX1Ofc9CbaByRj61XmuLWMfPMn5157NrkrA7pW6etVJ9RduWkPsO9S8VfZFxwlt2d3d61YQniQsR6Vi3/icjIhVVA9a42a8ds/Ngd/U1n3F0cE7sD161hKtJm8aEIm/qGvTSkhpDj0rCvdQZxyck1lXF76njt2qlPdEg49MCszW6Ww/U7w8rk5PWuU1i43AnPGav39wA5AG5j0ArjfGepNaPYW8c0UVxNmZy4yEQHABHucn8KpK5pSpSrS5I7mlYwmW6jJGMsPw5r3T4p+FhqPw+N1b3b74Y1kC5yHAHSvG/hXJF4iurMSIqEXIilwflOCOR7EGvevirZy6f4Iuxp0zZjgOEJ4I70nH3JXRw4mLpzUHumfMcajy129MUMDjinrgQqO9MJxnn6V5IxhzSfWlL0m4deKaA1B71IuPWkVSeKdtGelcJsSKBmp4+oquoIqxFipYz6P+AdzYp4CtkiRVmaV/PxjJfPf8MVra9pMiXVxKseIZSCp9+9eV/Am5+xeIQn2llhuAd0RPys4HB9jivfZGhvbV4wQcj9a+py6ccVhVB6OOiPLqt0Kzl0Z5NqVkUBODWYtw0OVcZ+tdV8S7yy8J+HZtY1KN2jV1jjRMbpXY8KCeB+PYGvIPBPj9fE3iq60i+sYbJJLczWJ35YbQCUb1yMn8KiUXGVj3MNh6lei60V7q6nbM0Mozhc+4qBo1PAOCenPSrH2Fyu5W4ojspn3BIpXKjOFUscfhSbIVjH1mS7tbcSWbIG3BWkbkKD3xWzYJERoT395PFFLE0lzMFADnIx9OuKvab4XfWdPkdLhF5+VHU4f15pVksdL0s6b4ggaOCBysE6AHZuPKMPr/SuOOJjKq4J/5X3t80KVRLRbo0NW8MJOY7iwme6teGGDlse/+NZF7oNy7bYr6a3iJ5jCj9D2rS0hxf6XBceEr9k+ySYwyEB1yc5U9s1tLqL/ANlXFx4gtAWgPLQDnb6//WrqeHjKTlSfK2r+XnqcznOPn+Z5B48+GerarL9o0jUExsA2TuevesnRom0Py7W6dEvSuD83DdiBXtyf2PfRM9jq0YI/hkGCPrXgvimK5a/Nxeb9jTSBHH3c57HvXNGFanL39n8zelPm0Z1SXzBQ2SWPTBzilN9MepZm9QMVX0Nom0+GR2DsV5z3q+32R85OPpW6KbIRfsCQTjPU0/7WSOOp9+aTFov8I+metQSmIKSBwegz0pk3HXFweVz+tUJnkkOT8o7U8jkhBk9qTyGI5Jz+gppiZTdUUEkl2PpVaaGVhknaOmAK1lttx4FTCzz9760XI1OfSwyThTk9ciuZ8XeFrS81GCWdG3FdpZTg9en616OYcfIiHnjpXaaN8MrDWNHjvtUuJ4CMunlsF49TkGtIRlJ2iDq+xXNexn/CL4a6bY6FFexJ5K7i8agnOf7xJ6msD42+LL3Tx/wjoSSR7lDtlA+UIDhsn1rvh4s0bw9pcenG7Xy4cpHNkBZMdcHufWvDPH+vw+JdXku7Zt8MO5UcchifQ9x71hiasFC0d+pyXnUqOUjkywCgYHSomY570rqQaj5JxXno2EJaq15dpar83zOeiD+tQ3995Un2a0/fTk7flGcH2966Lwv4TdWF7qw3zH5liPIU/wC16n2rppUebWRDl0RsNp0pia4tCl7bKMma3O5QPU91/HFVQKfceFfEOg3BvvDuoSyAdo22SY9MdGHtVZfFKzzmHxBpwiuAcNPBGInz/tJwp/DFcVfB1KW6NoVIy2LSjipEHtU8FtHdwfaNOuYryIdQhxIv1TqPr0piqQeRXG/M1RpaPPPFOiwzPE+4FHQ4ZGHQivoL4Y6/c3OgtPqUqT3SyGMlF25GBjI9a+c4HKuCMgjkV1vhfW9Rsrk3NjOInwFljcbo5B2yPX3Fb4LFvC1eboZV6Kqwsex/Ffw5bfELwHeaHJM9pOWWa1lAzsmXO3I7g5IPsa+dvCvwK+IFr4jhv7uGEfZZQ3nLNw46fL3PWvcvCPjaW61KOzvreCAupMbxyEhmHbBHpk9e1d5bamZH2kjH0r6anXo4uPNzeWxjQx2KwMHRh8L1szg7PS7hI0hlQiVBg571H9qutP1YadbKqS3cDKrH+92H44Nemi2tJX+0vGpbHWvAf2hPF7+E746xb2qSr9qWBSW2+XhSwYe+V4pV6MqCi4u7bt8goYn28mmuhvaFqWr2UEQubaW5iVePLO2WM/xY9R7GuoivdL8Raa8V3bGWDeqt5o2SBuuPftXL+HfGces232q68OX6M4DRyxbR5wx1K5O01pW6/wBty3Njcaa9jbXMWYC7jeZBzzjpXmrC06vuzafZrR+X9am07SfM1Yv6pod7HZRJ4VuoLO4jbAMikYTHQevOOvFVpDqWiONR1q+W4gkIjuI5VCrzxlajii1XSi32PUiyg/6q4Xev+NaVlqLaijWWp2McY27iSd8L4+vNZU4VqTSpTv5PT/gfeRr6r8TNe08I6lEmpQahbNARkHcQ+PQ+v41T1S+0/X4f7N+yRNo0B4+UfvGHcf41vyaNoF/AGSztUXs6qGjP4jpVDUfD1xHH/oUQZO3l8ipqYzli48vI3v8A129CoyjfVv5nkXjBbbQtTMOluzWwwTCxzsz6HrVJNe0+CwlvtRvVsoIhlmk7+wA6n2qLxTG8nim+WbdmNghU9iMVwPxN0uWbQ/PWTbHC4dl7Ht/WuvDe+ldnbSgpySZ6L4e8R+HvEDsuk6m9zKn3o3Qoceoz1FdKtrlBxge9fO3g7VovDmo2+q20MUu/McsOSMLkcg+v519O+H7aTVLeKW3jJDgEZHNbVIqLsjfHYX6u1bZmclqoHPH0qZLYHGBkV0t34U1OKLzTA5XHOKk8K6Vb3epLbSyjcAWKjrgdaizTSZ5/PGzlfY5v7FMRlIifwra8KeHJNUvQj8IOWPoK9NfTNMtbTC28XTHIzXP6RqukaTf3LSFYC5C5JwOTwPxreVFU5LnehyvFqUXyLUu6r4U0iDTCEtwHUcSZ+bNY93rGnP4YbSLe5aENGEfL/Mqk8/1qx4q8V2kGnyytcKqAE7mIAHvmvnfU1tNe1SbVmQyRn93CxJAK5J9emSaxxWKhTf7teRjShOorTZ1vxdHh27/sfR9N8m7l09XllC4dY9+MAn+93x+dec6gFjlEaqoA4wDwPyrRuXNsPIhVIQox8mMVjX0sVvC9xcyBEHUnufQeteVOo6s72OuMOSNipIoALMQAOST0FYc95cajdrpukRvI7nBZep9foPes7Xtalv3MUIMUAPCjq3uaueDtdXRnYmJPnPzNjk//AFq7qWHaV5GUpXdkeieEPCdposAnnKz3zDl+yey/410FuAs4zzmsXTfElheou2VQfQ1qxzK7BkII9RWl3c0SSWhsWNx5wwp47nsK8s+PHii1hijs7RYWni/5aFQSPxrU8T+L4bC0az04gHGN1eA+M7u6utVLyOXDDOc17aip6M81trY2tB8fRxzp9qElpIp+WaInA/qK9Q0Txql3Cr3gi1CI9J42AcfiOv4181Sbt5G3vU9he3thMJrOeSB/VTjP1HeuXE5NRqq8NH/XzNaWMnHSSufW1hNY34zYXCysR/qn+SQfh0P4VbtXlt58gFWHUEYr508P/EBlCw6xAQR0ngH81/w/KvUvDnjl54VC3MOq244IZsuv4/eH4185issrUN1p/XU9GlXhPY9WjmS4hCMCucEEHBB9Qexrf0rXvEFimyDWHkUdFuYxJj8eD+Zrz/R9d0u8wkVx9nkP/LKcgfk3Q/jitlrm7gbAXIPTIrzuapRfuto1cIz3PRLb4mJ5NqmozCykQ+Xdgg7Q5Hykex6j/wCtVjWLvStbuGsPFfh1zbIVkhumRZI5B2Yjnj+VeQ62b6Tyb2z8syrlZIZc7HH9PrXUeB/iNHaQJoWuWb+UvEEdwwBHtHJ91vocGvdwuPdSHJN/fr/XqjH2UYL3V/X9dzuvFOh3d/4LvbXwlqEUF28BS1nh6xnIPbkZ6ZHIzxVLwJpniGz8E2dl4mvTNq8IJkmVixDbiV56nAwM96kj1Xw8sy3Vk2qW8+clY4MZ9jjg1t3HibTZSpu4ZBbSfKtyqn5T6N6H611qnCcLRaT9b/juiHGeyVzldC+JWk65dXlrcWN0ZrJvJmntgro8g68A+3XvWg3iFLq9it4dLuLezZtr3EpAYZ77R2rI8I/DLTNButT1DwxffbEv3DtE0v3MEkBQf9496ofFOHxrofhxLnw/ppnuftAWWMoGLR4OcA8HnA9cZxTmqkal0vn/AMHYqKheyOtt9CudIJ+wXElrMCc4OUk9yPQ1O3iaS1Qw6nZSwSf89bcEq1ZsfiPWNJ0Oxa+0uTUEeNf3MeDLCxAyuc9Bzzz0qxceKLZoVew0S/edxx54Coh9SaztFq1049nr/XysKzb95XOY8Q+GLeJbrXiwhS9lVobeUnzWz1Y56Ak55rYsfAGgeIPC02k6myxtcR/PKhBKHqMGs7VLu5isr3UdWlE900R6fdQeiiuN0H4gW8Mz2H22NZouGUyDI4yAR+NZKvCM/djov6+RpVc1H3XZnP3fwCvdI8UwQvqK3mnmUFHjTG4Z6Nzx719IeCdFXw7aFLidZZG+6RgKF9hXlkfxI0yGXN9PJeThf3MNsu9j/T8TgViP498dzeIrjVHW3/s6VVWDTpGwYUHcOAcse/GOBjpVyxkIvme/QyqVa9ePJJ6H0Nr2rJHZSbcbgtcLqnjnTfDGm22o6qArXUoiDxxlmUEE5bHO0AcntxXn2p+PNdu7QQJp0VszffZp94A9gB/hWQhN04uL+Zri4xtDNxtHoB0Arnr5i5O6MqeGaVmepX/xW8OmyLx6xZuMcbJQzH8Ac15D4o1OTxlqakpOmnwyiWMEtG0jjOGxwQBk47557DMz2MSv5iRKrdmAwfzqEN5TfIcH1rjni6lRam8KEY7EU+lxMd1w08+OizzvIB+DE03zJAwt4QSGOAoGcnPAqa4unkUq7D8q5fWtfj06bFviW5BzgHhPqfWohCU3ZFu0Vc7D4nf2B4X0LTLUzCXWnBknRWO4+qlSPlAyMZx3rw/X9ckvLvZNIDJjKxL0QVB4517UZ9Rub2ffNqV0A7ykE8Y4PueK5DSmmF+JJw+ZM5Z85NfRUMFH47WPJrYtp8i1OjgnwzBupNPEnUk89qouNrYPWp4sOo55FbOnbY0hPQ1bC5kQ/I5UjpzXU6Nr15AyK85x6GuNhXpjtWzYTxiMJKo46etYzibxZzdtqd1e6PcXc4JmDEe1Y+qJutYZ87iepreuEWLw++xQu7risCYltIjJNd0Jcy5jgaadjJdF3n5RzSFIwPuirDAEH2qsx5NbJ3OtbCqkRIBAq/YRtBKJrd2icdGQ4P6VnqOa1LBjt/Coq3SLhFNm9Y+K722cR3qC4T++vyuP6Gu+8K+OpUVY7S8EsY628wzj8D0/CvJZgC4ohOJgRwR0IrzK2CpVVtY2UpRZ9L6X4r0u/QR3P+hynjLfNH+fUfj+das1lFcWuJFiuIZB1wGRh9elfN/h/W7861FYPIJInB5YfMMeh/xr0LRNX1Gxlza3TxjuvVW+o6GvExOXOk9GbQrKVz0mxOs6LIH0TUpUjU5+xXJMsDfTJ3L/AMBP4V0Wl/Eexs3I1fTn02R+Jo5h5lrMPZx90+m4CsLQLl9S0RL6dEWUtghBgH3xUtxGkkbK6hh05HauWGLqUZa9DRwUtUd5p+ueBtQd59L1i802RArEpGxTkZwp/i/Wun8P+K7G4Q6fb3r3k2flkuE2+Z+FfNWs2g8NWUuoaNcT20kfzGPdujf/AHlIIP8AOvUPhvqUviPwra6lfQwR3DrnMClcEdxya9qhmLtzwSt/XnYxlBNWken29zpOpzNDcRfYL+BiDFIMDPqPUH1FcHdeBtct/iVc+JhrNy2mSRbBYnJQHAGQc4xxnpnJrs9Ojj1zwpHeahGr3EfyCRRhiAcc1k2mtalperJYwXDSQPxtm+bH0rrqckmk9OZbrz7r/ImF7vl+45T4jk2+hzowI3YXP1NePXOn2d5cGSWzgkOchmjBP5mvon4ypDeeBrqaa3iMsSeYjquCGFfPlnK6yAg84Jry8XReHlZMtS51qjR0u2gsEyIUi9FVQAPyrVju1lbLqAevArBaWSQhnck1owf6tfoK85u+polY0JZYtvynJx6VXMnv+VREn9aZk5xRYaLDTPtxuOPrUE0gVSzEAdSc0DnA6VzOvzyz6ouns5WDAJC8bvrWtGm6s+VETlyq5B4g19mV4LFsIB883b8P8ao/DzTrTxdL4g06FJJLyHR57qydWPMsZDH65XIqt8XYYtO8QTaJaJ5dpDDGcZ+Zyy5JY9/0rZ/Zh1y78PeMbn7DFbt9ssmtpPNTdtUun3eRg19HhsJCktTzK1aUtjiPFEZt4NMkbO6SyUNkdwxH8sVxt/cE6pFH/wA80/U16f8AEa3ieeAlcCK4niRR0CgjAryW5cvr85b+/j8K9Cnrc5az0Rryz7xuNLDOQuQaiiAMWPelUBVYCoZrHY19OuxuAbpW7bLDN1IrkrYkNwelbFnI/XPasJxNoSP/2Q=="}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-42068-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: If I wanted to take a portion of the food item pictured, what utensil visible in the image would I likely use? answer: You would likely use the fork, which is resting on plate_1."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-43502-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: How could one describe the feature on some apparel items that ensures a snug fit without relying on an elastic material? answer: A feature like the belt_2's strap is used for wrapping around the waist to offer adjustability and maintain the fit of the garment, working in conjunction with the belt_2 as a whole."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-43504-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In rearranging this room, what could be adjusted to change the appearance of the bed or a couch? answer: The pillow_1 could be adjusted to change the appearance of the bed or a couch."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-43758-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: How would you describe the protective covering that encases the mechanism that allows one of the wall-mounted instruments to tell time? answer: The protective covering is described as the clock_1's case, which encloses the clock_1 on the building."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-43992-3", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Which object here is likely used to provide information about its contents, especially ingredients or nutritional facts? answer: The object likely used to provide information about its contents is the sticker on jar_2's body."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-44141-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What part of the canine in the image would be primarily responsible for picking up scents from its surroundings? answer: The dog_1’s nose is primarily responsible for picking up scents from its surroundings."