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Action_Prediction_gemini.py

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+import os
+import json
+import base64
+import re
+import asyncio
+import aiofiles
+from tqdm.asyncio import tqdm_asyncio
+from openai import AsyncOpenAI
+
+Model_name = "Gemini"  # 模型名称
+
+# ===== 配置项 =====
+TEST_JSON_PATH = "/code/CogReasoner/Test/Action_Prediction.json"  # 测试集 JSON 路径
+MODEL_NAME = "gemini-2.5-pro"  # 使用的模型名称
+MAX_SAMPLE = 44  # 测试样本数
+MAX_CONCURRENT_REQUESTS = 5  # 最大并发数
+ACCURACY_PRINT_INTERVAL = 10  # 每多少步打印一次准确率``
+OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Model_name}-Action_Prediction.json"  # 推理结果保存路径
+
+# ===== 初始化 OpenAI 客户端（对接 vLLM API） =====
+client = AsyncOpenAI(api_key="AIzaSyBCL2-lp3jOBPPZc7-5NsSy8r7wDFaqnFI",
+                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/")
+
+# ===== 提取模型输出的选项，如 G、A、B等 =====
+def extract_answer_letter(text):
+    match = re.search(r"\b([A-Z])\b", text.strip(), re.IGNORECASE)
+    if match:
+        return match.group(1).upper()
+    return None
+
+# ===== 异步处理单个样本 =====
+async def process_item(index, item, sem, stats):
+    async with sem:
+        image_paths = item["images"]
+        gt_answer = item["messages"][-1]["content"].strip().upper()
+        prompt = item["messages"][0]["content"]
+
+        # 编码所有图像为 base64 并构造 image_contents
+        image_contents = []
+        for path in image_paths:
+            async with aiofiles.open(path, "rb") as f:
+                content = await f.read()
+            encoded_image = base64.b64encode(content).decode("utf-8")
+            image_contents.append({
+                "type": "image_url",
+                "image_url": {"url": f"data:image/png;base64,{encoded_image}"}
+            })
+
+        # 构造消息
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {
+                "role": "user",
+                "content": image_contents + [
+                    {
+                        "type": "text",
+                        "text": prompt.strip()+"You should directly tell me your choice in a single uppercase letter, and do not output any explanation or any other contents.",
+                    }
+                ],
+            },
+        ]
+
+        try:
+            response = await client.chat.completions.create(
+                model=MODEL_NAME,
+                messages=messages,
+                temperature=0.1,
+                top_p=0.95,
+                max_tokens=7000,
+            )
+            pred_text = response.choices[0].message.content.strip()
+        except Exception as e:
+            pred_text = f"[ERROR] {str(e)}"
+
+        pred_answer = extract_answer_letter(pred_text)
+        match = pred_answer == gt_answer
+
+        stats["total"] += 1
+        stats["correct"] += int(match)
+
+        if stats["total"] % ACCURACY_PRINT_INTERVAL == 0:
+            acc = stats["correct"] / stats["total"] * 100
+            print(f"\n📊 Step {stats['total']}: Accuracy = {acc:.2f}%\n")
+
+        return {
+            "images": image_paths,
+            "ground_truth": gt_answer,
+            "prediction": pred_answer,
+            "match": match,
+            "raw_model_output": pred_text
+        }
+
+# ===== 主函数 =====
+async def main():
+    with open(TEST_JSON_PATH, "r", encoding="utf-8") as f:
+        test_data = json.load(f)[:MAX_SAMPLE]
+
+    sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
+    stats = {"total": 0, "correct": 0}
+    tasks = [process_item(i, item, sem, stats) for i, item in enumerate(test_data)]
+
+    print(f"\n🚀 Starting evaluation of {len(tasks)} samples...\n")
+    results = await tqdm_asyncio.gather(*tasks)
+
+    accuracy = stats["correct"] / stats["total"] * 100
+    errors = [r for r in results if not r["match"]]
+
+    # 写入输出
+    output = {
+        "metrics": {
+            "total": stats["total"],
+            "correct": stats["correct"],
+            "accuracy": accuracy
+        },
+        "errors": errors
+    }
+
+    with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+
+    # 控制台输出摘要
+    print(f"\n✅ Evaluation Complete")
+    print(f"🎯 Accuracy: {accuracy:.2f}%")
+    print(f"📁 Results saved to: {OUTPUT_JSON_PATH}")
+
+    print("\n❌ Sample Errors (up to 5):")
+    for r in errors[:5]:
+        print(f"- Image        : {r['images']}")
+        print(f"  Ground Truth : {r['ground_truth']}")
+        print(f"  Prediction   : {r['prediction']}")
+        print(f"  Raw Output   : {r['raw_model_output']}\n")
+
+    await client.aclose()  # ✅ 释放连接池
+
+# ===== 启动入口 =====
+if __name__ == "__main__":
+    asyncio.run(main())
+    sys.exit(0)  # ✅ 强制退出，防止异步底层未回收导致挂起

Action_Prediction_ours.py

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+import os
+import json
+import base64
+import re
+import asyncio
+import aiofiles
+import sys  # <--- 新增: 导入sys模块以支持sys.exit()
+from tqdm.asyncio import tqdm_asyncio
+from openai import AsyncOpenAI
+
+Model_name = "Stage_2"  # 模型名称
+
+# ===== 配置项 =====
+TEST_JSON_PATH = "/code/CogReasoner/Test/Action_Prediction.json"  # 测试集 JSON 路径
+MODEL_NAME = "qwen2vl"  # 使用的模型名称
+MAX_SAMPLE = 44  # 测试样本数
+MAX_CONCURRENT_REQUESTS = 5  # 最大并发数
+ACCURACY_PRINT_INTERVAL = 10  # 每多少步打印一次准确率``
+OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Model_name}-Action_Prediction.json"  # 推理结果保存路径
+
+# ===== 初始化 OpenAI 客户端（对接 vLLM API） =====
+client = AsyncOpenAI(
+    api_key="EMPTY",
+    base_url="http://localhost:8080/v1",
+)
+
+# ===== 提取模型输出的选项，如 G、A、B等 =====
+# 此函数无需修改，它能正确地从模型输出中提取第一个字母选项。
+def extract_answer_letter(text):
+    match = re.search(r"\b([A-Z])\b", text.strip(), re.IGNORECASE)
+    if match:
+        return match.group(1).upper()
+    return None
+
+# ===== 异步处理单个样本 =====
+# ***** 主要修改区域 *****
+async def process_item(index, item, sem, stats):
+    async with sem:
+        image_paths = item["images"]
+        # --- 修改开始 ---
+        # 1. 获取可能包含多个选项的正确答案字符串, e.g., "H,G,I,E"
+        gt_answer_str = item["messages"][-1]["content"].strip().upper()
+        # 2. 将答案字符串按逗号分割，创建一个包含所有正确选项的集合
+        possible_gt_answers = {opt.strip() for opt in gt_answer_str.split(',')}
+        # --- 修改结束 ---
+        prompt = item["messages"][0]["content"]
+
+        # 编码所有图像为 base64 并构造 image_contents
+        image_contents = []
+        for path in image_paths:
+            async with aiofiles.open(path, "rb") as f:
+                content = await f.read()
+            encoded_image = base64.b64encode(content).decode("utf-8")
+            image_contents.append({
+                "type": "image_url",
+                "image_url": {"url": f"data:image;base64,{encoded_image}"}
+            })
+
+        # 构造消息
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {
+                "role": "user",
+                "content": image_contents + [
+                    {
+                        "type": "text",
+                        "text": prompt.strip(),
+                    }
+                ],
+            },
+        ]
+
+        try:
+            response = await client.chat.completions.create(
+                model=MODEL_NAME,
+                messages=messages,
+                temperature=0.1,
+                top_p=0.95,
+                max_tokens=512,
+            )
+            pred_text = response.choices[0].message.content.strip()
+        except Exception as e:
+            pred_text = f"[ERROR] {str(e)}"
+
+        pred_answer = extract_answer_letter(pred_text)
+        
+        # --- 修改开始 ---
+        # 3. 检查预测的单个答案是否存在于正确答案集合中
+        match = pred_answer is not None and pred_answer in possible_gt_answers
+        # --- 修改结束 ---
+
+        stats["total"] += 1
+        stats["correct"] += int(match)
+
+        if stats["total"] % ACCURACY_PRINT_INTERVAL == 0:
+            acc = stats["correct"] / stats["total"] * 100
+            print(f"\n📊 Step {stats['total']}: Accuracy = {acc:.2f}%\n")
+
+        return {
+            "images": image_paths,
+            "ground_truth": gt_answer_str, # 返回原始的正确答案字符串
+            "prediction": pred_answer,
+            "match": match,
+            "raw_model_output": pred_text
+        }
+
+# ===== 主函数 =====
+async def main():
+    with open(TEST_JSON_PATH, "r", encoding="utf-8") as f:
+        test_data = json.load(f)[:MAX_SAMPLE]
+
+    sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
+    stats = {"total": 0, "correct": 0}
+    tasks = [process_item(i, item, sem, stats) for i, item in enumerate(test_data)]
+
+    print(f"\n🚀 Starting evaluation of {len(tasks)} samples...\n")
+    results = await tqdm_asyncio.gather(*tasks)
+
+    accuracy = (stats["correct"] / stats["total"] * 100) if stats["total"] > 0 else 0
+    errors = [r for r in results if not r["match"]]
+
+    # 写入输出
+    output = {
+        "metrics": {
+            "total": stats["total"],
+            "correct": stats["correct"],
+            "accuracy": accuracy
+        },
+        "errors": errors
+    }
+
+    os.makedirs(os.path.dirname(OUTPUT_JSON_PATH), exist_ok=True)
+    with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+
+    # 控制台输出摘要
+    print(f"\n✅ Evaluation Complete")
+    print(f"🎯 Accuracy: {accuracy:.2f}%")
+    print(f"📁 Results saved to: {OUTPUT_JSON_PATH}")
+
+    print("\n❌ Sample Errors (up to 5):")
+    for r in errors[:5]:
+        # --- 修复: 'image' 键应为 'images' ---
+        print(f"- Images       : {r['images']}")
+        print(f"  Ground Truth : {r['ground_truth']}")
+        print(f"  Prediction   : {r['prediction']}")
+        print(f"  Raw Output   : {r['raw_model_output']}\n")
+
+    await client.aclose()  # ✅ 释放连接池
+
+# ===== 启动入口 =====
+if __name__ == "__main__":
+    asyncio.run(main())
+    sys.exit(0)  # ✅ 强制退出，防止异步底层未回收导致挂起

Element_Attribute_gemini.py

@@ -0,0 +1,209 @@
+import os
+import json
+import base64
+import re
+import asyncio
+import aiofiles
+from tqdm.asyncio import tqdm_asyncio
+from openai import AsyncOpenAI
+from rouge import Rouge
+
+Model_name = "Gemini"  # 模型名称
+
+# ===== 配置项 =====
+TEST_JSON_PATH = "/code/CogReasoner/Test/Element_Attribute_249.json"  # 测试集 JSON 路径
+MODEL_NAME = "gemini-2.5-pro"  # 使用的模型名称
+MAX_SAMPLE = 249  # 测试样本数
+MAX_CONCURRENT_REQUESTS = 5  # 最大并发数
+ACCURACY_PRINT_INTERVAL = 10  # 每多少步打印一次准确率
+OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Model_name}-Element-Attribute.json"  # 推理结果保存路径
+
+# ===== 初始化客户端和 ROUGE 计算器 =====
+client = AsyncOpenAI(api_key="AIzaSyBCL2-lp3jOBPPZc7-5NsSy8r7wDFaqnFI",
+                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/")
+# 全局初始化 Rouge 对象，计算 ROUGE-1 分数
+rouge = Rouge(metrics=['rouge-1'])
+
+# ===== 提取模型输出的 Role 和 Name =====
+def parse_model_output(text):
+    if text is None:
+        text = ""
+    role_match = re.search(r"Role:\s*\[?([^\]\n,]+)\]?", text, re.IGNORECASE)
+    name_match = re.search(r"Name:\s*\[?([^\]\n,]+)\]?", text, re.IGNORECASE)
+    
+    role = role_match.group(1).strip() if role_match else None
+    name = name_match.group(1).strip() if name_match else None
+    
+    return role, name
+
+# ===== 异步处理单个样本，含推理与比对 =====
+async def process_item(index, item, sem, stats):
+    async with sem:
+        image_path = item["images"][0]
+        gt_response = item["messages"][-1]["content"]
+
+        # 加载并编码图像
+        async with aiofiles.open(image_path, "rb") as f:
+            content = await f.read()
+        encoded_image = base64.b64encode(content).decode("utf-8")
+        image_data_uri = f"data:image/png;base64,{encoded_image}"
+
+        try:
+            # 模型推理
+            response = await client.chat.completions.create(
+                model=MODEL_NAME,
+                messages=[
+                    {"role": "system", "content": "You are a helpful assistant."},
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "image_url", "image_url": {"url": image_data_uri}},
+                            {
+                                "type": "text",
+                                "text": (
+                                        '''
+                                        You are viewing a screenshot of a webpage where a specific element is marked with a red box. \nYour task is to predict its ARIA role and accessible name based on the context of the webpage and the visual appearance of the element. Please make your prediction using the following list of roles with their semantic descriptions, combined with visual clues from the screenshot (such as text, position, and style).\n** Possible Roles and Their Semantics\n1.link: A hyperlink used to navigate to other pages or resources.\n2.button: A button used to trigger actions (e.g., submit, confirm).\n3.textbox: A single-line text input field for entering free text.\n4.searchbox: A search input field for entering search queries.\n5.checkbox: A checkbox for multiple-choice options.\n6.radio: A radio button for single-choice options.\n7.slider: A slider for adjusting a range of values.\n8.spinbutton: A numeric adjuster for incrementing or decrementing values.\n9.combobox: A dropdown selection box allowing choices from options.\n10.option: A single option, typically within a dropdown or list.\n11.listbox: A list selection box displaying multiple selectable items.\n12.img: An image used to display visual content.\n13.form: A form containing a collection of user input controls.\n14.navigation: A navigation area providing links for the page or site.\n16.banner: A header, typically containing the site title or banner.\n17.contentinfo: A footer, usually containing copyright or contact information.\n18.article: An article, an independent content block (e.g., news, post).\n19.search: A search area, typically containing search functionality.\n20.heading: A heading used for content hierarchy (level may need to be inferred, e.g., heading level 1).\n21.list: A list containing multiple items.\n22.listitem: A list item, a single entry within a list.\n23.table: A table for displaying data in rows and columns.\n24.row: A table row containing cells.\n25.columnheader: A column header in a table.\n26.rowheader: A row header in a table.\n27.cell: A cell, a data item in a table.\n28.dialog: A dialog box, such as a popup window or modal.\n29.progressbar: A progress bar showing task progress.\n30.status: A status update providing dynamic information.\n31.paragraph: A paragraph, a block of text content.\n\n** Prediction Guidance\n1. Role:\nSelect the most matching role based on the element\u2019s visual characteristics (e.g., button shape, input field border) and context (e.g., located in a navigation bar or form).\nRefer to the role semantics to ensure the prediction aligns with its definition.\nIf uncertain, prioritize values from the above role list and avoid arbitrary guesses.\n2. Name:\nExtract the name from visible text on the element (e.g., \u201cSubmit\u201d on a button, a label next to an input field).\nIf no visible text is present, infer a reasonable name (e.g., \u201cunlabeled button\u201d).\n** Output Format\nPlease provide your prediction in the following format:\nRole: [role], Name: [name]\nRemeber the [] where  [role] and [name] MUST be enclosed in square brackets []
+                                        '''
+                                ),
+                            },
+                        ],
+                    },
+                ],
+                temperature=0.1,
+                top_p=0.95,
+                max_tokens=2048,
+            )
+            pred_text = response.choices[0].message.content.strip()
+        except Exception as e:
+            pred_text = f"[ERROR] {str(e)}"
+
+        # 解析 Role 和 Name
+        pred_role, pred_name = parse_model_output(pred_text)
+        gt_role, gt_name = parse_model_output(gt_response)
+        
+        # Role 匹配评估
+        match_role = pred_role == gt_role
+
+        # Name 匹配评估 (使用 ROUGE-1 F1 分数)
+        # 按照示例，处理空字符串以避免 ROUGE 库出错
+        pred_for_rouge = pred_name if pred_name else " "
+        gt_for_rouge = gt_name if gt_name else " "
+        
+        try:
+            # 将 gt 包装成列表，以匹配 ROUGE 库的输入格式
+            scores = rouge.get_scores([pred_for_rouge], [gt_for_rouge], avg=True)
+            name_f1 = scores['rouge-1']['f']
+            name_precision = scores['rouge-1']['p']
+            name_recall = scores['rouge-1']['r']
+        except Exception:
+            # 如果 ROUGE 计算出现任何异常，则该样本分数为0
+            name_f1, name_precision, name_recall = 0.0, 0.0, 0.0
+
+        match_name = name_f1 == 1.0
+
+        match_all = match_role and match_name
+
+        # 统计信息更新
+        stats["total"] += 1
+        stats["role_correct"] += int(match_role)
+        stats["all_correct"] += int(match_all)
+        stats["name_f1_total"] += name_f1
+        stats["name_precision_total"] += name_precision
+        stats["name_recall_total"] += name_recall
+
+
+        # 每 N 步打印一次准确率
+        if stats["total"] % ACCURACY_PRINT_INTERVAL == 0:
+            role_acc = stats["role_correct"] / stats["total"] * 100
+            avg_name_f1 = stats["name_f1_total"] / stats["total"] * 100
+            full_acc = stats["all_correct"] / stats["total"] * 100
+            print(f"\n📊 Step {stats['total']}: Role Acc={role_acc:.2f}%, Avg Name ROUGE-1 F1={avg_name_f1:.2f}%, Full Score={(role_acc + avg_name_f1) / 2:.2f}%\n")
+
+        return {
+            "image": os.path.basename(image_path),
+            "ground_truth": {"role": gt_role, "name": gt_name},
+            "prediction": {"role": pred_role, "name": pred_name},
+            "metrics_per_sample": {
+                "name_rouge1_f1": name_f1,
+                "name_rouge1_precision": name_precision,
+                "name_rouge1_recall": name_recall
+            },
+            "match_role": match_role,
+            "match_name": match_name,
+            "match_all": match_all,
+            "raw":pred_text
+        }
+
+# ===== 主函数入口 =====
+async def main():
+    with open(TEST_JSON_PATH, "r", encoding="utf-8") as f:
+        test_data = json.load(f)[:MAX_SAMPLE]
+
+    sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
+    stats = {
+        "total": 0, 
+        "role_correct": 0, 
+        "all_correct": 0,
+        "name_f1_total": 0.0,
+        "name_precision_total": 0.0,
+        "name_recall_total": 0.0
+    }
+    tasks = [process_item(i, item, sem, stats) for i, item in enumerate(test_data)]
+
+    print(f"\n🚀 Starting async evaluation of {len(tasks)} samples...\n")
+    results = await tqdm_asyncio.gather(*tasks)
+
+    total_samples = stats["total"] if stats["total"] > 0 else 1
+    
+    # 计算最终指标
+    role_acc = (stats["role_correct"] / total_samples * 100)
+    full_acc = (stats["all_correct"] / total_samples * 100)
+    
+    # 计算 Name 的平均 Precision, Recall, ROUGE-1 F1-Score
+    avg_name_precision = (stats["name_precision_total"] / total_samples * 100)
+    avg_name_recall = (stats["name_recall_total"] / total_samples * 100)
+    avg_name_f1 = (stats["name_f1_total"] / total_samples * 100)
+
+    # 错误样例收集
+    errors = [r for r in results if not r["match_all"]]
+
+    # 写入 JSON 文件
+    output = {
+        "metrics": {
+            "total_samples": stats["total"],
+            "role_accuracy": role_acc,
+            "name_metrics": {
+                "average_rouge1_precision": avg_name_precision,
+                "average_rouge1_recall": avg_name_recall,
+                "average_rouge1_f1_score": avg_name_f1,
+            },
+            "full_match_accuracy": full_acc,
+            "full_score":(role_acc + avg_name_f1) / 2
+        },
+        "errors": errors
+    }
+
+    with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+
+    # 控制台输出准确率
+    print(f"\n✅ Evaluation Complete")
+    print(f"🎯 Role Accuracy             : {role_acc:.2f}%")
+    print(f"🎯 Avg Name ROUGE-1 Precision: {avg_name_precision:.2f}%")
+    print(f"🎯 Avg Name ROUGE-1 Recall   : {avg_name_recall:.2f}%")
+    print(f"🎯 Avg Name ROUGE-1 F1-Score : {avg_name_f1:.2f}%")
+    print(f"🎯 Full Match Accuracy       : {full_acc:.2f}%")
+    print(f"🎯 Full Score                : {(role_acc + avg_name_f1) / 2:.2f}%")
+    print(f"📁 Results saved to: {OUTPUT_JSON_PATH}")
+
+    # 错误示例展示
+    print("\n❌ Sample Errors (up to 5):")
+    for r in errors[:5]:
+        print(f"- Image        : {r['image']}")
+        print(f"  Ground Truth : {r['ground_truth']}")
+        print(f"  Prediction   : {r['prediction']}")
+        print(f"  Name ROUGE-1 F1: {r['metrics_per_sample']['name_rouge1_f1']:.2f}\n")
+
+# ===== 执行主函数 =====
+if __name__ == "__main__":
+    asyncio.run(main())

Element_understanding_claude.py

@@ -0,0 +1,393 @@
+import asyncio
+import aiohttp
+import google.generativeai as genai
+import json
+import os
+import argparse
+import base64
+from tqdm.asyncio import tqdm_asyncio
+from typing import Dict, Any, List
+from datetime import datetime
+import numpy as np
+import boto3 # 引入boto3用于与AWS Bedrock交互
+
+# --- 配置项 ---
+# 1. 要测试的模型
+Test_Model = "Claude" 
+
+# 2. 输出文件路径
+OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Test_Model}-Element_Understanding_200_.json"
+Inference_output_file = f"/code/CogReasoner/Code/Evalaute/Result/Raw_Answer-{Test_Model}-Element_Understanding_200.jsonl"
+
+# 3. 评测模型 (Gemini)
+GEMINI_MODEL_NAME = 'gemini-2.5-flash-lite-preview-06-17'
+MAX_CONCURRENT_REQUESTS = 1 # 控制并发请求数
+
+# 4. 推理模型 (Claude on AWS Bedrock) - 在此处硬编码您的凭证和配置
+# !!重要!! 请在此处填入您的真实凭证
+AWS_ACCESS_KEY_ID = "AKIAYEDGY53YI74GRHPL" # <--- 在这里填入您的 AWS Access Key ID
+AWS_SECRET_ACCESS_KEY = "yAQVOVB1bbeykes6SCGEEuZZlzWPLaFtiEOGyNMk" # <--- 在这里填入您的 AWS Secret Access Key
+AWS_REGION_NAME = "us-east-1"  # 例如 "us-east-1"
+CLAUDE_MODEL_ID = "us.anthropic.claude-sonnet-4-20250514-v1:0" # 您希望使用的Claude模型ID
+
+# --- AWS Bedrock Claude 客户端 (来自您的示例) ---
+class BedrockClaudeClient:
+    """
+    一个用于与 AWS Bedrock 上的 Claude 模型交互的客户端。
+    """
+    def __init__(self, access_key: str, secret_key: str, region_name: str, model_id: str):
+        self.model_id = model_id
+        try:
+            self.bedrock_client = boto3.client(
+                service_name='bedrock-runtime',
+                region_name=region_name,
+                aws_access_key_id=access_key,
+                aws_secret_access_key=secret_key
+            )
+            print(f"Boto3 客户端成功创建，区域：'{region_name}', 模型：'{self.model_id}'!")
+        except Exception as e:
+            raise ConnectionError(f"创建 Bedrock 客户端失败: {e}。请检查您的 AWS 凭证和区域名称。")
+
+    def _parse_data_url(self, data_url: str):
+        try:
+            header, encoded = data_url.split(",", 1)
+            media_type = header.split(";")[0].split(":")[1]
+            return encoded, media_type
+        except Exception:
+            print(f"警告: 无法解析 Data URL: {data_url[:30]}...")
+            return None, None
+
+    def chat(self, messages: List[Dict[str, Any]], max_tokens: int = 1024, temperature: float = 0.1) -> Dict[str, Any]:
+        if not hasattr(self, 'bedrock_client'):
+            raise RuntimeError("Bedrock 客户端未成功初始化。")
+
+        claude_system_message = None
+        claude_messages_payload = []
+
+        for openai_msg in messages:
+            role = openai_msg.get("role")
+            content = openai_msg.get("content")
+            if role == "system":
+                claude_system_message = content
+            elif role == "user":
+                claude_content_blocks = []
+                if isinstance(content, list):
+                    for item in content:
+                        if item.get("type") == "text":
+                            claude_content_blocks.append({"type": "text", "text": item.get("text", "")})
+                        elif item.get("type") == "image_url":
+                            url = item.get("image_url", {}).get("url")
+                            if url:
+                                base64_data, media_type = self._parse_data_url(url)
+                                if base64_data and media_type:
+                                    claude_content_blocks.append({
+                                        "type": "image",
+                                        "source": {"type": "base64", "media_type": media_type, "data": base64_data}
+                                    })
+                claude_messages_payload.append({"role": "user", "content": claude_content_blocks})
+
+        if not claude_messages_payload:
+            raise ValueError("转换后没有有效的 'user' 角色消息可以发送给 Claude。")
+
+        body = {
+            "anthropic_version": "bedrock-2023-05-31",
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+            "messages": claude_messages_payload
+        }
+        if claude_system_message:
+            body["system"] = claude_system_message
+
+        try:
+            response = self.bedrock_client.invoke_model(modelId=self.model_id, body=json.dumps(body))
+            response_body = json.loads(response.get('body').read())
+            response_text = ""
+            if response_body.get('content'):
+                for content_block in response_body['content']:
+                    if content_block.get('type') == 'text':
+                        response_text += content_block['text']
+            return {"response_text": response_text}
+        except Exception as e:
+            error_message = str(e)
+            if hasattr(e, 'response') and 'Error' in e.response:
+                error_message = f"{e.response['Error'].get('Code', '')}: {e.response['Error'].get('Message', '')}"
+            raise RuntimeError(f"调用 Claude 模型时出错: {error_message}")
+
+
+# --- 提示模板 (无变化) ---
+def get_gemini_evaluator_prompt(ground_truth: str, model_answer: str) -> str:
+    """为Gemini评估器创建详细的提示。"""
+    return f"""You are a meticulous and impartial AI evaluator for a web UI understanding benchmark. Your task is to assess the quality of a candidate model's answer by comparing it strictly against a ground truth reference.
+
+Your evaluation must be based *exclusively* on the information provided in the "Ground Truth Answer". Do not use any external knowledge or make assumptions beyond what is written in the ground truth.
+
+Evaluate the candidate answer on three specific aspects: Appearance, Position, and Function.
+
+**[Ground Truth Answer]**
+{ground_truth}
+---
+**[Candidate Model's Answer]**
+{model_answer}
+---
+
+**Evaluation Criteria & Scoring:**
+- **Score 1:** Completely incorrect or missing.
+- **Score 2:** Mostly incorrect, with a minor element of truth.
+- **Score 3:** Partially correct, but misses significant details mentioned in the ground truth.
+- **Score 4:** Mostly correct, with only minor inaccuracies or omissions compared to the ground truth.
+- **Score 5:** Fully and accurately captures all relevant information present in the ground truth.
+
+Your response MUST be a single, valid JSON object, adhering to the following structure. Do not add any text before or after the JSON object.
+
+{{
+  "appearance_score": <integer_score>,
+  "appearance_justification": "<Your brief justification for the appearance score, referencing the ground truth>",
+  "position_score": <integer_score>,
+  "position_justification": "<Your brief justification for the position score, referencing the ground truth>",
+  "function_score": <integer_score>,
+  "function_justification": "<Your brief justification for the function score, referencing the ground truth>",
+  "overall_score": <A final holistic integer score from 1 to 5, considering all aspects>,
+  "overall_justification": "<A final summary of the model's performance on this example>"
+}}
+"""
+
+# --- 辅助函数 ---
+def encode_image_to_base64(image_path: str) -> str:
+    """将图片文件编码为base64字符串。"""
+    try:
+        with open(image_path, "rb") as image_file:
+            return base64.b64encode(image_file.read()).decode('utf-8')
+    except FileNotFoundError:
+        print(f"警告: 在路径 {image_path} 未找到图片文件")
+        return None
+
+def create_model_payload(user_prompt: str, image_base64: str) -> Dict[str, Any]:
+    """为模型创建兼容OpenAI格式的JSON负载。"""
+    return {
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": user_prompt},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/png;base64,{image_base64}"}
+                    }
+                ]
+            }
+        ],
+        "max_tokens": 1024,
+        "temperature": 0.1
+    }
+
+# --- 核心异步函数 ---
+async def run_inference(item: Dict[str, Any], claude_client: BedrockClaudeClient, semaphore: asyncio.Semaphore) -> Dict[str, Any]:
+    """执行推理阶段（使用Claude），并返回包含答案的关键信息。"""
+    async with semaphore:
+        image_path = item['images'][0]
+        user_prompt = item['messages'][0]['content']
+        model_answer = None
+
+        image_base64 = encode_image_to_base64(image_path)
+        if not image_base64:
+            model_answer = "Error: Image file not found."
+        else:
+            payload = create_model_payload(user_prompt, image_base64)
+            try:
+                response_data = await asyncio.to_thread(
+                    claude_client.chat,
+                    messages=payload['messages'],
+                    max_tokens=payload['max_tokens'],
+                    temperature=payload['temperature']
+                )
+                model_answer = response_data['response_text']
+                await asyncio.sleep(10)
+            except Exception as e:
+                model_answer = f"Error during Claude inference: {e}"
+        
+        return {"id": item.get("id", os.path.basename(image_path)), "model_answer": model_answer}
+
+async def run_evaluation(item: Dict[str, Any], gemini_model: genai.GenerativeModel, semaphore: asyncio.Semaphore) -> Dict[str, Any]:
+    """仅执行评估阶段，并将评估结果添加到item字典中。"""
+    async with semaphore:
+        ground_truth = item['messages'][1]['content']
+        model_answer = item.get('model_answer', '')
+        evaluation = None
+        if "Error:" in model_answer or not model_answer:
+            evaluation = {"error": "Skipped evaluation due to inference error or empty answer."}
+        else:
+            eval_prompt = get_gemini_evaluator_prompt(ground_truth, model_answer)
+            try:
+                response = await gemini_model.generate_content_async(
+                    eval_prompt,
+                    generation_config={"response_mime_type": "application/json"},
+                )
+                evaluation = json.loads(response.text)
+            except Exception as e:
+                evaluation = {"error": f"Error during Gemini evaluation: {e}"}
+        item['evaluation'] = evaluation
+        return item
+
+def calculate_summary(results: List[Dict[str, Any]], model_name: str, benchmark_file: str, evaluator_model: str) -> Dict[str, Any]:
+    """计算评估结果的摘要统计信息。"""
+    scores = {"appearance": [], "position": [], "function": [], "overall": []}
+    successful_evals, failed_evals = 0, 0
+    for res in results:
+        eval_data = res.get('evaluation', {})
+        if 'error' in eval_data or not eval_data:
+            failed_evals += 1
+            continue
+        successful_evals += 1
+        scores["appearance"].append(eval_data.get("appearance_score", 0))
+        scores["position"].append(eval_data.get("position_score", 0))
+        scores["function"].append(eval_data.get("function_score", 0))
+        scores["overall"].append(eval_data.get("overall_score", 0))
+    average_scores = {
+        "appearance_avg": round(np.mean(scores["appearance"]).item() if scores["appearance"] else 0, 3),
+        "position_avg": round(np.mean(scores["position"]).item() if scores["position"] else 0, 3),
+        "function_avg": round(np.mean(scores["function"]).item() if scores["function"] else 0, 3),
+        "overall_avg": round(np.mean(scores["overall"]).item() if scores["overall"] else 0, 3)
+    }
+    summary = {
+        "test_metadata": {
+            "model_tested": model_name,
+            "benchmark_file": os.path.basename(benchmark_file),
+            "evaluator_model": evaluator_model,
+            "test_date": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        },
+        "evaluation_summary": {
+            "total_samples": len(results),
+            "successful_evaluations": successful_evals,
+            "failed_evaluations": failed_evals,
+            "average_scores": average_scores
+        }
+    }
+    return summary
+
+# --- 主程序入口 (已恢复硬编码) ---
+async def main():
+    parser = argparse.ArgumentParser(description="分阶段Benchmark工具：可独立进行推理或评估。")
+    parser.add_argument("--gemini_api_key", default="AIzaSyBCL2-lp3jOBPPZc7-5NsSy8r7wDFaqnFI", help="您的Google AI Studio API密钥。")
+    parser.add_argument("--benchmark_file", default="/code/CogReasoner/Test/Element_Understanding_sampled_200_clean.json", help="包含测试数据的JSON文件路径。")
+    parser.add_argument("--output_file", default=OUTPUT_JSON_PATH, help="保存最终评估结果的JSON文件路径。")
+    parser.add_argument("--concurrency", type=int, default=MAX_CONCURRENT_REQUESTS, help="最大并发请求数。")
+    parser.add_argument("--inference_output_file", type=str, help="[推理模式] 推理结果要保存到的.jsonl文件路径。如果未提供，将使用默认路径。")
+    parser.add_argument("--evaluation_input_file", default=Inference_output_file,type=str, help="[评估模式] 包含模型答案的.jsonl文件路径。")
+    parser.add_argument("--mode", choices=['inference', 'evaluation'], help="明确选择脚本运行模式：'inference' 或 'evaluation'。")
+    args = parser.parse_args()
+
+    if not args.mode:
+        args.mode = 'evaluation' if args.evaluation_input_file else 'inference'
+
+    if args.mode == 'inference':
+        print("--- 进入 [推理模式] (模型: Claude) ---")
+        
+        # 检查硬编码的凭证是否已填写
+        if "YOUR_AWS" in AWS_ACCESS_KEY_ID or "YOUR_AWS" in AWS_SECRET_ACCESS_KEY:
+            print("错误: 推理模式需要 AWS 凭证。请在脚本顶部的配置项中填入您的真实凭证。")
+            return
+
+        try:
+            # 使用脚本顶部的全局变量初始化客户端
+            claude_client = BedrockClaudeClient(
+                access_key=AWS_ACCESS_KEY_ID,
+                secret_key=AWS_SECRET_ACCESS_KEY,
+                region_name=AWS_REGION_NAME,
+                model_id=CLAUDE_MODEL_ID
+            )
+        except Exception as e:
+            print(f"初始化 Claude 客户端失败: {e}")
+            return
+
+        inference_output_path = args.inference_output_file if args.inference_output_file else Inference_output_file
+        
+        try:
+            with open(args.benchmark_file, 'r', encoding='utf-8') as f:
+                benchmark_items = json.load(f)
+        except FileNotFoundError:
+            print(f"错误: 在 {args.benchmark_file} 未找到Benchmark文件。")
+            return
+
+        for i, item in enumerate(benchmark_items):
+            if "id" not in item:
+                item["id"] = f"{os.path.basename(item['images'][0])}_{i}"
+        
+        semaphore = asyncio.Semaphore(args.concurrency)
+        
+        inference_tasks = [run_inference(item, claude_client, semaphore) for item in benchmark_items]
+        inference_results = await tqdm_asyncio.gather(*inference_tasks, desc="Inferring with Claude")
+
+        output_dir = os.path.dirname(inference_output_path)
+        if output_dir and not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+        
+        with open(inference_output_path, 'w', encoding='utf-8') as f:
+            for result in inference_results:
+                f.write(json.dumps(result, ensure_ascii=False) + '\n')
+        
+        print(f"\n推理完成！结果已保存到: {inference_output_path}")
+        
+    elif args.mode == 'evaluation':
+        print("--- 进入 [评估模式] (评估模型: Gemini) ---")
+        evaluation_input_path = args.evaluation_input_file if args.evaluation_input_file else Inference_output_file
+        if not args.gemini_api_key or "YOUR_GEMINI" in args.gemini_api_key:
+            print("错误: 评估模式需要Gemini API密钥。请使用 --gemini_api_key 参数。")
+            return
+        try:
+            with open(args.benchmark_file, 'r', encoding='utf-8') as f:
+                benchmark_data_list = json.load(f)
+                benchmark_data_map = {}
+                for i, item in enumerate(benchmark_data_list):
+                    item_id = item.get("id", f"{os.path.basename(item['images'][0])}_{i}")
+                    benchmark_data_map[item_id] = item
+            with open(evaluation_input_path, 'r', encoding='utf-8') as f:
+                model_answers = [json.loads(line) for line in f]
+        except FileNotFoundError as e:
+            print(f"错误: 无法找到输入文件 - {e}")
+            return
+        
+        items_to_evaluate = []
+        for answer in model_answers:
+            item_id = answer.get("id")
+            if item_id in benchmark_data_map:
+                full_item = benchmark_data_map[item_id]
+                full_item['model_answer'] = answer['model_answer']
+                items_to_evaluate.append(full_item)
+            else:
+                print(f"警告: 在原始benchmark数据中找不到ID为 '{item_id}' 的项，跳过。")
+        
+        if not items_to_evaluate:
+            print("错误: 没有可供评估的数据。请检查ID是否匹配。")
+            return
+            
+        semaphore = asyncio.Semaphore(args.concurrency)
+        genai.configure(api_key=args.gemini_api_key)
+        gemini_model = genai.GenerativeModel(GEMINI_MODEL_NAME)
+        
+        evaluation_tasks = [run_evaluation(item, gemini_model, semaphore) for item in items_to_evaluate]
+        final_results_list = await tqdm_asyncio.gather(*evaluation_tasks, desc="Evaluating with Gemini")
+
+        summary = calculate_summary(
+            results=final_results_list,
+            model_name=Test_Model,
+            benchmark_file=args.benchmark_file,
+            evaluator_model=GEMINI_MODEL_NAME
+        )
+        final_output_object = {"summary": summary, "results": final_results_list}
+
+        output_dir = os.path.dirname(args.output_file)
+        if output_dir and not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+            
+        with open(args.output_file, 'w', encoding='utf-8') as f:
+            json.dump(final_output_object, f, indent=2, ensure_ascii=False)
+        
+        print("\n--- 评估完成！摘要如下 ---")
+        print(json.dumps(summary, indent=2, ensure_ascii=False))
+        print("--------------------------")
+        print(f"\n完整结果已保存到: {args.output_file}")
+    else:
+        print("错误: 模式不明确。请使用 --mode 'inference' 或 'evaluation' 来指定运行模式。")
+
+if __name__ == "__main__":
+    asyncio.run(main())

Next_Page_Prediction.py

@@ -0,0 +1,134 @@
+import os
+import json
+import base64
+import re
+import asyncio
+import aiofiles
+from tqdm.asyncio import tqdm_asyncio
+from openai import AsyncOpenAI
+
+Model_name = "OpenWebVoyager"  # 模型名称
+
+# ===== 配置项 =====
+TEST_JSON_PATH = "/code/CogReasoner/Test/Next_Page_Prediction_100.json"  # 测试集 JSON 路径
+MODEL_NAME = "qwen2vl"  # 使用的模型名称
+MAX_SAMPLE = 93  # 测试样本数
+MAX_CONCURRENT_REQUESTS = 10  # 最大并发数
+ACCURACY_PRINT_INTERVAL = 10  # 每多少步打印一次准确率
+OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Model_name}-Next_Page_Prediction_100.json"  # 推理结果保存路径
+
+# ===== 初始化 OpenAI 客户端（对接 vLLM API） =====
+client = AsyncOpenAI(
+    api_key="EMPTY",
+    base_url="http://localhost:8080/v1",
+)
+
+# ===== 提取模型输出的选项，如 G、A、B等 =====
+def extract_answer_letter(text):
+    match = re.search(r"\b([A-H])\b", text.strip(), re.IGNORECASE)
+    if match:
+        return match.group(1).upper()
+    return None
+
+# ===== 异步处理单个样本 =====
+async def process_item(index, item, sem, stats):
+    async with sem:
+        image_path = item["images"][0]
+        gt_answer = item["messages"][-1]["content"].strip().upper()
+        prompt = item["messages"][0]["content"]
+
+        # 编码图像
+        async with aiofiles.open(image_path, "rb") as f:
+            content = await f.read()
+        encoded_image = base64.b64encode(content).decode("utf-8")
+        image_data_uri = f"data:image;base64,{encoded_image}"
+
+        try:
+            # 推理请求
+            response = await client.chat.completions.create(
+                model=MODEL_NAME,
+                messages=[
+                    {"role": "system", "content": "You are a helpful assistant."},
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "image_url", "image_url": {"url": image_data_uri}},
+                            {
+                                "type": "text",
+                                "text": prompt + "Directly give the answer letter (A, B, C, D, E, F, G, H) without any explanation.",
+                            },
+                        ],
+                    },
+                ],
+                temperature=0.1,
+                top_p=0.95,
+                max_tokens=512,
+            )
+            pred_text = response.choices[0].message.content.strip()
+        except Exception as e:
+            pred_text = f"[ERROR] {str(e)}"
+
+        pred_answer = extract_answer_letter(pred_text)
+        match = pred_answer == gt_answer
+
+        stats["total"] += 1
+        stats["correct"] += int(match)
+
+        if stats["total"] % ACCURACY_PRINT_INTERVAL == 0:
+            acc = stats["correct"] / stats["total"] * 100
+            print(f"\n📊 Step {stats['total']}: Accuracy = {acc:.2f}%\n")
+
+        return {
+            "image": image_path,
+            "ground_truth": gt_answer,
+            "prediction": pred_answer,
+            "match": match,
+            "raw_model_output": pred_text
+        }
+
+# ===== 主函数 =====
+async def main():
+    with open(TEST_JSON_PATH, "r", encoding="utf-8") as f:
+        test_data = json.load(f)[:MAX_SAMPLE]
+
+    sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
+    stats = {"total": 0, "correct": 0}
+    tasks = [process_item(i, item, sem, stats) for i, item in enumerate(test_data)]
+
+    print(f"\n🚀 Starting evaluation of {len(tasks)} samples...\n")
+    results = await tqdm_asyncio.gather(*tasks)
+
+    accuracy = stats["correct"] / stats["total"] * 100
+    errors = [r for r in results if not r["match"]]
+
+    # 写入输出
+    output = {
+        "metrics": {
+            "total": stats["total"],
+            "correct": stats["correct"],
+            "accuracy": accuracy
+        },
+        "errors": errors
+    }
+
+    with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+
+    # 控制台输出摘要
+    print(f"\n✅ Evaluation Complete")
+    print(f"🎯 Accuracy: {accuracy:.2f}%")
+    print(f"📁 Results saved to: {OUTPUT_JSON_PATH}")
+
+    print("\n❌ Sample Errors (up to 5):")
+    for r in errors[:5]:
+        print(f"- Image        : {r['image']}")
+        print(f"  Ground Truth : {r['ground_truth']}")
+        print(f"  Prediction   : {r['prediction']}")
+        print(f"  Raw Output   : {r['raw_model_output']}\n")
+
+    await client.aclose()  # ✅ 释放连接池
+
+# ===== 启动入口 =====
+if __name__ == "__main__":
+    asyncio.run(main())
+    sys.exit(0)  # ✅ 强制退出，防止异步底层未回收导致挂起