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCACqAQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDgrLypbaNGumn8048qMFHBwOQT2BH6H1rn7q7Z7hxYrPHa4McqyfLhs9Dzntz9a0ZbKKXSIJbO5WK8jjb7Srvhmb+Hbk9sHjvxUE2nRCR5rm7uJJpBttpFGY3kGCwYLyOh+uc18/BxTdw5JOPKv6/U5R/Ntb0NZpIykYZlAJ4GCvTp/wDqrRjW5ZraCVfJkQgsQAoBOQM/pnvVu2h1RY1up7Z4Y5SCHUcbQc529cY9fWrs0sr6mGhjkihVz5jGTJAwWyBjsR/hXRKd7GajKK1RUntJt0Lx7ZAxWTdjGVBK7tp7cd8UyWVLac74BIGUB3Eh+7xwCfTpnH8qsG7N3G265eOUKQGmfJ2/3eeAAMjA9apz7XuGc3McjY4yN2V4wfbv/wDXqI67kyqJq1icS2j6cts8UySqzbZhIADgjggDnk5B/wAKZqGoR7QYkdWfhs8qMY5/pj/CoJ/JS6xNamMM4AcHaSPQcY+mPWmgWP2pmE7yROo+aROMjk5/KtItEylJqz2CS9vODNzBKQI5cEdeefercF5GbWOe4igHH7pmHzY9unJ+tMa+F5BHaqjxshIC5K7ieo9h398mmaObSSyaW7EjQxBUWKJvmdgORznAx6c/rWkIuWlrCUeZ2juNv78tbsZLgRlcNtUBgxyck/nxVWyUqyySAjD565xnsM8HIz+ddzoug+HfGXh28k8P6bc2Gp6ejSzQyStIkiAE5Bbvntx9O9cfZ3iGI28xgkZ49uEjAycdBjv2zWko8uiQVKTpWuakUkEsscsaK4YkSBwVyfUjkemMHjpUlytrb3FtJBOIJZnyFSQHyzzlcYzj7vsenasyP7KIbjzbpkIQ4hQDBweq4HB/z7VPfSQm1SJWjaTcI4ig2seCMnnkDkcev1rLk1sZKWtzSvtVe0vzErvLGhMe8FSpYnIZSCcHGDj6dOlEV4t/FLLOs0pcrmYsUztzjpjPOCPTisPT4/syTxT2MJLbAQ67hnI5C/TJ4z1qWS5hTR3NrfzwyFQk0BjyGBxjkDpyOvoPwaivslNLoWljlFxLPa3t1JFkRJ5n3lyCRkYCj7pz14qSDUbWBmEkEE4VvkYSN3Pt2ySPf8K2PDWk3l0d2pX7wwKUDrOh+dSuApB4GMcZ6gHHUZ9I0j4eafquo21rBp9sbGW7jM7A+Y3ljK7I2GdvyqWyQOucd6qNpuzLVO5y2k+EtUfUpHvze6NZmRTDPcSiN2jBKv5QPMn4dPxr6G8WXekWPwOuPDzavHDdpYRmMSTiOUobgRq5PHVhg/jXLePPFOit8JNQ0CbWbSXVtLUXemtcTrm6hViFKNzlwoOV4ORjA7fN3j34pWfiPQNNsIdOa21jS4XgNyJvOjnjLFsqp+5z2BOa7qUfZrTcGlG/KfQUmq6DpHwavfCc2sLf6g0ly8E0KvIC2xihDHr84HTPSvDNb1uO/wBFtrUWUkE0S+UzDbgkgE/LjtzXI6J4w1KfQ4tPeScWpmyyMcAMF5Ix7HHQUy+1qO5t1niKFUZVVWIy3GSScYGD2rnxE5zlsQ5VLcqOg1fVNCmMdzcWLwwGBYzbBgxBAwTux1Pqee3GKpwPpDWKvb2DsspAcSTjAO4jHzcZPb9a5OKw1XUb91s45JIeZBkkIid2HqAOuKWdJodOmVpIp7S2Zgm47BLyAdhIBbsehwPyrOdPm0vqS4Sa3Nm41xXuY7e0FxH5hKbmbAVi2DwOOnXrW2stpYW8t9DcwS3oCoEVGcjDbTtbOOnIx0/LPm9kwa5JNu5Odwj3ZCrn866WynnFssNu7BSCVZeScjnn2yeKVSitLMHCMOh1umT6hrl9BNd4Au5tsJYYCHLDLDOf4T6ct7VPo3iG50+b7JJIYjLmORkyFZckggdSeeG5IzmuesnuIIWup5bhjLE+8R91PUgHrnGf/wBVJr15oiWCW9vE010kg3pcOQY1wcBSOMHoce3tXM6ScuW10/wBKLeiH3Fvbf2mjG6hM2PkB3MWGOw9R6eozmstZbee7isr67kKNOUVi+EGW6nPRRzz7+3OZdeVc3kRln8tcFcqO+ODn1JNVhFJbyLcz7JmTBwPmXr0I6etd1Gk7ascI2R6y89voXh9L3TNUgsJW2FLYcuyAArLhhw7DJ4yCMfdzzzvirVW1nW4RbjytqoJXICtNKeS4TAABB4H0rn31O48RXrS3JhdtwcFkA2gYXGBwBjjHSp9VhltrkzWFtHIm7yx5bru2qB1PbPqeo71dOk4L3ty2k2fXo8T6DJt33HxB2sdo83wxaOM+n+oqFtd8IF1Jl8XDzG2gN4MtTuI7f8AHt1r0e0jX5Yz/Bcqefov/wBerGvqlna2Er4WP7eFJPbKuB+uK8iGLqyvdL8T6GVOEXa7/D/I8svvFngSxgeW/vPE9vGGIZ5vB1qgBzg5JtuvSsfw7feBvHGm+J5fEt5PdeG9NvoPsdzbaSttcIJFbh1hjy3IHOMd+K7v43WazeB758bt5D89vmz/AErg/wBlQLEPGlvjA2QSY9MLJTWKnOOqQnSir6lRvAf7Psl8Zo/HmqWhbLYuQybTjH8UQqq/wm+EF1cqbb4yWGCCBFcPCV+h+Za+hb6Jp7O38qKF8plxIM5AA4Hv71Wl0bSbuyljuNOs3xJMvzQKeck9x7UoYvn+wvvZlLDw0u/wR893XwH8KX21IPjNoMsAUKoLxkH14844px/Z0SRD5PxP8OzLsITBC5PbOGOR617tqHhPwzHFYyvoGkYMlsGLWcfIMig549M1DqfgbwdcRx+f4U0JZTvR9tnHgEHnB2jPSr+tqEbuHXv/AMAn6tCTtf8AD/gnztq/wXt/Dt2keqanbardTWN7ewy6fO3lq8ELSKpBGeSo49O9cN4Lt4y8s8G23nt43VUK8ZbABI6njP5CvSfhnY28PxA1jSwI4FEF8ERAAiBraRcZ7AnkV55p9rb+G7ac3Jin1KQhZWGStv3OGzhieATjHp3NdVKFSrGcorRGS5aVeC0/L5nb+BdRvtNku5bu/sWs59LuRdqkIDI6qWDbv4R909+/FeHO0E05uIZi0TH92/8AEuScdgAcDPvXpvhq0sr+xvoJ7qW2/tKYWbmI7iIyjOzEHGRuRAQTyCwzzWB8QPAeoaJCNUsZxq+jDlri3iC+SxBVQ69V6AA9DyM1tThKUOdLQWZNOaRyts4gkEbOsyBmeOQkZz1IY456dOlaH2uwtxH5DF0VgVCgsCe6njtkdu/tk1/DsNxfXK2VhCssxDsIF4MmFycZ/i64qzaaRqkkkSxww3UE86pi3O4hl3DqcDIwD6HcOeaNL7nmcrkXYTJJE09pcvCzAxxsXGYzyQM9wfTqK1NFvre1mU3gt3lkj3HzFARZN3LEYwG654wc57VSNnDCradcW/2TU4xJGRGQY/NH8JxknkdsjPTFdfpmn32kS2jalYTNd20bTRs+nqUQHGQ6kAqAQAA39/gcisZJT0aNadFvRM6650jULq2tPEOn2kl3pLQea/BMaKmPMZcNwc4HKkDHFexeHm0nwZ4D1DV7SQavqMFqb0wKFS4LBCRmPdwQuSSMHaCO1ea6Z440a3spLK40NNIZ7aWIyWK4BdwvzsmR6c9c15l+0H4mj1H4i6bqGgahHG0VgqC/BdJGYlhscYXBGSB7N19OjD0qNN3j2NqlKVJXZz9/pC/FLxleX/g2yuYprtYpLuzklVI472UksItxOU3k4ycj1xwPP7vTDbk27Nb/ALgtFIxwGDBjkfTOTjP0r3z9mW0SyvbnVmIlaCd57iRhtA8mF5SQBx1x+deJ3niDSZXkWKRGlcE5aMspO7pg4569iKca7lOVloYVdIRcVqzHjjle1iRBJ5cHyo4bjqScHp3qvYQRvNFGDJ+8JycZ4zgnH+P6VrnULJsx7MvJ9xUG5Qvrt64zxjPvUXnw/v2iISBCYxEgAB4+7kcjvnGTVuUmnoYc0uxpabLFbIW8+7e6kQr+6VlYxnhR755/AYqvaR3d9u+1RM4wfLMoO7bnvk5C8Hn3rNS9gmWU2rNsDbWZQQSfTJ696YuvfZ2SKaCSeCNTvKjbvYggFj35x+AFZeyktUPlb2Nq6tbDTI7dlMbXcbsyzBshs8bD0yRxgeh71VTUo4bdjJHtlBOSc4ySDnj04A/GuRhuJEeVI/MgglkDbADjG7sPbNaos5WkBiKukWM7gD24OPrn3rWNHTVlOn3ZszeII7dt4faZImQblJGTxjHtVO3tnnle7WNre3kbdiXoBnt3OP5VV+zxtc+bIFlWAZCIA27jGDkcdqbc3UlywMZETktuLHOFrSNOMdhqKS0HyKtujlmjlbIKKQcLj+tWrYx3VsPOfAU7lRslg2PToayFsrw3kcTcRkFlkc7QQO/9K0LyDb5kkDRwQAbizDO05OQDzmtB26FnVNQ8i1KrHCieYSQihRu7A4PWq8TXuppD5W2GHGZJSuBgcckdfpVJ7sagkVvHavPFG44HQHuT9R61Zt3eS0YJEltDG21MghSx5x/Oj0HayP0lvFEGpsB8v79McHn5sYq34kAm0KNyTmPUIiPxcL/7NUWoyiW7vEEbeZCY2wR977xBB/z0p+qof+EfulckOtxbyEA5xiVM/wAjXzVC3PJev5Hu1NYxfoc38Vx5nw+vWAyPJJ/8dY15d+y9NGfFHi2zY8yWMbfqR/WvXfiLAx8A36Aj5YGDD8CD/OvGP2ZyF+I+uwtkB9PBX6CRf/r0oO0S3rzI988Nz/brCwuo5MwtbbCu3ndtGGz9O1T2x2m9QAnbqDgg/wC1EG/9mpnhu1ex0izkjdWSRlml3HO0FBwo+o/WpYhKt7e+dGsbyXSSYV9w5jVP/ZSadKPLFmM3eWhFrzj/AIRXzxx5cKv0zjY+f6VZ1O2Z7TyJsFhcybwvoST/AFqG6jafwrdRjOTazquOuRVvVJYYi9yzFXZkzxw2QMZ/FqJxvRu/6/qw4u1Sy/r+rnzF4quk8NX+qRRIBNd3DG5kX74UMdij2HXHv3wK8q16RrqeSeCflpD8yYIII9/wruPi3LPc+LdWlQdLlwMdsHH9KsfDLV/hHYaIlprWmvqOq7t9y107iONichFC4GAMCvrqc1Soxi1pY8Wac6jdzg/D1zeWwwrLubKs6rtBBGOR64J6cV0uhayIdSls58y2l3byQT25OVkRicr+XI9CAa9St9T+DEkQkXw7o8asRyLiZMk/Q1HKnwOa8t5Ws0s5mYNG1tqzEnDdlfIPPFZrEUox5V+T/wAjSUZTlzSZ4CuiHT1kmk1OwidJFRDtnVgckMeIxgKVKk57/XEzpp0V5bXOm63axw2sWWWMuzZU7mblcEFm9+E+lfaXh3wP4b1Kyl1TZJe29/K1xD9oiTcqtjKkEHILbjn0OO1Gp/Bj4a6jAYp/ClhESAPMt4fJcY9GTGK8yVJNmqpwS/4c+VdR8NReM9Xs9XtprGz0h/mMct6XkfDk529jt2jGe3bIr6Ag0rwBqeiNG2q6jealFahLea9nDzqyg4xgHGeMr0yM4zzTrD9nzwvb/bsTYaW732pCuRDBlcRlS2GIAYbvfv0Hfad8N/A1hMs9p4fs4ZU5VgpyD7E1SjJbM0Th1WpFZ+B9DutEhs9aittakAP+lyKFlkBJK5x1IBAz3xXyB+1x4U8JeHfEkVxoWvxvIItz6Y2WaIhiGGQML0Bw3JyccAV9zRabYx42JjA474xXxn/wUFg0lPG2graQRRalJYsbqfecvGGwisuMcYbB9+2K2hdvUictLXIPgxO+nfATxPrDKQ50W/kUrz80hW3UfrXzJMPIn8wcsCCUPrX0rdONC/ZG1ES4U3UNhaDBILeZMZj+OF5r5vEcUr7UmZAT3Ofw9azw2qlLu2TV0svIkSQSSLICu0N+8YE8Hvx37VDFqCpdxr9+BHLY243E9Tx+lLNAYm8rkeZjjON39KsWNgiShpy7OBknI2qfx6mulq5i7WG2SkKJvkEbjkK/8xTSH/c3Er+VFGc7T1Y/571ZWzwxlSR5B1Y8Y+hovvssca5V8kgiRiPlPtjinYlMjtre8km89rhQsg3KoJLAdj7UxTewliZAoYEl26DjGKrTNLEoe4V0TbtVtuSc89f89arRytJKsYkLpnA3j5R2ycUFJFq1vJ7qQw5yT8iDdtGP8a34FhEQJIigt2BJVsliBVG2sI4tsVwsUq5+/GTkNj6cU7fZW+7bHcSK7c7lwGIOMUiW09i3qOowLbDzQWeQYX+InnOMnOKoi9iht5FdJ4klG0b/ALxPGcZqKKN52E1vA0mw7UCsFUDqc561YurySBzDLCidSHlyQnoRjg9+lNBboMF5LGI7a2SRC7Lyycduvc8Y5rSvLh4rVz5Vs4i5+Z8hjgfN6H6Vm2t/cTztMJB5MZ2qypjI70ttfI83loiOoBwBnA/+tSZLXkfpJdfET4WJIXbxppD7/lYx3AfHX+6Dmq958SfhepZZPERlEgzhLWdx9crGfU1O2nWkcf7q1giXP3UjCgZQDoB61f8ADsNvcyEMiOJLbv26H+dfOwxcZTsqaX3nvSocsbuT/AxNW+I/w7u9Lmiu49bnt3XEwi0m6wAffYMD3rzL4f694CPxgmvPBdpqiWkeizvdxSxP5jlHVvkDMckgHA4r1XxJbSS2WuxStvAtIyig4GSzHP6Cvn34DqLf412iH5fNtriP/wAh5/pTdfmVuVL0CFNJ6SZ7ja/FHw39jiSLQfGH2fblFGjN93pjOe1Sf8LP8Omdmbw54vMrBSf+JM/IB4OM1p6I7S6Zbh2zg3if98zHH/oNO1JVBS53/KbaNuT0/wA5rNYmX8qFGlB9X/XyKVn8RdEYhbfwf4xcMGXH9lFQc8kfM4FT3fj+w8iWKbwJ4vKxbGZWso16fd/5a+36VfsWja7is92G+1Px7GJv8aXVlbzrzOHBt4cepwT/AI1TxklTbUV9wewh7Szb+8+TPGklzP4wv91nPZGW4dtk8qFowSSAy4xnnnBP1rk5tD1WyE7S2RWOeQtvjAdOTnhhnFdJ8briTT/iHrEE1wplWUMpcbFYFQQM9BgHFcVZ+Kb2LAjmaBieBkj9RxXr08ViOWLkrryMHRwruk7M2hEv9jXoP3oljfjqBvC/+zCqPiC0a81uG4VowrFkKxfdBBGfz610vhzxnO8n2a7MFyu3DJPGsgI4yMMOecfpXQ6pplheS22pCHStI052E0u+PY6MD8yrt4YHHHcdGxwTpPEykrJWZVLCU4tTbvE9fk+JmheFhF4bGq3FoLGJIvIERbZ8oONzDnr3NVV+MelAsRrGqsCc8BT/APqry/xH4X0jxv4o1PVdF8U28UlzKZY4ruHag4wqF1ZsHgDJUVylt4Zm0rWptI1+2lhlYiNjnDRN/CynoR0PoRXkzw0r3lUlr5mHJeWulz3xvi1Zq+W1TViCu5A4+8D0OM9PemTfGG3BA/tDU4yehRE4+uf8K5Txj4b0C08NWkWoXUts0iqIJQcybI1wMfKc4GRt4BLZ64ryfW9J1DSNTktftEd1APminQkrNGeVcdunbscjtUyoNT5faO/qxzpcjtc98uPjOACEuL9jjPO0fyNfLP7S2v3fiTxlBdPqeq3ayLmCO7fcsAY/cj9BkE/lWtJDdlGG51wcZIH51kf8I7dar4k0rzgGC3UYJBGdu8ZyPpmurDQdGXNKV16mTi2j0348eVpfwI0PR2U7brWegPVbe2Cj9ZK+Z5orXzFdHaMFvusM5r6M/aljvLnRvBWmWa7hHaXN7KuR1lmKj9Iq+d7zS9WL/vLSVUHXvj8q7MI0qS7k1tZsmF3Ohw8cpAGcngZ7CqtzJdvdRtK6oFION4IH1qvPb3MalH8xQvJDAioW81gqEDLHCmuq5lym5C0ZD7LuKQ7S3KnIOeuO49v/AK1Ir3LuzW5gO/7oZuW9cdhmvZ/hH4y0zwfDp2kyeH7TVNOmjAuopIYy00rr8xLlSSOcbemPzrjPjtpfhzw341kXwtLFJY6hCuoQwmPa9p5hJ8g5HRSDgjquKyhWUpNGk6DjBSORht5mtzLdFztBLKzD5j9T2qG21GKSeQJsgVVI5PLewqjLf3MsW1pQiYIZVPBB9v8APWooGtJLctcRuzrwoQYAFbXMeXuXrm5ubeVt4SQkYGwdvrTYTJeTgzJuROFjztyT7VWeW2it98DStIMbcnhD7Uy3dZIw1xdspBJOD81IdjXmd5d0aSRwr/qkwMAN6YFVIrIsHa+nyUyAgPCkHv7VQ+1Mo2wNsGfvBcH86fDJiWONZFI535HB/CgLaGrFIkoMUMe9VyQVBC9OhNQ2Yt4ZHndozIxIAU5AB/nUE8kEIIhZ9p5fpj8h71mu0gPmhSqNwCBgGhiSP1Nu2CtFfKNpceW6E7Rk4IP16ipNISD7VaOsYSN7RXAXHXA5yKhEQntbRZGODdyow7N8koH8hUthCovrNAdqpAyKo4GA7D+lfIwilZ+n6n0Le6K+sGO3m1t7yaOKN9Oj++wAypm559cg184/DyQaZ8a9LkJYnzbhTtwSf3bDivpU/Z9Rv7gyRxzJNY4w4yNykdj3BNfNeln7J8bNG3DBGrGP8CxH9a6EZwvzan0X4di+z26wECTyr+/TJ44LyuP0xVa9kM+jRyKEZTpzbgARuZQBj25q9aIsSTvMyBDqUpLHgYZG6/nWVfytcB4tPjkjiCyxtM0fyuMjOwHtxjdjHpnrWKWzQ476mzYSCXVY/lQMLlGUZweVwf0NP1e4iVppJHZVFud20cnYw4wOSOP85qhpzrFq0TSBQ7tAwbHYsBXOfEi91bRbW51JlVNOt5tpYRNK/lls9McfNx6YwciocJyptRet2TWbUrrsfKnxyvk1L4k64yRusZunwsg5AzxkV5fcfarOTak8kcTHjHK/keK9K+I91Df+JdQ1J9qvdTvMygYxk5xWjY2PhtPh6utaxp0d3phd45pdoSeGYAEKHyMg5XjD9TwPvV9NSTjCKfkeS/ebY34V20BhGparq9neGOAyLau0YWMkcPJj5wFzk9CTgDJIqX4t3ktn4ghsfMlitI7aGaJJj84SSJZACB/F82D6dO1WbfwBp2paPD4v+HmsSQWEBEl1bX+4mF4zncxUMCO/zDv6VpftU+HtRuNci16JA2bOBrtYnEnlsYxySAOM7hnHYetY1JtyTb/4B00U7NJHmWjawsOoCWe6mi5wzJ2HXoOvNfTXgEeGvGeiyX+sWsk82nRx28M6TMu8BS2Dj0PTPrivjnTI7q8uIbe0jkJkbbuPCj6noBXvWh+KE8IC2sFurGeW2CST2gLbfMYsvXGC2CDgkdOtKcXOSUfyudNDDzrqyX36Lu9fJH0C2gaH4t8OWC6nHI/2d5Y4mil2smcHn16VXHww8G+RGJLa8Df7NydufYE1X8I+KNJvtH0/UtOTymlBNzCw4SXJB6+vr6EV048QWikFxG+f7rf/AFq8vF4udOtKK/JHPieWFWUZbnOH4S+C5OfLuh/22J/rWbr3w28K6DpcmrWX2priB08oGQkZLAc/gTXer4h0zja20HnJP6cVieO9Rg1HSrWwtZY3kuLuNdqPuIByOn1IrCOMnN8un3IygqbloN174Z+GvEMOm3GsC8+0Q6bBBiOTAAC7un1Y1jSfAvwK3Ik1YHttkU4P5V6TqNyi3sqRy24CsVA83HA4/pUTXLKo+ZN3XAkHStHi6sdFa3oXyp7nmY+Angpggmu9T3Y+Y/u8E+vK1U1b4FfDuw0+W8urrVNiDpiLkngD7nfpXqouJSqtyBnrk4rzT9ofWL+18IwLabtrSF5GBPAUcD9a6cDVq4ivGm2rN9v+CZ1VGMHI8q8IaJoeoeNo9Ht72xsbLzfLUyxBmGDjBccc4+8RjnmvRvE37N/w913UpdRv7zWPtMgUN5UkaKMAAADbgDgV4P4astR1jW4V0aM7i3mQtHMInMq87QxJwckY6fpX1d8Otb1rUdBNt4kURa7Zx5uI8qxki7OGThiOhIHocV62a8tC0sPZPqv1M8NUq1I8s9Ujym4/ZV+HwyYrnxEwHpdw/wDxFUn/AGV/CBOI7vxGq5zj7XBj/wBF1722q2qD5piOxBJz/KopPEemoBm6UZ9zmvF/tOuu33f8E1agt2eASfsq+GvNYfatfERA27bqDdn0wU6U6P8AZY8MqhAvfEXP8Jmt/wD4mveT4jsQOL1Md8tilj8Q2cjKkU3mOeAqKSSfpT/tWv2X3P8AzJah3PAm/Zc8NgEG88QYPbzbfj6fLVV/2YfCMbbpNa1yE54Dz2wx/wCO19BXnie2tZmilgYOnDKVIIPoRWXfeM7jn7JaBeoy6FwfwDAij+1q63ivuf8AmS+RdTxGH9mfwizh11/WJSOxntz+mKtt+zP4dkiaM6nrZVuSN0H6ccV7Dp/iXV7hVM19p1qc9TpUrADtyJ8/p+FbWmSapfSKyeLfCwHJ8t7KVH+hzKMH863jmFeWkVH7/wDgh+7fU7GPQblEVpriCMLdGbvjBBBH15NULm+8LaWwk1TxRo9i0TSYM15EuQWLd296+XZ/hJr+pxKdW1vX7+Yk7jMzENz3LMSO3Pfn0rQs/ghYxTK0emmVTJuKzOPlGeATgFsf571Lngqdl+p1uVS+skj2uf4n/B3RFUy+PNPlkRNjNb5n3gkE8RqeeBXluveK/g9rviTTbjwcNTuNch1i3lkuZYJY4QC43A78DJ7AKTnjvWjbfC1EubkpY2ccc6nzVECFXbcDnBPX8u1a1l4AuorMwzxWgAfzIwIkUA+u0DqO3NTLG4XleiQRlyu7mdDp3xE0yXxFqWn2/hXU7s2Um2VXiRC0u4qXXLnK7Rxxk/pSR/FH7RqnkR+ELeCNFLSSTXi53E/c2CMnIGDyQOeM1ix+Cb8arFfLdKjKxIVYzgH+8QMDJz19eewqebwJBcSyyXE0yMT1icsSB0OCM5/P69q5VmVGEraW9CHUg5atj7j4r+JLQEw6FoNggACC7mkVjwOOcY5IHr3xXC/FX4jeIdXQxr4jsLaCKUKLW3Lp5mc/eAJLZBPUYIHSu4j+HHhyRXae3nkkYliZN4Occ89Kqa38LvDt9ptyh02S5vtpe2eWQgCYA7CGHTn1rT+1qbfLd/cKU6XRHx9rZE2pXEh4Bc4Yfl+Vb512ZPgP4l8I3dvIUN9a6jbzAnafmCMD2/ukevPXAq945+HPijw9dRHVdPSJbgt5WyZJA23Geh7ZHXFbGjeBvFes/BvxFb6XZSXsz3trHHbRkFvLXfJJtz77DtB5PbNerHEUnHm5lYxVOfLzW0Oe+C80badfh2H7mBm/coy3ABG07ZFGCDnlH3KRnit