Next_Page_Prediction_gemini.py

@@ -0,0 +1,132 @@
+import os
+import json
+import base64
+import re
+import asyncio
+import aiofiles
+from tqdm.asyncio import tqdm_asyncio
+from openai import AsyncOpenAI
+
+Model_name = "Gemini"  # 模型名称
+
+# ===== 配置项 =====
+TEST_JSON_PATH = "/code/CogReasoner/Test/Next_Page_Prediction_100.json"  # 测试集 JSON 路径
+MODEL_NAME = "gemini-2.5-pro"  # 使用的模型名称
+MAX_SAMPLE = 93  # 测试样本数
+MAX_CONCURRENT_REQUESTS = 10  # 最大并发数
+ACCURACY_PRINT_INTERVAL = 10  # 每多少步打印一次准确率
+OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Model_name}-Next_Page_Prediction_100.json"  # 推理结果保存路径
+
+# ===== 初始化 OpenAI 客户端（对接 vLLM API） =====
+client = AsyncOpenAI(api_key="AIzaSyBCL2-lp3jOBPPZc7-5NsSy8r7wDFaqnFI",
+                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/")
+
+# ===== 提取模型输出的选项，如 G、A、B等 =====
+def extract_answer_letter(text):
+    match = re.search(r"\b([A-H])\b", text.strip(), re.IGNORECASE)
+    if match:
+        return match.group(1).upper()
+    return None
+
+# ===== 异步处理单个样本 =====
+async def process_item(index, item, sem, stats):
+    async with sem:
+        image_path = item["images"][0]
+        gt_answer = item["messages"][-1]["content"].strip().upper()
+        prompt = item["messages"][0]["content"]
+
+        # 编码图像
+        async with aiofiles.open(image_path, "rb") as f:
+            content = await f.read()
+        encoded_image = base64.b64encode(content).decode("utf-8")
+        image_data_uri = f"data:image/png;base64,{encoded_image}"
+
+        try:
+            # 推理请求
+            response = await client.chat.completions.create(
+                model=MODEL_NAME,
+                messages=[
+                    {"role": "system", "content": "You are a helpful assistant."},
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "image_url", "image_url": {"url": image_data_uri}},
+                            {
+                                "type": "text",
+                                "text": prompt + "Directly give the answer letter (A, B, C, D, E, F, G, H) without any explanation.",
+                            },
+                        ],
+                    },
+                ],
+                temperature=0.1,
+                top_p=0.95,
+                max_tokens=4096,
+            )
+            pred_text = response.choices[0].message.content.strip()
+        except Exception as e:
+            pred_text = f"[ERROR] {str(e)}"
+
+        pred_answer = extract_answer_letter(pred_text)
+        match = pred_answer == gt_answer
+
+        stats["total"] += 1
+        stats["correct"] += int(match)
+
+        if stats["total"] % ACCURACY_PRINT_INTERVAL == 0:
+            acc = stats["correct"] / stats["total"] * 100
+            print(f"\n📊 Step {stats['total']}: Accuracy = {acc:.2f}%\n")
+
+        return {
+            "image": image_path,
+            "ground_truth": gt_answer,
+            "prediction": pred_answer,
+            "match": match,
+            "raw_model_output": pred_text
+        }
+
+# ===== 主函数 =====
+async def main():
+    with open(TEST_JSON_PATH, "r", encoding="utf-8") as f:
+        test_data = json.load(f)[:MAX_SAMPLE]
+
+    sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
+    stats = {"total": 0, "correct": 0}
+    tasks = [process_item(i, item, sem, stats) for i, item in enumerate(test_data)]
+
+    print(f"\n🚀 Starting evaluation of {len(tasks)} samples...\n")
+    results = await tqdm_asyncio.gather(*tasks)
+
+    accuracy = stats["correct"] / stats["total"] * 100
+    errors = [r for r in results if not r["match"]]
+
+    # 写入输出
+    output = {
+        "metrics": {
+            "total": stats["total"],
+            "correct": stats["correct"],
+            "accuracy": accuracy
+        },
+        "errors": errors
+    }
+
+    with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+
+    # 控制台输出摘要
+    print(f"\n✅ Evaluation Complete")
+    print(f"🎯 Accuracy: {accuracy:.2f}%")
+    print(f"📁 Results saved to: {OUTPUT_JSON_PATH}")
+
+    print("\n❌ Sample Errors (up to 5):")
+    for r in errors[:5]:
+        print(f"- Image        : {r['image']}")
+        print(f"  Ground Truth : {r['ground_truth']}")
+        print(f"  Prediction   : {r['prediction']}")
+        print(f"  Raw Output   : {r['raw_model_output']}\n")
+
+    await client.aclose()  # ✅ 释放连接池
+
+# ===== 启动入口 =====
+if __name__ == "__main__":
+    asyncio.run(main())
+    sys.exit(0)  # ✅ 强制退出，防止异步底层未回收导致挂起

Next_Page_Prediction_ov.py

@@ -0,0 +1,127 @@
+import os
+import json
+import base64
+import pickle
+import re
+import uuid
+import asyncio
+import aiofiles
+from tqdm.asyncio import tqdm_asyncio
+from PIL import Image
+import requests
+
+Model_name = "OpenWebVoyager"
+TEST_JSON_PATH = "/code/CogReasoner/Test/Next_Page_Prediction_100.json"
+MAX_SAMPLE = 93
+MAX_CONCURRENT_REQUESTS = 10
+ACCURACY_PRINT_INTERVAL = 10
+OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Model_name}-Next_Page_Prediction_100.json"
+
+SERVER_URL = "http://127.0.0.1:8080/predict"
+
+def extract_answer_letter(text):
+    match = re.search(r"\b([A-H])\b", text.strip(), re.IGNORECASE)
+    if match:
+        return match.group(1).upper()
+    return None
+
+async def process_item(index, item, sem, stats):
+    async with sem:
+        image_path = item["images"][0]
+        gt_answer = item["messages"][-1]["content"].strip().upper()
+        prompt = item["messages"][0]["content"]
+
+        # 异步加载图片并处理为base64(pickle)
+        try:
+            async with aiofiles.open(image_path, "rb") as f:
+                raw_bytes = await f.read()
+            image = Image.open(image_path).convert("RGB")  # 强制为RGB
+            pickled_images = pickle.dumps([image])
+            base64_encoded_images = base64.b64encode(pickled_images).decode("utf-8")
+        except Exception as e:
+            print(f"❌ 图像处理错误 @ {image_path}: {e}")
+            return {
+                "image": image_path,
+                "ground_truth": gt_answer,
+                "prediction": None,
+                "match": False,
+                "raw_model_output": f"[ERROR] {str(e)}"
+            }
+
+        # 构造请求体
+        prompt_text = " Thought this task and finally, directly give the answer letter (A, B, C, D, E, F, G, H) without any explanation." + prompt
+        user_content = f"{prompt_text}"
+
+        payload = {
+            "id": str(uuid.uuid4()),
+            "conversations": [{"role": "user", "content": user_content}],
+            "images": base64_encoded_images
+        }
+
+        try:
+            response = requests.post(SERVER_URL, json=payload, timeout=120)
+            if response.status_code == 200:
+                result = response.json()
+                pred_text = result.get("text", "").strip()
+            else:
+                pred_text = f"[HTTP_ERROR {response.status_code}] {response.text}"
+        except Exception as e:
+            pred_text = f"[NETWORK_ERROR] {str(e)}"
+
+        pred_answer = extract_answer_letter(pred_text)
+        match = pred_answer == gt_answer
+
+        stats["total"] += 1
+        stats["correct"] += int(match)
+
+        if stats["total"] % ACCURACY_PRINT_INTERVAL == 0:
+            acc = stats["correct"] / stats["total"] * 100
+            print(f"\n📊 Step {stats['total']}: Accuracy = {acc:.2f}%\n")
+
+        return {
+            "image": image_path,
+            "ground_truth": gt_answer,
+            "prediction": pred_answer,
+            "match": match,
+            "raw_model_output": pred_text
+        }
+
+async def main():
+    with open(TEST_JSON_PATH, "r", encoding="utf-8") as f:
+        test_data = json.load(f)[:MAX_SAMPLE]
+
+    sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
+    stats = {"total": 0, "correct": 0}
+    tasks = [process_item(i, item, sem, stats) for i, item in enumerate(test_data)]
+
+    print(f"\n🚀 Starting evaluation of {len(tasks)} samples...\n")
+    results = await tqdm_asyncio.gather(*tasks)
+
+    accuracy = stats["correct"] / stats["total"] * 100
+    errors = [r for r in results if not r["match"]]
+
+    output = {
+        "metrics": {
+            "total": stats["total"],
+            "correct": stats["correct"],
+            "accuracy": accuracy
+        },
+        "errors": errors
+    }
+
+    with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+
+    print(f"\n✅ Evaluation Complete")
+    print(f"🎯 Accuracy: {accuracy:.2f}%")
+    print(f"📁 Results saved to: {OUTPUT_JSON_PATH}")
+
+    print("\n❌ Sample Errors (up to 5):")
+    for r in errors[:5]:
+        print(f"- Image        : {r['image']}")
+        print(f"  Ground Truth : {r['ground_truth']}")
+        print(f"  Prediction   : {r['prediction']}")
+        print(f"  Raw Output   : {r['raw_model_output']}\n")
+
+if __name__ == "__main__":
+    asyncio.run(main())

Popup_Close_UI-TARs.py

@@ -0,0 +1,110 @@
+import json
+import os
+import re
+from PIL import Image, ImageDraw
+
+def parse_click_coordinates(raw_output: str) -> tuple[int, int] | None:
+    """
+    从模型的原始输出字符串中解析出点击的 (x, y) 坐标。
+    """
+    match = re.search(r'\((\d+),\s*(\d+)\)', raw_output)
+    if match:
+        x = int(match.group(1))
+        y = int(match.group(2))
+        return (x, y)
+    print(f"⚠️  警告: 无法从 '{raw_output}' 中解析坐标。")
+    return None
+
+def annotate_image(
+    image_path: str,
+    coords: tuple[int, int],
+    output_path: str,
+    box_size: int = 50,         # 增大方框尺寸
+    crosshair_size: int = 25,   # 十字准星的半径
+    line_width: int = 5,        # 增粗线条
+    color: str = "red"
+):
+    """
+    在图片的指定坐标上绘制一个带十字准星的醒目红框。
+
+    Args:
+        image_path (str): 原始图片的路径。
+        coords (tuple[int, int]): 要标注的 (x, y) 坐标。
+        output_path (str): 保存标注后图片的路径。
+        box_size (int): 标注框的边长。
+        crosshair_size (int): 十字准星从中心点向外延伸的长度。
+        line_width (int): 标注线条的宽度。
+        color (str): 标注的颜色。
+    """
+    try:
+        with Image.open(image_path) as img:
+            img = img.convert("RGBA")
+            draw = ImageDraw.Draw(img)
+            x, y = coords
+
+            # --- 绘制外部方框 ---
+            half_box = box_size // 2
+            box_coords = [
+                (x - half_box, y - half_box),  # 左上角
+                (x + half_box, y + half_box)   # 右下角
+            ]
+            draw.rectangle(box_coords, outline=color, width=line_width)
+
+            # --- 绘制中心十字准星 ---
+            # 水平线
+            draw.line([(x - crosshair_size, y), (x + crosshair_size, y)], fill=color, width=line_width - 1)
+            # 垂直线
+            draw.line([(x, y - crosshair_size), (x, y + crosshair_size)], fill=color, width=line_width - 1)
+
+            img.save(output_path)
+            print(f"✅ 已标注并保存到: {output_path}")
+
+    except FileNotFoundError:
+        print(f"❌ 错误: 找不到原始图片 '{image_path}'。")
+    except Exception as e:
+        print(f"❌ 处理图片 '{image_path}' 时发生错误: {e}")
+
+def main():
+    """
+    主函数，读取JSON，处理并生成标注图片。
+    """
+    input_json_file = '/code/CogReasoner/Code/Evalaute/Result/Test-UI-TARs-Single_Step.json'  # 你的JSON文件名
+    output_dir = '/code/CogReasoner/Code/Evalaute/Result/Single_Step_UI-TARs'        # 输出文件夹名
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    try:
+        with open(input_json_file, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+    except FileNotFoundError:
+        print(f"❌ 错误: 找不到JSON文件 '{input_json_file}'。")
+        return
+    except json.JSONDecodeError:
+        print(f"❌ 错误: JSON文件 '{input_json_file}' 格式不正确。")
+        return
+
+    for i, result in enumerate(data.get('results', [])):
+        print(f"\n--- 正在处理第 {i+1} 个结果 ---")
+
+        raw_output = result.get('raw_model_output')
+        image_list = result.get('images')
+
+        if not raw_output or not image_list:
+            print("⚠️  警告: 此条目缺少 'raw_model_output' 或 'images'，已跳过。")
+            continue
+
+        coords = parse_click_coordinates(raw_output)
+        if not coords:
+            continue
+
+        original_image_path = image_list[0]
+        base_name = os.path.basename(original_image_path)
+        name, ext = os.path.splitext(base_name)
+        new_filename = f"{i+1:02d}_{name}_annotated{ext}"
+        output_image_path = os.path.join(output_dir, new_filename)
+
+        # 调用增强版的标注函数
+        annotate_image(original_image_path, coords, output_image_path)
+
+if __name__ == "__main__":
+    main()

Popup_Close_claude.py

@@ -0,0 +1,345 @@
+import os
+import sys
+import json
+import base64
+import re
+import asyncio
+import aiofiles
+from tqdm.asyncio import tqdm_asyncio # Used for progress bar in async tasks
+import boto3 # Import boto3 for AWS Bedrock interaction
+
+Test_Model = "Claude" # Define the model name for testing
+
+# ===== Configuration Items =====
+TEST_JSON_PATH = "/code/CogReasoner/Test/Popup_close.json" # Path to the test set JSON file
+MODEL_NAME = "us.anthropic.claude-sonnet-4-20250514-v1:0" # Specify the Claude model name for inference
+MAX_SAMPLE = 60 # Maximum number of samples to test
+MAX_CONCURRENT_REQUESTS = 1 # Maximum concurrent requests (set to 10 for async processing)
+ACCURACY_PRINT_INTERVAL = 10 # Print current accuracy after processing this many samples
+OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Test_Model}-Popup.json" # Path where inference results will be saved
+
+# ===== AWS Bedrock Claude Client Class =====
+class BedrockClaudeClient:
+    """
+    A client for interacting with the Claude model on AWS Bedrock.
+    AWS credentials are configured directly in this code (for demonstration).
+    """
+    def __init__(self, access_key, secret_key, region_name, model_id):
+        """
+        Initializes the Bedrock runtime client with provided keys and region info.
+        """
+        self.model_id = model_id
+        
+        try:
+            self.bedrock_client = boto3.client(
+                service_name='bedrock-runtime',
+                region_name=region_name,
+                aws_access_key_id=access_key,
+                aws_secret_access_key=secret_key
+            )
+            print(f"Boto3 client successfully created in region '{region_name}' for model '{self.model_id}'!")
+        except Exception as e:
+            raise ConnectionError(f"Failed to create Bedrock client: {e}. Please check your AWS credentials and region name.")
+
+    def _parse_data_url(self, data_url):
+        """
+        Parses a Data URL (e.g., data:image/png;base64,iVBOR...)
+        Extracts media_type and Base64 data.
+        """
+        if not data_url.startswith("data:"):
+            print(f"Warning: Not a standard Data URL format: {data_url}")
+            return None, None
+
+        parts = data_url.split(',', 1)
+        if len(parts) < 2:
+            print(f"Warning: Incomplete Data URL format: {data_url}")
+            return None, None
+
+        metadata = parts[0][len("data:"):].split(';')
+        media_type = metadata[0]
+        base64_data = parts[1]
+
+        if "base64" not in metadata:
+            print(f"Warning: Data URL does not contain 'base64' encoding identifier: {data_url}")
+            return None, None
+
+        return base64_data, media_type
+
+    # This method is kept as `def` (synchronous) because `boto3` client calls are synchronous.
+    # It will be called within `asyncio.to_thread` in `process_item` to avoid blocking the event loop.
+    def chat(self, messages, max_tokens=1024, temperature=0.7):
+        """
+        Sends messages to the Claude model and gets a reply.
+        This function is now fully compatible with OpenAI-format message lists,
+        including handling system messages and embedded base64 image_url.
+        """
+        if not hasattr(self, 'bedrock_client'):
+            raise RuntimeError("Bedrock client not successfully initialized.")
+
+        claude_system_message = None
+        claude_messages_payload = []
+
+        # Convert OpenAI format to Claude Bedrock format
+        for openai_msg in messages:
+            role = openai_msg.get("role")
+            content = openai_msg.get("content")
+
+            if role == "system":
+                claude_system_message = content
+            elif role in ["user", "assistant"]:
+                claude_content_blocks = []
+                if isinstance(content, str):
+                    claude_content_blocks.append({"type": "text", "text": content})
+                elif isinstance(content, list):
+                    for item in content:
+                        if item.get("type") == "text":
+                            claude_content_blocks.append({"type": "text", "text": item.get("text", "")})
+                        elif item.get("type") == "image_url":
+                            image_url_dict = item.get("image_url", {})
+                            url = image_url_dict.get("url")
+                            
+                            if url:
+                                base64_data, media_type = self._parse_data_url(url)
+                                if base64_data and media_type:
+                                    claude_content_blocks.append({
+                                        "type": "image",
+                                        "source": {
+                                            "type": "base64",
+                                            "media_type": media_type,
+                                            "data": base64_data
+                                        }
+                                    })
+                            else:
+                                print(f"Warning: Could not parse image data from Data URL {url}, skipping this content block.")
+                        else:
+                            print(f"Warning: Unsupported OpenAI content type: {item.get('type')}. Skipping this content block.")
+                
+                if claude_content_blocks:
+                    claude_messages_payload.append({"role": role, "content": claude_content_blocks})
+                else:
+                    print(f"Warning: '{role}' role message has no valid content, skipping.")
+
+            else:
+                print(f"Warning: Unsupported OpenAI message role: {role}. Skipping this message.")
+
+        if not claude_messages_payload:
+            raise ValueError("No valid 'user' or 'assistant' role messages to send to Claude after conversion.")
+
+        # Build the request body
+        body = {
+            "anthropic_version": "bedrock-2023-05-31",
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+            "messages": claude_messages_payload
+        }
+        # Add system message if it exists
+        if claude_system_message:
+            body["system"] = claude_system_message
+
+        try:
+            response = self.bedrock_client.invoke_model(
+                modelId=self.model_id,
+                body=json.dumps(body)
+            )
+
+            response_body = json.loads(response.get('body').read())
+            
+            response_text = ""
+            if response_body.get('content'):
+                for content_block in response_body['content']:
+                    if content_block.get('type') == 'text':
+                        response_text += content_block['text']
+
+            usage = response_body.get('usage', {})
+            prompt_tokens = usage.get('input_tokens', 0)
+            completion_tokens = usage.get('output_tokens', 0)
+
+            return {
+                "response_text": response_text,
+                "prompt_tokens": prompt_tokens,
+                "completion_tokens": completion_tokens
+            }
+        except Exception as e:
+            error_message = str(e)
+            if hasattr(e, 'response') and 'Error' in e.response:
+                error_message = f"{e.response['Error'].get('Code', '')}: {e.response['Error'].get('Message', '')}"
+            raise RuntimeError(f"Error calling Claude model: {error_message}")
+
+
+# ===== Extract Answer Letter from Model Output =====
+def extract_action_from_output(text: str):
+    """
+    从模型的详细输出中提取最终的行动指令。
+    它会寻找 "Action: action_name [node_id]" 这样的格式。
+    
+    Args:
+        text (str): 模型的原始输出字符串。
+        
+    Returns:
+        str or None: 格式化后的答案，如 "CLICK(41)"，如果找不到则返回 None。
+    """
+    # 正则表达式寻找 "Action: "，后面跟着一个单词(action)和一个用方括号括起来的数字(node_id)
+    # re.IGNORECASE 使得 "click" 和 "Click" 都能匹配
+    match = re.search(r"Action:\s+(\w+)\s+\[(\d+)\]", text, re.IGNORECASE)
+    
+    if match:
+        action = match.group(1).upper()  # 提取动作并转为大写, e.g., "CLICK"
+        node_id = match.group(2)         # 提取节点ID, e.g., "41"
+        return f"{action}({node_id})"    # 组合成与Ground Truth一致的格式
+        
+    return None # 如果没有找到匹配项，返回 None
+
+# ===== Asynchronously Process Single Sample =====
+async def process_item(index, item, sem, claude_client_instance, stats):
+    async with sem: # Control concurrency with semaphore
+        image_paths = item["images"]
+        prompt = item["messages"][0]["content"]
+        gt_json_str = item["messages"][-1]["content"]
+        try:
+            gt_data = json.loads(gt_json_str) 
+            action = gt_data.get('ACTION', gt_data.get('action', '')).upper()
+            node_id = gt_data.get('NODE_ID', gt_data.get('node_id'))
+            gt_answer = f"{action}({node_id})"
+        except (json.JSONDecodeError, TypeError, AttributeError):
+            print(f"警告: 无法解析 Ground Truth: {gt_json_str}")
+            gt_answer = "INVALID_GT_FORMAT"
+
+        image_contents = []
+        for path in image_paths:
+            try:
+                async with aiofiles.open(path, "rb") as f:
+                    content = await f.read()
+                encoded_image = base64.b64encode(content).decode("utf-8")
+                image_contents.append({
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/png;base64,{encoded_image}"}
+                })
+            except FileNotFoundError:
+                # 如果图片找不到，记录错误并跳过这个样本
+                error_msg = f"[ERROR] Image not found at {path}"
+                print(error_msg)
+                return {
+                    "images": image_paths,
+                    "ground_truth": gt_answer,
+                    "prediction": None,
+                    "match": False,
+                    "raw_model_output": error_msg
+                }
+        pred_text = "" # Initialize model prediction text
+        try:
+            # Send inference request to the model
+            # The ClaudeClient.chat method is synchronous, so we run it in a thread pool
+            response_data = await asyncio.to_thread(
+                claude_client_instance.chat,
+                messages = [
+            {"role": "system", "content": "You are a helpful assistant that analyzes UI screenshots and determines the next action."},
+            {
+                "role": "user",
+                "content": image_contents + [
+                    {
+                        "type": "text",
+                        "text": "Determine the single, most direct action required to close or dismiss the popup.\n\n The original Task is: " + prompt.strip() + "\n\n## Output Format:\nPlease output only the action in the format: `Action: action_name [node_id]`Remeber the [] where [node_id] MUST be enclosed in square brackets []. ",
+                    }
+                ],
+            },
+        ],
+                max_tokens=2048, # Limit max length of generated text
+                temperature=0.1 # Control randomness of generated text
+            )
+            pred_text = response_data['response_text'].strip() # Extract model generated text content
+        except Exception as e:
+            pred_text = f"[ERROR] {str(e)}" # Capture exception and log error message
+        await asyncio.sleep(10) # 暂停0.5秒。根据你的RPS限制调整这个值。
+                                 # 例如，如果RPS是2，你可能需要等待0.5秒。
+        pred_answer = extract_action_from_output(pred_text) # Extract predicted answer from model output
+        match = pred_answer == gt_answer # Check if predicted answer matches ground truth
+
+        stats["total"] += 1 # Increment total processed samples
+        stats["correct"] += int(match) # Increment correct count if match
+
+        # Print current accuracy periodically
+        if stats["total"] % ACCURACY_PRINT_INTERVAL == 0:
+            acc = stats["correct"] / stats["total"] * 100
+            print(f"\n📊 Step {stats['total']}: Accuracy = {acc:.2f}%\n")
+
+        return {
+            "image": image_paths, # Original image path
+            "ground_truth": gt_answer, # Ground truth answer
+            "prediction": pred_answer, # Model predicted answer
+            "match": match, # Whether it matched
+            "raw_model_output": pred_text # Raw model output text
+        }
+
+# ===== Main Function =====
+async def main():
+    """
+    Main execution function, responsible for loading test data, creating and running
+    asynchronous tasks, collecting results, and saving them.
+    """
+    # AWS credentials and model ID (fill in your actual values)
+    # WARNING: Hardcoding credentials directly is insecure. For production, use environment variables,
+    # AWS CLI configuration, or IAM roles.
+    aws_access_key_id = "AKIAYEDGY53YI74GRHPL" # REPLACE WITH YOUR AWS ACCESS KEY ID
+    aws_secret_access_key = "yAQVOVB1bbeykes6SCGEEuZZlzWPLaFtiEOGyNMk" # REPLACE WITH YOUR AWS SECRET ACCESS KEY
+    aws_region_name = "us-east-1"
+    aws_model_id = MODEL_NAME # Using MODEL_NAME from config
+
+    # Initialize AWS Bedrock Claude client
+    try:
+        claude_client = BedrockClaudeClient(
+            access_key=aws_access_key_id,
+            secret_key=aws_secret_access_key,
+            region_name=aws_region_name,
+            model_id=aws_model_id
+        )
+    except Exception as e:
+        print(f"Failed to initialize Bedrock Claude client: {e}")
+        sys.exit(1) # Exit program
+
+    # Read test set JSON file
+    with open(TEST_JSON_PATH, "r", encoding="utf-8") as f:
+        test_data = json.load(f)[:MAX_SAMPLE] # Load data and truncate based on MAX_SAMPLE
+
+    sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS) # Create semaphore for concurrency control
+    stats = {"total": 0, "correct": 0} # Initialize statistics
+
+    # Create tasks for each item
+    tasks = [process_item(i, item, sem, claude_client, stats) for i, item in enumerate(test_data)]
+
+    print(f"\n🚀 Starting evaluation of {len(tasks)} samples using Claude on AWS Bedrock...\n")
+    results = await tqdm_asyncio.gather(*tasks) # Execute all tasks concurrently with a progress bar
+
+    accuracy = stats["correct"] / stats["total"] * 100 # Calculate final accuracy
+    errors = [r for r in results if not r["match"]] # Filter out all non-matching (incorrect) samples
+
+    # Prepare JSON structure for output
+    output = {
+        "metrics": {
+            "total": stats["total"], # Total samples
+            "correct": stats["correct"], # Correct samples
+            "accuracy": accuracy # Final accuracy
+        },
+        "errors": errors # Detailed info of incorrect samples
+    }
+
+    # Write results to the specified JSON file
+    with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False) # Save JSON in a readable format, ensure Chinese support
+
+    # Print evaluation summary to console
+    print(f"\n✅ Evaluation Complete")
+    print(f"🎯 Accuracy: {accuracy:.2f}%")
+    print(f"📁 Results saved to: {OUTPUT_JSON_PATH}")
+
+    print("\n❌ Sample Errors (up to 5):")
+    # Print detailed information for the first 5 error samples
+    for r in errors[:5]:
+        print(f"- Image          : {r['image']}")
+        print(f"  Ground Truth   : {r['ground_truth']}")
+        print(f"  Prediction     : {r['prediction']}")
+        print(f"  Raw Output     : {r['raw_model_output']}\n")
+
+# ===== Entry Point =====
+if __name__ == "__main__":
+    asyncio.run(main()) # Run the main asynchronous function
+    sys.exit(0) # Force exit to prevent the async event loop from hanging