7x9438VancJ9khNjbQ2UdnJHIyssgQbQxDMTuwBz7Z9a8/wDD0eteFr7ULDU4tR0xzbMZoJEaJ8r8y5BGfpjH1rMuXm1CYH7HeXDyDejXAOCD3yxPFdEOSSfW/Y9bLpOlTdRThHp71728kuhIb1pJv9Lvwpj+5HAgbH0VeB9TmtTT9US51O1gWHyrVpAj78PLIWOGcnnLnsP59sE28cZZbzUIkwceRZqXY+27GB+IrQ8P3y6dqcF5ZW5tjG3+tlIklf1A6hfw/IVovcVtvzOilOVea9mnN62duWKurX/E+k/ApktbS6tVt5b2/ebfKRBhYW3H5Qey4AH4LUln4muo5vs+paTdwXBYKwRg4AA5Occj3Ga9F0vU9Kv9E0rWlsbmd5rdGZoY2LRnAzgDGQDnpn8a2zNb3CvbwyqMjkrlDnHTPUfzr4vEYvmrSdSOp4tWsnN+0jd7b9tP0ODtJrG4XbHe3MD8EJLDsJz0I4wQeRx6H0prgWuvaVJJcNjzVky6MvRhjpzjjqPrXX/2FYxOtwy3MjK3mKZLosVPI43fX+VWdPEFharFDbllj43TT73HqNxyfwrGOJUZXjE54zjF3scZbae+qI99bXbNuc7vLcgE55PX8abJpF5EzPKxkVeQqfebjJ4rr59asrZiDHHEBzuCBhn8CKzbjxrYxM/+js46BlQjd9c0lVn2M3KJnRaReyASRh9svzFZZSdvvj1z6VwXxyF3p3hRJ5pVjVZsHC/f4xxkkH8vxrsr74l6bGuxHAYHhI0Znz9OK8z+M1/qHxC0W206wtby2MU4cPPCVB4xnoP1zXqZf7WlXjUnZJeZtKm3H3lb5oxP2d9QguvEl9YkpIWhEynam+KRTwykEHkHacqw5HSvbpLwabm/F0Ybm2+RJCUITI6ZwM57g884I4r5n0Dwv4k8FeMtP1aAx3Me8B3gfbnPBUg8/j0r1y8ijvwFvjJcqowquWyvPoD1z/WuvNa8atSNWEunTuc/M6UU4/I9J0+XTdRbyf8ARbi5HMnlzjOSO4HT8qtyaPZBiWj+znj/AJbjj8xXl1lKbFwtsDHtHy4zheMevH196fNfSTN+9eR/clq8VSvq2Crp/Ej0n7Pp1mRcQhL2YNtSORg8ef8AaA6/Q1v6V4sto7pFlgso2iAWeURhNo7hSvIP0ryW4u0j8H3zpM0V0x/ckdmGMY981kwarOEH20sZ3ZQWXhXYrkkfiDX0eDlTpUox6vXzNoNPbqeheLtU0DSfE4/sy0n1SGZg08lw5CxM3O0YwXOCDz0B9a1NTvNB1nQrq+0rRLi0ubAqJ5IkKwuMDOAcZHI+Yd+K+f8AxPrOqWmpPdWAnnYt++RW9PukZ784x34rrFvtdvND0aP+1zZpJsup4oGZXKhmHksynBBAGccEHj1p1VQhzXVk/wAH+e5U3yx1OiOoqDuNtHgf3mHNPGtysmI4IBk8YOK5wzbVwtwAfYEcUnmPghWMmSOSRz7d68W1N9Tk5vM918+M7co5B4YgjIHPpUczweX8xkI4HDlev0ql59umVO9jncA0YyozxngVGL6ztzJMdttGPvhUO4+/FeT8S0NrmraiMoAqzkqMj5iSOO9O+3wRPiR5EGPmJX9azjfQZyk0qAjnkDnnjGRk1NFepJlFildgn3ZQVz+J4/DNHL0C5buZYZTgyEpjBIUnr9DUCtGkY2XAP70kHyQoC+np+NQfaJCpeaFbcA5wMNg+575qrY3RuTt+zu8OSrOWT5SDg8cmhxa6DNlpgFBimjY91LgE1FJdXqxb1jyc/d3g/oOKrXk8SALCYkIySRtJP1444qKa6tHmQhZd4+9tGOfX/IqpR02Jeh5H8aLm71HxTBZ3EuWht0RCeBl+Sf1H5V6x4S8PW3hHRItKtoiXaNJLmRmJEkrKMkdvQD6V5J8YFQeMLS6hLhZ4UPzeoYj/AAr2S+mMtzG/3m8iNXPLbTsXtXo1mvqkUtD0a0rYWKXU+fvjfbrc/Em8nu5obaJYoxmTqR5Y4AH1rwjVrOGMxkwSMki/IgDP5mMjAH4dvSvof9obSo/7YsdSjYPFdQm3kwMYaMj1/wBkgfhXhnimBbK/ZVubvEEaqixyOdgIBwAOmSf1r1srlGUbK+3Q9bAtypKUeXRfb2+Xmc9HDIJAblBZx46yFVwPRUyCT7Va0N7Vr6G4u7H7QkRDETPtTA/2ASD261GbO7lXzb2aKziUZjF3cfM30GST+lS20FvtDz3lxID/AA2sG0f99OMV7Ki47K3qJV4VvdqSc/KCdvm/+Cj60+A/iaK58D/ZdUuxJPBO6J/DuUjIwF7Ak+9dld6xpIQphpS+CEw7EYHUHtXjv7OupGK1ngh0qa2t9h3X0pL73BGFJAKqcHpnnI4Hf3COcy7I2AIYZXCBRjnn6V8fmtKNPFSe99dDxsxgoYiXJDkT2XVfi7ej1MO91TSpollljvoXA5RldWxnpx0/Cn295p7Wpe2sLp42+7sgw3Hqc5PXvWu1xKCYtsQJ/vMFz6Gq8KXRZ5BZxPtbAzjbn0yM46/rXC5O1kn+hw3e1zA1HzZI8Q6XqbbsBcxrj8fSuY1Kz1MySNJpsqL0VXcbuvUc8ivSItSnjYRGzhRTjdsfheOegx/KpJLqFSDI8DBuitJx/wDrqVPl6E8qPHrfS7y0T9zZSJIx3O4Kl2Pqf88VA/25HCeVKuOu7/8AXXss8cDoxiMcUmcZOCGOD61h6rbLjCzWwlYdHTP8q1VZvVinHmd29TyPWLa7vLIwW4KzI4ZWduPpk1pNJKzF3PzHrnsa3tR0qVmLRSWzlThhsK/Sueu2kthtfyg390emfauhS5o2voRKT5FB7IUvKMH5mycYxjFAOchmYZPc8Cs+a/kyWEqYHvVY31790LgeobirVC+qMbo6zQ5NPUNDePgOCBvByue+TWa0tqJ2geRdzFkQjg56Aj3rnpbu/wB5Ks+7sc5qFZ9cdwy3TKo5xtxj8a6Oa3LtdGntNvI6eLw5aPokV7unnmMqhsMxDb1yvyjjghh9a0/ESw22i6SLiYLPFCbd0cjeApyM/ga57TNb12xtJLaCWPyJIykgOR8pYnrkEYJ4IPHFR2HmxqR5qsM9WcDd9Sf8a2r14Si1G7ua1JqSdi4HVskfOvY5B3UABRlEK49TioTcJvKvGmc4PzfyxSPIAVzGmCcDLkfqa4HFLoc3KfRImsogfnjLLwSG3H/61PWSxeNJHuYfmHDLGMn9elcXd+J9JECmK5hfaMbBFk4HoKzm8XwsFgt7W9kULz5URjwP84rj9m+x6HtUejzppgUFoI3Y/NnaOD689MVWaTRcPbxyQPIf4MfMp/D/ADzXD2Wv6kQjL4bnkWQli88gHb9OO1Ldaprsc4kttI020BwDK8u5hjjovJxQ6dwc+p3NpNZgeRaIHYckBCACP0FN+RTHst1jck5Xcq5+vXivMLqbxZf3QaXWoLeLjJty2eeuMjr+NTSaFqFwxOqa7dzKmSo37QfrjHT60lSfcn2jfQ7+0ubwu73cFrb7jghZd5P1wMf/AK6jkvdPilVJ7uKB14BkA/Q9+9cTp2lXNps8jVsxYO8FAeT0J654796jOm3Q3rK9iF5Kg/eOeM8CpdJPVEth8bbaF9PsNRjjUtbXGx2UdVfnOe4yP1rs7nV7SS5ge3dnjmtopVGzoCoI59RyPwNcRc6cXhe1uJmMEmAwLZAIIPcVoW99bW9ha2sawSi3UoCYtzYJzjPoCcj6mupyp/V/Zzeqd0dDqxlSUG9UzI+M8UOr+F42sN11dQ3CSeXEGdlXlT26cjsOleAfEeeZ9XhgeaaCKC2i3RKdpDhACTgZ/M96+lJvEdulrJJHIMx54gh+br6Y5r58+JmoG78TX9yjon215m4GD/q42VTz1yO3evX4dhzzlBOySvf1Zth8whho2lTU+19kzivsj+dI5tZneJFd/l/hYgA+p6iui03QXfUY0u1nazWZRM0MZEmw4wy7gQc5HbuPWsiK/jui04uObm0ZCQik7kbd6/Sup8I3Mc2owiNpJ/MtWAAT7zJyowFPbZ+VfY08HQknfUqrxDjrWptQXZJf8E+hvBPhLSPCmnzJplxLJHOVkaSScMsgxgMOgGR7enXityS7tRLh541dcgZkHQ//AF64+2+zXEMRSIzTmND5SMV2ggYPOAABzUsek2bSMs87xuTkfvske2K/MsZTlGtKM3dptfccFSrOtL2k3dvqzqA9s7v9pmtp4uiKQF28c4qwNQtbWLy4N20DgKVC+3Submt7eOEgea4TsI8kj86g8xQFxasoZtuCATn8PrWFna3QnU6WTUoSgYBUlOTsLbhn0yOKjGqWzOxeB89GIcYB9q5q/F3tV7dhE4Bb5ouU5xznH6ZrMtxds0gF0rbwZAGUKfQ/XnAz+dU6PQTujrpr2y5ZBJlCCpD479/WoLrWrJydy28LpgbyAzk46ZrknjuZRiV/OfJBjBJI46n8j37VFBBBeLcJbJ517CoKxx7d3+1n09s4q6dC+iJ5nc6OfVoZEJWaKRuu5jhufQYrFuZLeXIHmDngZx+VU4I5ZYwSkm4jlXXYcURWNzPAS1n2PyhyfzNbKNndoltshktrQyZPmcj7rAY+ucVn3cFqrNi5ZM8fMxH5CtSeNYFK3cDwAMFHmIW6/TjvUTW9ok4hcqj7dw+RsYyR347VSk2iWjIH2f7sVwhYdRyaYXEUmWnbGcAbelamq6fqdvEr2kKXAHTc+MDPpWZLZ30iZkgjXjlCdw+oNXF93oLkZIqo3PmKwORgZIpEEQPyFGfocdaqhZYVDbYrcYwX8zJHHvUoCsdsxSbvuZAO/rTeuidxEpGV3KpX/ZbgUx2A6Rhu+Ac1KiQJ8o3IPXJINMEG9CqzdejF+ama12LsrHrRjgimDW1hbhuSHDKpz9AM1KLi5tgBLYQtH0xG2Svb+VZV9qUpkGy1c7Se4B/DB9v0pkt+JG8zzZlkxtJLAA/iK5HdnRc6D7asb7SbVYwcMh5Izz1/EVDLe2JYsHVZeiMrDB/A1xmpusr+Wslw7g5be5GR+GM+lZ9tC6qlnCcCLJVmmyRznHPUdvpSULrchzkd9d6/ZW5AnhMhY5CLGcse54qtd69bXG2WK3WNCfm3wFePX9K4tJL0rMse6RVP3thxkHkZPU5z0qeGO5mXEkMxbg7TJwP8/wBKHdrlbFzS6nU22r6dcKUt3Zivy7SgAzx7cU25SWVVRHWME8eYAc9+o9axLaySUeWJJGYk5VmwO3OaLqPUIELxm4kQbSq7gOnJwT/n8qIyvoUm2apS4nYCVggBwdkm05/rQ1pfx/NbvGykZO9uc+vTA/8Ar1zx1SSIAXd44yoLrHztx3zVn+0Hd2i8qVm2ZIdiuR6/1pyfKtiW7dDSNhrkk/y3cEcZ5OTn69AK8W+Nuk3Vh4hX5ozPMBciXZ958Ybt3wfxAr1G8vrmMGOO3mBBwNk2BxnufpXn/wAXjLfmz1BrYBox5QbIK4DE5Lc/3iORXrZDWti0rWumv8iXJHkOmzNHf/Zg8YDSGZM5xhgAVGPQgfmK6CyuHs51H2kxi1uBgDPCE4zyfQrWTLoWp6trvk6XB5stvNJJtUBUVO4549APXIq0gmW7MMqravKphlBXDIwGORgY4IP/AAGvtqdWMZ8l9UKUXJXPojwbqqT+FUESee1vIYwN2AVPzAnIHZu3pWje3U4eNLazjKscSEPg9PbmuD+GUWu6fBnWQxieLhZNocMOjep6t1PcV2RnzzH5RBB3KpwR+VfF51Tg8bKcJJp6/Pr+KLhdRsx+oxXMjeY+8AHeRGCSRn3OO3OKxL1LkSQss1+1qVOFgi8oK2SQCfvYByfz9cVfur0Ww8xmaNWxwoz/AJNZk3iKJG2i9lYE8r5efrXDFrdIltXLEF5ZopEuoTwwleURcsp7qeRwf61Doq2kl3OLu6N3HJiEOXcNF82d52+oXt0yeMkGrFvd2l7AJ2jSaNlIJKdfqPTFS2r2turbozDETwhAAU+w/PpTjPlskvvKUVa5m3kU1uPMN40KpuZFRTIzryACDyTyOf61HZRpp1ypjM9o0qByChZmJP3yuRg5Bzj0xwOmqz2IMjMqEs2WKseQffPoKYjWzt+7CBWk3ZJOQTk5B/Gj2tlYnlV9y6/ieySyeSTUFYyOCVHybW56A5B4HHPbvTLXxKZbmSF2kMZGGYShjkg4bAPbPGPf3FUxaac+QdPtWH8WEADf405bTTiRD9kiiUjaW2DAPHGRzWU6rt7pavfc1J7lpEwmoTls/MpbAOf1qrLd3qqVuNskY4BHX9aLPSbO0kZonOSeB5u4LgdgexpVhWK63C5dV+6UwDk565zXK9tAaZn3N/LbyBScJ6MMr/n/AOvQbzzgXRYZgCAcSfMferoktDGYypuAuTiQLt//AF9qqSPaWgJg0+2QdfkUA/pWkEyHoVJbNJiJJrYmQMcLuDn680x7NGQgw7TnB34IpZ9akcHYRHkcK8fPPTmoB9mkka4Z183aA2CdpxnpXSua1kxN3GfZhFhQyYxtIHSopP3ZIXcw67QwpRb2ZmMkcf708n5vWpPIkBJztA5BXFNQb3YrX2OjSeZZR+9klJAZN0eCPfg+/So58yYims7gLu3b0UqvPf8AHpVeL7fdqkhY2YH/ACzkba3/ANersWUlLG5JIA3FWwBx/Pis+SK3NLlTzdPhZE/02ZiMYDdM47+n/wBetabSohdIiSJIoQMJTPkrx0OO9ZMlzBJI8kd0zMWwCE3EdRnB9s1TudQvliw0Mp2nCyI2Ax6dB2+vSs5PVJF+0iotWOwij8jCs9o0fViWYsCOnHfmpGk00ujS2y7kBYbWHPA6c98iuFbXNTWFkl09/MVQ23rvHQ49Tx+tXNMnvdSszLNHJbtj/Vl+AfXp6U3Fx94hVHfY6qO80iKXyg1xg5+VpehOeM/mKbJqWnPbiJkZFB43fNx7flXIwrbLciOe3ugAxU4OPTp7c/57aUn9lQO2ZrtFZs7AmQDz0J6en4UrXHzy6my11CInVRGAT+7YqPmz7VTuAu0zFXcK3WH7wzwRioIbqyPzrG0b5yPM4x0yDSy3y4cSyxI3oo+9/nH60lT7q4uaIslujwnCOjA7x5gyRzyCPzqpdWCGwklnigkVMNlo8HI9j/npTbrV1WZA8ytGwwWAA/HP5ce1VrkGR5DbX6mIrhlc/dFawm41E1pYuEkpKS6FfSzGdajKoP3kbJgce/8ASszxVp1odda6dF8ySNTnHJI4/oK5fwL4/nuPHS6XqOmQ2nliVTucswYA8EYHpW7498S6VbahbLHZSyP5cjymEqMKNvOGIz17V71SnOOOjG26O76xT9vzJ6HYQpe3ieZbwAhokdZJMbQ20Agc+2fxqE297EgNxBBEQCSFUkn16VmRvbajYwtDfXFuskEZVWPGCvt35FW7e3mt4PMk1RZienmnaMfrnpXi17+1kvN/mcVSSlJtbF2RLySAXEVsktuCAzk8Bj0z6HrVNklfcPItQD0BAyfw+tEtpbTv5iXJ3sBujVtqscdc0yB76KcR/YIxFuI3F8nHQHP4k1g5Jr3TKUiSM3MSbVihWNTgBYzxzTJhNMPntVmRgOv48/59ajEk9wv+tmtpQOPK5AP0I9xUtnHKp2i5mLheHd+G9/fNKXNa9xc1yEWiKz+VBOoPzMoOOfSljsnMfmYmXA+71OB7Vd+1XCSeTIjkAEqT09ufxpx1S3jVNz/vDyAGxgZ601rZNByp7oiWykRC0TS4P8LdPXpSlZ/LKgJDjIJI4IzTJbyO4VxG8jbScqH5qkt3NJbjfZyIehQsC68kdc/U0rJPRB7vQtpDcQoQDCEC9s5H50ycsqmWTdlM/OvpVKKKaB2ZbiSRGHKSHOKlWeAZzkkHB2Z/Oqv2FdDUu/tBaOEySY6qB7+tOC6hnJCOAf7uGA9Kessm0rGAGx+VRQ3F88jxizM4RQxxJn68D0q0nsNKL0SEmuoLa423CBVJ6/eNSLeW7F0jQkr38vgZHapIrxmOWheOTGMjjFRyzCSfz3V5HC7AR1OatNW1NGkkMDbnO35snlRTfMfBUKqPnaQeD+tH28H5BFIMqTtYZNNW5YLkRvjGMSDNS6nkYtq5sX97YKYmuLrzW3ZAYEH/AD9atQ3Nvd258uON4uuA2CR257f0rOuoonktt8aNkkHKg5GKl2rwdozgdvY0+TmV0awTb1LpuQrBjp52seWBA9M/zqRr+JTmSBYwOAB3B9jxWTcMwmjUMcbG4z71nyMzr87FslQcnOeKJrVCk7HQS6lp5jWOQRhwML8vft+NZlzqdm0aySQDKjnseeP6/pWLqBK2hKkggrjHbk1ooqta4ZQ37o9R7Gs52VrGfM2U9U16WLaEWJdp2jecYz3NQRa7K8eyUyMpxl4zuU5H/wBesHxKqm3VioJDnBI6davMSsQKkr9PqK1VKNkyGnvcvTanuUF7iQ7hwCwBx/SnwXVtIf3kkwB9+tcfeE+cpyc7v61eR3FuxDsMdOenSiVJLYlM6hZ7QqFaMSrtC/PySB61G8tuYzFFLBBAqksJHYMW9FwDWLAT9mY5Odzfypk4DeUW5JYA579aIwSeuppCaT1RhaZoKDxtPrczPGkkjtGpO/JwQM4IPPt07Vb1jw0niG9W51S8C4TYsUf8Azk4Pck5ycV0yqotlIUD7p4H1p+xDcxAouCeRj3Fd88yrOaasmtL26GntHsWrNIxCkD3IMKLiPAwwAGM46Z+lVr60tXl2PFcyBQRv8zGQO/6/p71c08DyozgZ2f1apLb5sbuf3mOa4ZVJObk+ono3EyLSexs8pClzKY8Y8wk7ccZHtk/ma011eSSNXWPKnIBxwP61YvEX5PlHT0rntRAUEAY/dHpU80W9jKV0adzqGqyHdEqLzwQQen+RRFqEkkKLfQsBncu3tj+uMcVyekO5WAF2wUfPPXBFaunkl4cknKnP/fQrScFsRFu50L3okx5chQH+Ejvnrn8aav7xvMeALInAbOM/wCf61TsQDcMCMgKev1qd+CuOPu/zrnaUbotS0JTGHG+OfyiPlwuD0zUMheJGkW+5HAynSqsJKzyYJHzEcfU1b0r94+JPn+RT83NPYqLGm9eNGTzSxxnaFI5p73hQ7Fik/eDh0HToefStIgZPA+8KrTcGRRwNg4/75pxSlKzAhSW6Mg5TgDn1qjf6pdWl0rJveND+9jhOGI9ux4I69elOuXdWXazDjHB961Y449sJ2Lnb1x7Uk+SoXTnKnL3XYgttfv75DFe2S28UJIjkfCs4PcjoOg4+tPlnlIPklRyduWGGrO1f54k3/N8+Oef4DQnEEWOMrz78Vcm6kuYJycpXbNB3uOqZD9iV6f55pQ3znz5I8sMfKcZPpjtTLdm+zH5j9zPXvu61BGA0SlhuOAcn1wKzcboSbtY/9k="}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-45554-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: If someone were to check the duration of a cooking task, which part of the personal accessory would they most likely look at to read it precisely? answer: They would look at the watch_1's window embedded within the watch_1's case for a precise reading."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-46464-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where is the most likely point at which the visible light diminishes, signaling an entrance to a different part of the location? answer: The most likely point at which the visible light diminishes is near box_2's side, marking an entrance to a different part indicated by the presence of box_2."