Popup_Close_ov.py

@@ -0,0 +1,169 @@
+import os
+import json
+import base64
+import pickle
+import re
+import uuid
+import asyncio
+import aiofiles
+from PIL import Image
+from tqdm.asyncio import tqdm_asyncio
+import requests
+
+Model_name = "OpenWebVoyager"
+TEST_JSON_PATH = "/code/CogReasoner/Test/Popup_close.json"
+MAX_SAMPLE = 60
+MAX_CONCURRENT_REQUESTS = 5
+ACCURACY_PRINT_INTERVAL = 10
+OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Model_name}-Popup_close.json"
+
+SERVER_URL = "http://127.0.0.1:8080/predict"
+
+def extract_action_from_output(text):
+    match = re.search(r"Action:\s+(\w+)\s+\[(\d+)\]", text, re.IGNORECASE)
+    if match:
+        action = match.group(1).upper()
+        node_id = match.group(2)
+        return f"{action}({node_id})"
+    return None
+
+async def process_item(index, item, sem, stats):
+    async with sem:
+        image_paths = item["images"]
+        prompt = item["messages"][0]["content"]
+        prompt = prompt[prompt.find("OBSERVATION:"):]
+        gt_json_str = item["messages"][-1]["content"]
+        try:
+            gt_data = json.loads(gt_json_str)
+            action = gt_data.get('ACTION', gt_data.get('action', '')).upper()
+            node_id = gt_data.get('NODE_ID', gt_data.get('node_id'))
+            gt_answer = f"{action}({node_id})"
+        except (json.JSONDecodeError, TypeError, AttributeError):
+            print(f"警告: 无法解析 Ground Truth: {gt_json_str}")
+            gt_answer = "INVALID_GT_FORMAT"
+
+        image_list = []
+        for path in image_paths:
+            try:
+                pil_image = Image.open(path).convert("RGB")
+                image_list.append(pil_image)
+            except FileNotFoundError:
+                error_msg = f"[ERROR] Image not found at {path}"
+                print(error_msg)
+                return {
+                    "images": image_paths,
+                    "ground_truth": gt_answer,
+                    "prediction": None,
+                    "match": False,
+                    "raw_model_output": error_msg
+                }
+
+        try:
+            pickled_images = pickle.dumps(image_list)
+            base64_encoded_images = base64.b64encode(pickled_images).decode("utf-8")
+        except Exception as e:
+            error_msg = f"[ERROR] Image encoding failed: {e}"
+            print(error_msg)
+            return {
+                "images": image_paths,
+                "ground_truth": gt_answer,
+                "prediction": None,
+                "match": False,
+                "raw_model_output": error_msg
+            }
+
+        system_prompt = "You are a helpful assistant that analyzes UI screenshots and determines the next action."
+        prompt_text = prompt.strip()
+        user_content = "Determine the single, most direct action required to close or dismiss the popup." + prompt_text
+
+        payload = {
+            "id": str(uuid.uuid4()),
+            "conversations": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_content}
+            ],
+            "images": base64_encoded_images
+        }
+
+        try:
+            response = requests.post(SERVER_URL, json=payload, timeout=120)
+            if response.status_code == 200:
+                result = response.json()
+                pred_text = result.get("text", "").strip()
+            else:
+                pred_text = f"[HTTP_ERROR {response.status_code}] {response.text}"
+        except Exception as e:
+            pred_text = f"[NETWORK_ERROR] {str(e)}"
+
+        pred_answer = extract_action_from_output(pred_text)
+        match = pred_answer == gt_answer
+
+        stats["total"] += 1
+        stats["correct"] += int(match)
+
+        if stats["total"] % ACCURACY_PRINT_INTERVAL == 0:
+            acc = stats["correct"] / stats["total"] * 100
+            print(f"\n📊 Step {stats['total']}: Accuracy = {acc:.2f}%\n")
+
+        return {
+            "images": image_paths,
+            "ground_truth": gt_answer,
+            "prediction": pred_answer,
+            "match": match,
+            "raw_model_output": pred_text
+        }
+
+async def main():
+    with open(TEST_JSON_PATH, "r", encoding="utf-8") as f:
+        test_data = json.load(f)
+
+    if MAX_SAMPLE:
+        test_data = test_data[:MAX_SAMPLE]
+
+    sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
+    stats = {"total": 0, "correct": 0}
+    tasks = [process_item(i, item, sem, stats) for i, item in enumerate(test_data)]
+
+    print(f"\n🚀 Starting evaluation of {len(tasks)} samples with model '{Model_name}'...\n")
+    results = await tqdm_asyncio.gather(*tasks)
+
+    valid_results = [r for r in results if r is not None]
+    if not valid_results:
+        print("\n❌ No valid samples were processed. Evaluation cannot be completed.")
+        return
+
+    final_total = stats["total"]
+    final_correct = stats["correct"]
+    accuracy = (final_correct / final_total * 100) if final_total > 0 else 0
+
+    errors = [r for r in valid_results if not r["match"]]
+
+    output = {
+        "model_name": Model_name,
+        "metrics": {
+            "total_processed": final_total,
+            "correct": final_correct,
+            "accuracy": accuracy
+        },
+        "results": valid_results,
+        "errors": errors
+    }
+
+    with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+
+    print(f"\n✅ Evaluation Complete")
+    print(f"🎯 Accuracy: {accuracy:.2f}% ({final_correct}/{final_total})")
+    print(f"📁 Results saved to: {OUTPUT_JSON_PATH}")
+
+    if errors:
+        print("\n❌ Sample Errors (up to 5):")
+        for r in errors[:5]:
+            print(f"- Images       : {', '.join(r['images'])}")
+            print(f"  Ground Truth : {r['ground_truth']}")
+            print(f"  Prediction   : {r['prediction']}")
+            raw_output_snippet = r['raw_model_output'][-200:].replace('\n', ' ')
+            print(f"  Raw Output...: ...{raw_output_snippet}\n")
+
+if __name__ == "__main__":
+    asyncio.run(main())

Result/Raw_Answer-CogReasoner-User_Intent_Prediction.jsonl

@@ -0,0 +1,42 @@
+{"id": "6ccbade8-62c4-41b8-99bc-2ef2d7c157e1.jpg_0", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The filter section on the right side of the page has been expanded, revealing additional filter options such as \"Material,\" \"Number of drawers,\" and \"Features.\" The \"Material\" section is now visible with options like \"Wood (including board),\" \"Metal,\" \"Solid wood,\" etc.\n2. **User Action Inference**: The user likely clicked on the \"Filter and sort\" button to expand the filter options.\n3. **Intent Reasoning**: If the user clicked on the \"Filter and sort\" button, then they are likely trying to refine their search results by applying specific filters. This suggests they want to narrow down the selection of nightstands based on certain criteria.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"Solid wood\" option under the \"Material\" filter is now selected, and the number of items displayed has decreased from 80 to 10. The filter section on the right side of the page now shows \"Solid wood\" as selected.\n2. **User Action Inference**: The user likely clicked on the \"Solid wood\" option to apply this filter.\n3. **Intent Reasoning**: If the user selected the \"Solid wood\" filter, then they are trying to view only nightstands made of solid wood. This indicates a specific preference for solid wood materials in their search.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user first expanded the filter options by clicking on the \"Filter and sort\" button. Then, they selected the \"Solid wood\" filter to refine their search results to only show nightstands made of solid wood.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and view nightstands made of solid wood. The user is specifically interested in this material and is using the filter options to narrow down the search results accordingly."}
+{"id": "26c5fa4a-431d-4697-b1cb-7c2d82a4b3e1.jpg_1", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The search bar now contains the text \"Fried Bombay Duck,\" and the search button has been clicked. The page has not yet navigated to a results page, indicating that the search is in progress.\n2. **User Action Inference**: The user likely typed \"Fried Bombay Duck\" into the search bar and clicked the search button.\n3. **Intent Reasoning**: If the user typed \"Fried Bombay Duck\" and clicked the search button, then they are likely trying to find recipes related to \"Fried Bombay Duck.\"\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The page now displays a list of recipes for \"Fried Bombay Duck,\" including various types of fried dishes with the keyword \"Fried Bombay Duck\" in the title or description. The search bar still contains the text \"Fried Bombay Duck.\"\n2. **User Action Inference**: The user likely waited for the search results to load after clicking the search button.\n3. **Intent Reasoning**: If the user waited for the search results to load, then they are likely trying to view and explore the available recipes for \"Fried Bombay Duck.\"\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user typed \"Fried Bombay Duck\" into the search bar, clicked the search button, and waited for the search results to load. The search results page now displays various recipes for \"Fried Bombay Duck.\"\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and explore recipes for \"Fried Bombay Duck.\" The user's actions indicate a clear intent to search for and view recipes related to this specific dish."}
+{"id": "358cdc88-5796-4105-b94d-38c580231f89.jpg_2", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a general landing page for a parenting website with sections like \"BabyCenter is your parenting partner,\" \"Popular tools,\" and \"Getting Pregnant.\" The second webpage is focused on \"Baby Names,\" featuring a search bar, a section titled \"Baby Names,\" and various categories like \"Top baby names,\" \"Baby names by origin,\" and \"Baby names by theme.\"\n2. **User Action Inference**: The user likely clicked on a link or navigation option related to \"Baby Names\" from the landing page.\n3. **Intent Reasoning**: If the user navigated from the landing page to the \"Baby Names\" page, they are likely seeking information about baby names, such as ideas, advice, meanings, and popularity. The user's intent is to explore resources for naming a baby.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows the \"Baby Names\" page with a search bar and various categories. The third webpage shows the same \"Baby Names\" page, but the search bar now contains the text \"Carl,\" and the page displays a list of baby names starting with \"C.\"\n2. **User Action Inference**: The user likely typed \"Carl\" into the search bar and pressed enter or clicked the search button.\n3. **Intent Reasoning**: If the user entered \"Carl\" into the search bar, they are likely trying to find baby names related to \"Carl.\" The user's intent is to explore baby names starting with \"C\" to potentially name a baby \"Carl.\"\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started on the landing page of a parenting website, navigated to the \"Baby Names\" page, and searched for baby names starting with \"C\" by entering \"Carl\" into the search bar.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find baby names starting with \"C\" to potentially name a baby \"Carl.\" The user is actively exploring name options and is likely in the process of selecting a name for a baby."}
+{"id": "0113d857-adb4-456b-8b75-9cd22446be52.jpg_3", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a list of Ohio land for sale with various filters on the left sidebar. The second webpage shows a filtered list of \"Ohio Undeveloped Land for Sale,\" with the \"Undeveloped\" filter applied. The number of listings has decreased from 9,436 to 2,812.\n2. **User Action Inference**: The user likely clicked on the \"Undeveloped\" filter option in the sidebar.\n3. **Intent Reasoning**: If the user clicked on the \"Undeveloped\" filter, then they are likely trying to narrow down the search results to only include undeveloped land for sale in Ohio.\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows the filtered list of \"Ohio Undeveloped Land for Sale.\" The third webpage shows a detailed view of a specific property listing, \"2 Acres • $119,900,\" with a \"Contact Seller\" button visible.\n2. **User Action Inference**: The user likely clicked on the \"Contact Seller\" button for the \"2 Acres • $119,900\" property.\n3. **Intent Reasoning**: If the user clicked on the \"Contact Seller\" button, then they are likely trying to contact the seller of the specific property to inquire further about the land.\n\n### Final Summary:\n- **Trajectory Recap**: The user first filtered the search results to only show undeveloped land for sale in Ohio. Then, they selected a specific property listing and clicked on the \"Contact Seller\" button to contact the seller.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to contact the seller of a specific undeveloped land for sale in Ohio to inquire further about the property."}
+{"id": "f2eea470-c966-4ecd-a7ae-ed2caaa30fe5.jpg_4", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a section titled \"Attending a Driver Training School\" with a brief overview of driver training schools. The second webpage focuses on \"Driver Training Schools,\" providing more detailed information about the program, including the number of sessions, requirements, and options to get a license.\n2. **User Action Inference**: The user likely clicked on the \"Driver Training Schools\" link or button, which is visually distinct and leads to a more detailed page.\n3. **Intent Reasoning**: If the user clicked on \"Driver Training Schools,\" then they are likely trying to gather more specific information about the driver training program, such as the number of sessions, requirements, and options to get a license.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage provides a general overview of driver training schools, while the third webpage focuses on \"90-Minute Parent/Teen Component,\" which is a specific part of the driver training program for students under 18.\n2. **User Action Inference**: The user likely clicked on the \"90-Minute Parent/Teen Component\" link or button, which is visually distinct and leads to a more specific page.\n3. **Intent Reasoning**: If the user clicked on \"90-Minute Parent/Teen Component,\" then they are likely trying to gather more specific information about this particular component of the driver training program, such as the curriculum, requirements, and enrollment details.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user navigated from a general overview of driver training schools to a more specific section about the \"90-Minute Parent/Teen Component.\" The user clicked on relevant links to access detailed information about the program.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to gather detailed information about the \"90-Minute Parent/Teen Component\" of the driver training program. The user is likely preparing for driver training and seeking specific details about this component to understand the requirements, curriculum, and enrollment process."}
+{"id": "2496c31e-197e-4787-a049-578b6eabcd70.jpg_5", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a list of cryptocurrencies, with \"Litecoin\" being one of the entries. The second webpage is a detailed page for \"Litecoin,\" displaying its price, market cap, volume, and other metrics. The transition indicates a move from a general list to a specific cryptocurrency's detailed page.\n2. **User Action Inference**: The user likely clicked on the \"Litecoin\" link from the list of cryptocurrencies.\n3. **Intent Reasoning**: If the user clicked on \"Litecoin,\" then they are likely trying to gather more detailed information about this specific cryptocurrency, such as its price, market performance, and other relevant metrics.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage is the detailed page for \"Litecoin,\" and the third webpage shows the \"Historical Data\" tab for \"Litecoin,\" displaying a table of historical price data. The transition indicates a move from the general details to a specific historical data view.\n2. **User Action Inference**: The user likely clicked on the \"Historical Data\" tab.\n3. **Intent Reasoning**: If the user clicked on the \"Historical Data\" tab, then they are likely trying to analyze the historical performance of \"Litecoin\" to understand its price trends over time.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started by viewing a list of cryptocurrencies, then clicked on \"Litecoin\" to access its detailed page, and finally clicked on the \"Historical Data\" tab to view its historical price data.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to analyze the historical performance of \"Litecoin\" to understand its price trends over time. The user is likely interested in evaluating the cryptocurrency's past performance to make informed decisions or gain insights into its market behavior."}
+{"id": "32aa9179-09ff-4caa-af3b-eff7b38f5b32.jpg_6", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a search bar with the query \"Linkedin learning\" and a dropdown menu with search suggestions. The second webpage displays a list of search results related to \"linkedin learning,\" including courses, posts, jobs, groups, people, and more courses. The \"Courses\" tab is highlighted, indicating it is the active section.\n2. **User Action Inference**: The user likely clicked on the \"Courses\" option from the search suggestions or the dropdown menu.\n3. **Intent Reasoning**: If the user clicked on the \"Courses\" tab, then they are likely trying to find specific courses related to \"linkedin learning.\" This suggests the user is interested in exploring educational resources or learning materials.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows a list of search results under the \"Courses\" tab. The third webpage is the LinkedIn Learning page, which includes an \"About\" section, a \"Newsletter,\" and a \"Page posts\" section. The \"About\" tab is highlighted, indicating it is the active section.\n2. **User Action Inference**: The user likely clicked on the \"About\" tab from the search results.\n3. **Intent Reasoning**: If the user clicked on the \"About\" tab, then they are likely trying to learn more about LinkedIn Learning. This suggests the user is interested in understanding the platform's mission, team, and offerings.\n\n---\n\n### WebPage 3 → WebPage 4:\n1. **Visual Difference**: The third webpage shows the \"About\" section of the LinkedIn Learning page. The fourth webpage is the LinkedIn Learning page, which includes an \"About\" section, a \"Newsletter,\" and a \"Page posts\" section. The \"Newsletter\" tab is highlighted, indicating it is the active section.\n2. **User Action Inference**: The user likely clicked on the \"Newsletter\" tab from the search results.\n3. **Intent Reasoning**: If the user clicked on the \"Newsletter\" tab, then they are likely trying to access the latest updates and content from LinkedIn Learning. This suggests the user is interested in staying informed about new courses, events, and other relevant information.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started by searching for \"linkedin learning,\" then navigated to the \"Courses\" section to explore educational resources, followed by the \"About\" section to learn more about the platform, and finally the \"Newsletter\" section to access the latest updates.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to gain a comprehensive understanding of LinkedIn Learning, including its offerings, mission, and the latest content and updates. The user is likely a student, instructor, or someone interested in professional development and learning resources."}
+{"id": "42b5cfdd-c73e-49bb-bb0e-214da54c8d08.jpg_7", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a search bar with the placeholder text \"Hong Kong\" and a dropdown menu listing popular cities. The second webpage shows the same search bar, but the dropdown menu is now expanded, and the text \"Park\" is typed into the search bar.\n2. **User Action Inference**: The user likely clicked on the search bar and then typed \"Park\" into it.\n3. **Intent Reasoning**: If the user typed \"Park\" into the search bar, they are likely trying to search for attractions or tickets related to parks. The expanded dropdown suggests they are exploring options or confirming their search query.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows the search bar with \"Park\" typed in, and the third webpage displays a list of park-related attractions and tickets, including \"The Jurassic Dinosaur Adventure Park\" and \"Ocean Park Hong Kong.\"\n2. **User Action Inference**: The user likely clicked the \"Search\" button after typing \"Park\" into the search bar.\n3. **Intent Reasoning**: If the user clicked the \"Search\" button, they are trying to find specific park-related attractions or tickets. The displayed results indicate they are exploring options for parks in Hong Kong.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started by typing \"Park\" into the search bar, then clicked the \"Search\" button, and the page displayed a list of park-related attractions and tickets.\n- **Overall Intent Deduction**: The user's final goal is to find and explore park-related attractions or tickets, specifically in Hong Kong. The user's actions indicate a clear intent to search for parks and view available options."}
+{"id": "80cb2b9b-6cd9-4d64-8083-dbcdc49f8464.jpg_8", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"Last Name\" field has been filled with the text \"Carlone.\"\n2. **User Action Inference**: The user likely typed \"Carlone\" into the \"Last Name\" field.\n3. **Intent Reasoning**: If the user filled in the \"Last Name\" field, they are likely progressing through the form to provide necessary information for the BBB Accreditation application. This suggests they are preparing to submit their application.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"Email\" field has been filled with the text \"vito.carlone@godfather.com.\"\n2. **User Action Inference**: The user likely typed \"vito.carlone@godfather.com\" into the \"Email\" field.\n3. **Intent Reasoning**: If the user filled in the \"Email\" field, they are continuing to complete the form. This indicates they are ensuring all required fields are filled out accurately to proceed with the application.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user filled in the \"Last Name\" field with \"Carlone\" and the \"Email\" field with \"vito.carlone@godfather.com.\" These actions suggest they are systematically completing the form fields necessary for the BBB Accreditation application.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to complete the BBB Accreditation application form by entering the required personal and contact information. The user is likely preparing to submit the form to proceed with the application process."}
+{"id": "f2d7b54d-3718-418c-94f2-e6c49042ac0d.jpg_9", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"Career/Education Level\" filter is expanded, showing a list of education levels. The \"High school diploma or equivalent\" option is highlighted, indicating it has been selected.\n2. **User Action Inference**: The user likely clicked on the \"Career/Education Level\" filter to expand it and then selected the \"High school diploma or equivalent\" option.\n3. **Intent Reasoning**: If the user expanded the \"Career/Education Level\" filter and selected \"High school diploma or equivalent,\" they are likely trying to refine their job search results to include only positions that require a high school diploma or equivalent education.\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"Career/Education Level\" filter now shows \"High school diploma or equivalent\" as selected, and the \"Display Results\" button is highlighted, indicating it has been clicked.\n2. **User Action Inference**: The user likely clicked on the \"High school diploma or equivalent\" option and then clicked the \"Display Results\" button.\n3. **Intent Reasoning**: If the user selected \"High school diploma or equivalent\" and clicked \"Display Results,\" they are likely trying to apply this filter to update the job search results to only show positions that match their selected education level.\n\n### Final Summary:\n- **Trajectory Recap**: The user expanded the \"Career/Education Level\" filter, selected \"High school diploma or equivalent,\" and clicked \"Display Results\" to apply the filter and update the job search results.\n- **Overall Intent Deduction**: The user's final goal is to refine their job search results to include only positions that require a high school diploma or equivalent education. This is evident from the user's actions of selecting the appropriate filter and updating the results accordingly."}
+{"id": "2bca6168-ca65-404d-9e56-f92b7ba8ecc7.jpg_10", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The second webpage shows a dropdown menu under the \"FIND STORAGE\" tab, which includes options like \"Self Storage,\" \"Vehicle Storage,\" \"RV Storage,\" etc. The \"Self Storage\" option is highlighted, indicating it has been selected. The rest of the page remains unchanged.\n2. **User Action Inference**: The user likely clicked on the \"FIND STORAGE\" tab, which triggered the dropdown menu. Then, the user clicked on the \"Self Storage\" option.\n3. **Intent Reasoning**: If the user clicked on \"Self Storage,\" they are likely trying to explore self-storage options. This suggests they are interested in finding a self-storage facility or learning more about self-storage services.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The third webpage shows a dropdown menu under the \"STORAGE 101\" tab, which includes options like \"How Big of a Storage Unit Do I Need?\" and \"How to Choose a Unit.\" The \"How Big of a Storage Unit Do I Need?\" option is highlighted, indicating it has been selected. The rest of the page remains unchanged.\n2. **User Action Inference**: The user likely clicked on the \"STORAGE 101\" tab, which triggered the dropdown menu. Then, the user clicked on the \"How Big of a Storage Unit Do I Need?\" option.\n3. **Intent Reasoning**: If the user clicked on \"How Big of a Storage Unit Do I Need?,\" they are likely trying to determine the appropriate size for a storage unit. This suggests they are in the process of selecting a self-storage facility and need guidance on unit sizes.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user navigated from the main page to the \"FIND STORAGE\" section, selected \"Self Storage,\" and then moved to the \"STORAGE 101\" section to explore the \"How Big of a Storage Unit Do I Need?\" option. The user's actions indicate a focused interest in self-storage unit sizes.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to determine the appropriate size for a self-storage unit. They are likely in the process of selecting a self-storage facility and are seeking guidance on unit sizes to ensure they choose the right one."}
+{"id": "8a45644f-1527-4401-a627-135d8bda3ab6.jpg_11", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a search interface for flights from Columbus to New York City. The second webpage displays a list of flights from Toronto to New York City, with a departure date of 4/8/2023 and a return date of 4/15/2023. The \"From\" field is now filled with \"Toronto (YTO)\" and the \"To\" field is filled with \"New York City (NYC)\". The \"Find flights\" button has been clicked, and the page now shows a list of available flights.\n2. **User Action Inference**: The user likely clicked the \"Find flights\" button after entering the departure and destination cities and dates.\n3. **Intent Reasoning**: If the user clicked the \"Find flights\" button, then they are trying to search for available flights from Toronto to New York City for the specified dates. The user's goal is to find and possibly book a flight.\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows a list of flights from Toronto to New York City. The third webpage shows the same list of flights, but the \"Sort by\" dropdown is now open, and the \"Price\" option is highlighted. The \"Price\" option is set to sort the flights by price.\n2. **User Action Inference**: The user likely clicked the \"Sort by\" dropdown and selected the \"Price\" option.\n3. **Intent Reasoning**: If the user selected the \"Price\" option, then they are trying to sort the flights by price. The user's goal is to find the cheapest flight option available.\n\n### Final Summary:\n- **Trajectory Recap**: The user started by searching for flights from Columbus to New York City, then clicked the \"Find flights\" button to view available flights. They then sorted the flights by price to find the cheapest option.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and potentially book the cheapest flight from Toronto to New York City for the specified dates. The user is focused on minimizing the cost of their travel."}
+{"id": "7862788a-f7f7-46ee-b71f-373ec9a05345.jpg_12", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a search form with fields for \"From,\" \"To,\" \"Depart,\" and \"Return\" dates. The \"From\" field is filled with \"Ahmedabad,\" and the \"To\" field is filled with \"Dubai.\" The \"Depart\" date is set to \"Wed, Apr 5,\" and the \"Return\" date is set to \"Thu, Apr 6.\" The \"Search\" button is visible, and the page is ready for the user to initiate a search. The second webpage shows the same search form, but the \"Search\" button has been clicked, and the page has transitioned to a results page displaying flight and hotel options.\n2. **User Action Inference**: The user likely clicked the \"Search\" button to initiate the search for flights and hotels from Ahmedabad to Dubai.\n3. **Intent Reasoning**: If the user clicked the \"Search\" button, then the user is trying to find available travel options (flights and hotels) for the specified dates and locations. The user's goal is to view the search results to plan their trip.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows a list of hotel options with a filter applied for \"4.1\" star rating. The third webpage shows the same list of hotel options, but the filter for \"4.1\" star rating has been cleared, and the list now includes hotels with a 4.2 star rating.\n2. **User Action Inference**: The user likely clicked the \"Clear\" button next to the \"4.1\" star rating filter to remove it from the search results.\n3. **Intent Reasoning**: If the user cleared the \"4.1\" star rating filter, then the user is trying to expand the search results to include hotels with a higher star rating. The user's goal is to find a hotel that meets their quality preference.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user initiated a search for flights and hotels from Ahmedabad to Dubai, viewed the initial search results, and then cleared a filter for hotels with a 4.1 star rating to view hotels with a 4.2 star rating.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find a suitable hotel option for their trip from Ahmedabad to Dubai. The user is specifically looking for a hotel with a 4.2 star rating or higher, as indicated by the clearing of the 4.1 star rating filter. The user is likely trying to ensure a high-quality stay during their trip."}
+{"id": "fb5c47bb-9bb9-4633-a1c6-16f4c4198919.jpg_13", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a search bar with the term \"Paracetamol\" entered, and a dropdown list of search suggestions appears. The second webpage displays search results for \"Paracetamol,\" including a link to \"Paracetamol Tablet - Amazon - Amazon Official Site.\"\n2. **User Action Inference**: The user likely clicked the search button or pressed enter after entering \"Paracetamol\" in the search bar.\n3. **Intent Reasoning**: If the user entered \"Paracetamol\" and initiated a search, then they are likely trying to find information about the drug, such as its uses, dosage, side effects, or related products.\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows search results for \"Paracetamol,\" and the third webpage displays detailed information about \"Paracetamol,\" including its uses, dosage, side effects, and interactions.\n2. **User Action Inference**: The user likely clicked on the link to \"Paracetamol Tablet - Amazon - Amazon Official Site\" or a similar link that led to the detailed information page.\n3. **Intent Reasoning**: If the user navigated to the detailed information page, then they are likely trying to gather comprehensive information about \"Paracetamol\" to understand its uses, dosage, side effects, and interactions.\n\n### Final Summary:\n- **Trajectory Recap**: The user entered \"Paracetamol\" in the search bar, initiated a search, and navigated to a detailed information page about \"Paracetamol.\" The user's actions indicate a focused intent to gather detailed information about the drug.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to obtain comprehensive information about \"Paracetamol,\" including its uses, dosage, side effects, and interactions. The user is likely preparing for personal use or seeking medical advice related to the drug."}
+{"id": "bc9c7b93-f2a6-4fa2-bf10-7f604004f259.jpg_14", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The search bar now contains the text \"Landscape Contractors,\" and the search button has been clicked. The page has transitioned to a search results page for \"Landscape Contractors\" near Milwaukee, WI.\n2. **User Action Inference**: The user likely typed \"Landscape Contractors\" into the search bar and clicked the search button.\n3. **Intent Reasoning**: If the user typed \"Landscape Contractors\" and clicked the search button, then the user is likely trying to find a list of landscape contractors in the Milwaukee area.\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The search results page now displays a list of landscape contractors, including their names, ratings, and contact information. The user has selected \"Sunny Home Improvement LLC\" from the list.\n2. **User Action Inference**: The user likely clicked on the \"Sunny Home Improvement LLC\" link in the search results.\n3. **Intent Reasoning**: If the user clicked on \"Sunny Home Improvement LLC,\" then the user is likely trying to gather more detailed information about this specific landscape contractor, such as their services, ratings, and contact details.\n\n### Final Summary:\n- **Trajectory Recap**: The user searched for \"Landscape Contractors\" near Milwaukee, WI, and then selected \"Sunny Home Improvement LLC\" from the search results to view more detailed information about this specific contractor.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and gather detailed information about a specific landscape contractor, \"Sunny Home Improvement LLC,\" in the Milwaukee area. The user is likely considering hiring this contractor or evaluating their services."}
+{"id": "8ea2d5ea-90a1-4c33-b109-83254d815bad.jpg_15", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"Phone\" field has been filled with the number \"6571572589\". The \"Business Postal Code\" field is now active, and the user has typed \"60068\" into it.\n2. **User Action Inference**: The user likely clicked on the \"Phone\" field to enter the phone number and then proceeded to the \"Business Postal Code\" field to enter the postal code.\n3. **Intent Reasoning**: If the user filled in the phone number and then the postal code, they are likely completing the form to apply for BBB Accreditation. The user is ensuring that all required fields are filled out correctly.\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"Business Postal Code\" field now contains the number \"60068\". The \"No\" radio button is selected, indicating the user's answer to the question \"Have you been in business for more than six months?\".\n2. **User Action Inference**: The user likely clicked on the \"No\" radio button to select their answer regarding business duration.\n3. **Intent Reasoning**: If the user selected \"No\" for the business duration, they are providing accurate information about their business experience. This step is part of completing the form to ensure the application is as precise as possible.\n\n### Final Summary:\n- **Trajectory Recap**: The user filled in the \"Phone\" field with \"6571572589\" and the \"Business Postal Code\" field with \"60068\". They also selected \"No\" for the question about business duration. These actions suggest the user is systematically completing the form to apply for BBB Accreditation.\n- **Overall Intent Deduction**: The user's final goal is to apply for BBB Accreditation by completing the required form fields accurately. The user is ensuring that all necessary information is provided to successfully submit their application."}
+{"id": "eb67042a-f0d5-48eb-9c16-8ef93871397c.jpg_16", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"Year\" filter section is expanded, showing a dropdown menu with year options. The \"From\" field is set to \"2010,\" and the \"To\" field is set to \"2023.\"\n2. **User Action Inference**: The user likely clicked on the \"Year\" filter to expand it and view the year options.\n3. **Intent Reasoning**: If the user clicked on the \"Year\" filter, they are likely trying to refine their search results by selecting a specific year range for the Toyota Corolla models.\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"From\" field now shows \"2023,\" and the \"To\" field shows \"2023.\" The list of cars displayed has been updated to show only cars from 2023.\n2. **User Action Inference**: The user likely selected \"2023\" from the \"From\" and \"To\" fields in the year filter.\n3. **Intent Reasoning**: If the user selected \"2023\" in both fields, they are trying to narrow down their search results to only include Toyota Corolla models from 2023.\n\n### Final Summary:\n- **Trajectory Recap**: The user expanded the \"Year\" filter, selected \"2023\" in both the \"From\" and \"To\" fields, and the search results were updated to show only cars from 2023.\n- **Overall Intent Deduction**: The user's final goal is to find and view Toyota Corolla models specifically from the year 2023. This is evident from the user's actions of refining the search results by selecting the appropriate year range."}
+{"id": "5f35baa2-d228-4fa1-894a-6cebed718b9f.jpg_17", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a general overview of Amazon jobs with various job categories and a \"View all jobs in your area\" button. The second webpage focuses on the \"Amazon Pharmacy\" section, displaying images and information about Amazonians working in Pharmacy. The \"Find jobs in Pharmacy\" section is also visible, indicating a transition from a general job search to a specific job category.\n2. **User Action Inference**: The user likely clicked on the \"Amazon Pharmacy\" link or section from the first page.\n3. **Intent Reasoning**: If the user clicked on \"Amazon Pharmacy,\" then they are likely interested in exploring job opportunities within the Pharmacy department. This suggests the user is narrowing their job search to a specific area of interest.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows the \"Amazon Pharmacy\" section with a \"Full Time\" filter applied, as indicated by the \"Full Time (23)\" label under the \"JOB TYPE\" filter. The job listings are now filtered to show only full-time positions.\n2. **User Action Inference**: The user likely clicked on the \"Full Time\" checkbox under the \"JOB TYPE\" filter.\n3. **Intent Reasoning**: If the user selected the \"Full Time\" filter, then they are specifically looking for full-time job opportunities within the Pharmacy department. This indicates the user's intent to find and apply for full-time positions in this area.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started on a general Amazon jobs page, navigated to the \"Amazon Pharmacy\" section, and then applied a filter for \"Full Time\" jobs. The user's actions indicate a focused search for full-time pharmacy-related job opportunities.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and apply for full-time job positions within the Amazon Pharmacy department. The user's actions are consistent with a specific interest in this area and a preference for full-time employment."}
+{"id": "08be352c-4e74-4549-acda-1d9b42f4b438.jpg_18", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The number of tickets displayed has changed from 2 to 1. The price range slider has been adjusted, and the ticket listings on the right side of the page have been updated to reflect the new ticket options.\n2. **User Action Inference**: The user likely clicked on the \"1 Ticket\" button or adjusted the price slider to change the ticket quantity and price range.\n3. **Intent Reasoning**: If the user clicked on the \"1 Ticket\" button, they are likely trying to purchase a single ticket. If they adjusted the price slider, they are trying to find a ticket within a specific price range. The updated ticket listings suggest the user is exploring available options based on their preferences.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The ticket quantity has been changed from 1 to 1, and the price range has been adjusted to $200. The ticket listings on the right side of the page have been updated to reflect the new ticket options.\n2. **User Action Inference**: The user likely clicked on the \"1 Ticket\" button again or adjusted the price slider further to change the ticket quantity and price range.\n3. **Intent Reasoning**: If the user clicked on the \"1 Ticket\" button again, they are likely trying to purchase a single ticket. If they adjusted the price slider further, they are trying to find a ticket within a specific price range. The updated ticket listings suggest the user is exploring available options based on their preferences.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started by viewing a page with 2 tickets available. They then adjusted the ticket quantity to 1 and updated the price range slider. The user continued to explore available ticket options based on their preferences.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to purchase a single ticket for the Phoenix Suns vs. Denver Nuggets game. The user is specifically looking for a ticket within a specific price range, as indicated by the updated ticket listings and price slider adjustments. The user is likely trying to find the most suitable ticket option based on their budget and preferences."}
+{"id": "42f06fb7-d9a4-463b-beea-da8f34414004.jpg_19", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a general \"Drugs & Medications A to Z\" page with a search bar and various navigation options. The second webpage is a more specific page titled \"Drugs: V,\" which lists drugs and medications that start with the letter 'V.' The content is focused on this specific subset of drugs.\n2. **User Action Inference**: The user likely clicked on a link or button that navigated them from the general page to the specific page for drugs starting with 'V.'\n3. **Intent Reasoning**: If the user navigated to a page listing drugs starting with 'V,' they are likely trying to find specific information about drugs that begin with this letter. This could be for personal use, research, or to gather information about particular medications.\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage lists drugs starting with 'V,' and the third webpage shows a list of drugs starting with 'V' in a more detailed format, with additional options like \"Patient Info\" and \"Professional Monographs.\"\n2. **User Action Inference**: The user likely clicked on a link or button that expanded the list of drugs starting with 'V' to include more detailed information.\n3. **Intent Reasoning**: If the user expanded the list to include additional options like \"Patient Info\" and \"Professional Monographs,\" they are likely trying to access more detailed and specific information about the drugs listed. This could be for personal use, research, or to gather professional advice about these medications.\n\n### Final Summary:\n- **Trajectory Recap**: The user started on a general page listing all drugs and medications, navigated to a specific page listing drugs starting with 'V,' and then expanded the list to include more detailed information.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and access detailed information about drugs that start with the letter 'V.' This could be for personal use, research, or to gather professional advice about these medications. The user is specifically interested in finding more detailed and specific information about these drugs."}