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-48256-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In a chilly environment, what part of clothing would provide warmth around the midsection and cover the arms as well? answer: A sweater’s body and sleeves would provide warmth. The sweater_1’s body covers the midsection, while the sweater_1’s sleeve covers the arms."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-48592-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Looking at the device that allows for time measurement, what part directly interacts with its environment to display the time? answer: Clock_1's hand is the part of the time-measuring device that directly interacts with the environment to display the time."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-49126-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where would someone need to place their remote control if they wanted it to align with the lower edge of the screen after finishing watching television? answer: They would place the remote control on the television_set_1's bottom, aligning it with the lower edge of the screen."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-49269-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where would someone hold onto if they wanted to pick up the beverage container without touching its main body? answer: They would hold onto the cup_1's handle to pick up the cup_1 without touching its main body."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-49653-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: Where can one find the main compartment area that could hold various items? answer: The main compartment area can be found within handbag_1's body of handbag_1."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCAEAAJkDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDwqPpSsDjIFEY46139vdrBo+mlhFgWi9UHqck1rJ8quNs5z4W8/EbRux88n/x1q+oDKiIAWGe/NeI6JrujMvnutkwRtvmRhflP1HfFdBb614bkXLajEpPXdJiuerVbtZFRPQWishem8EMfnkbS/c1J56/3q82fXvCsUrxya/ZI6HDKbkAqfQjNOh17w3OxWDXLdyoyQl0CQPXg1g619CrHo3np/eoEynuK4E6jpZX5NXj/AAuv/r1G2p2KnH9txr6Zu+v61LrWHY9C85fUU7z19RXna6palsDW1OegF0P8alGoA/d1dj9JxU+2HY74zZ7ik8wetcEdSlAyupsf+2gNQTa5PECf7SY/iKXtUGx6KJF9eaeJVx1ryeXxbfI2Eui31A/wqN/Geqxjlh+Kj/Cqu30DmSPUNdtIdRsxDIHcB92EYKTwR1P1qnp+jwW91HIizrtYOCWUgECvOP8AhPdWUkDy2/Cpbf4gaqXA2RflTvO1rBzK57TbMM18p/Eo4+I3iLt/xMJv/QjXqcHxEv4SDJHCAOvyZ/rXkXjm4N34y1S7PW4n848Y5YBv60qV+fVFykpRsihFg455rrPKrkITwOTXe+WPQflW9R7ExVzh4bnRSv8AyH9MH1d//ia6S+lU6bZ+RPHPH9jG2SM5VvvcivFAq9xXr2ixiTwtpAPQ2YB/76at2246nMc/4GDHw5fZJJF4jfmjf4VpHvnPStbQfD8cmm3Nnozm3kaaN3a7l3LgBuBtTPercvgbX5fmTV9F57CRh+hSt78ug1qeQ+PVB8X6oFXn7Sx3fXFWPhsD/auoev2Bj/5Ejrv9Q+F2t3F7dXct7okstyMOXdjg8cj5eDx1qtpXw11fSLiSaPUtKV5IzGx8x2+U4OPuewrFKwWYsDosfLDJ61yfxFCNc6eR1Nq3/o1663XNC1fStHur2S906WO2jMreWG3kDsCVArm5o9K8SWWn3VzcXtqyROgCRqwP7xjnqPWiUlLYbRyPh5j/AMJDpwzgG7iH4bxXpspAmkGOfMbP51iaV4S0iPV7OddTv223CMAbZOcMP9utG91PTw9wyC9H7xthkiVQxye4Y/ypXUdwRT8XOR4TvSrEESQ8jqPmNedi5uQeLiX8HNdj4gvmutNe02KYpXDZD/MdpP8Aj+lYlhpOnXMuybUntAem6Hfz+YqHKL1Bo7mCVxpmmnzGz9ghYnPOSgOf1p0FxcEz7ppCBbTkZc8EROQfzqrfanoemC0sJdRnkaKzgXclrlSPLHOd1P0m90bUrqW0tb+48yS1nUF7Rgq/uX5Jyafu2GefR63rEeCuqXg/7bN/jXaeFdU1Gbw6J7i9nkkN3IoZnJOAiHGfxNY58GrjjWrb8YJP8K2rW3sNB8OW0F/qsAMl1MyMsMhB+WLP8P8AnNK0V0EdDpl7K9pIZJGclsAls44FV/EaTNrtywjcjK844+4tRaJPZT2Lmyu0ukEhBZUZcHA4+YCud8ZeJPEFp4q1G3tdd1KCGObYkcd06qoAHAAPFZyTcvdLi7I24y4IzGfyr03yT/zzf/P4V4H/AMJl4sVePEmrD/t6f/GvfP7T13/oJXX/AH8NY1udW/z/AOAaQkj51A557V7B4aQv4Y0hgOlt0/4G1eQjGcdK92+HVusvhDSZWH3Ij+kjV2r9Ucy3LfhEqZrpscb1x+TV0YYDqK9u+PVlaw+CraaC0t4mF7HykSqcbH9BXhYOSKVaalN2LpvQewzzUEignJANWtpK5NQMuTWLbNEc74+jz4N1jA/5dJD+leRaCrDRbIYPAf8A9DNe83saSQNHKivGy4ZGGQw9CO9dD4Y0HQ3tn3aLp8mxSVU2646Z9KlVVCWonG7Pn1ZmtoTP5wiMRDZZc9/T/PNUNEs7nXdcs9KUhZrl3DyMOF/iPHTP+NdJ8WpLafUEjshHHEuFfbEsKsxIztVQPlGQOcn3qn4QvILLxm91cJIVit9yhAOWIGMdqidXnTki4QXOlLY6nW/hlaRWggikbz0TG5jkY69K8r8SaS2jXr2buSCQAwOe/IFexL42eW3vWvoSjxNiL90f3nOMccenPHWuA8U3UmoQveyWU0V1GCTIqAIRjn8cfyrkw866naex6GKpYd006e5xMsbOmXUeYB8ueuPStPwGCPEcfYG2uf8A0RJTdNWCTUIPNBWIyKJAW5ABAIPH1r7K0v4R/DlbS2uU8M2vmtEG8xZJVJ3LycbuOteippL3jynHsfL3kydNufpWH48gJsdMQnB3zsPx8sf0r7Hf4XeBe2iAf9vEn/xVUNQ+EHw+vggudDZ/Lzs/0qUYzjP8XtTden3/AAE0z5X+HtsU0SbuTcE5H+6tcx42OfF+rZ/5+5B+uK+kPG3gvQfDOqx6foNm9tbPCJnQys+WJIzliT0Ar5u8ZHPi3VyP+f2X/wBDNTCSlK6BqyMgjivrT7EnoPyNfJn1r7G2r7f981niUna44nzYvgvWj0On4/6/4v8A4qvY/AVlNY+ErKzufL82ONg2yQOv32PUEg1xkaDA4BIrvvCwxoUOOMBv5mu6UbJeqIW59H/tBRbfh5Gx4xdwkfkR/WvnpX+brX0Z+0OufhiW/uzwn/x4f4182AnPIrCfxMKbsi7vzgdfao5W2g5pkbnrUU7ktnNQaJjZpODz2rt/BrAqwKt2zx7VwErHGTXfeCRu3Y9R/Kuap8aKi9Ty748aKtt4mW+mJljaLzVTytpxkjOR97Brm/hqumahrmoyTRJOYoQ2xGIIJJ5BHuOlfQPxN03R9Y8KT2t3qum6deIpa2nuplQIe4OTnBHB/D0r5ct4pvBfxJsrax1mz1KS6CLctaktEVkYfJnjcRw2R7UnDmTUXqa06ihUTa6npumLHLpWpQwafLMJT5ZBDsY1646Ec8fpWTp8NlcJPZSWjFoeGZy236EHg102pz2cNl5VyGt5weM9GH1riPEmo/ZdGvDZyrbhsIsxU4TcQC2BycAk1513N2j1PdnOEYOT8+hwngeJ73xnbwokU0bX6sBM21SN/OT2BANfe+n3X2vT4Lry3j81A+0kEjI9RkV84/AT4M6fLqI1648WaPrenxqQLfTpSzEnp5hIBT6Yz719KJGkQVI1CqvQDtXrVLdGfNp3I3YZ7/lULyKCee9WnAxxVVurfX+lc8hnmHxLxL4lTocWyD9Wr5B8Vhj4m1UlSM3s3b/bNfX/AMRhu8UY/wCmEY/U14fqGramNQuQNQu9gmcAeccAbjWtBtN2KcbxR46OvXFfZGV/2a8Zg1PUd6n7ZMTnHJDfzFe4fbJv+esf/fA/woxMpO2gRpnjHlBVyD36V3HhYH+wYuM8sP1NceEiEYZZASCOK7jwem7w+hxzl/516lXZeqMFufT/AMTtGu/EHw3u9Ps133eI5UQnBbaytgZ4zgGvlnxDBqmi3QtrrR7oS4JZXwpBz+NfZRPmW8JKtzEB9088CuQ8ceDdP8SxoZ2ngmiUrHIidj2II56VyYiM3FunuFJpS97Y+T11x1yG0ycEcY3f/Wpg1W5m/wBRpNy+f7p/+tXr158N30uSV3P2xNx24UofTnrVa18PNDGscSCBdw3Y6n6n+lcEZYhuzVjscaNrq7PKpxrlzYXJtdHliuFjJi89vlJ9+9YtpB4kniEviPX7u2hdgotrd/JRj2BI6/lXtHjLT4bfSUiGcytz1GQO354ryfxXA2qaYbJE3XDTBdv+1jg/pmiU3zcr3PLxNa1Tkjoamh+D7Kaynvb23FvYjhZkQtLJyOh5P1P86WXw34IXXRqWm2klxqKy7o5HmZyGxgAr2PoCPyq3rFrFZWml6bBetcRW1okXm7+HYDJP5k1yeoXujaLqsVylxJJfxEMBEwJU5z8xNcSqznNxTdvI+lw2CoUKKqSs27at/kepXvhqXUHtBerFDG8iRs29WZdzBen1NYHif4d2NxqdnoF3fyQfaXZZ2CqPJTblW6kNuPAHHrVXQfG91eXttPcXRe3jnjd4iqlsBgeCB7Vf8V+ItDbxdp/iJNc1qK3sYJllsWi3RTb9x3HnnBbgH0XGK3peyjsrP7zmxLrpa25fLYgs/Bmm+A9Qn1Hw/rCx30o+z+fdTNGNnPygIRuyccn04rYT4s6r4durbT9X0tr5WjMkshnIkU5wFXIII4zyc81hX+seHPEUt4L23tRau6TSJcttVU58vIHVuCc9qy/iksFv4fsnQDAnURkNuAUq3Q+mK551puur3107fqdEsPB4KUoW016N/wBfefQvhbxBpvibSE1LSbjzIicOrLh427qw7H+dXWV8t8w6+ntXyj8PPGN74P1WLUIyXgOFu7cHiSP0/wB4dQfX2Jr6rs7u3vbKK8tZllguEWWJx/ErKCD+RrsT7niwnzI808ebm8WsCQSEjH6V4Fd5N3OfWVj+pr6B8ZYfxlIP+uQ/QV85zszTPyTlyf1rfD/Ezf7KLlrzPH/vCvcMj1avCbAMbuEZPLj+dey+c3qa0rdA6HLL4D8XYBTw5f8Ar/q811nhrTr3TdJNnqNtJa3MZctG64YZ5HH0r323zsHNeaeOwT4mvv8AcQ/+OCuyrU5klY5YvU+ircf6PZHuUX/0CnyIM4qO0VjZ2JMn/LNCOB/cqSUS/wB8f981T3IuUWt0aNlZcguxP51i6tpMZw0aj746D3rccXGDtljABPBjJ7/WqWoytbWsl1d3UEdvCPNkcxkBVXkknd6CsZNJFp2PA/jXqdnZa7Fp095EkiW4JQsAQWOR+mK8zsYvsX2u/uoQlxIMlQ5bamDjr3Pf6gV0vjPV9L8Q+Ptc8QafNLd2jiGG3Z49qEhBuIBPqB1A7VzOsuI9NJfc0kp3EbsFvbPpXkTerfc82o+aq7dTzTxLFfq7paXsaQSZbLuxMY7gduKzi1qb22s7cPJIYdzyuxJYY4FbeuhDHICFZ24OBwOwArl5YprS9iucYMf3R2x3FdVN8ysdsVsdl4ZmQIwON4Iwc8+9WfEMrzW7QIBudefpkf8A16w/C4jYXbuiKUlJ3y5Ix12jHQ4PepZrwI9/fFWMcaDy0bqeuB+f86zdK0rnfVxl6PJ1Ze0O/tbfWdSnnUSxMkNqiY6gfM+PUgH9a1vFGtWmqxxWNrCPLUHDSgMVOCAR6d6898QOLS1srOeVlZiXml7h253fhx+VbOiQyW7Rm7dJZ8bQU+6PUjpycU6lNWU77HIq9SNP2cXoyGOOTz/Lufk2nGwfxe+fSvpz4D6z/aXgdbKRszadIYcZ/wCWZ5T+o/4DXz3f2q3EKsFO5Dwe+K7b4Ba+um+Nxpc8gVdQiMI3cBnHzIR69CP+BVjzX1IhL3j0DxUd3jaYd/MiH/jq1877GZicEV9D6v8AvPiDj/p7iH/oNekxadprctpentz3tk/wp/WVQlqr3PSpU+eO58c6ah/tC2yOsqj9a9L+2L6j9K+ibLRNGklUPommHPf7In+FYX9nWP8A0D7X/v0KJ42E7e6y3Q8zq7fhBivNPHpx4nvh6xJ/6BXptup2ivMviAjf8JZdnJx9nUEdvu//AFq9Wp0PMh8R9Dae8Z0zT/3i5MUeef8AYqeSSPn94n4NUOm/8gmwbcOYYv8A0EVPKMnIIxWzuSimZIwXyy/eP8QrnviGn2nwLr0ELBpH06cIFPJPltjFdDn55Af7/wDQVFdRrNCYnGUfCsPUE4NYTjeLRWh8ZII7bTbSzhKgEZ+UY5Izz781meKJ/wB+wB4UAKK6r4oeE9Q8G+Iltp1kk0+X/j1udvytg8AnoGxwRXn2vXLz3LQ2sLyuf7qkngZ4HevJcZc1mcUabUtTnbrbLexxA52Hew/T/GoPFEKJZjA+YsAKsaFaSy3LNOCJJHLP3wOw/LFTeM4gscZ6Kx2k+lbrSUUdKetihZ26RXk6KTkKvf2x+PSrNrC8srKMEDL/ADDIyB8ufxOfwqpqwaDX5o4nP+rUsB3960PDkny3sj52RopOfx/wqm3ycxDetzkfEMN1qGqtAqRzzWsYMiDJUnq3vgDFdT4avrXUNJ8r/lrFtjmB6hgBzXJ6bPIni+Kfeq7pT5jOcAhuWFa2r2g0LUX1O0kLLKAXiVTtYf41VRXSg/katdDrEBUDLZB4NNtNNkvr2xt4HMdybuPyHBwVbeMHPaqOlalBdxK0bBgwB6113w6sP7T8c6PallUC5WViT1CfOR9cLiuOzjIhLU9P1Lb/AMLJwTn/AE6PP/jteqQLzXls6eZ8TgoAGdQT9CK9ZhjPeuXFfGvQ9rC/Cy7p4/fD0wf5VzmwV1FkjKxY9AjH/wAdNc5t9jWfqbvQ3LeWLYDu/SvM/iE4/wCEruSpO0wJ29q9VhVlUA5ryv4pEJ4nmfP/AC7R/wBa+mqdDxI/EfQOleb/AGJpxwmDBCRwf7oq1KspIOI/1qtobK2gaWQSc20B/wDHVq5JjPOB+NbPdmZnFZfNmx5fD+/90UxhJkA+X94evrUxP72bnPzj/wBBFRyEAZyOo/mKzaRRW1KxttQtHtL62tbq3f78Uyb1P4EV5v8AEPwp4b0DwRrt7o+g6ZYXk1uLcTQwgPiRgrAHtkE9K9RkPoRXm/x1uki8Lw2m/wCee6RgM9Qqtn9Stc9d8sHYVR2g2fLunaU1sks0g+YcE1z/AItXzrdYgPvSIM/VgP616PrMGQqRttQDnA6muK8VWjGxd48F4mEvA5O05/pXnQneabOSnU5ndnJX0ZfW7hhkqkapnHetC0UweG9QOMF5AgPf/OKjs1M8d645Z24X3GMfyqxcyL/ZktsAQ5mVsH02nmuiT9y3oaX944ieBooWnIyROp+uSK7SS1W904W0r5O35H7dOKz5dMe40qVYc+YNrpnuQc1XttS1JY/s7wHCkZCjmlVvUStujSV3sVNI0vUYb/yomkVt20oF3cj2r134S+GNf1DxHZXMIe3NlMs0szcKgB6e5IyMe9egfDX4dWOoeH9P1o3aLb3cKybYkO8dmUk9CCCD16V6npun2unWa2tlAsESk8KOpz1J7mh3esjWMG3dnm8Qz8TlcgYGoevoa9ZheM/8tF/OvLtPXPxKB4ONQbqM9Ca9cifjmKA/WJf8K8zFfGj18IvcZNaOhDjeD+7f/wBBNYOF/wBn8hXRKsRimZreDIic7hGAQQp56VmbF/542/8A36X/AArNXNHuaZHFeU/FGIv4ilUHk20f/s1esMMV5R8UyV8UkDobRCf++mr6epsjxI7nu3h9UPhfSmMSc2lvk7Rz8q1eeGIdIIsf7orO8ONK3hHSBtTH2O353HP3F9q0G84/3P8Avo/4Vs9yCl5UZuJwY04YY+UcfKKbLFHjIiTOR/CPWpVEhmmwEzkZ5Pp9KjmEgHOzGR3Pr9KjSxSHFEwf3af98ivFv2gbsDW9OsRjasG9gAOMsf8A4kV7OzSjIAT8z/hXz58ZPNu/iPPN5y/Zra3SNo1HJkxzk9gAfzJrixnwpGOJdqdjhtSEMFs7PIWcjgeprjNbeVtKvJ1AVFUKxb0YhT+hrqdXRbid2Zj6YB6VzfxB2WXhu1sIQBPeTbznqVUc/qRXn01eSOSlpZdWchpsbrabwd4Y56cirEcaMuxVAYy5Y9zheP61JHpeow6YkzQv5ZHZDwPyp+lQyiWQtlhwQSDjvnBNdU0+Vs6uVp3ZasYQAy9BjFVpbbY0j7RljWnbLtZuKSRAcgiuJz1Kue2/s369Fe+Gp/DkoH2jT2MkeT96Jzn9GP8A48K9SEa+WPlH+TXy98MNYPh/xtYXwfbF5oin9DG/ynP04P4V9S4baB8v51005KSOmlK6PMdFXPxEGRj/AE6TgfVq9ZhGR3rynw783xFJ/wCnuY/+hV6zbj864MV/ER6uF+AnIxaXB6fuH6/7pqhtrSlBNlc/9cW/lVXZ71MUaNEzTQj/AJaCvKPirIj+JlZWBBtEHH+81ewPn1NeQ/GHnxTCO5tE/wDQ3r6Wrsjw4bnuPhZ93g/R+Rj7Fb/+gLWmzY6sPzrF8Hoh8GaM5jT/AI8YP4