+{"id": "09059c15-25c0-451a-a6af-5557b7f0e82e.jpg_20", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows the Best Buy homepage with various sections like \"Top Deals,\" \"Deal of the Day,\" and \"Our featured offers.\" The second webpage shows a search bar with the term \"Samsung TV\" entered, and a dropdown list of search suggestions appears below the search bar.\n2. **User Action Inference**: The user likely typed \"Samsung TV\" into the search bar.\n3. **Intent Reasoning**: If the user typed \"Samsung TV,\" then they are likely trying to search for Samsung TVs on the Best Buy website. The dropdown list suggests that the user is in the process of refining their search.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows the search suggestions for \"Samsung TV,\" and the third webpage displays a list of Samsung TVs with various models, prices, and options to add to the cart.\n2. **User Action Inference**: The user likely clicked on the \"Samsung TV\" option from the search suggestions.\n3. **Intent Reasoning**: If the user clicked on \"Samsung TV,\" then they are trying to view the available Samsung TVs on the Best Buy website. The user is likely interested in browsing through the product options to find a specific TV model.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user navigated from the Best Buy homepage to the search bar, entered \"Samsung TV,\" and selected the search suggestion to view the available Samsung TVs. The user then viewed a list of Samsung TVs with various options to add to the cart.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to browse and potentially purchase a Samsung TV from the Best Buy website. The user is specifically interested in Samsung TVs and is likely looking for a specific model or comparing different options."}
+{"id": "9f428247-d3fa-4c51-ba2f-97fe7b4ef9ad.jpg_21", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"Destination or property\" textbox has been filled with the text \"Dublin,\" and a dropdown menu has appeared, listing locations and properties related to Dublin.\n2. **User Action Inference**: The user likely typed \"Dublin\" into the \"Destination or property\" textbox.\n3. **Intent Reasoning**: If the user typed \"Dublin,\" then they are likely trying to search for hotels or properties in Dublin. The dropdown suggests that the user is in the process of selecting a specific location or property.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The dropdown menu now shows \"Dublin, Ireland\" as the selected option, and the \"Search\" button has been clicked, as indicated by the updated page state.\n2. **User Action Inference**: The user likely clicked on \"Dublin, Ireland\" from the dropdown menu and then clicked the \"Search\" button.\n3. **Intent Reasoning**: If the user selected \"Dublin, Ireland\" and clicked \"Search,\" then they are trying to find hotels or properties in Dublin, Ireland. The user's goal is to view available accommodations in that specific location.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user typed \"Dublin\" into the destination textbox, selected \"Dublin, Ireland\" from the dropdown menu, and clicked the \"Search\" button. The user's actions indicate a focused intent to find hotels or properties in Dublin, Ireland.\n- **Overall Intent Deduction**: The user's final goal is to search for and view available hotels or properties in Dublin, Ireland. The user's actions are consistent with this intent, as they have specified the location and initiated the search process."}
+{"id": "9809c03d-61c1-4640-8bf6-46ce20a88578.jpg_22", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"Home type\" dropdown menu is now open, displaying options such as \"House,\" \"Apartment,\" \"Condo,\" and \"Townhome.\" The \"Apartment\" option is highlighted, indicating it has been selected.\n2. **User Action Inference**: The user likely clicked on the \"Home type\" dropdown and then selected the \"Apartment\" option.\n3. **Intent Reasoning**: If the user selected \"Apartment,\" they are likely trying to filter the search results to show only apartment listings. This suggests they are specifically interested in apartment rentals.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"Beds / Baths\" dropdown menu is now open, displaying options for selecting the number of bedrooms and bathrooms. The \"Any\" option is highlighted, indicating it has been selected.\n2. **User Action Inference**: The user likely clicked on the \"Beds / Baths\" dropdown and then selected the \"Any\" option.\n3. **Intent Reasoning**: If the user selected \"Any\" for beds and baths, they are likely trying to broaden their search criteria to include all possible apartment listings regardless of how many bedrooms or bathrooms they have. This suggests they want to see the most comprehensive list of available apartments.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user first filtered the search results to show only apartment listings by selecting \"Apartment\" from the \"Home type\" dropdown. Then, they further broadened their search by selecting \"Any\" for beds and baths from the \"Beds / Baths\" dropdown.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find a suitable apartment rental in New York. They are specifically interested in apartments and are open to all options regarding the number of bedrooms and bathrooms. The user is likely looking for the most comprehensive list of available apartments that meet their basic requirements."}
+{"id": "604b9910-0079-4b25-ae5e-62c4ff2b9964.jpg_23", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The location in the top navigation bar has changed from \"Columbus, OH, USA\" to \"Austin, TX, USA\". The list of concerts has also updated to reflect events in Austin, TX.\n2. **User Action Inference**: The user likely clicked on the location dropdown and selected \"Austin, TX, USA\".\n3. **Intent Reasoning**: If the user selected a new location, then they are likely trying to view events specific to Austin, TX. This suggests the user is interested in exploring concerts, sports, or theater events in that area.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The list of concerts has been further refined to show events by the artist \"Taylor Tomlinson\". The other elements on the page, such as the sports and theater sections, remain unchanged.\n2. **User Action Inference**: The user likely clicked on the \"Taylor Tomlinson\" link within the concerts section.\n3. **Intent Reasoning**: If the user clicked on \"Taylor Tomlinson\", then they are specifically interested in events related to this artist. This indicates the user is looking for concerts or other performances by Taylor Tomlinson in Austin, TX.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user first changed the location to Austin, TX, USA, then navigated to the concerts section and selected \"Taylor Tomlinson\" as the artist of interest.\n- **Overall Intent Deduction**: The user's final goal is to find and view events, specifically concerts, by the artist Taylor Tomlinson in Austin, TX. The user is likely planning to attend a concert by Taylor Tomlinson and is using the website to explore available dates, venues, and ticket options."}
+{"id": "2456573b-41d2-4ead-947a-1b299832e608.jpg_24", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a general job opportunities page with various job categories like \"Fulfillment Center Associate,\" \"Sortation Center Associate,\" etc. The second webpage shows a dropdown menu under \"Job Opportunities\" with a list of job categories, including \"Amazon Pharmacy.\"\n2. **User Action Inference**: The user likely clicked on the \"Job Opportunities\" button to expand the dropdown menu.\n3. **Intent Reasoning**: If the user clicked on \"Job Opportunities,\" then they are likely trying to explore specific job categories available at Amazon. The appearance of the dropdown menu suggests the user is narrowing down their job search to a particular category.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows the dropdown menu under \"Job Opportunities,\" and the third webpage shows the \"Amazon Pharmacy\" category selected, with a link labeled \"Amazon Pharmacy.\"\n2. **User Action Inference**: The user likely clicked on the \"Amazon Pharmacy\" link in the dropdown menu.\n3. **Intent Reasoning**: If the user clicked on \"Amazon Pharmacy,\" then they are specifically interested in exploring job opportunities within the Amazon Pharmacy category. This indicates a focused intent to find relevant job listings for this role.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started on a general job opportunities page, expanded the dropdown menu under \"Job Opportunities,\" and then selected the \"Amazon Pharmacy\" category. The user's actions indicate a step-by-step narrowing of their job search to a specific category.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and explore job opportunities within the Amazon Pharmacy category. The user is likely interested in understanding the available roles, benefits, and application process for this specific job category."}
+{"id": "8f08ba33-3b08-445b-8c5e-b552a79953a5.jpg_25", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The location field in the search bar has been updated from \"New York, NY\" to \"San Diego,\" and a dropdown menu has appeared with various locations in San Diego, CA.\n2. **User Action Inference**: The user likely typed \"San Diego\" into the location field.\n3. **Intent Reasoning**: If the user typed \"San Diego,\" then they are likely trying to search for hair salons in San Diego, CA. The dropdown menu suggests that the user is in the process of selecting a specific location within San Diego.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The location field now shows \"San Diego, CA,\" and the dropdown menu is no longer visible. The \"Find\" button has been clicked, and the page has likely navigated to a results page for hair salons in San Diego, CA.\n2. **User Action Inference**: The user likely clicked the \"Find\" button after selecting \"San Diego, CA\" from the dropdown menu.\n3. **Intent Reasoning**: If the user clicked the \"Find\" button, then they are trying to view the available hair salons in San Diego, CA. The user's goal is to find and possibly contact a hair salon in that location.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user initially searched for hair salons in New York, NY, but then updated the location to San Diego, CA. They selected \"San Diego, CA\" from the dropdown menu and clicked the \"Find\" button to view the results for hair salons in San Diego, CA.\n- **Overall Intent Deduction**: The user's final goal is to find and possibly contact a hair salon in San Diego, CA. The user's actions indicate a clear intent to search for local hair salons in a specific location and proceed with finding a suitable option."}
+{"id": "0daf346d-efba-43d4-96db-259ea7f2fa6f.jpg_26", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a general clearance page with various categories like \"Women,\" \"Men,\" \"Girls,\" and \"Boys.\" The second webpage focuses specifically on the \"Dresses\" category, with a list of clearance dresses displayed. The \"Dresses\" category is highlighted, and the number of products under this category is shown as 190.\n2. **User Action Inference**: The user likely clicked on the \"Dresses\" category link to filter the clearance items to only show dresses.\n3. **Intent Reasoning**: If the user clicked on the \"Dresses\" category, then they are likely trying to browse and shop for clearance dresses. This suggests the user is interested in finding discounted dresses.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows a list of clearance dresses, and the third webpage displays a detailed view of a specific dress. The dress is the \"Women's Nine West Button Down Dress,\" and it is marked as a clearance item with a price of $7.00.\n2. **User Action Inference**: The user likely clicked on the image or title of the \"Women's Nine West Button Down Dress\" to view more details about this specific dress.\n3. **Intent Reasoning**: If the user clicked on a specific dress, then they are likely interested in learning more about this particular dress, possibly to consider purchasing it. This suggests the user is evaluating the dress for potential purchase.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user navigated from a general clearance page to a specific clearance dresses page and then to a detailed view of a specific dress. The user's actions indicate a focused interest in browsing and evaluating clearance dresses.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and potentially purchase a clearance dress. The user's actions show a clear progression from browsing general clearance items to focusing on a specific dress for more detailed evaluation."}
+{"id": "4f7ad134-aaa7-4257-829b-eb91febf74a6.jpg_27", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"SAIL FROM\" dropdown has been updated from \"Brisbane, Australia\" to \"Brisbane, Australia\" with a green checkmark, indicating a selection. The \"DATES\" dropdown is now visible, suggesting the user is about to select travel dates.\n2. **User Action Inference**: The user likely clicked on the \"Brisbane, Australia\" option in the \"SAIL FROM\" dropdown.\n3. **Intent Reasoning**: If the user selected \"Brisbane, Australia,\" they are likely planning a cruise that departs from Brisbane. The next logical step would be to select travel dates.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"DATES\" dropdown is now open, and the \"NOV\" option is highlighted, indicating it has been selected. The \"DURATION\" dropdown is also visible, suggesting the user is about to select a duration for the cruise.\n2. **User Action Inference**: The user likely clicked on the \"NOV\" option in the \"DATES\" dropdown.\n3. **Intent Reasoning**: If the user selected \"NOV,\" they are likely planning a cruise that departs in November. The next logical step would be to select a duration for the cruise.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user selected \"Brisbane, Australia\" as the departure port and \"NOV\" as the departure month. The user is in the process of planning a cruise that departs from Brisbane in November.\n- **Overall Intent Deduction**: The user's final goal is to book a cruise that departs from Brisbane in November. The user is systematically selecting the necessary options to define the cruise itinerary."}
+{"id": "3307bd5c-663a-4fb9-b768-90897e95fef5.jpg_28", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The checkbox labeled \"I'm travelling for work\" is now checked, indicating a change in the user's selection.\n2. **User Action Inference**: The user likely clicked the checkbox to select the \"I'm travelling for work\" option.\n3. **Intent Reasoning**: If the user selects the \"I'm travelling for work\" checkbox, they are likely trying to specify their travel purpose as work-related. This could affect the search results or offers they receive.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"Search\" button has been clicked, and the page now displays a confirmation message indicating that the search has been triggered. The search results or relevant content are not yet visible.\n2. **User Action Inference**: The user likely clicked the \"Search\" button after selecting the \"I'm travelling for work\" checkbox.\n3. **Intent Reasoning**: If the user clicks the \"Search\" button, they are trying to find accommodations based on their specified travel purpose. The confirmation message suggests that the search is in progress, and the user is waiting for the results.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user selected the \"I'm travelling for work\" checkbox and then clicked the \"Search\" button. The search is in progress, and the user is waiting for the results.\n- **Overall Intent Deduction**: The user's final goal is to find accommodations for a work-related trip. They have specified their travel purpose and are now waiting for the search results to display relevant options."}
+{"id": "e54fc5b9-07bf-43c2-868e-bfe02d552c81.jpg_29", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"DESTINATION COUNTRY/TERRIORY\" field has been filled with \"UNITED STATES,\" and the \"ACCOUNT NUMBER (RECOMMENDED)\" field has been filled with \"344565432.\"\n2. **User Action Inference**: The user likely clicked on the \"DESTINATION COUNTRY/TERRIORY\" field and selected \"UNITED STATES\" from the dropdown. Then, they clicked on the \"ACCOUNT NUMBER (RECOMMENDED)\" field and entered \"344565432.\"\n3. **Intent Reasoning**: If the user filled in the \"DESTINATION COUNTRY/TERRIORY\" field, they are likely trying to specify the destination country for their shipment. By entering an \"ACCOUNT NUMBER (RECOMMENDED),\" they are following the system's recommendation, possibly to ensure accurate tracking. The user is preparing to track a shipment by providing necessary details.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"SHIP DATE\" field has been filled with \"10/20/2023,\" and the \"TRACK\" button has been clicked, changing its appearance to indicate it has been interacted with.\n2. **User Action Inference**: The user likely clicked on the \"SHIP DATE\" field and selected \"10/20/2023\" from the calendar. Then, they clicked the \"TRACK\" button.\n3. **Intent Reasoning**: If the user selected a ship date, they are specifying the date of their shipment. By clicking the \"TRACK\" button, they are initiating the tracking process for their shipment. The user's goal is to track a specific shipment by providing all necessary details.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user filled in the \"DESTINATION COUNTRY/TERRIORY\" field with \"UNITED STATES,\" entered an \"ACCOUNT NUMBER (RECOMMENDED),\" selected a \"SHIP DATE\" of \"10/20/2023,\" and clicked the \"TRACK\" button to initiate the tracking process.\n- **Overall Intent Deduction**: The user's final goal is to track a specific FedEx shipment by providing all necessary details such as destination country, recommended account number, ship date, and then initiating the tracking process. The user is likely trying to monitor the status of their shipment efficiently."}
+{"id": "86376987-29c2-4b2a-9558-2cfb1a33d26d.jpg_30", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a general landing page for Udemy with various course categories and promotional content. The second webpage is focused on \"Drawing Courses,\" with a section titled \"Drawing Courses\" and a brief description about the category. The content is more specific to drawing, with images and descriptions of drawing courses.\n2. **User Action Inference**: The user likely clicked on a link or button related to \"Drawing\" or a similar category on the first webpage.\n3. **Intent Reasoning**: If the user clicked on a link related to drawing, then they are likely trying to explore courses in the drawing category. This suggests the user is interested in learning about or engaging with drawing-related content.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage is the \"Drawing Courses\" page, and the third webpage is the \"Pencil Drawing Courses\" page. The content is now specific to pencil drawing, with a section titled \"Pencil Drawing Courses\" and a featured course titled \"How To Sketch and Draw a Realistic Puppy in Charcoal.\"\n2. **User Action Inference**: The user likely clicked on a link or button related to \"Pencil Drawing\" on the second webpage.\n3. **Intent Reasoning**: If the user clicked on a link related to pencil drawing, then they are likely trying to explore courses in the pencil drawing category. This suggests the user is interested in learning about or engaging with pencil drawing-related content.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started on the general Udemy landing page, navigated to the \"Drawing Courses\" page, and then to the \"Pencil Drawing Courses\" page. The user's journey shows a progression from a broad category to a specific subcategory.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to explore and potentially enroll in pencil drawing courses. The user's actions indicate a focused interest in learning pencil drawing, as evidenced by the navigation through the relevant course categories."}
+{"id": "e4d86e4b-ecb7-46ee-84e8-f17d108d5f82.jpg_31", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a general view of the Audio Library with various playlists and music categories. The second webpage displays a list of playlists under the \"Pop\" category, with a \"Pop\" filter applied.\n2. **User Action Inference**: The user likely clicked on the \"Pop\" category or a similar filter option to narrow down the music selection to pop music.\n3. **Intent Reasoning**: If the user clicked on the \"Pop\" category, then they are likely trying to find and explore pop music content within the Audio Library.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows a list of pop music playlists, and the third webpage displays a detailed view of the \"Rockin\" playlist, with a \"Rockin\" filter applied.\n2. **User Action Inference**: The user likely clicked on the \"Rockin\" playlist to view its contents.\n3. **Intent Reasoning**: If the user clicked on the \"Rockin\" playlist, then they are likely trying to explore the specific tracks within this playlist, possibly to find a particular song or to listen to the playlist as a whole.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started by viewing the general Audio Library, then filtered the music to show only pop music, and finally selected the \"Rockin\" playlist to view its contents.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to explore and potentially listen to pop music, specifically within the \"Rockin\" playlist. The user is likely interested in finding a particular song or enjoying the playlist as a whole."}
+{"id": "8c042495-ec25-48f1-b5be-e311402405df.jpg_32", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The filter modal is now open, displaying options for filtering groups, posts, and comments. The \"Groups\" option is selected, and the \"Apply\" button is visible.\n2. **User Action Inference**: The user likely clicked the \"Filter\" button to open the filter modal.\n3. **Intent Reasoning**: If the user opened the filter modal, they are likely trying to refine their search results to focus on specific types of content, such as groups related to getting pregnant.\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"Apply\" button is now highlighted, indicating it has been clicked. The filter modal remains open, but the \"Apply\" button is the focal point.\n2. **User Action Inference**: The user likely clicked the \"Apply\" button to apply the selected filter.\n3. **Intent Reasoning**: If the user clicked \"Apply,\" they are trying to apply the filter to update the search results to match their specified criteria, such as viewing only groups related to getting pregnant.\n\n### Final Summary:\n- **Trajectory Recap**: The user opened the filter modal, selected the \"Groups\" option, and clicked the \"Apply\" button to filter the search results to show only groups related to getting pregnant.\n- **Overall Intent Deduction**: The user's final goal is to find and view groups specifically related to getting pregnant, likely to connect with others or seek advice in this area."}
+{"id": "3dd3b016-f654-4d39-8006-7bb210d199f1.jpg_33", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The number of jobs found has decreased from 2916 to 225. The \"State\" filter has been updated to \"NY,\" and the \"Radius\" filter has been updated to \"20 miles.\" The job listings have been updated to reflect these filters.\n2. **User Action Inference**: The user likely selected the \"NY\" option from the \"State\" filter and the \"20 miles\" option from the \"Radius\" filter.\n3. **Intent Reasoning**: If the user selected these filters, then they are likely trying to narrow down the job search results to a specific state and radius. This suggests the user is looking for jobs within a particular geographic area.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"Actions\" dropdown menu is now open, showing options such as \"Apply,\" \"Save to My Profile,\" \"See all jobs from this company,\" \"See who you know on LinkedIn,\" and \"Report this job.\"\n2. **User Action Inference**: The user likely clicked on the \"Actions\" button next to a job listing.\n3. **Intent Reasoning**: If the user clicked on the \"Actions\" button, then they are likely interested in taking a specific action related to the job, such as applying for it, saving it for later, or reporting it. This suggests the user is actively considering the job and wants to take further action.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started by filtering job search results to a specific state and radius, reducing the number of jobs found. They then clicked on the \"Actions\" button for a job listing, opening a dropdown menu with options to apply, save, see all jobs from the company, see who they know on LinkedIn, and report the job.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and apply for a job within a specific geographic area. They are actively considering a job and are exploring options to take further action, such as applying or saving the job for later."}
+{"id": "ca717773-4a78-494e-adc5-b5d78be8f63e.jpg_34", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The search bar now shows the text \"Milwaukee\" in the \"Near\" field, and the dropdown menu is expanded, displaying location suggestions such as \"Milwaukee, WI,\" \"West Milwaukee, WI,\" etc.\n2. **User Action Inference**: The user likely typed \"Milwaukee\" into the \"Near\" field.\n3. **Intent Reasoning**: If the user typed \"Milwaukee,\" they are likely trying to find businesses, charities, or categories in the Milwaukee area. The dropdown suggests they are refining their search to a specific location.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The dropdown menu is still expanded, but the \"Near\" field now shows \"Milwaukee, WI,\" and the \"Search\" button is highlighted, indicating it has been clicked.\n2. **User Action Inference**: The user likely clicked the \"Search\" button after selecting \"Milwaukee, WI\" from the dropdown.\n3. **Intent Reasoning**: If the user clicked the \"Search\" button, they are trying to execute the search for businesses, charities, or categories in the Milwaukee, WI area. The highlighted button suggests the action was successful.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user typed \"Milwaukee\" into the \"Near\" field, selected \"Milwaukee, WI\" from the dropdown, and clicked the \"Search\" button. The search was executed, and the page likely updated to display relevant results for the Milwaukee, WI area.\n- **Overall Intent Deduction**: The user's final goal was to find businesses, charities, or categories in the Milwaukee, WI area. The sequence of actions—typing the location, selecting it from the dropdown, and clicking the \"Search\" button—clearly indicates this intent."}
+{"id": "507520aa-73a4-4b62-940e-c907e3eb7108.jpg_35", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage is the USPS homepage, featuring sections like \"See Incoming Mail in Your Email,\" \"Search or Track Packages,\" and \"Featured USPS Products & Services.\" The second webpage is the \"Find USPS Locations\" page, which includes a search bar for entering a city, state, or ZIP code, along with options for location types and filters.\n2. **User Action Inference**: The user likely clicked on the \"Locations\" link in the top navigation bar of the first webpage.\n3. **Intent Reasoning**: If the user clicked on the \"Locations\" link, then they are likely trying to find a USPS location near them. This suggests the user's intent is to locate a USPS facility, such as a post office, to access services or obtain information.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows an empty search bar in the \"Find USPS Locations\" page. The third webpage shows the search bar filled with the ZIP code \"90210,\" and the \"Search\" button is highlighted, indicating it has been clicked.\n2. **User Action Inference**: The user typed \"90210\" into the search bar and then clicked the \"Search\" button.\n3. **Intent Reasoning**: If the user entered a ZIP code and clicked the \"Search\" button, then they are trying to find USPS locations within the specified ZIP code area. This suggests the user's intent is to locate USPS facilities near the ZIP code 90210.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user navigated from the USPS homepage to the \"Find USPS Locations\" page, entered the ZIP code \"90210\" into the search bar, and clicked the \"Search\" button. The user's actions indicate a focused intent to find USPS locations near the specified ZIP code.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to locate USPS facilities, such as post offices, near the ZIP code 90210. The user is likely seeking information about USPS services, availability, or operations in that specific area."}
+{"id": "a63e680d-d1b1-45af-ac94-dd80a9b95605.jpg_36", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"Recommended\" dropdown menu is now open, displaying sorting options such as \"Distance to university,\" \"Recommended,\" \"Price low to high,\" and \"Price high to low.\" The \"Recommended\" option is highlighted, indicating it was selected.\n2. **User Action Inference**: The user likely clicked on the \"Recommended\" dropdown to view and select a sorting option.\n3. **Intent Reasoning**: If the user opened the dropdown and selected \"Recommended,\" they are likely trying to sort the properties based on the most relevant or popular options, possibly to find the best-suited accommodation.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The properties are now sorted by \"Price low to high,\" as indicated by the updated sorting label at the top of the page. The order of the properties has changed, with the lowest-priced options appearing first.\n2. **User Action Inference**: The user likely clicked on the \"Price low to high\" option within the dropdown menu.\n3. **Intent Reasoning**: If the user selected \"Price low to high,\" they are trying to find the most affordable accommodation options available. This suggests they prioritize cost over other factors like distance or popularity.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user initially viewed a list of properties sorted by recommendation. They then opened the sorting dropdown and selected \"Price low to high,\" which reordered the list to display the lowest-priced options first.\n- **Overall Intent Deduction**: The user's final goal is to find and view the most affordable student accommodation options near the University of Leeds. They are specifically looking for the lowest-priced choices, indicating a focus on cost-effectiveness."}
+{"id": "2c68fd85-cf37-406d-8d40-77120358414c.jpg_37", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The first webpage shows a general shopping interface with various categories, promotions, and a sidebar menu. The second webpage displays a specific category page for \"Toilet Paper,\" with a list of products and a navigation bar indicating the current category.\n2. **User Action Inference**: The user likely clicked on the \"Toilet Paper\" category from the sidebar menu.\n3. **Intent Reasoning**: If the user clicked on the \"Toilet Paper\" category, then they are likely trying to browse or purchase toilet paper products. This suggests the user's intent is to find and possibly buy toilet paper.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The second webpage shows a list of toilet paper products with a \"30 results\" count. The third webpage displays a detailed view of a specific toilet paper product, \"Charmin Ultra Soft Toilet Paper,\" with detailed information such as price, brand, and customer ratings.\n2. **User Action Inference**: The user likely clicked on the \"Charmin Ultra Soft Toilet Paper\" product from the list of toilet paper products.\n3. **Intent Reasoning**: If the user clicked on a specific toilet paper product, then they are likely interested in purchasing that particular brand and model. This suggests the user's intent is to view more details about the product and possibly add it to their cart.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user navigated from a general shopping interface to a specific category page for toilet paper, then selected a particular product, \"Charmin Ultra Soft Toilet Paper,\" to view more details.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and possibly purchase a specific brand and model of toilet paper. The user's actions indicate a focused intent to explore and select a toilet paper product for purchase."}
+{"id": "3467a166-04ce-4bc9-9a2e-47bc034d5a9d.jpg_38", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The price per ticket has increased from $80 to $103. The ticket listings on the left side of the page have been updated to reflect this new price. The seat map in the center of the page remains unchanged.\n2. **User Action Inference**: The user likely adjusted the price filter on the right side of the page, increasing the minimum price from $75 to $97.\n3. **Intent Reasoning**: If the user increased the price filter, they are likely trying to find tickets that are more expensive than the default minimum price. This suggests they are looking for higher-quality or more desirable seating options.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"Zones\" filter on the right side of the page is now expanded, showing options for \"Lower Tier,\" \"Floor,\" and \"Upper Tier.\" The \"Lower Tier\" option is selected, and the seat map has been updated to show only the lower tier sections.\n2. **User Action Inference**: The user likely clicked the \"Zones\" filter to expand it and then selected the \"Lower Tier\" option.\n3. **Intent Reasoning**: If the user selected the \"Lower Tier\" option, they are likely trying to narrow down their seating options to the lower tier of the venue. This suggests they are prioritizing lower-tier seating, possibly for better views or more comfortable access.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user first increased the price filter to $97, then expanded the \"Zones\" filter and selected the \"Lower Tier\" option. These actions updated the ticket listings and seat map to reflect higher-priced tickets in the lower tier of the venue.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find and select higher-priced tickets in the lower tier of the venue. The user is likely prioritizing both quality and accessibility in their seating choice."}
+{"id": "f4a0f46a-6075-492c-a6d3-c8adeaad7857.jpg_39", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The number of job listings has decreased from 5 to 2. The job listings now show only \"Customer Specialist\" positions. The \"Full Time\" filter is applied, as indicated by the \"Full time\" tag in the filter section.\n2. **User Action Inference**: The user likely clicked on the \"Full Time\" filter option.\n3. **Intent Reasoning**: If the user selected the \"Full Time\" filter, then they are likely trying to narrow down the job search results to only full-time positions. This suggests the user is specifically looking for full-time job opportunities.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The job listings remain the same, showing only \"Customer Specialist\" positions. The \"Full Time\" filter is still applied, and the number of jobs remains at 2. The \"Most recent\" filter is now applied, as indicated by the \"Most recent\" tag in the filter section.\n2. **User Action Inference**: The user likely clicked on the \"Most recent\" filter option.\n3. **Intent Reasoning**: If the user selected the \"Most recent\" filter, then they are likely trying to sort the job listings by the most recent postings. This suggests the user is interested in the latest job opportunities available.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user first filtered the job search results to show only full-time positions, reducing the number of listings from 5 to 2. Then, the user sorted the filtered results by the most recent postings, further refining the job listings.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to find the most recent full-time job opportunities at CarMax. The user is specifically interested in the latest full-time job listings and has filtered and sorted the results accordingly."}
+{"id": "19f2ca13-87e2-48e2-8302-8507689d68c6.jpg_40", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"Shape\" field has been updated from \"Any shape\" to \"White.\" The rest of the page remains unchanged.\n2. **User Action Inference**: The user likely clicked on the \"White\" option in the \"Shape\" dropdown menu.\n3. **Intent Reasoning**: If the user selected \"White\" for the shape, they are likely trying to narrow down the search results to only those pills that are white in color. This suggests they are refining their search to find a specific type of pill based on its visual characteristics.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"Shape\" field has been updated from \"White\" to \"Round.\" The rest of the page remains unchanged.\n2. **User Action Inference**: The user likely clicked on the \"Round\" option in the \"Shape\" dropdown menu.\n3. **Intent Reasoning**: If the user selected \"Round\" for the shape, they are further refining their search to only those pills that are both white and round in shape. This indicates they are trying to narrow down the search results to a specific combination of color and shape.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user started with a general pill identifier search, then refined the search by selecting \"White\" as the shape, and finally selected \"Round\" as the shape. The user is progressively narrowing down the search results to find a specific type of pill that matches both the white color and round shape.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to identify a specific pill that is both white and round in shape. The user is likely trying to find a particular medication that meets these visual criteria."}
+{"id": "78d773dd-a767-49c0-bd5e-f823d55b577a.jpg_41", "model_answer": "I have received a total of 3 webpage screenshots. Let us reason step by step to analyze the user's intent.\n\n---\n\n### WebPage 1 → WebPage 2:\n1. **Visual Difference**: The \"SAIL TO\" dropdown menu is now expanded, displaying a list of destinations such as Alaska, The Bahamas, Bermuda, Canada & New England, Caribbean, Europe, Greenland & Canada, etc. The \"SAIL FROM\" dropdown is still collapsed.\n2. **User Action Inference**: The user likely clicked on the \"SAIL TO\" dropdown to view the available destination options.\n3. **Intent Reasoning**: If the user clicked on the \"SAIL TO\" dropdown, then they are likely trying to select a specific destination for their cruise. This suggests they are in the process of planning their cruise itinerary.\n\n---\n\n### WebPage 2 → WebPage 3:\n1. **Visual Difference**: The \"SAIL TO\" dropdown is now closed, and the \"SAIL FROM\" dropdown is expanded, showing options like Alaska, The Bahamas, Bermuda, etc. The \"DATES\" and \"DURATION\" dropdowns are still collapsed.\n2. **User Action Inference**: The user likely clicked on the \"SAIL FROM\" dropdown to view the available departure options.\n3. **Intent Reasoning**: If the user clicked on the \"SAIL FROM\" dropdown, then they are likely trying to select a departure location for their cruise. This suggests they are continuing to plan their cruise itinerary by specifying both the destination and the departure location.\n\n---\n\n### Final Summary:\n- **Trajectory Recap**: The user first expanded the \"SAIL TO\" dropdown to view available destinations, then expanded the \"SAIL FROM\" dropdown to view available departure options. The user has not yet selected a specific destination or departure location.\n- **Overall Intent Deduction**: Based on the step-by-step reasoning, the user's final goal is to plan a cruise itinerary by selecting a destination and a departure location. The user is in the process of gathering information to make these selections."}