R/cWtgrGCRsT/vkVvJaszK+5fOl54JHf2pkzDYcEdu/vTwsZnkyi9Fx8o44pkqrs/1aD/gIqGikwY8/fXrXzP47W8i8Saqt1EqTtMzujHsxJB+hHSvpeRBk/u0/wC+RXxd+0vPOfjJrz288lrLClvGrROVOPJQ9vrWNXCvENRTtYxxEOeK1NuO2tZZNso8t+qqOuK8w+N+vxWGu6dYww+eYYGeVfMZNu4jaMjvhf1qfRviJqFjKINctjfQ/dE0aBZV/D7rfpXmnjC+n1XxDe6mzO6yv8pdSDtAAHHbgdK5aWDnCp7+yMsPRkp3kb+n+ObdIdkmlzkHAP8ApO88e7Dj8KveFtefVNemtVt/Ih8ouimQu3BA5J4PB9K84BVdpLlTnmuk+H8qJ4qhIcKrLICTxxtJ6/hW9alH2crdjtktD09E2P8AWonBOcdaoaj4h0yzX5rqOaTPEcJDsf6CuQ1fXb/UnMQb7Nbsf9XGTlh7nv8AyrzKWEqVXe1kZJHq3w20KDxh4qOhW+oQxvFEbi4I+ZljBVTj/ayw/nX1XaRGK0hiALBEVcsck47n3r5E/ZNbyvjDFAqqwl064U8dMbW/pX19HENqfKOgrrlQVF2R0Ukkjy7woVPxBZmIAFxMTn/gVesW8sPH71MfWvJ/AzE+PwwCn95OSCAR0b1r2KF8jBhtyMdPJX/CvIxN/anrYb4Bt5cRR6dcuJFOIscH1IH9aTMf95fzqe6jifTbhRbwDCgjEQGDuFP8uL/n2t/+/C/4UoNlSFYcV5D8ZAF8T2fBJNoM4/32r2BhxXkfxpQf8JDYk45tMYz/ALZr6atsjxIfEew+C2kPgbR2JXH2CHtz90VsMz5xmPP0NYPgWVD4D0dS6kiwj4z/ALIrcaWHn94n/fVby3ZkRHeJXwU6L2PvTJPM2kkrj6Gl86ISuRKhyF/iHvTZJoyp+dfzqbFoc5l3HJTr6Gvj79qTT/svxfvJj/y+2VvPkDrhdh/9Ar7CMkZcjzE/Ovm79s3TAl74e1+LDIY5LOUg55B3p/N/yq6EkqiJqq8D5r1KPJIPQZ5/CucvkUxsBzjpXQ6hKd5GTzmucv8AowHQk960rbk09iHwys48ZaIbZRLOL+Axoy7gzeYuAQeoqtLJLeajdXU7bpZ53lcnuzMSf1Neh/sz6CNf+O3hiB08yC0uPts+egWEF+f+BBR+Ncn4s0l9C8Y63o0oKtZ388GPZZCB+mK51ubvYhtYo8ADPHqasmNVAx69TUVqfLGSO2KkeRSy84xWjM9z2z9jzR3ufH+ra0E/d2Fh5QY9N8rAD/x1Hr6ojLhV4GMDv/8AWrx39j7TI7P4Z3eq7GWbUtQkJZuAyRgKuPbJf8c17OpACjg8DvXnV3ebN4bHknw5Jbx5nB/5bH+dexwDkV5B8NEP/CZlj3ikNex265rxMT/FPWw/8MllA+w3H+6B1/2hU20+9NZc2dx6YX/0IVYwPU0oCk9Sk80IHL15P8ZmSTXrAo4OLZgT6fNXr7FsHLGvKfjUoOsaaScn7O+P++h/jX0lZPlPGj8R6Z8P3b/hX+j8niyQdfat5mYn7x/OuZ+H5LeBdJHmMMWoGBj39q3wDn/WP+Q/wrpluZdRd2JGyT90f1pWJKE5yMHvURUiUjzW6A9B7+1LtOxh5jdPQVmykWyScgE/nXk/7VemLf8Awg1C4bcXsLi3uEx/vmM/o5r1TaRn5m5+n+Fcd8abR7z4T+KbeNmZzpkjgYHOz5v6VF7NMtq6Z8AamcNweSM8/SsK8PyEcE1t6gC/zKCV7HtisC6bOT2rrq7mVPY+kv2BdEWTXfFHiORATb20NnExHQyMXfH4Iv51wv7W2kJpXx51d0UCPUIYL0DHdkCsf++kY17r+wrp/k/Cm/v+jXurS84/hRIlH65rzz9vGw+z+P8Aw3qIUZudMeIvjqY5ScflIK5U/wB4jd/CeAIT5Xc0wnBGOuOKkU5jXn/OKt+GrNNT8V6RpswLRXV7DA6rwSryBTg/QmtpbmS1Pv3wHpjaL4E0LSZIoY5LTToYpFiOU3hBuwe/zZOe9ayhRCCVH3c9ParAtlhiWCI4jjUIgxnAAwB19KiaNhalg3/LPPT2rypO7bOo8n+GUgHitmkYACF+Tx3Feu288BA/fL+deV/CJmTxS8sbYP2Zxn8RXssN1OV5fj/dFePiH+9PTo39noRo8b2k+11bhen+8Km86D/nqKnM832Vm3YIZcEAetWvOl9R/wB8iqhcht3MlhxXk3xxJTVNIYE8xSj9Vr1ojivJ/jso+26OT/cmH6pX0dZ+6eRH4kd58NpS3gTSvkJ/cEZ4/vNXSKzMi/I3T2rk/hirv4E0tgrECNxwP9thXTRzERjgngdq6GZPckO4SHIP3fUUoLcjY3I9qYZczr8h5Q9vcVIjZP3D0x0qHsUibeQv3GNcv8Wbr7N8MPFM/ln5dIuPvDgZjYZ/Wupjjd0VtpIIB6V5b+1ZenTfgjrgJZTd+TbKegBaVSc+2FIrHd2NOh8SXLRHSlEcqsckFh1xXNXZCkdx2rVjktIopDc3cZLdEj+asK7k6nB3N6/4V2V3cxpo+/f2PrP7J8BNBfac3D3U/A9Z2A/RRXnP/BQGzY6N4Q1QLxFd3MDH/eRGH/oBr2P9ny2OlfBXwXYygiU6SszAjn5zv/8AZ64v9tfTU1H4Ltd/ZmkfT9RgmDf88w26Mn6HeB+Irhv+8+Z1W90+LYTvtlK4+tbHgKSO28f+HZ3BZI9VtGYL1IEq9K5y3mijjCF8ZPQ8VYjungu4LmCUJJDKsiMp5VlIIP6V1y1MEtT9MpQ2WG0+nSoZ1KafJkHiE8f8Bptrdw3Wnw3kUiyRzwrKjjoysuQR9QaZfTL/AGVcMf8Angxz/wABNeRLQ6keW/CA58Qy+otm/wDQlr2K3+7ivHvg0AfENxjtan/0Ja9ihGAOOteTX1qs9Kj/AAy04xZnOeXQfrVmoZP+PLH/AE0Sp8j1NaR2M76syWuIe7Y/A15L+0DcIRozxuCQZgSP+AV7CS3HJryL9o0E2ejMe0so/Ra+hq/CeStzivCfjnxytxp/hnRNae3ikn8iFBCjbN7kk5Izxkmvbk8OxkEr4h8UAA8EaxMM/rXg11c2fhnSPDWsaIvk69MlxPPOx8wBd7RrhGyoyA3OO1OX4r+OE4GqQt7m0i/+Jro1lFWdjnlfmdj3OTQXDceJfFQx0/4m8prlviLc33hbw8NTsfEviRrv7RGkKXGpySITnJ3L3GAa81b4teOD11G3/wDAOP8AwrqPA0198S471PFUhms9P8swC3UQnzX3ZJKjnCr096hxmle4XsY//C4/Hij5b6HHbMZP9a4z4x/EPxV4p8FvperXcU1r9ojlZFjxkqeO/vn8K9qb4Y+ECuPKvdw7fajx+leb/tA+DvDvhzwPFPpqXS3E94kRMk5cKNrEnH4ClGcXJJIq7sfMmp3IVtkEMaE5y4GDWLd4ViCQzsK09bTaokxjcTnHY96xcM5JwSTwK0qPU0gtD9Cfh9pWoTeA/Dlw3ijxLDI+k2zbI9Q2qgMSnAG3ge1eZ/tYatrGi+BZtCXXNTv7PUo42nW+n80qUmBXacDHI569Ky/DXxU8WWvhrSrWGSx8qGzijQG1U4VUUD8eKwvjDrF34w+F2taxrTI93ps1nDbeSgjXbLI+7I7/AHa5XGcZXvoVc+f7aU4CyorKDwcVJcuhwka446iq0DEIRmlUjcM8GuhsLH3R8GdU1rxV8PrTUJvE2s2rwhbQQ2kyJEqJFHtwGRucHnnrXS6rpuqJplyT4x8SsqQOdhuo9pAU8ECPpXhfw78U6n4P+G/h06QLd4tTtXuZxcKXxKkrxHbgjA2ovHPNa0nxX8SXUEtvLb6aFlUxsVhcHDDB/irhnCbk3F6FI7X4KTxLr13JKwUfZdo/Fh/hXsEV3a5H70flXj/7PUzjWdSMbEEWyjI4/ir3KGeYsP3jfnXh1V++Z69Nv2aGSXEBs8hycSKTwfenfbbb/nof++TVm6lk8mIh23ecOc+xqTzp/wC+351sk+hkrmb2xXk/7RKg6VpTHtO4/wDHRXrYj4z3ry/9oe1ll0HTDFE8jC6bIRckfJXvVfhPLs7nO+CfhjpvirwtZavfatqKTOsiCOHy9qKrsABuUn1P41q/8KP0Fumr6vnHTdF/8RXnWkeJvGWjWK2Ol6nfWtshJWJYEIBJyfvIT196tN45+IB4Otah7j7NF/8AG6PaR6MiVKTZ18/wZ0KNlX+1tV5P96L/AOIrkofFs/w61bV/D2hwWl7bR3hLTXasZGYKqkfIVGAQccVAfGfjsyB/7XvSexNtF/8AG65e6067vLqW4uFvXmmcvI+0ZLE5J+76k0KrFPV3BUpHcr8YNZcySHS9JEm3IwsmD/4/XmPxz+I95r+jW1jeQWlrhzMBBvy3buT71bfRrhfuC+HuFGf/AEGsrV/BtvqciyX9rfTOi7VbpgenC0/b007pD9izxjUL5riMOqHY4/IiqKyhhxuPHFez/wDCvtICiMafegDtuP8AhQnw+0pCCthek+pJ/wAKl4iLepooNHtHhH4R6Ne+EdHuzrmoLJNYQSOixoAhaNSQMjtmuB/aI0WLwR4Pl0Gzmlu4tXkgmllnADIYnfAXbxg7jnPtWzD4n8Zwoqx6neRqihVC2sQAAGAP9XWJ41i1LxkkK+I5r29WEYQeWqY6/wB1R6msvaLmvzaAoStqj51cxoNxO056URAHacgj6169/wAK00XnNjfN9XP+FPX4caLkEafejH+0f8Kt4iBXIzsfgXp9t488J6fod3cNYroVrKI5IV3NLvnLENk4GNwxiu21P4V6Xp+nXd1Hrd7IYImlCtCgBKjIH04rz/wbZ3vhBpX8PyX1m0y7H+RXyMg/xKf7ordudf8AFd1DJDcajfyRyqVdfIjAYHqOErmqT5pXjKyGoPqd7+zwv/E11Vh/zxjH6mvc7YZYcV4h+zrBMl7qzSxug8uLG4Yzy1e5WuNxxXlVFesz06b/AHSJLn/VRf8AXUfyNSfhUdz9yEf9Nf6GpK1SMikZwvAjl/74NVZ5kY/Osuen+rb/AAq6c+tIc9c17jueecjr3h60vWWSKMxSM43EJgH8Ky30CBGIaRgfda7q44mt8/8APQCknt0k5IyaxdNMpSaOEOiW3aX9BQdFt8f679K7JrNORtpptEz92p9mi+ZnEzaLb/LibuO1PbRrYj/XD/vmutntV8yIbRy9PNon9yp9mm3cfMcT/YduP+Ww/KgaJbk/64flXYPaJu4WmfZV/u0vZoOY5STRbb5QJR1GeKedGtduPOHT+7XUi0Usg2jqT+hqRrRNp+QflSVNBzHHf2JbdpR+VKNEt/8AnqPyrq/sy/3aT7Mv9wUvZoOY5d9KtW2ASZ+bP3far1j4cF1ho45pIwcMUjz+FbyWXmTwqEwuSCce1b0FssMYSMbQKynFLY0jqY1hpslqR5drdqBxxCa2YBIgyLS7yexjNTrG3qfzqRNy85b86xdPU15nYrymZki/0W4BEhO3ZyRipd83/Pldf981LOC3kHn757+1P+f1P50RRN2z/9k="}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-50614-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: On a particular timekeeping device where the numbers 6 and 12 are prominently visible, can you tell me the enclosing structure that contains the numerical face? answer: The enclosing structure containing the numerical face with numbers 6 and 12 prominently visible is clock_12's case."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD//gAMQXBwbGVNYXJrCv/bAEMABQMEBAQDBQQEBAUFBQYHDAgHBwcHDwsLCQwRDxISEQ8RERMWHBcTFBoVEREYIRgaHR0fHx8TFyIkIh4kHB4fHv/bAEMBBQUFBwYHDggIDh4UERQeHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHv/AABEIAQAAwAMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APrvWuY40yAAS5z3wP8A69TabCyRebL/AK2QDd+XAplxD9o1GPcMpEuT6ZJ6fpV2m3pYd9DE8UXfkxrDEYmuXHyRtJtJBIBb8Ac18xeEvjHb/DzX7/SdW09LjTvt8g82OXbOArFc4PDdM4469a9v8XXPmfFGztyeIbI/hk5rwzwx4K0fXPFniPUdatWa1SZysrEhC7McLx98j+5nvmqS0Ei1+0Np/hH4u+Fn8ceAdSg1DVLCDGpWMZxcPAvIfy+u5OemQVyM8Cvkq18N69qcpXTtIvbsZ+/FCSn/AH10/WvpnWrPwT4T1OC70bSFOsKxEUkQZ5C46iNMnB/l3YVk39j4r1bYNRuU0e3mP7m2VTPcuPQRpx+Hz4osM8aPw01iO1ia7e1tZW5dZbhQU9jyaVPhzcFwF1eyOM5/fL6cfrX0JovwC8T6mokksby2Rh9/UdSFsT/2ygUkfQ4NM8U/Am+8PxrJK1jKWiMiCPVbxC5DKpQE55w4PToDSWoHz3N4A8U2sSy29vFffLkG2uF3Ic+hIJ49KWDV9d0ARWuvaPNLAJTKYr2Boy3ABKvxzxjPP619EX/wU8W6Gnnx22rRDGTJaXKahGv1VlEv5Cqdtb6xDpVxDqsFtq+mZEUgiXcBjs0L5IIycjkj0osgPn+LxeyuqwtdW+XJVA4dAT+Xt+Vdbfa3ItklzI2I4wJAhbOcfzyR/Kuy1z4QeGNSe11rw7CbKWMMXtlcmCd8HbjJJQhscDjjGAa8v8ZpNpt79juYWha3zK8bjHC8KPoWP6UAaXw7s28afE22OtlJre2Iur9VCqJnUfLHjvyAv03V+gOiafCYoLp0Q/u4mjjA4jYJjI9+Tj0r5Z+Cvw/eL4IT6kLac65fn+145Amf3cZCiPd/eKOzY/2q+hvhP4nGv+H4Y7qSMX8I2OgIBdRjD4/T6ijoJndKAwIIBHpXCePZJJfFOk2rEbIkkuRznjAH4cg13O5YkaR2Cqo3MT2A715j431KGLW9VvIygjsrZbVMf3mG8/8AoRpoSPIvjFrpiJuYpNrmZpUPX7uNv65r5/vL4zXm9znc+W/Pmuy+LWtebdvb+ZzHhMH8z+pNeZpckSPJ5mGVTg4zk/j9azk7s2irI3La/Ml08jMfmcn3GTXcaNqklnYXdwyNMFtg/low3EEjp798fhXk8EpBHPtXrnwW8HXPjyLXdLim8l0sd8Uu/AWQMCgPqCVAI9CT2oQPY2tHiXXdKstO1NsO0v2nTbsDhGznZn0KnaQecH2qG/0ONY3tZYEjVP3ZQgALjjFY+hm8trWXTbvzbfULF2XyZM8TJuDqOw4CEf8AAvWvRr7S7XX9Okgu4SyzRKsysuCGwOcHoe4pvVXIi7Ox8w+O/Dp8P6z+5KvazHdGVOQp/umvUfhVotuunW+oxgMJgGQ/zFbHxA+GXhnSvBNzcNcPZ3EhH2QSOW3yD0UD0zzjgGrnwG8PzTeG57CLUoLkLMXhJUp5RI5U5zxnn8TWkGKR90AYooorMR4n44XUdQ+It/Dpc628oQRS3DDPkRYG9x7gZx74rkvGOomzgtdH0iI/OgjtLfdyA2cMzddzgF2bqFHHLCu31GFmtte1VW/e31+1uhB58sOFP5k/oK4+3sjJ40vL+4XLRErED/Dlyv8A6DGg+grS/QEO8FeAnF4sMBjl1WZA95qM0eVt4+gwo6DqFQYHBJ6GvY9A8DeHNMsnjt4FuLqUfvb6Rg08h927Dn7g+XtiovAG6205ytqZLif99IynGRnaoHt8p4+vrXUQsZiWe2MTA4yev51LYEGjWU1naLayzmcRnCOygHb2H4dKffafBdSRPOvmCLcQjAFSSMcg9cdqZr+pppOkz3zRiQQruKFtuRnnnB7V4ZF+0LqMmvX2l6f4XGu3ivtttOsJT9oUY5aRuUxnjAOfYdKWu4HvEj+XjcrHc2BgE8+/p9a5LxF4LTX2l1G6eG21QptiMC/IB1CyNjMn1OMdh1zwVr8T/ibc6rdWz/D+WDMe6K2nt3DQkdd0gba49Co59q6jwN4l8R65cynxNbpo9vHHyqN5UjMcEEDJIXB9aYWZwN/p8unT3ETWwSSJj9qXGSfU4Hp6jqPwx5h8Y/BEPiex+0wt5V3bNGXlxnzbdm7nuVORnvwe9fQHxBgsINbs5rG5WRbmNg5aXfuZeoJPqCK841FPKDQZBQ21xFjH8A2EfkaAPR/hElrZ+GHf7Qv2VgdkRACwqoI4PoVCnB6YrC+CWg/bPiL4n8bwzyHSmkaysImHyhhtMhX0XgD/AHifTnyTQPGN++hp4T0qTfqurTi2tUJyBz8zN6KBkn2BHevqnwbo1p4d8MadolkWaCzhCbz96RurOfdmJY+5pIHoX9ZkS20i6uZMeVHE7Pk9tp/+sPxr528c6mLLRWad83ErNdXR7EksSPzwK9V+JXiJEtjpSF1JJnl3LtHlqfkA9csCc/7B7EV8s/FvxI/2a5hDf8fI4yeVRTx+Zz+VDY0jybxfqD3moyyu2WZsn6msFJZFRgGIV/vD1wadczNNOWPTrUe4ZAHQe1QWWIXBIr69/YdSxttF1h5p0XULx08uNjy0SZyR+J/SvkG1QyzIiDl2wK+hPhT4i0OOCx0q8uZfsFs2EubZ9lzYSZ5ljYdieSvKsO1NaMHsdV+0f4YTT/HsWuWEQRr4eY4UDmaIjn/gSkD8D61Fo9++mX6tP+9UmIHd6cYz77cfma9T+KsNrqug6TJIyTtDqEZMgI/eLgjJI6ZUhq8v8T6fDFo8sqOAUOMb8kBdo57iqehKVynp2gx+PtduvEuuwyyWJmkh02wmJCxRK2N7L3ZuTjtxXPNqFv4C8Z67YWgCWpWJ4kB+7uXOKW5+IWuWutReG9A0Br25aFJlYPtXa3fPb6/WvNviNrEt94uvpZCok3rHIqNkBkUKwz35Bpx3A/TCmTuI4XkPRVLH8BT6zPFU/wBl8N6lcZx5drIR/wB8mpJPItOuWvtCKGQrvEpQ7sfOXYjP44rLVwuqG5bcFn+YZ7HkkfmW/SsDT9dksIxDIDsQ70YdPcH/AD61srd2+px+faMHfjfED82f7w/wq3owPVvCSTaloCxxXUkKxsUYK2Md+3J6+1dDaxWul2jRCYsYwZHySWx3OOTXjnhrXJbC8gnivJo0RiJFQ/K2RjDKeuOuODkV1nizxz4e8I+E59fub+GV5IyVLPu46ZfHOSei+vA7mk0B5p8XvG02uJqqvrqWvhWFxFcBSqONnLfMPm3E4AGc+3euB+H/AO0J8OvBVslro/gq481F8t7x9vmyj1Jzn8K8T+IvjGbxZfeXb266dokDs1tZxgKMk8uwHBY/p0FcwlpPINyRxKvYODk1LZVj7JH7XnhGU+W2iahHngPxgfhS2/7WHgeSZhc6RcsjtwPJGQPc45r42FjdZ5Fvx9f8ad9jnDcxWx49W/xpXCx9WeKP2gPh5rV/BLbm4sYokK7DbtySeTwPp+Vc3e/FHwXfrOY9bEUjwGKPzIJBtJOWY/L6Y/KvneSxnz8lvb4/32qNrC52n/Ro/wAJKdwse4/A1NLj+Kr61b6xZ6qSoSBYN4MCn7xIcDHTqM+lfWieLLezF0Zone3t4wu9WHzTEKyxgdcsHGD0GDmvzb0i+vdG1OO+0+R7S8t23IQ3Tn9Qa+k/A3xDh8S6JJf3UiHU4IcS268BDjb5mO5PTPYYH1L2FY6Xx1rhjjubi6n3TT5lnbcW2jsozzgDAH0Hc181fEDVWu9Sf5t2TlcdNvQfpXZfE7xJKITDvOZywlwfu46L+GQT+HpXj9zM7OzOxZicUr3GhXJ4A/GhQQNxqJMlsZqYkthR0HApFGpoNnLOlxPHnciEIfQngGq/h7VbrQtUUZPyHBBPBHp9P/117XpXwq8Tab8P7PVYLWyvpruI3EtiCyXSqBnCH7pYLztI7nmvHfFdtDNH9ttskZwcrgjnoR2IPBphY+n/AIa+J01rQrCxm3yR2rGZQBuklGzATHtjb7DHpS+OpFtvDE8wwJZsocHqc5P868b+BXiU2NwIpCG8v+Fj2bg/lwfzr0TxNrNnq5u7PzVEkkYAd2ysfI3Mffpj64pp9BW1ucXceJYfDt/caypjW5bS0WD0aQs+0fqT+FeaeHLTUfEOtW9hZwS3V7eThI1XJZ2Y/wCTVvx9qk95qMmnwIGsLMoiv5Q3K+MkBu3bIB7V3H7MirZ+No9QvbG7jhSJ1jv44yfs0vZx6ntj3x3qloJn6LVy/wAVLgW3gLVGzjfGIx/wJgK6gCuA+PFx5Pgfy84M11Gn4DLf0pLck8LgtWvpPs6oGDA7s9AO5NebeKdbvvCWuJuuJoJM5g7K+OoJ79unvXufhe0W00Zp5FAmuxuyeojHQfiefyrzb4uaHpWq2kt1rl4LTSbVg88oH7xgP4I/9pjwKuQIZ4c+Iceq6Je63rNq1hbWYAbUVbbvfsgH/LQ+2Pyrxf4keNtQ8aamXcm20qBibe2B4/3m9WP/ANYVW8Z+KJfETwWlvAunaFYjbY2KH5UH95v7znuayLaHcwd1wB91fT3PvWbZSQ61tmch3XCg/Kp7e5960YIGY4WmQAAFycKOpPQV6v8ADb4Sa14kWO91LzNO098MExiWVSOG54UfqfapGeXyxRREebIq57E1bi0XU58NbaTqNwG6eVbO39K+p2+Fmh6R4YurfRtGsLy+MYOy7UkXJUglGbOQWxgNngkHHGK67wxPpWsaPBqdhABFMWykiAPE4JVkYdmUgqR6igLo+JLzTr+y/wCP/Tb+zT+9Pbui/mRUKwh13xsrr2KnNfZ3xCWCCz0q2e3ha11HUUsbtWXrBJHJvwRjb90ciuFi+FXh7xj4L0zWoNPj0O/u7RJUNnlQqEfICDw2V25z69adxHy1f2nm8MNjj7relQ6JfX+kaml5YyGC8gOQAMqy9CCO6n0rv/iB4I13wjc+Xq1uJLZmxHdxr8jc8Bh/C36Hsa4m8tm3bgxSRfuuOo/+tSuVYueKimq282taWjJAjf6Xas257dv72epQ+vboexrijKWfJ7dK6Ozvbyxu2v7bZFOPlZcghxgZBU9VIzweOcUmt6LZujarYAxQCNZZ7MEM1vnuOcmM/mOh9aaJMOJj/jXq/wCzl4Rt/EvjdLy/i8+y0vZcvDtz5r7wEXHpk5PsteeeF9OfXtcSziQQIQWJAGFUDJNfR3wp02LwLHqGoS38UEV3aLDGSOYypY7+fvHLeg6UAe6jS4lQFJGLqJAue+4AHPvgYzXzB+0h4Si0bxmt3aoI7TXITM6AACO4X74wOm4EH6g17P4W+IlvMLazu9U03VZSAHmtj5UvT73lN97nspz7GsL9py1tL74eRarA4le3vop0cH+E/umHtw350DWjPlrwXdPp/iKHt+8Ct+eK9F0K9eXxFcStGHt7G4UhPLyrMSCMgfe+U55z/SvL+YtZEi92/pXeaPqclpK9zaSNE80gLEdeEA60IbPSbHwvopkS51i2htYxMzxQvFtUbiSWI7nOTzXQNquiWEDR2ZaXbkRqkYUE9h7CvMbfUJJSDLKztjOWOc5q5LNcG3Jt2jDghvmQsCB9CKtJoR+gdeafHNGvV0LSlOPtFy7N7KqjJ/AE16PczCGIuQWPRVHVj2ArzD4s3ZtdWtbq4Kh7ewYLg8B5Gwcfgh/OktyDiPFOpRWiMsWUjjTb6BVwAMn0rwT44aheanImnXUv+jWkr7IlG0Bl2jJ9eM9fU1r+KfE95qPjXSdMu7tv7Pe+AMUeUV9smFDDuenPf26DF+MKD+3r5vV3b/vpUP8AWmxpHlv2YCQOR8m0Mg9AR/OrkKZCjuaYw/1Wf+ea/wAq0dIthd3tva7tgkcKzf3R3b8Bk/hUFHrHwD+HMWuSjXNZtRLZKSLeCVNyTEcMzDuB2Hc16Jp2t+JfBPiJtC1DSoLrSwD/AGfbWakSNbKOPILHErIPvRMQ/UqWHAZF8Q/DngmPTdJtbJr6waxjlFzZzLtVTkL9/apyBn7wPI4NdJb6/wCDPiNp0mkI08zlRMsUkMkMsTKRtkjfGAwJGGVj1oJZ0+i61pmtafHqGmXIuIHJXhSrKw4ZWUgFWB4IIBFQaRo0en6vfahaytGt/te4thjyzMODKB1DMMA84O0HrzWjbwiGFUBJIGCTyT+NPUc96QHPfEPw3P4m0EWcOoCxmhk86KXy9+1tjr0yOz/pW1bmx0fRYo2kW3tLG2VSW4CRxp1PsAKtggcGm3lrb31pJa3UKTQyoUeN1BVlPUEHqKAPEtY8QjxbqLaFdadeammvXCFrCKUxmzs1AKLxjErAiWTJAVWVSQSoryH4s+BrjwXr5t0V5NNuWJtZTk7T1MbHuQOh7j8a+rvBXgzRvC0l9PYJI9zdSEvNK250j3bhGp67ckkk8sSSxJqj8WPC9j4m8JXlndlI8IXjmdtoiccq+T6d/bNEvIpM+Hry3WTBBKOOVYdQarQXF1bXKXNs3lXcHykdnU5HQ8EEcEe9at7G0UzQybd8bFGKnIJB7HuPeqk0alSxwCoz9eR/jQgaNfwpo8U3jHTZ9PRraO6QyzxK2ViKk7wv+ycDGemcV7FqNxPeSC5sYBLdMNkJkwVjA4LAEEAk9yDj0NcN8MbeL+z59QP+sgSSNDnpuwf6V7L8KfD0eva9BZS7hAkZkmx12qAMfiSPzphseQ+LbbxjEn2i+t4NShT5mQSeaVHsCAR9UKmmWXiy517wxe+H5rxmW6iCRvcMSUZWBCOe4yAA55XgNwQ1fSeufCvw9caxc6dam40y5kh861nhlYgEHB3KThhyOD/dNfNnxS8HaloWt3Tm2W31ezHmXMUS/uruLoJo89cjOR9Qfc2Dc80mtpV1TypI3SRHKurDBUjqDWtZTLFCSZAG87ATHIAA5z071rWos9Wtl1Af8fEceX/21AwD7kcDPcY7hq5vzNyEKcr5rH3HSiO43sdTa3BDRujg5ONvfit3TjNMsjggBF3NkgHnA/HrXH204jiiB/vBs9xWzpWoyiOSGN2xIy5Hc4zj/PvWiEfotLLczzKsaqqlgYyeoG1ssf04+leGftJa4lpqrwCTiCBQxzznGa9yS3mklEZk+QMRIRwcbV+UV8WftOeJDP4t1WBH+9cyIOeiodo/lSIR57reorPrfhq8WSNiLgE7WBIxMOo7d+tdP8YoSNUllBJDk/j+7jrye2u3aW1LEYhuQRwM9VPXv0r1z4tFnvInGcEYx9Yl/wAKUikeWY+WI/7H9TXe/A23S5+JmlI/RC7jjPIXj+dcCxIih7fKf/QjXbfBG/Wx+Jmju7FVkkaL8WU4/XFQNn0Z4q8Cahrd3JeA+F5UZV8r7VpUguIxtHHnRyq3r2GM4rE0NvCvw1k1K8mdRqLhI5rWCczsxHIEe8BzneDhi2MdetdF40+Jlh4Wv49FOkX99qTRK6RqyIrA5Awclm6H7qtXJJpupfEeW7HiTw7/AGNGqL9kcKVlGSc8vh85287FH1od7CRb1v43aVbWNvJZabdyXVxIUSC5UwAY5JLYI/LPvityx+LfhGXQ11GXUYo5PJ82S2LqZU45XGeTXG6p8DYhZRNpeqyxXcbnLzjzAwIwQQfb6Uj/AAH8NwaL5M0JmuvL2tOfvE+o9Km47HXeEvi34b8Q6tHp8QuILmWTy4Y2UNv75ypIwO/p716Uki4GK+QoPh34h8I3X9oWM0M/2S4FwgCnzMIcgAnjJr2fwT8ZvC2q6SZdSlbTbyFik1q6MzqcnoFBJ4GTgcU7roJo9I1y1uL6zNtband6azsMz2oTzAByQN6sBnkZxkVkW3gnw/Dci8uYrrVLwKyi41K7kuWAYEHAY7RkEggADBNWNZ8SWFl4Uk8Rwn7dZxxCdPIYEyqcAbSeO46/jisTQPij4P1ho40vpbOaRgipcxEKWJwFEi7oyc8Y3Zp6iPmn47aXFpfxH1COGNY4ZtkyKibVG5egHYZFcDLjY4/2T/KvVv2mGB+IZX5gyWse4Hr3x/OvLCC8oRVJLAgAe4pIvodd8N7rbb3VuCVBjYtj9P5mvpn4AN9nur7UHA8pIoopG/uhiTn/AMdH+cV8q+BJUQz4P7yRdpHopxz+eB+NfVX7M80dzo+qRyAOJbe3JU8gghwafUXQ9e1a3ikvrK9S3WWSJmXzNxBRGU5IxweccH1zXn/xu8IN4p0uxbT1RdXtpZJLVyMeZiJmMJP919oHscHtW9qXifTvC91p2m6uz2tveym2trhzmNWVNw3ufu5xgZ7it+cKZ7d/KV9rE7s42ZUjI9c5x+NXuRsfntqduNE8Qh4o2is73cyxuMGN+Q8Z9MZIx9RXLXc0Wn399bybyFYPGccshUYr6G/a38KLpmsDUrOEC21RmuEx/wAsrlceYMejj5vrur591m2l1S0sLi1RpLksLZlHVg33fyP86laMt6q4/QptV1/UYdL0izWSZgT8zcKo5LMewFeh+H7Wx8LhZ9Quor24JZC6MPkYAHgfw4z9fftWJJPa+AtKfTtOMc95c2xWe8HUueGCnsFzgD3zVzwp4Lv9ZjttR8TT3Ok6PK8axN5ReWYuQucKPkXdtG5h3HWhu4LQ/Rqe5RZTggqg3sRzxgn+lfmv8WdTfUPEl5PJJuJmc5+pr9CPFusaf4e8M3q3Vyv2lrZo0Cqcs5Rto9gdp6+lfmh4hmeeZ5GbcSSaolKxQsXJhlf+5Mp/Q/4V7p8SdktrbuXCsUiZffMbD/P0rwrTVIsbrI/jjP6PXtHjS5iuItDtPtEMUlxbQNGsjYB4YdfxFDGeZSZ8iIkHGWH/AI8an067lsb23vbc7ZreVZUPoykEfyrTvtFuLZnsJEZbqNmdUYf61ep2+pHPHccjvjHiRgcEHnjkVAz668E2WneKvC+mS6TrN/p1rJGzXHkTbrqcE5KGd90iAMWyFPfgiu20bQNE8P2L2+mWNvYwcvK4+83cu7nlj3yxNfLnwO8dHwlqzafqFwyaZcuDvxnyX9f90969s+IWrXOrW62iLDPpO2IGzguA02qXUpPk2zAcpDxvc/xL7A5Nb2Ed7pOoWWr6bDf2My3FtOu+KUKQJFzjcM9v8+lW5Igy4Iqh4b06407Rre21C8N5e7Q1xOBtVpMchFHCoPuqo6AD3q0b61F1Fai5ieaXeEQHk7Mb+npkZ+tIRSv9LgnRlaNefavBviP8Nr228YRa5osIaNxiaPcUA65PA5BBwa938Va/p3hvSW1TVmkjtldY8xxmQ7m6DAHc8fUj1rD8GeLtJ8Z3eoQ2MM0f2PYR52AZVYuNwAJ4yjDr6HvSa6jTPIvhB8Rr7SvFlj4RnSKPSVZrZVnysls2WO0P/EM4ABAOCO/X3DVvDvhy8vI9VuNIsTeQuskd0IgkisDkEsMEjPY5FeK/FL4SyaRrA8R+H7eW6gedZNir5ktvKW44OdyEngnO3O1sqQU7Pxj49XQPhz9ovri0OtbDbPBbS7glwBhlI6jGeRzjnk8Et7XA+evivLZT+PNSTToxFaxS+XGqsWAx1xntkmuSYlZAw/hxVoyPNK8sjbndizMepJ5NWLHTmuw7M3lQoN0shH3RnA+pJ4A7mhFMr6Bc/ZZJHGM7RgeoDAn+VfSP7Mt7eNdtZWN1DFIbaRNs0e9HMbjCnBBHBJyPyNfPl1oNxpukf2rdSww27cKrth23cHA74zXo/wABfEcWjeKdPvZpNlv5sckjYJ+SRDG5/Agmqe6EtUz6E+N/hpvGHwz1C2ul+zahYIb1FhZnXcit8oOBuDLntkHHpTPgP4zuvF3hAR31siXGnpFAZ45d63ClPlk6cMQMke9dZoHiG012SdILa4ijXhPPUK8i4++Y8lkU8gbwpPYV8+fEbVLn4D/EeXU/DthHJo3iCDc1kciJJ0YbiuDwSD+pqiDvfjB4G1HxLo/ijVryXzZobdP7HtxwEWL53yPVzvX6H3AHyHohktNSkjjcggiSNh1weQR+Nfal34x8Rav4J0TxB4V0W3vP7StxJPC6vKYmI5X5SvAIIJPtXyN410qbw943ntp4WgMNwUaJlwVUkSIp/wCAkCpkXHsdL8GfBbeKdabxTrcObTzWe0gdeHOfvkHsOw716z4Y0DUtb0y0lSTBhlnhmvbkvmVEuXaNo4xhWIG1lb7oO3rtxVH4XamH1GSxDQC2bS7W5tYEXBUFpFf68hfbGK9UsHITyx6enTimkJs5/X766urDULy+kL3E0bl+cgEjGB9OBXxl4gR21G5DxgFHKMFXGMfL0H0r648Qz40mcZ+/tUf99D/CvnX4n6S1j4ie8jjxBeLv4HG7ow+uefxrorRUWkhe0lUblLdnB26hYbhB32Nj6bh/Wuw1vVLXxFYWFrkK1jaRwKw+8GA5P0zn2ritTWWFRKnBXgj1qGC4cv8Aa7Vysi/fTuP8RWHQL2PQ9F16OaFNB8UZG0AWt+D80fpuPUjPfqP1qTW9JmiuRFcbRctzFMMbLkHpz0Dn16N7HrzFpfWuq23kXICTD9D6itrQddFjF/YfiAfadMf5YpcZaHPp7eoqRlPZ1V12spwQeCK7/wCE/wAS5fBcps721Fxp80weV40USqcYBz/EAABg9hxiue1rTPJRGlmWWGQZtr4NlXXsrn+TdR0b1rFdGiLRTxlXU4II5FID7R8LeL9E8R2K3OmahDcKVywRvmT2ZeoNZ81ky/EzQ76CGR4hZX6TShchCTAVDHoCcMRnrivkG3a4spRdWF5NazL0eJyp/Suk0H4m+MdHvLq5jvobqW7ZGuJLmEO0hRQq5PXgDFK2txn1h4+0g674N1XSlA82a3YwE/wyr80Z/B1WvGfgfpviLR/FaX8+lz2envbNFcC4IRhGwBjO0/MdpjjHTqz+lcqPjd4ymjwlvpoxklhE3f8A4FXL6x4w8UaveNeXOoNC7xCBjCPLzGCSFOOSAST+NF2Kx9G/Eb4qaF4YtZIhOt1f4IW2ibJzjgkjoK+XvGHiPUfFGqS6jqTrvkYkKFwF+n4AflVKTaHZ2O925LE5JNRwW0t1cBE2jqzMxwqKOpJ7AUDH2Fo1y3VY40G6R26Iuep/w79BXV2EVjpunDWdZV4tNt2zBbnHmXEmMcjux/JRkepKaXYWMGmtquou1to9nhssMPcv2OOuT2H8P1JI868b+JL7xLfhyvlW0YKQW6fdiT0+pxyaaQNlLxp4pvvE2rvc3LBIVO2GFPuRqOij/HvXV+ANQYW1uA4DRFojk9j8y/8Ajwx+NeaAYbnqK6XwlcmO78jdtEowD6N1B/MCmxJn3r4X8XeE7PTvDQsNMs7a8123DmOyt0jKsoIO7aB1kBUZ759K0fir4I0zxvosdlqEKSeU4kiYjlT6j8K8N/ZrWDVfE/kFpY7yILNFx/q4QT5uCejZ2px2kY9uPqAyJ5xtyw83Zv2/7OcZ/Oq3Qnoz5Rt9W8a/BrxbPo8d3BcaDPm7it7nO3y1Yb0iP8LEMePUCud/aA1PS/EfiGDxNorb7e/s4pWyMFZUJUg/8B2V9SfETwxpPiHSXtdStDMjgxq6YDxbuC6ntjr+FfOnxp8B23hLwvpiQ3s90DPNFul28AqCAAAMfd75qJbFR3Nf4E3qN4bMTKPOil2MQBkrtBXP5mvRvFOvjw74R1DWFUNNBAfIQ87pDwo/MivD/gpqK2mrXkE0qojwxy/MdoBGQfoBVzxL4l1Txz48tvCGhQi60pmi8ydMgRH+J9wODx0z6/SqT0BrU7/xHLmyRc/ekH6A1ymqWFpqdqba8hEkZOR2Kn1B7Gt7xBJlIF92P8qzZQqeXg8sgY+xyf6YrqrfGzGOxV0H4BaX4q0r7XH4qSwdpShhms2kIXIAO5Tg9/yryX46/DH/AIVp46Hh2y1KTUtlpHM85h8vDPnK4yeMAfnX2F4Es7W18K6ZIFSK7uIjOzOQFlXzWGCexAAOff8AA+dfHXR49a+IN7dSjzMQwoGZcEgRLXLdttGj2PkaWCVV85QUkXqB/MVbtdR86PybjBJGOe9dt42tdI0TWYIZCxlhlSSaKNckL1weQAfauH8WvpVzqz3OiRSW9s6gmOQYIbvjGRj8aAOg8MeJJNH3WN4hvNIlPzxE/NGT/Evof0NdJfWFvHaw3VvN9q0mX/UXCDLQf7LAc7R6dR2yOK8phupYztkBI/Ouj8LeIrrRpWNuVns5R+/tZD8rj29D70mCOmuLDA2rc2gB/iF1Hj/0Ks99PKv81za49ftUfP8A49WgPiJo+4k+EYi/GM3h/wDiaT/hYtgchPCFlz3e4Y/0pWHcpRW8caYN1bZ9rmP/AOKqU2kMnP2q3P8A28p/jUp+IkIxs8J6WvrmVjUbfEJ+SvhzRl49WP8AWgB62UPa5tP+/wAD/KtbR9MtvLluruQQaZbjzJ5WGDKR0464z0HXPJ7AYf8AwsaaNsjQ9DH1iY/1rN17xPd69bH7T9mt7aL5vs9uhVWPqcnmgDZnbUfiNqwt7UnT9BsCFBxxGDxnHdz+QFdtD4M0PToIUsNOhdcFZJZBuc9O56dP1rT+FGjQ2PgqytDbndLCtzKzD7zSc/ywPwrotQhiggYk7VGSzM36kms5SLijwL4laHBbGS4hhihRADkAD2x+Of0ribVzHIpU8jkV3njHUIvFHik6ZZSIbCzJd23gCZx6H0z0/E1xmrWjWGpSQD7md0ZHoe1XF6WFJdT1H4U+L5/DvifTtfgyTDLulQfxoeJU/FTuHuT6V9leGdch1XxLqYtbg3FpNFDcWrg5XaYkJx9d4yPavzz8OXvlXHks4QPgqx/hYdD9Ox9ia+mf2YvGltZ6sdB1BhCJxILZnb/loSmYs/8AAcj1/EVS0Jep9G3M0cty9oQfMRFkIxwQ24D/ANBNeKftCK0WhWNncoJfLvGeN2HDxmJ+ue69PyPevYNUvnttSs0LZguQ0fbCuOh/HIFeI/tL6hFd3ukaHFJmYEyOByRuwoonsENzwm6a6t761tbGBZGvAYCHPygEEZb1A64719F/CPwpZaDYRyRjfcP800rfedz1J/z6V4TdQqvimziKlsXIVR+JP9K+lfCsuISnIUYAJ6k7c5/z6Uo7jkee6uPNvraEnbuUDPpliKoXsgN7LggqrbFweMLwP0FWr91bVE+9mNBn2wN1ZQPGMc10zd5MzWx7v8JPFmhT6dp2g3xMNzHF5CmbBR8nOA3bPoce2a5fxw8UvjjVbmVdyR3Ehx7Jxj9K4O+0u5iaF7BWSWTJCsSUYAZIB/hP6VqeIZ2tfDWo3DMfMW1fJJ5yRj88msIyUi5R5T5r8Uyvfatc3UhaSaeVpGJ9ScmrGh/DfxNrRiaO1S0SYFomuSV3j1AxmvRfgj4Ji8UeJ57m84trIeYcqDl84QEHqOCce1fQF9osckUJu0iElu+6NoiVH19s9xkipk7AkfG2s/DLxNYyzRRQQX7wrukS3Y7lH0IGfwriZo5oHeKRHR0OGRxgqfcV9k+J5dE0AX2q391bWbSDYZC5YsVHACdSeegrwr4iQaZ4p0641/R9Ou7aaxO2Rpk+a6ixnzDjoR79v0Sk+o3E8lLHPOBVzTl+0zLDFEZJCc7R6UyeD92SoJJ4A9zXqfhzwXdWOjRtZTW8OoOFd5Jod6n1Q9wPcU27CSucjB4T1O58treJCCNxJOP8irK+AtcCBpZIF3ejE4/IV39hqn9kkWviTTn0w5xFOh320h9BJ/D7BsV3FtYwzQB1KurDKspyCPUGpbaKsj5n1i0axuHtpVYTRtsbPTB+6w9j+lQ3IxHlCQrAAivYPiN4QtrtDdMjCRSQCnHGOh9Rxn615n9hdYr2ykC+YiGaEk4LBPvKPX5cn8KakJxPpb4eXqah4U05o0PlLZQgP/eOwAj8CKg+Iej3Os+Fr3TrOZYZplChm6dRkH69K84+BPjaC1tG8MX9wsJdibGZzhQSeUOe+eR65Ir2i9lhCrHuAZ/u5PLHGazasy0z55svhPqKxebe6hBCxyCkaF8duuR9ayPFPgePQrN7pL6WeRf7ygCvetWkSORYMHe4JGBwP8/0NeL/ABd8RJHBNo0ckbzu/wA/lnPlp6H/AGie1OLbYNJI86ebGyROmOtdd4Y1ozhQAWuEADIOsg6Ar/tgfmPfFcHFKVG1vu9vavQPhv4N+3RjxJrly+m6HbHzPMyVecg/w/7OeM9zwPbaxlc9a0n4reK4NBt4ZLqLVrRH/cTSgmVMDGCw7jP8XORWZBqep61rkviPWXxsBdWYbQzDOCB/dXOSfauKvNe0U6pNcWVhq2lqzfJcW8yuzqOhkjYYP1zn3q0mq299C2NfjvXA+W1niW03MOhY9Gx6bsVJRoabqElz8RdMSGBJCGHMj4KAknp3O0H0r6C0rW47TwzbatPE6bhHtgLAEyyEIsZJ6fMwGe1fMfgaG/tPGMGoa1BcWv7xikhXdG24Y++Mg9exr3fX9T0W98KXNjf3gs/9XOkjRB1DxsJF4OA4yvIzyM01KKdg5ZPUzdTuGXU7phjO5k6dB92qcJzIuCByOtMlcyStIx5Zix+poRdzBclc9wMkVtJ9SEjtr7UY5NPsbO7jWCWd1XaX+U5POCOemax/iNOIfCdyuD++kjj6e5Y/+g1UikuLzVbNnWO4SJwGccMmORlccd/Tt1rK+KOrRrHa6buy5kLkf8B4/mawppW0LqXvqXfgrfaHYQ6rDqunTXkzhJLdYvvDG4Nk5G0cjk10mu+Pb27llsPBWkPd3kiASzMpMdu2AOT3x0zwPrXlPhXVLzRdctdTsSongfeoYfK/qp9j+hr3j4a+ItG1CHWL/wA+0s7yTdKdP4j2kAkkD+LPHzDknJPbDkrhF2OU8N/DLddNrHjG8Gs6lu3JG5LLCvHBHfH0ArS8c2V/aeGdSjbS7aeweykEs8U4Xb8pGdpHIHHQ16ZcXttDo8N/PFHHC+1nDsBsB6n8P5ZrwX4q67AsuoaH4d1GeTTbpg99vOURs5IRjyc8fXpz2iw+bQ8V8EaMLrxFaiSMOkKmdlPovT9cV7dYWm5V/d7cdu9cF8PHtIvEl9PNILdYrF5FZs7ERSuS30GK7+38X+DogFfxFpwbHaXINKV7jjsaJ0+KW2eGWJZEcYKlQR+R4qDw94esdIEzWUL24nILwLITEpGeUXOFz7e1Taf4p8L393FZWOvWFxczNtjjjlyzH0ArfELZXCg88544/wD14pagYGq2cFwotXYCSUHYpH3sDnFeW+NPBV2zJJZBAY33E5ww/Q17k0HI4qG609JFJdQfwpDufHmtafdafcFJojG+T8uMD/gJ/pW1onxL8UaTHHA10t5FEMRpdKWKj0zkH86938WeCrPUDmSBHUqw5HavD/Hvhmy0S5MUfmYPyqgzyfb8O9WmnoyWraopa/8AEnxPqruTeR2isu3FvGFIHsxy361xzCSV8ks7ueT1JNXV02V51hjQvKx4Uc4+tet+C/BWi+FdGXxH4slEt2WBt7ULkL6cfxOew6Acn0q7JEas5/wJ4CtYbAeI/GP7jTowHjtm4M3pu9j6dT9Oa6DW7+48QzxGSF4dNTiysEwu/HGTjgAfkvQZPFN1K+vvEOopdX0ZEW4mzsg3Cj++x/m34D20oIVtELswe4cAO5GAAOiqOwHYVLkVaxhXvh7zgry3sscwGAIwPLUegU5/Pqe9ZFzodygOUt7wfUxv/UV2Mk25lUKWkb7qjkn8KtzaB4hnhEsHhjV5F2/fW1bn355oA86t3udLcvaXd/ppJ6c7OnqpOfyq3J4k1W5tootQa31iFGyoJKuh9QUIOfrV/VBJbTtDdW9xbSAlStxGU+YcEc96zZrW0lz5tsrN2xwR75FIo9gDCrekgvqEICqw3fMG6Y71Rz0zWv4Vt2udTZY1Zn8v5VUZJO4AAD1refwszjubs+l6er29/bMVnQnepGGxgj8v0rhPir4P1TW2i1HRpEM8Yw8LPsJI6FSePwNX9a8a6nLqUlpa6esK2shh/wBKTBBHBGMZz+HatnTdTnm04T3YQSH+FFIxUU6TUR1Zq9zwaSTxlpZkM+lXMiQnDt5BYL9WXikt/iAGVUvbBXx3VuR9Dwa9g1WZoPCHiK5B2EQy4wemIsD0r5gKsKGhI9XtfHWkXG1Z2ljC/dEkjkD9abqPiGzu23rdRMvYBgAPwrysIx6Lz7VcsrcvIpcA96VgPd/hDaw65Ya/FvZFlVIGkiOJFBBPynHvXT/8IPrFqVOm+IoZlHATUdMhm/NgAa5T9mmaGPV9U0+RyrypHKg3cNjIPHc8ive4bT5sFfvZxUO9y0eeaFo3iWz1m2F7ofhhrTfmS6skMbx8E7gjDg59D3ruI4MkYFUfEPi/wvoNzFY32qQm9lcItrADLLknHKrnb+OK6MWv3dp4wenehpgZ622TnGaS7g/0ZzgjHcda1hBgDrTbuHMDDuVoSFc+cvFvxeeHWTZ6VYJNFBM0UjzkhpCOCFx078nP0rhfE+r3PibVEdLYQlSSq7t23PUk1a+Jfh9tA8Z3s8eViuGeRVP8LE84/M1x0NzNIzjOEBI2jv8AX1qkluJtnbaXaaboluhWRZ9SlkjwykERDcD17kj8s+tauuJPqWrrLdOZZoE8qJGY7M7jk/44647da5S0G42+4/d29umO36V2d0B/a7jbhd2MfnQ9hrcmsbVLdGffvkflnI5PYdOg9AOBWt4R8N3nizXk0qxByfmmlIyI09f8B3rOZDCgJyPl7+navoD4G+GbzRPBsHiSwsHv77UfnmtvtQizDk7CoYbS4xnkrkOeelSkN6Ggvw60nwh4eTUtL0+Oa7tAzTXTJvnRShVpFHRtuQxQ8EA45xXQeAvFGj+JYp9PRki1C1Cx3UarxuZeWjP8a+jD29RXRaJ4j0nVZZLCNprTUY1LSWF3EYp0A6nafvD/AGlJHvXz5ruiXfgT4y2msWc8qWIuTPHZK/VCMPHGvTlS2F7EADA6XZIjc7jwhFD4l8Qaj4Z8R2lpfW40/fIslsn+vV1imbOMks4Y/jXmPxp+BbeHLWTXvB/mXFjuVZdNbLOpZgo8k8k8kfKfzr6B8KaP4eur2Px5oRuHbUrdgcsQrq7BjlG5Vgy8jjnOas6xFdX3iywR4ZV0/Tonui2cLLO4KRr9FXzCR6stDSGmfLRIJrS0ICRpo2mlt0cKrzRw+YVTncPYn1weM1lkZPWrdnoqarEWW4aGeNsRsjlWUkYByDkVdR2jcIK8iDXdFh0HxBaX2j6kmo6deKyyoH3mKTsMkDAOTwehU+1bpCuiyBSN2A4bJB6DAxjvz9KqQaVqVpaX9pqU/wBqlYqYpCo3nAyMnqefWr/h6fzIN8UQlaNN7bhwq9Mn9B+NbYaScdSnCPOynr+kXF5o1/ZSPHCLuIo2yM/KCMZ9zivCvE3gu+0aT/SIt0DH93Ogyjf4H2r6EutY8gGa524Y4LkcA/0qIXtrqNqY/IW8ifKujRgqwzjHT9KdSClqZcyvofMq6aVPvVuxtGV8MvavWtf8D6dO0lxo0v2aUKS1rM3H/AT1H0OfwripNOmtJZI7mJonXqrDH4+9csouL1NFZrQ3PhDe/wBh/EDTrl/9VO5tJBnA+fG3n67a9D+Kl14qTxRdaVcajqP9nKBLDDYIlpD5LdDPdPwOQwwM5x2ryQxsSyISCG3AjgggZr2/wnYaNrXw507XPE0Nz4lu9IaZ0gUedPtdxhGUn58ccnPBpIR5z4f0PUNZWS18NWEt+rErMmjhre2Hr517J87+6rwexr6I8FWt5beF7Sx1KaxkvbSPyZfskxkRMD5Vy3OQMDnk4zXNaV4T17xxHb3HigyeHvD8fzQ6BZSFHcdvOdcYGP4FA/CvSdO0uw0rTYrDTrSK0tIE2xxRKAqj6dz79TVN3EVHi27fpUN1GTCTirtjeafq2npe6ZeQXluzMolicMuVOCMjuCKSeMeWeMUkgZ8r/tFwMupxTHplh+leQaaUjnV2UsnmZI9favdf2mYQkUMmP+W2P/HTXhtkvygkfxn+VAHRRMGcNGCisy/KPaurvXH9sEHpvBJB+tcykjBUcDZvxkKSBXQamwXUlJ4yqmjoNbmjdEGI5PB6cV9s+FbeOz0HT7OMFRBaxRhTjIAQAdPpXw+j+aypwxxgZr6bh8C6lrGkaNqXh7XWtLIadCBp0l7eLGSQXLCSKZSCd+OVbGPSlEcz1fyx54cqCyrxkdM+n+ewryb9pDwzc6t4ZfUtPXF9YqZInH3kODkj14J4ruPAWm3+kaK9nfW0cEwlLApqM14HBA+bfMA4/wB3oPxqHxt4i8P6a0el6tqNrbz3oKQxyuF3kj/PWmyVucl+zP4vt/EPwzgtCiw3mkyGzuIwScHqrEkDJIOTx1zXprkM4BG4MMV8W2/jKPwp8Trx9P1K6tVW9jQ+V88c0LYBG3lWHcE9CBj3+0hholdRkA5B9jT1A+OieeK6LwpCGUsVB3kZ46gZrm1Y49q6zwgQyBUyWjXLD68iiq/dKp/EW9RjCXMmT93jHpVa1uE0LQ9WmjG5rp02qq5bG7lRnvk8fhUt+zPM/Y7qrj7NKRZXYDRXGUwfcf8A1qdOVkkKW7Ga3qekzWs7OkEFpBmKSGSPO5Cy4fONylSVH4EE0jeJrO3tbbVNM+bTWaWOV3iGWZo0QO5O043bunXHHpXl/jN4dO8Tzw+I1uFtriFI1uYix+ZCNkn1A6jnP61za+JfDVpPcPYaJLdSNIfs/wBtm3RomB/AuCTnJALY55zW1yD1WfWp7yUL8k9sAP32z5VOOzYBIHTAxjHeo9TvdH1uKSxvlYXGALe7TGIgAcjbxlScd/U8muK0DUPEHjzVbbRobjfcSkpCgxHHHxk4VRhRx2FaGp2lvotjHAt6lzqTbluQnK25BIMZPRm4zkHiqaTVgTsY93E1tOVZcMrEj0Yetd38J/GH/CLasy3LFtPvABICPunoGz6Cuc0WwbWra6gCu91HCZbYKwwxUjcp75Izj3qDRbm3tHnS8Dtb3FrLFtA/iKnYf++ttc1SHKXFn2Dpzhk8xGV4yMhlOQR6iuE+PXiibSvD39gaVKqarqsThpN2PstqqkyzH04BUe545FcV8H/iTHpiRaBrhdLXKpa3DHO3JwFOewz17Vd8WeCdX1D4gJPrV3DcaXq12Gnu87IzBGQYLMZJ2gt8zH+LaMc9ZiDRb+B9sdJtraV0nhn1tFOn6YznbZ6dDnE0g6b3JJJ6lnGOM49WuU+Xp3rzz4Vr/besa/44YsIb67FnphAwDaQZUED0ZsnHsK9Blk+XB/vDFVfUR86/tQRgaZE4H/LwP/QWr5/svMIYIAW3EgEdeK9K+I9zjw5qsJYs03ivUiuT0VG6D8xXm9nhQT79jQxHSmQzQQO5BJ2k4Het3XkMep256iSJDkVzx/cacs+AVTGSTgD05qxo3iQapfrDdxQwiSNY4JMZ8th2OfWkUb9q6KhfJDqwIIPT1/HpX038O/H1rovwah1KW2lvX02T7FJFEyptOfkZ2Y4CkFRnk+gJr5fSQB3LKUPKuhPKMO3+e1egfBzWYn8SxaBqVzOujakdlzAkm1JWxgbz1xycYIwcHPFJaMp6q56Fp/xg8S6lJrmNNcsLNhZRWVszi2mzxvZuckHPz7D8ownevJNW8M/Ebx7bxazPa6pfmIfZWkumVJY2LhiygjJUEY64wa+pPB/g3+z3+3ao1sGXKWem2IMdjYx5ONicb5CPvSMM9hgddbSta0S81vUNFsrmNr3TmC3EY4K5VT+IG4An14609SbnkvgT4EaDpMunardwC51KEB3eQ78ydSeffpXt8cbC0RAQCgwQWz+tThABgDFRNuj+U8qQc0WsK9z4zBA4rp/h1rFvNe3NrBYSJJECs7yNwQOAQOev4Vzl5azWrBZtvzDI2sDmu78K2FpBZvdwKymSMOS4w3IzzU13aJpRV2VGnScPKqFVbkA9RWdqAGY5mWV2QnYqDkn9MfWruVy/91mrN1q3SW4st7FURy2ByWJIA7/r2q1oZsb4strG7QWl9FHKkwLIj4PK4zj8xXlereCoLPUk1GxtvtUEUgka380oxwegPpXpHjxf3tsxblYnP5kf4VQ8NQz39vftPkW9tHuWcg/eyAE988/lTuCV9jzyPxB4gRL+20Sxi0mB2JdbZQk+0/wmQgEjrwMdfSsW3vtQgkCX9neJFnllTJ/Wu0NzZ380wgtREobCSZ+8RwT9O1CSXVrteS0Fyg4JhfDr+DYz+BFCmPl7jvDd5dCOOXTrO8tkQ71luAhlkI6BQADt/E/hVnX0huSdSt18maTP2q3CY8tic719FPp2PtirNt4q0Ej7PcXCQyHho7iMwtn8cZ/8ep08llc4msnJ56htw/px+FEncpJGbafarxViSSMPGjMGlfaBhd2c9jxn611XgT4gPaLcaX4hiN3pN3D5U6LGCw7BjnqQe+K5C5drAzZ2NHIpBzyRyCP0GKqQRmSeZYVJVAzcN0UHrz16isnoNdj6w8Iaj4buPD9ha+G5o1sraNUjhXgoPQjr1/WtmZht3DPXtXx1pWualovmm0nlVipKushVlBxnpx6nGK7qy+L+v2caR2s0WoKEUhp4vLZiR8y4HAIJ4xnOKExOJxnxAjDeDNUvH48vxXfjP+/I4/mBXlFxfPAzRKijODvPX8K9a+IPji11KznsZfB9tb6dO3nSrG2C0+Sd/wAoAP8AOvHJpIrkuqgqVJ2hjzt+tXe5FrE73s91xcStJ0GCePypYZBBOVJDIcdKz1Yo2DwanQ7hgn8aAPR9Cv21O1O/Ml7Cg3AdZ4h3H+0v6ium8FOF8UaW8UiHe+07zhTkEDn0Oa8k0TUbmxuYpIZmjkjYNEwHRh2Pt/jXounXcVwkerWhEWH/AHiD/lhN1x/useR7/WkUmfUfwl+KR1qx1DQZoo21yzEosoXfaLsLnaNx/iGAD7c+tV/gzpmq2PiT/iZb0vbyGfUdS3rh2BlMUKt6BnNzLj3X+6K+bDdXE3iJ7y0nNpcPN5iNGShRs8EY6HvxXuXwj+LelaZbxaJ4mhtrG6hijto7+NDsliTOzeeoxk88jk8DJpoGux7ZrGvppWvafp8wVLe7hYhyeVk86GJVx6EzD8q19Ve6j024lsYluLpI2eGJzhZGAyFz2z0z2zXnPjyWPXbaz1TS7y0kSO2miiYTDDTSNE0ABHHMkaDnGMivQG1GFYRKSBnkr1x7U0yWj8+NI8VXlpeZu9QtrpHYMY5Dgp3OCB+le9eC/FGja3oEj2c8byqo3xg8pxjp1x+FfHhlf7zPnsBXbfBy38S3fjixl0S1vbhElVLtoo2dEhJ+beegGM9aVSKmioT5WfQyo2FB55qDUFBvbKLIw0qA5XOctkfTpXW6fosTXVst5IYo5mOGAzgcj+YNbeoaRp+m3zPbpGHjJAnYBmj6bTgnjndkjHUUnKwcjZ5x4x0qJtQtbi9YJYQQbpRuw0gBJKj3Pr71sJomn6lp81zcXdzbWcBaGGztVVEjBYrjc2SX56jJ5p/iyRb7TLlZYiJUjaS3Zzg7lJDEEeuce/8AKAM11oEkNxeRWe+RGSVpPmYscsnA9sg981LuzqhNU4Wjv1Zy48JaEJJFjW9ssAiGMurfPnCr344z171z2s6Slm3kxXCu0gJUOdrZQZPHpz146GurL6fYvcQRX0kl8CqmCJ2PlnoGJb06+3PrVIXd5NDC0dmkssiPBIrBCzBT1znJ4zxx/SldmVkeYX1wH8yK4tfOVRycK6kD09aykvNFgm82CObT5EYEeWrBc+4GQfyxXqeqaVpMNpcJcWJkuJRtBEhZId34Ak9OccegrzTW/Dlx8zwMqoighQcjp2PUjtk0JicexU1XxZKGY+ZDdxtxhV2Mp9hx+X5elXPDzX99ZC8azkSzZ/3MioxRh0IzjHB7dvpisSy0V7ltyjZ5fO5/ugk9Sc8V7r4Svby00vT5rQ3MEaSKsuVKJNtBByvO5W7EVTloKMXc8suwqu54B6cHtVbUVkikyUIYDpnj1r3nzNQmhMV7a2jO/wA6qtuhjKk/cOere/pVZdB0+bUWOqaVpt3AqkSD7MqgkgkEFCGXHTHfjHc1KZTjc8AWSR2ZGaXbxnaxHTHFYOr2oluGeJAjKc5AxkV9LaX4F8FXs4e70+aGMttmNnO4AOSMIDuzwM57cZxmsfXvg94Tu7Qzade60rI2JUlZAydSCuI8Nx9PrT5kiXBnzTcxsj4bG70BzRBKwUpng9QfWvZr/wCDmlLb/aD4hv7EPJsQXdojbsjjo45/DtWdcfAnxAZCLDWtInYEDZM7QNz0ySCo+pNVzIhwZ5kvzDIrf8Maq1lcFnUyxOnl3EWf9ah/qOorbuvhD4+spXjOmWtwyHDLBfRMRx/vCshvBnjO3/er4a1VlVsb4rdpFB+q5FK4WaOunt5JhHJbyCX93vhnHWRM8H6jof8A9eKFxcXUkn7/AHFhwvOcD2pPCNzLaXC6dq0NxYxSuDG00ZQ28vTOG/hbkH/9dd5qHhq5dlk/sy3kQMTmKVkyM9OjVS1C9tzk9F1i9sJlaC6mRVYNtSQrkjkHjuCAc9jWtf8Ai3xDdYgn1zUZkZQSFuDznnn8KdL4cdDu/s+8hBz9x1cf+PFapjQbcEhpdQiY9AbUv/6ATQVdH//Z"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-52000-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In which object could one look to check the time, and what part would they specifically be looking at? answer: To check the time, one would look at clock_1, specifically at clock_1's hand which indicates the time."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-52537-0", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: What part of the vehicle allows a driver to see the road ahead while being protected from the elements outside? answer: The car_(automobile)_1's windshield allows the driver to see the road ahead while being protected from the elements."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-53888-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: During a busy work session, on what surface are the laptop's resting pads likely to be placed to provide stability? answer: The laptop's resting pads would likely be placed on the laptop_computer_1's back for stability."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-54912-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: In the case of a toast being proposed, which item would people most likely use to participate in the clinking of glasses? answer: They would most likely use glass_(drink_container)_1, specifically holding it by glass_(drink_container)_1's body and clinking using glass_(drink_container)_1's bottom."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-55848-4", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: For typing on the first electronic device from the left, which part is specifically designed to present the user with a tactile interface? answer: For typing, the user would make use of the laptop_computer_1's keyboard on the laptop_computer_1."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}
{"custom_id": "MMR-57095-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "qwen-vl-max-latest", "messages": [{"role": "system", "content": "You are an expert Vision-Language Model assistant specializing in transforming explicit questions about images into sophisticated, implicit instructions. Your goal is to reframe direct queries into nuanced directives that guide a user or another AI to the same answer through observation and inference, rather than by a direct ask.\n\nYour Task:\nGiven an image and a corresponding explicit question-answer (QA) pair, your task is to:\n1. Convert the explicit question into a compelling implicit instruction that leverages the image content and guides the user to deduce the information sought by the original question.\n2. Classify the ​​reframed question​​ into one of five analytical dimensions (Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections) based on its core intent.\n\nMethodology: Reconstructing the Query Using Analytical Dimensions\nTo create the implicit instruction, you will:\n1. Actively draw insights from the provided image.\n2. Utilize one or more of the five analytical dimensions to adjust the original question, add contextual information, and reconstruct its intent into an implicit directive.\n3. Assign a ​​classification​​ to the reframed question based on which dimension(s) it primarily engages (e.g., questions about object positioning vs. symbolic meaning).\n4. The implicit instruction should make the answer feel like a natural consequence of observation or analysis tied to the classification.\n\n1. Spatial Relationships – Used to add spatial descriptive attributes\n1.1. Positional Arrangements\nOriginal Question Type (Location/Identification): What is the fountain in the middle of the photo like?\nImplicit Instruction (using positional arrangement description): Among the series of landscape structures in the square, please describe in detail the facility that is neither located at the very edge nor immediately adjacent to buildings, but is roughly in the geometric center area and jets water upwards.\n1.2. Directional Orientation\nOriginal Question Type (Location/Identification): Find the person in the picture who has their back to us.\nImplicit Instruction (using directional orientation description): Among the multiple individuals in the scene, please identify and describe the person whose main body part (especially the face) is oriented roughly opposite to the observer's line of sight.\n\n2. Visual Attributes – Used to add visual descriptive attributes\n2.1. Color\nOriginal Question Type (Location/Identification): How is the green apple in the basket? (Assuming there are red and green apples)\nImplicit Instruction (using color attribute description): In the container holding various fruits, please focus on the spherical fruits whose skin presents a hue similar to leaves or unripe bananas, and describe the condition of one of them.\n2.2. Shape\nOriginal Question Type (Location/Identification): Find the square cushion.\nImplicit Instruction (using shape attribute description): Among the multiple cushions on the sofa, please point out the fabric item used for comfortable leaning that has an outer contour with four roughly equal sides and internal angles close to right angles.\n2.3. Size\nOriginal Question Type (Location/Identification): What is the tallest book in that pile of books?\nImplicit Instruction (using size attribute description): Among the several books stacked together, please identify the printed material that significantly surpasses all other books in the vertical dimension, and describe its cover.\n2.4. Material Properties\nOriginal Question Type (Location/Identification): Which sculpture is made of stone?\nImplicit Instruction (using material attribute description): Among the multiple displayed artistic forms, please identify the work whose surface presents natural rock texture, feels cold to the touch, has a hard texture, and typically possesses a certain sense of weight.\n\n3. Functional Context – Used to add functional or behavioral descriptive attributes\n3.1. Item Purposes\nOriginal Question Type (Location/Identification): Find the knife used for cutting vegetables in the kitchen.\nImplicit Instruction (using item purpose description): Among the various utensils in the kitchen, please identify the hand-held metal tool that typically has a sharp single or double-edged blade and is designed for segmenting or slicing food ingredients.\n3.2. Human Actions\nOriginal Question Type (Location/Identification): Which waiter is wiping the table?\nImplicit Instruction (using human action description): Among the staff in the restaurant, please locate the employee who is currently holding a cloth or cleaning supplies and whose upper body and arms are repeatedly performing a wiping motion on a flat surface.\n3.3. Environmental Conditions\nOriginal Question Type (Location/Identification): Point out the rabbit in the snow. (Assuming a complex background where the environment helps)\nImplicit Instruction (using environmental condition description): On the vast ground covered with white ice crystals, carefully search for and point out the small mammal that contrasts with the surrounding snowy white environment, which it might be using for camouflage.\n\n4. Logical Reasoning – Used to add logical judgment attributes\n4.1. Quantity Comparisons\nOriginal Question Type (Location/Identification): Which team has the fewest people? (Assuming there are three teams)\nImplicit Instruction (using quantity comparison description for differentiation/location): Separately count the members of each clearly distinguishable group of people in the frame, and then indicate the group whose total number of members is at the lowest level in comparison.\n4.2. Conditional Evaluations\nOriginal Question Type (Location/Identification): If I were going out, which umbrella should I take? (Assuming one good umbrella and one broken one)\nImplicit Instruction (using conditional evaluation description): Examine all available rain gear in the image, assess their respective conditions and functionality, and select the umbrella that would provide reliable shelter in the event of wet weather and currently has no obvious damage.\n4.3. Causal Relationships\nOriginal Question Type (Location/Identification): Which child knocked over the milk? (Assuming spilled milk and several children nearby)\nImplicit Instruction (using causal relationship description): Observe the location where the milky white liquid was spilled and the position of its overturned container, and considering the positions of nearby children, their expressions, or any traces on their hands, infer the child most likely responsible for this accident.\n\n5. Semantic Connections – Used to add symbolic or emotional descriptive attributes\n5.1. Cultural Metaphors\nOriginal Question Type (Location/Identification): Find the traditional Chinese painting that features bamboo.\nImplicit Instruction (using cultural metaphor description): Among several traditional Eastern paintings, please identify the artwork whose main subject is a plant characterized by its hollow, segmented stems, often used in a specific culture as a symbol of gentlemanly qualities (such as integrity, modesty, and resilience).\n5.2. Emotional Expressions\nOriginal Question Type (Location/Identification): Which member of the audience looks the most disappointed?\nImplicit Instruction (using emotional expression description): Among the people watching the game or performance, find the individual whose facial expression (e.g., downturned mouth, dull eyes, furrowed brow) and body posture most clearly convey negative emotions, such as unmet expectations or dissatisfaction.\n5.3. Symbolic Meanings\nOriginal Question Type (Location/Identification): Identify the decoration on top of the wedding cake.\nImplicit Instruction (using symbolic meaning description): Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\n\nInput Format You Will Receive:\n​​Image​​ (required)\n​​Original Question​​ (required)\n​​Answer/Context​​ (optional): May include partial or no contextual constraints\n\nOutput Format You Should Generate:\nParse the reframed ​​question​​ and its ​​classification​​ into JSON format. Follow this structure:\n\n{\n    \"question\": \"[Implicit instruction guiding observation/inference]\",\n    \"classification\": \"[One of: Spatial Relationships, Visual Attributes, Functional Context, Logical Reasoning, Semantic Connections]\"\n}\n​​Example Output:​​\n{\n    \"question\": \"Observe the multi-tiered celebration cake and locate the small decorative object placed at its very top, which typically carries auspicious meanings (such as figures of the newlyweds, symbols of love, a shared future, etc.) and serves as a finishing touch for the overall ceremony.\",\n    \"classification\": \"Semantic Connections\"\n}"}, {"role": "user", "content": [{"type": "text", "text": "question: If an individual were to pick up this item, which part would likely provide the necessary grip? answer: An individual would likely pick up the item by its knife_1's handle, which is specifically designed to be held comfortably and safely."}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}]}], "temperature": 0.1, "response_format": {"type": "json_object"}, "max_tokens": 200}}