Result/Raw_Answer-Gemni-User_Intent_Prediction.jsonl

@@ -0,0 +1,42 @@
+{"id": "6ccbade8-62c4-41b8-99bc-2ef2d7c157e1.jpg_0", "model_answer": "Based on the visual changes across the three images, here is a prediction of the user's intent and navigation path:\n\n1.  **Initial Search & Exploration:** In the first image, the user is on the main \"Nightstands\" category page. They are browsing a wide variety of options. Their initial criteria seem to be that the item is a nightstand and is available in \"Columbus, OH\".\n\n2.  **Considering Refinements:** In the second image, the user has opened the \"Filter and sort\" panel and expanded the \"Material\" category. This action indicates they are no longer satisfied with a general browse and have a specific characteristic in mind. They are now evaluating the different material options available, such as wood, metal, and rattan.\n\n3.  **Applying a Specific Filter:** In the third image, the user has made a decision. They have clicked the \"Solid wood\" checkbox. The product grid has updated to show only solid wood nightstands, and a \"Solid wood\" filter tag is now active.\n\n**Predicted Intent:**\n\nThe user's intent is to **find and purchase a solid wood nightstand**. They began with a broad search and then deliberately used the filtering system to narrow down the results to match their specific preference for a durable, high-quality material."}
+{"id": "26c5fa4a-431d-4697-b1cb-7c2d82a4b3e1.jpg_1", "model_answer": "Based on the sequence of screenshots, here is a step-by-step analysis of the visual changes and the user's inferred intent:\n\n**Step 1: Initial State (First Image)**\n*   The user is on the Cookpad homepage.\n*   The search bar is empty.\n*   The page displays the user's search history (e.g., \"Pad Thai,\" \"Shrimp\") and recently viewed recipes.\n*   **Inference:** The user has just arrived on the site or is about to begin a new search for a recipe.\n\n**Step 2: Performing a Search (Second Image)**\n*   The user has typed \"Fried Bombay Duck\" into the main search bar at the top of the page.\n*   The rest of the page content remains unchanged from the first image.\n*   **Inference:** The user has decided what they want to cook and is initiating a search for that specific dish.\n\n**Step 3: Viewing Search Results (Third Image)**\n*   The page has now loaded the search results for the query.\n*   The heading confirms this, showing \"Fried Bombay Duck recipes (32)\".\n*   The main content area is now a list of various recipes for \"Fried Bombay Duck,\" each with a photo, title, cooking time, and ingredients.\n*   A \"Filters\" section has appeared on the left, allowing the user to refine the results.\n*   **Inference:** The search has been successfully executed, and the user is now browsing the available recipes to find one that suits their needs.\n\n**Overall User Intent:**\n\nThe user's overall intent is to **find a recipe for \"Fried Bombay Duck.\"** They navigate the Cookpad website by starting on the homepage, using the search bar to look for a specific dish, and then evaluating the list of resulting recipes."}
+{"id": "358cdc88-5796-4105-b94d-38c580231f89.jpg_2", "model_answer": "Based on the visual transitions between the webpages, the user's final objective is **to find a name for their baby boy.**\n\nHere's the reasoning:\n\n1.  **Initial Interest (Image 1):** The user starts on the \"Getting Pregnant\" section of the BabyCenter website. This indicates their journey begins with an interest in conceiving or the very early stages of pregnancy.\n\n2.  **Shift in Focus (Image 2):** The user then navigates to the \"Baby Names\" section. This is a significant shift from the \"how-to\" of getting pregnant to the \"what's next\" of planning for an actual baby, suggesting they are either newly pregnant or very optimistic.\n\n3.  **Specific Action (Image 3):** The user interacts with the \"Baby names finder\" tool and specifically filters the search for \"Boy\" names. This final, focused action clearly shows their immediate goal is to explore and select a name for a male child."}
+{"id": "0113d857-adb4-456b-8b75-9cd22446be52.jpg_3", "model_answer": "Based on the sequence of these three webpage snapshots, we can analyze the user's actions and infer their goal. This is a classic example of a user refining their search and then taking action on a specific item of interest.\n\nHere is a step-by-step analysis of the user's journey:\n\n### **Step 1: Broad Initial Search (Image 1)**\n\n*   **Website:** The user is on \"Land Watch,\" a website specializing in the sale of land, farms, and rural properties.\n*   **Initial Goal:** The user starts with a broad search for **\"Ohio Land for Sale by Owner.\"** This indicates an initial preference for dealing directly with property owners, possibly to avoid real estate agent commissions or to have a more direct negotiation.\n*   **Filters:** At this stage, the only significant filter applied is the location (Ohio) and the sale type (For Sale by Owner). The user has not yet specified price, acreage, or property type.\n*   **Result:** This broad search yields a large number of results (4,596 listings), showing a mix of properties, including some with existing houses and others that are just land.\n\n### **Step 2: Refining the Search (Image 2)**\n\n*   **Change in Strategy:** The user has modified their search. The primary filter is no longer \"For Sale by Owner\" but has been changed to **\"Undeveloped\"** land in Ohio.\n*   **Inference:** The user likely realized their primary goal is to find raw, empty land, rather than a property with a house. The \"For Sale by Owner\" criterion might have been too restrictive or less important than the type of land itself. They are now focusing on land that could be used for building a new home, for recreation (like hunting), or as an investment.\n*   **Result:** This refined search is much more focused. The number of listings has been significantly reduced (to what appears to be 1,25"}
+{"id": "f2eea470-c966-4ecd-a7ae-ed2caaa30fe5.jpg_4", "model_answer": "Based on the layout and content shifts across the three images, here is a prediction of the user's navigation intent:\n\nThe user is likely a parent or a young driver who has failed the Virginia driver's license exam three times and is now trying to understand the mandatory requirements to be eligible to test again.\n\nHere is a step-by-step analysis of the navigation path that supports this conclusion:\n\n### **Image 1: Broad Inquiry about Driver Training**\n\n*   **Page Title:** \"Attending a Driver Training School\"\n*   **Content:** This is a high-level, general information page. It serves as a starting point for anyone interested in driver training. It covers multiple topics, including:\n    *   General Driver Training Schools\n    *   Virtual Driver Training Schools\n    *   **CDL Driver Training Schools**\n    *   Driver Training for Applicants Failing Three Times\n*   **Predicted Intent at this Stage:** The user has a general question about driver training in Virginia. They could be a new teen driver, an adult getting a license for the first time, or someone interested in a commercial license. The path is not yet clear.\n\n### **Image 2: Narrowing the Focus to Standard (Non-Commercial) Driver Education**\n\n*   **Page Title:** \"Driver Training Schools\"\n*   **Content Shift:** The most significant change is the **disappearance of the \"CDL Driver Training Schools\" section**. The content is now focused exclusively on standard, non-commercial driver education. New sections appear, such as \"Requirements to Get a License,\" which specifically mentions rules for drivers **under 18 years old** and the involvement of a **parent/guardian**.\n*   **Predicted Intent at this Stage:** The user has filtered their search. They are not interested in a Commercial Driver's License (CDL). The focus on underage requirements and"}
+{"id": "2496c31e-197e-4787-a049-578b6eabcd70.jpg_5", "model_answer": "Based on the sequence of screenshots, the user is likely **researching the historical price performance of the cryptocurrency Litecoin (LTC)**.\n\nHere's a step-by-step breakdown of the user's actions:\n\n1.  **Image 1:** The user starts on the main page of CoinMarketCap, which lists cryptocurrencies by market capitalization. Their cursor is hovering over Litecoin (LTC), indicating their interest in this specific coin.\n2.  **Image 2:** The user has clicked on Litecoin and is now on its dedicated \"Overview\" page. They are viewing current price information, charts, and general statistics.\n3.  **Image 3:** The user has navigated from the \"Overview\" tab to the \"Historical Data\" tab for Litecoin. This page shows a detailed, day-by-day breakdown of past prices (Open, High, Low, Close), trading volume, and market cap.\n\n**Conclusion:**\n\nThe user's progression from a general list to a specific coin's overview, and finally to its detailed historical data, strongly suggests they are performing an analysis of Litecoin's past performance. This is a common activity for someone considering investing in a cryptocurrency, conducting market research, or performing technical analysis."}
+{"id": "32aa9179-09ff-4caa-af3b-eff7b38f5b32.jpg_6", "model_answer": "Based on the sequence of these three images, the user's likely intention is to **learn about and explore the LinkedIn Learning platform.**\n\nHere's a breakdown of the user's journey:\n\n1.  **Initial Search:** The user starts by typing \"linkedin learning\" into the main search bar on LinkedIn. This indicates a clear and direct interest in this specific service.\n2.  **Reviewing Search Results:** The user is then presented with a comprehensive search results page that includes courses, jobs, groups, and the official LinkedIn Learning company page.\n3.  **Navigating to the Official Page:** Instead of clicking on an individual course or job, the user navigates to the main \"LinkedIn Learning\" company page.\n\nThis final step is key. By going to the official page, the user shows they want a high-level overview of the platform—what it is, what it offers, and what kind of content it posts—rather than just diving into a specific course. They are likely evaluating the service for their professional development needs."}
+{"id": "42b5cfdd-c73e-49bb-bb0e-214da54c8d08.jpg_7", "model_answer": "Based on the user's navigation path across the three images, we can infer the following about their goal:\n\n1.  **Initial Intent:** The user starts on the \"Attractions & Tickets\" page of Trip.com, indicating a general interest in finding activities or sights to see.\n\n2.  **Narrowing the Location:** The location is set to \"Hong Kong,\" showing the user is specifically interested in attractions within that city.\n\n3.  **Specifying the Attraction Type:** The user clicks on the search bar and types \"Park.\"\n\n**Conclusion:**\n\nThe user's goal is to **find and likely book tickets for a park in Hong Kong**. The search results suggest they are looking for major theme parks or recreational parks, such as Ocean Park or Hong Kong Disneyland."}
+{"id": "80cb2b9b-6cd9-4d64-8083-dbcdc49f8464.jpg_8", "model_answer": "Of course. Here is a detailed analysis of the structural and content-level changes across the three webpages to predict user intent.\n\n### Summary of User Intent\n\nThe user is a business owner or representative who is actively seeking to get their business accredited by the Better Business Bureau (BBB). They have moved from an initial stage of interest to the action of providing their contact information. Their clear intent is to submit a lead generation form to be contacted by a BBB representative for more information about the accreditation process, benefits, and requirements.\n\n---\n\n### Detailed Analysis\n\nThe analysis is broken down into the structural and content-level changes observed across the three images, which represent a sequence of user actions.\n\n#### **Image 1: Initial State & Latent Intent**\n\n*   **Structural Analysis:** The webpage is static. It presents a clear value proposition for BBB Accreditation with a prominent call-to-action (CTA) form titled \"Learn more about BBB Accreditation.\" The form fields (First Name, Last Name, Email, etc.) are present but empty.\n*   **Content Analysis:** There is no user-generated content. The page is in its default state.\n*   **Predicted Intent:** The user has navigated to this specific page, indicating a pre-existing interest in what the BBB offers. Their intent is to **evaluate the possibility of getting their business accredited**. They are currently in an information-gathering phase, reading about the benefits like \"Greater Exposure,\" \"Enhanced Credibility,\" and \"Differentiation.\" The empty form represents a potential next step.\n\n#### **Image 2: First Engagement**\n\n*   **Structural Analysis:** The overall page structure remains unchanged. The significant change is within the form itself: the \"First Name\" input field has transitioned from an empty state to a filled state.\n*   **Content Analysis:** The user has typed \"Vito\" into the \"First Name\" field.\n*   **Predicted Intent:** The user's intent has progressed from passive information gathering to **active engagement**. By starting to fill out the"}
+{"id": "f2d7b54d-3718-418c-94f2-e6c49042ac0d.jpg_9", "model_answer": "Based on the series of images, we can infer the user's goal with increasing specificity by observing their actions.\n\n**User's Goal:** To find a logistics-related job in or near Elmont, New York, that requires a high school diploma or equivalent and falls within a middle-income salary range.\n\nHere is a step-by-step breakdown of the user's navigation path and what it reveals:\n\n1.  **Initial Search (Established before Image 1):** The user has already performed a search. The \"Current Filters\" section shows their initial criteria:\n    *   **Field:** \"logistic\"\n    *   **Location:** ZIP code 11005 (Elmont, NY) within a 20-mile radius.\n    *   **Salary:** \"Middle Income Jobs ($30K-$69K)\".\n    *   This initial search yielded 2916 results, which is too many to review effectively.\n\n2.  **Refining the Search (Image 1 & 2):** The user opens the \"Filters\" menu to narrow down the results. They scroll down to the \"Career/Education Level\" section, indicating they want to filter based on educational requirements.\n\n3.  **Applying the Final Filter (Image 3):** The user makes a specific choice. They select the checkbox for **\"High school diploma or equivalent\"**. This is the key action. It shows they are specifically looking for jobs that do not require a college degree.\n\nIn summary, the user is methodically refining a broad job search for a \"logistic\" position to find roles that precisely match their location, desired salary, and educational qualifications."}
+{"id": "2bca6168-ca65-404d-9e56-f92b7ba8ecc7.jpg_10", "model_answer": "Based on the visual progression in the screenshots, the user is attempting to **access informational resources about storage options.**\n\nHere's a step-by-step breakdown of the user's actions:\n\n1.  **Image 1:** The user is on the homepage of the \"Extra Space Storage\" website.\n2.  **Image 2:** The user moves their cursor to hover over the **\"STORAGE 101\"** link in the main navigation menu.\n3.  **Image 3:** A dropdown menu appears under \"STORAGE 101,\" revealing links to educational content such as \"Storage Types\" (Self Storage, Vehicle Storage, etc.) and a \"Storage Unit Size Guide.\"\n\nThe user's goal is not to immediately find a storage unit near them, but rather to first learn more about the services offered and determine what type or size of storage they might need. They are in the research phase of their process."}
+{"id": "8a45644f-1527-4401-a627-135d8bda3ab6.jpg_11", "model_answer": "Of course. Here is a detailed analysis of the structural and content-level changes across the three webpages to predict the user's evolving intent.\n\n### Overall User Intent Prediction\n\nThe user's journey demonstrates a classic progression from **exploration** to **evaluation** and finally to **refinement**.\n\n1.  **Initial Intent (Image 1):** The user is in the early, exploratory phase of trip planning. They know their origin (Columbus) but are open to destinations and dates, seeking inspiration and price estimates.\n2.  **Developing Intent (Image 2):** The user has made key decisions. Their intent is now evaluative. They have a specific destination (New York City) and dates, and they are actively comparing concrete flight options based on primary factors like price and time.\n3.  **High Intent (Image 3):** The user is now refining their search with high specificity. They are moving beyond basic criteria and using advanced filters to find the optimal flight that meets nuanced preferences (e.g.,"}
+{"id": "7862788a-f7f7-46ee-b71f-373ec9a05345.jpg_12", "model_answer": "Based on the user's navigation path across the three images, we can infer their goal with increasing specificity.\n\n### User's Goal Analysis:\n\nThe user's primary goal is to **book a short, one-night, round-trip package deal (flight + hotel) for two adults from Ahmedabad to Dubai, with a specific requirement that the hotel stay includes breakfast.**\n\nHere is a step-by-step breakdown of how we can infer this from the images:\n\n**Image 1: Initial Search & Intent**\n\n*   **Action:** The user is on the Trip.com homepage and has filled out the search form.\n*   **What they want:** A \"Bundle & Save\" package, which combines flights and hotels. This indicates they are looking for a complete travel package, likely for convenience and potential cost savings.\n*   **Destination & Origin:** They are planning a round-trip from Ahmedabad (AMD) to Dubai (DXB).\n*   **Dates & Duration:** The trip is for a very short duration, just one night (departing Wednesday, April 5, and returning Thursday, April 6).\n*   **Travelers:** The booking is for 2 adults and 1 hotel room.\n\n**Inference from Image 1:** The user wants to find a convenient package deal for a quick, one-night trip for two to Dubai.\n\n---"}
+{"id": "fb5c47bb-9bb9-4633-a1c6-16f4c4198919.jpg_13", "model_answer": "Based on the sequence of these three webpage snapshots, the user is likely performing the following task:\n\n**Researching the drug Paracetamol to learn about its uses, dosage, side effects, and other important information.**\n\nHere's a step-by-step breakdown of the user's journey:\n\n1.  **Image 1:** The user is on the homepage of Drugs.com and has typed \"Paracetamol\" into the search bar. This is the initial step of their research.\n2.  **Image 2:** The user has executed the search and is now on the search results page for \"Paracetamol.\" They are presented with various links related to the drug, such as FAQs, drug interactions, and the main drug information page.\n3.  **Image 3:** The user has clicked on the main link for \"Paracetamol\" and is now reading a detailed consumer information page. This page provides comprehensive details about the drug, including what it is, how to take it, dosage instructions, side effects, and contraindications."}
+{"id": "bc9c7b93-f2a6-4fa2-bf10-7f604004f259.jpg_14", "model_answer": "Of course. Here is a detailed analysis of the structural and content-level changes across the three webpages to predict the user's intent.\n\n### Overall User Intent Prediction\n\nThe user's intent is to **find and evaluate reputable landscape contractors in the Milwaukee, WI area for the purpose of hiring one.** They are using the Better Business Bureau (BBB) specifically because they value trust, reliability, and established business ratings in their decision-making process. They are currently in the information-gathering and comparison phase of their customer journey.\n\n---\n\n### Step-by-Step Analysis of the User Journey\n\nHere is a breakdown of the changes across the three images, which represent a clear sequence of user actions.\n\n#### **Image 1: The Landing Page - Initial Exploration**\n\n*   **Structural Analysis:**\n    *   This is the BBB homepage, featuring a prominent central search module.\n    *   The layout is designed to serve multiple user intents, with clear sections for \"Find a Business,\" \"File a Complaint,\" \"Start a Review,\" and \"Get Accredited.\"\n    *   The primary call-to-action is the search bar, indicating that finding a business is the most common user goal.\n\n*   **Content-Level Analysis:**\n    *   The \"Find\" search field is empty but shows placeholder suggestions like \"Construction Services,\" \"Plumbers,\" etc.\n    *   The \"Near\" location field is pre-populated with \"Milwaukee, WI,\" likely based on the user's IP address or a previous search.\n    *   The rest of the page contains general information: news, popular categories, and promotions for the BBB itself.\n\n*   **Inferred User State:** The user has just arrived on the site. Their goal is not yet specified, but the pre-filled location suggests they are looking for local services.\n\n#### **Image 2: The Search Query - Defining Intent**\n\n*   "}
+{"id": "8ea2d5ea-90a1-4c33-b109-83254d815bad.jpg_15", "model_answer": "Based on the sequential changes in the screenshots, the user's objective is to **inquire about getting their new business accredited by the Better Business Bureau (BBB).**\n\nHere's a breakdown of the user's journey:\n\n1.  **Initial Intent:** The user is on the BBB's \"Get Accredited\" page and begins filling out the form to learn more. They enter their email (\"vito.corleone@godfather.com\") and business name (\"Mafia Services\").\n2.  **First Action:** In the first screenshot, they indicate their business has been in operation for more than six months by selecting \"Yes\".\n3.  **Correction/Clarification:** In the final screenshot, the user changes their selection from \"Yes\" to \"No\" for the question \"Have you been in business for more than six months?\".\n\nThis specific change from \"Yes\" to \"No\" is the key indicator. It shows the user is specifically trying to get information about accreditation for a business that is **less than six months old**. They are likely trying to determine their eligibility or the specific process for a new enterprise."}
+{"id": "eb67042a-f0d5-48eb-9c16-8ef93871397c.jpg_16", "model_answer": "Based on the provided webpage snapshots, the user is performing the following task:\n\n**Shopping for a used Toyota Corolla on the CarMax website.**\n\nHere's a breakdown of the evidence:\n\n1.  **Website:** The user is on CarMax.com, a site for buying and selling used cars.\n2.  **Initial Search:** The page title clearly states \"Used Toyota Corolla for sale,\" and the \"Make\" and \"Model\" filters are set to \"Toyota\" and \"Corolla,\" respectively.\n3.  **Refining the Search:** The third image shows the user actively using the \"Year\" filter to narrow down the results. They have selected a range that appears to start from **2016** and goes up to the most recent model year available.\n\nIn summary, the user is looking to purchase a relatively recent model (2016 or newer) of a used Toyota Corolla."}
+{"id": "5f35baa2-d228-4fa1-894a-6cebed718b9f.jpg_17", "model_answer": "Based on the progression across the three screenshots, the user's overall navigation goal is to **find a part-time job in an Amazon Pharmacy**.\n\nHere's a breakdown of the user's journey:\n\n1.  **Screenshot 1:** The user starts on the main Amazon hourly jobs landing page, which displays a wide variety of job categories. They identify \"Amazon Pharmacy\" as a category of interest.\n2.  **Screenshot 2:** The user has clicked on the \"Amazon Pharmacy\" category, navigating to a page that lists all available pharmacy-related jobs.\n3.  **Screenshot 3:** The user refines their search on the pharmacy jobs page by applying a filter. They have specifically checked the \"Part Time\" box under the \"JOB TYPE\" filter, indicating they are looking for a position that is not full-time."}
+{"id": "08be352c-4e74-4549-acda-1d9b42f4b438.jpg_18", "model_answer": "Based on the user's journey across the three screenshots, their probable goal is to **purchase a single ticket for the Phoenix Suns vs. Denver Nuggets game with a budget of around $200.**\n\nHere's a breakdown of their actions:\n\n1.  **Initial Search (Image 1):** The user starts by looking for two tickets, sorted by the lowest price available.\n2.  **Refining the Search (Image 2):** The user changes the quantity from two tickets to just one, indicating they are likely going to the game alone. They explore the available single-ticket options.\n3.  **Setting a Budget (Image 3):** The user significantly refines their search by adjusting the price filter to a maximum of $200, showing they have a specific budget in mind for their single ticket."}
+{"id": "42f06fb7-d9a4-463b-beea-da8f34414004.jpg_19", "model_answer": "Based on the sequence of screenshots, here is the inferred user objective:\n\nThe user initially wanted to understand the **food and alcohol interactions for the allergy medication Allegra (fexofenadine)**.\n\nAfter reviewing that information, their objective shifted. They then navigated away from the Allegra page to the A-Z drug index to **find information about a different medication that starts with the letter 'V'**. They are likely browsing alphabetically because they are unsure of the drug's full or correct spelling."}
+{"id": "09059c15-25c0-451a-a6af-5557b7f0e82e.jpg_20", "model_answer": "Based on the provided screenshots, here is a step-by-step reasoning of the user's objective:\n\n**User's Objective:** The user wants to find and likely purchase a 55-inch Samsung TV.\n\n---\n\n### Step-by-Step Reasoning:\n\n1.  **Screenshot 1: Initial Search Intent**\n    *   The user is on the Best Buy homepage.\n    *   They have navigated to the search bar at the top of the page.\n    *   They have typed the query \"samsung tv\" into the search bar.\n    *   **Inference:** The user's initial, broad objective is to browse the selection of Samsung televisions available at Best Buy.\n\n2.  **Screenshot 2: Search Suggestions**\n    *   As the user types their query, a dropdown menu appears with search suggestions.\n    *   These suggestions include refinements by screen size (e.g., \"samsung tv 55 inch\", \"samsung tv 65\") and specific product recommendations.\n    *   **Inference:** The user is still in the process of searching. The website is offering ways to narrow down the search, but the user has not yet selected one of these suggestions. They proceed with their general \"samsung tv\" search.\n\n3.  **Screenshot 3: Filtering for a Specific Size**\n    *   The user has now landed on the search results page for \"samsung tv\". The page displays a list of various Samsung TV models.\n    *   The crucial action is visible in the left-hand sidebar under the \"Filter Your Results\" section.\n    *   The user has clicked on the checkbox next to **\"55\"** under the \"TV Screen Size (in.)\" filter.\n    *   **Inference:** This action demonstrates a clear refinement of the user's initial goal. They are no longer interested in all Samsung TVs; they are specifically looking for models that are 55 inches in size. This is the strongest indicator of their specific objective."}
+{"id": "9f428247-d3fa-4c51-ba2f-97fe7b4ef9ad.jpg_21", "model_answer": "Based on the sequence of screenshots, the user's objective is to **find and book a hotel in Dublin, Ireland**.\n\nHere's the breakdown of the user's actions:\n\n1.  **Screenshot 1:** The user is on the Ryanair website and has selected the \"Hotels\" tab, indicating their intent to search for accommodation. The cursor is active in the \"Destination or property\" search box.\n2.  **Screenshot 2:** The user has started typing \"Dublin\" into the destination field, which triggers a dropdown list of location suggestions.\n3.  **Screenshot 3:** The user has selected \"Dublin, Ireland\" from the suggestions, confirming their desired location for a hotel search."}
+{"id": "9809c03d-61c1-4640-8bf6-46ce20a88578.jpg_22", "model_answer": "Based on the sequence of images, the high-level task being performed is to **sort the search results for rental apartments in New York by price, from lowest to highest.**\n\nHere's a breakdown of the steps shown:\n\n1.  **Initial State:** The user is on the Redfin website, viewing a list of \"New York Apartments for Rent,\" which are initially sorted by \"Newest.\"\n2.  **Action:** The user clicks on the \"Sort\" dropdown menu to see the available sorting options.\n3.  **Final State:** The user has selected \"Price (Low to High),\" and the sort filter on the page has been updated to reflect this choice."}
+{"id": "604b9910-0079-4b25-ae5e-62c4ff2b9964.jpg_23", "model_answer": "Based on the sequence of webpage states, the high-level task being performed is:\n\n**Changing the location to browse for events in a different city.**\n\nSpecifically, the user is changing their location from \"Columbus, OH, USA\" to \"Austin, TX, USA\" on the StubHub website to see a list of concerts, sports, and theatre events available in the Austin area."}
+{"id": "2456573b-41d2-4ead-947a-1b299832e608.jpg_24", "model_answer": "Based on the user's browsing journey, their probable goal is to **find and apply for an hourly job at Amazon**.\n\nHere's a breakdown of the user's actions:\n\n1.  **Image 1:** The user lands on the Amazon jobs homepage, which highlights \"Higher pay starting at $19/hour\" and displays various hourly roles like \"Fulfillment Center Associate,\" \"Delivery Station Associate,\" and \"Grocery Warehouse Associate.\"\n2.  **Image 2:** The user opens the main navigation menu to see the site's structure.\n3.  **Image 3:** The user expands the \"Job Opportunities\" section within the menu to see a comprehensive list of available job categories.\n\nThis progression shows a clear intent to explore the different types of hourly positions Amazon offers before likely proceeding to search for specific openings in their area."}
+{"id": "8f08ba33-3b08-445b-8c5e-b552a79953a5.jpg_25", "model_answer": "Based on the sequence of screenshots, the user's objective is to **find a hair salon in San Diego, California.**\n\nHere's a breakdown of the user's actions:\n\n1.  **Screenshot 1:** The user starts their search by typing \"hair salon\" into the business/service search field. The location field defaults to \"New York, NY\".\n2.  **Screenshot 2:** The user changes the location by typing \"San Diego\" into the location field, which triggers a dropdown list of suggested locations.\n3.  **Screenshot 3:** The user selects the specific location \"San Diego, CA\" from the dropdown list, confirming their intended search area.\n\nThe user is preparing to execute a search for hair salons within the San Diego, CA area."}
+{"id": "0daf346d-efba-43d4-96db-259ea7f2fa6f.jpg_26", "model_answer": "Based on the sequence of screenshots, the user is shopping for discounted clothing on the Kohl's website and is refining their search.\n\nHere's a step-by-step breakdown of their actions:\n\n1.  **Initial Search:** The user starts in the \"Clearance Clothes\" section and has filtered the results to show only \"Bottoms\" (like pants and shorts).\n2.  **Intention to Change:** The second image shows the user's cursor hovering over the \"Dresses\" category in the left-hand filter menu.\n3.  **Refined Search:** The final image shows that the page has updated. The user has successfully clicked on \"Dresses,\" and the product grid now displays only clearance dresses.\n\nTherefore, the user is trying to **change the category of clearance clothing they are browsing from \"Bottoms\" to \"Dresses.\"**"}
+{"id": "4f7ad134-aaa7-4257-829b-eb91febf74a6.jpg_27", "model_answer": "Based on the sequence of screenshots, the user is trying to **search for a cruise on the Carnival website**.\n\nHere's a step-by-step breakdown of their actions:\n\n1.  **Image 1:** The user has clicked on the \"SAIL FROM\" dropdown menu and selected **Brisbane, Australia** as their departure port.\n2.  **Image 2:** The user has moved to the \"SAIL TO\" dropdown and selected **Papua New Guinea** as their destination.\n3.  **Image 3:** With the departure and destination ports set, the user has clicked on the \"DATES\" dropdown and is selecting **November 2023** as their desired travel time.\n\nIn summary, the user is attempting to find a cruise that departs from Brisbane, Australia, travels to Papua New Guinea, and sails in November 2023."}
+{"id": "3307bd5c-663a-4fb9-b768-90897e95fef5.jpg_28", "model_answer": "Based on the provided screenshots, here is a step-by-step reasoning to identify the user's objective:\n\n1.  **Identify the Platform and Context:** The user is on the Booking.com website, a platform used for booking travel accommodations. The user is logged into their account (\"Smith Jones\"), which suggests they are actively planning a trip.\n\n2.  **Analyze the Search Criteria:** The primary search form on the page shows the user's specific inputs:\n    *   **Destination:** \"Manila\"\n    *   **Dates:** A 2-night stay from Monday, April 10th to Wednesday, April 12th.\n    *   **Occupancy:** \"2 adults, 1 child\" in \"1 room\".\n\n3.  **Confirm User Intent:** The \"Your recent searches\" section corroborates the information in the main search bar, showing a recent search for \"Manila\" with the same occupancy details. This confirms the user's intent is deliberate and not an accidental entry.\n\n4.  **Synthesize the Objective:** By combining these observations, we can determine the user's clear and specific goal.\n\n**User Objective:** The user wants to find and book accommodation in Manila, Philippines, for a short family trip for two adults and one child, from April 10th to April 12th."}
+{"id": "e54fc5b9-07bf-43c2-868e-bfe02d552c81.jpg_29", "model_answer": "Based on the sequential changes in the screenshots, the user's objective is to **track a FedEx package using a reference number instead of a standard tracking number.**\n\nHere's the breakdown of the user's actions and the interface's response:\n\n1.  **Initial Action:** In the first screenshot, the user has navigated to the FedEx tracking page, selected the \"Track by Reference\" tab, and entered a reference number (`33455654345`). This indicates they do not have a standard tracking number and are attempting an alternative lookup method.\n2.  **System Prompt:** In the second screenshot, the interface has updated. It now highlights the \"DESTINATION COUNTRY/TERRITORY\" field in red and displays other optional fields. This is the system's response, indicating that a reference number alone is not sufficient and more information is required to identify the specific shipment.\n3.  **Pending Final Action:** In the final screenshot, the \"TRACK\" button has become active (changed from gray to orange). While the user has not yet filled in the required destination country, their clear intent remains to execute the search once they provide the necessary details.\n\nIn summary, the user wants to find the status of their shipment but only has a reference number, so they are attempting to use FedEx's reference tracking feature, which requires additional information to work."}
+{"id": "86376987-29c2-4b2a-9558-2cfb1a33d26d.jpg_30", "model_answer": "Excellent analysis. Based on the visual evidence, here is a prediction of the user's intent and navigation path.\n\n### Overall User Intent\n\nThe user is on a journey to find and likely purchase an online course for a specific hobby. Their navigation pattern shows a clear progression from **broad, unfocused exploration** to a **highly specific, refined search**, indicating they are moving from the \"discovery\" phase to the \"evaluation and selection\" phase of their decision-making process.\n\n---\n\n### Step-by-Step Navigation Analysis\n\n#### **Screen 1: The Homepage (Broad Exploration)**\n\n*   **Layout & Content:** This is the generic Udemy homepage. It features a wide variety of topics (\"Python,\" \"Javan\" [likely a typo for Java], business courses, design, marketing, etc.). The content is designed to appeal to any potential user, showcasing the breadth of the platform's offerings.\n*   **Predicted Intent:** The user has just arrived on the site with a general idea of \"I want to learn something new.\" They are in an exploratory mindset, browsing the main categories to see what piques their interest. They have not yet committed to a specific subject.\n\n#### **Transition from Screen 1 to Screen 2: Narrowing Focus**\n\n*   **Layout & Content Shift:**\n    *   The main heading changes from the generic homepage banner to **\"Drawing Courses.\"**\n    *   A breadcrumb trail appears: `Home > Lifestyle > Arts & Crafts > Drawing`.\n    *   All course listings are now exclusively related to drawing"}
+{"id": "e4d86e4b-ecb7-46ee-84e8-f17d108d5f82.jpg_31", "model_answer": "Based on the sequence of webpage states, the user is performing the following high-level task:\n\n**Find a rock song for a video using the TikTok Audio Library.**\n\nHere's a breakdown of the user's journey across the three images:\n\n1.  **Initial Exploration:** The user starts on the main \"Audio Library\" page, browsing through \"Top Music.\" They have started playing a song called \"Rockin.\"\n2.  **Broadening the Search by Category:** The user navigates from the general music list to the \"Playlist\" view to see curated collections of music.\n3.  **Focusing on a Specific Genre:** The user selects the \"Rock\" playlist to explore more songs within that specific genre, likely because the song they were listening to (\"Rockin\") is in that category."}
+{"id": "8c042495-ec25-48f1-b5be-e311402405df.jpg_32", "model_answer": "Based on the series of webpage images, we can infer the user's goal with a high degree of confidence.\n\n**Primary Goal:** The user's primary goal is to **find and join an online community or support group on the BabyCenter website for people who are actively trying to get pregnant.**\n\nHere is a step-by-step analysis of the user's actions that supports this conclusion:\n\n1.  **Initial Search and Context (Image 1):**\n    *   The user is on the BabyCenter website, a platform focused on pregnancy and parenting.\n    *   They have specifically searched for the term \"**getting pregnant**\".\n    *   They are on the \"**Community**\" tab, not the \"Expert\" tab. This indicates a clear preference for peer-to-peer interaction and support rather than professional articles or advice.\n    *   The search results are a list of community groups like \"Getting Pregnant's GOT PREGNANT!\", \"Getting Pregnant Swap,\" and \"Christians: Getting Pregnant, Pregnancy.\"\n\n2.  **Refining the Search (Image 2 & 3):**\n    *   The user clicks the \"**Filter**\" button to narrow down the search results.\n    *   Inside the filter pop-up, the user's focus is on the \"**Type**\" of content. The \"**Groups**\" option is selected. This is a crucial step, as it shows they are not interested in individual posts or comments but in finding a dedicated group to join.\n    *   They have left the time filter on \"All Time,\" suggesting they are looking for established, long-running communities, not just recent activity.\n\n### Summary of Inferences:\n\n*   **Intent:** The user is actively trying to conceive. This is evident from the search term \"getting pregnant\" and their presence on a pregnancy-focused website.\n*   **Need:** The user is seeking support, advice, and a sense of community. By choosing the \"Community\" tab and filtering specifically for \"Groups,\" they are demonstrating a desire to"}
+{"id": "3dd3b016-f654-4d39-8006-7bb210d199f1.jpg_33", "model_answer": "Based on the provided series of screenshots, here is a step-by-step reasoning of the user's objective:\n\n**User's Objective:** The user is looking for an entry-level, full-time job in or related to logistics within a 20-mile radius of zip code 11005 (New York). After an initial broad search, they have refined the results and are now considering applying for or saving a specific Administrative Assistant position.\n\n---\n\n### Step-by-Step Reasoning:\n\n1.  **Initial Broad Job Search (Screenshot 1):**\n    *   The user starts on the Ohio Jobs search page (powered by Monster).\n    *   They initiate a search using the keyword **\"logistic\"** and the location (zip code) **\"11005\"**, which corresponds to a location in New York.\n    *   The"}
+{"id": "ca717773-4a78-494e-adc5-b5d78be8f63e.jpg_34", "model_answer": "Based on the sequence of screenshots, the user is trying to **find a business that provides a specific type of service in the Milwaukee, WI area** using the Better Business Bureau (BBB) website.\n\nHere's the breakdown of the user's actions:\n\n1.  **Image 1:** The user is on the BBB homepage. They have either entered or have \"Milwaukee, WI\" pre-filled as their location and have their cursor in the \"Find\" search box, ready to type.\n2.  **Image 2:** The user clicks on the \"Find\" box, which reveals a dropdown list of popular business categories like \"Construction Services,\" \"Restaurants,\" \"Plumbers,\" etc.\n3.  **Image 3:** The user begins typing \"Ser\" into the search box. This filters the dropdown list to categories containing that text, specifically \"Construction **Ser**vices\" and \"Auto **Ser**vices.\"\n\nThe user's goal is to search for a local service provider (likely an auto or construction service) in Milwaukee."}
+{"id": "507520aa-73a4-4b62-940e-c907e3eb7108.jpg_35", "model_answer": "Based on the visual progression of the screenshots, the user is attempting to **find a USPS location, specifically a Post Office, near the ZIP code 90210.**\n\nHere's the step-by-step breakdown:\n\n1.  **Image 1:** The user is on the USPS homepage and their cursor is hovering over the \"Locations\" link in the top navigation bar, indicating their intent to find a physical location.\n2.  **Image 2:** The user has clicked on \"Locations\" and is now on the \"Find USPS Locations\" page. Their cursor is in the search box, ready to enter a location.\n3.  **Image 3:** The user has typed the ZIP code \"90210\" into the search field. Their next step would be to click the \"Search\" button to see a list of Post Offices within 20 miles of that ZIP code."}
+{"id": "a63e680d-d1b1-45af-ac94-dd80a9b95605.jpg_36", "model_answer": "Based on the sequence of screenshots, here is a deduction of the user's navigation goal:\n\n1.  **Screenshot 1:** The user has searched for student accommodation near the \"University of Leeds\". The results are displayed in the default \"Recommended\" sort order.\n\n2.  **Screenshot 2:** The user has clicked on the sorting dropdown menu, revealing options like \"Distance to university,\" \"Price low to high,\" and \"Price high to low.\" This shows an intent to reorder the search results.\n\n3.  **Screenshot 3:** The user has selected \"Price low to high\" from the dropdown. The property list has now been re-sorted, with the least expensive option, \"St. Albans\" at £254/week, now appearing at the top of the list.\n\n**Conclusion:**\n\nThe user's overall goal is to **find the most affordable student accommodation** available near the University of Leeds. They are using the website's sorting functionality to rearrange the properties from the lowest price to the highest."}
+{"id": "2c68fd85-cf37-406d-8d40-77120358414c.jpg_37", "model_answer": "Based on the provided screenshots, here is a step-by-step reasoning to identify the user's objective:\n\n1.  **Step 1 (First Image):** The user is on the Target homepage. They have moved their cursor to the \"Categories\" dropdown menu in the top-left corner. Within this menu, they are hovering over \"Household Essentials,\" causing a sub-menu to appear. This indicates their initial interest is in shopping for household goods.\n\n2.  **Step 2 (Second Image):** The user's cursor has moved down within the \"Household Essentials\" sub-menu and is now highlighting \"Toilet Paper.\" This action shows a clear refinement of their search from the broad category of \"Household Essentials\" to a specific product type.\n\n3.  **Step 3 (Third Image):** The user has successfully navigated to the product listing page for toilet paper. The page heading is \"Toilet Paper,\" and the breadcrumbs at the top confirm the path taken: \"Target / Household Essentials / Paper Products / Toilet Paper & Wipes.\" The page displays various brands and packages of toilet paper for sale.\n\n**Conclusion:**\n\nBy following the user's navigation from the homepage to a general category and then to a specific product page, it is clear that the user's objective is to **shop for and purchase toilet paper**."}
+{"id": "3467a166-04ce-4bc9-9a2e-47bc034d5a9d.jpg_38", "model_answer": "Based on the sequence of these three webpage snapshots, we can analyze the user's step-by-step process for purchasing tickets.\n\n**Overall Goal:** The user is trying to buy four tickets for the Kevin Hart comedy show and is using the website's filters to find the best option for their budget and seating preference.\n\nHere is a breakdown of their actions:\n\n### Step 1: Initial Exploration (First Image)\n\n*   **Action:** The user has selected that they need **4 tickets**. They are looking at the initial, base prices for various sections. For example, Section 36A is listed at **$80 per ticket**.\n*   **User's Intent:** The user is getting a general sense of the ticket availability and the starting prices. They are likely comparing different sections to see what's available in their potential price range. The filter \"Show prices with estimated fees\" is turned **off**, so they are only seeing the pre-fee price.\n\n### Step 2: Seeking Price Transparency (Second Image)\n\n*   **Action:** The user has toggled the **\"Show prices with estimated fees\"** filter to the ON position.\n*   **Result:** This has caused all the ticket prices in the list to increase significantly. The same tickets in Section 36A that were $80 are now shown as **$103 per ticket**. The price range slider on the right has also updated to reflect these higher, all-in prices.\n*   **User's Intent:** This is a very common action for a savvy ticket buyer. The user wants to see the **total cost per ticket**, including service fees and taxes, to avoid surprises at checkout. They are now evaluating the options based on the final price they will actually have to pay.\n\n### Step 3: Refining the Search by Location (Third Image)\n\n*   **Action:** After seeing the final prices, the user has now expanded the **\"Zones\"** filter.\n*   **User's Intent:** The"}
+{"id": "f4a0f46a-6075-492c-a6d3-c8adeaad7857.jpg_39", "model_answer": "Of course. Based on the sequence of these three webpage snapshots, we can analyze the user's actions and infer their goal.\n\nThe user is looking for a specific type of job at the company CarMax. Here is a step-by-step breakdown of their process:\n\n### **Step 1: Initial Broad Search (Image 1)**\n\n*   **Action:** The user has performed an initial search for jobs in \"Springfield\".\n*   **Result:** The website returned 5 job listings. It's important to note that this search pulled results from both **Springfield, Illinois** and **Springfield, Missouri**.\n*   **User's Goal:** To see all available CarMax positions in any city named Springfield. At this stage, they are casting a wide net to see what is available.\n\n### **Step 2: Filtering the Results (Image 2)**\n\n*   **Action:** The user has refined their search by applying two filters:\n    1.  **Job Category:** They have likely selected \"Sales\" (as the \"Auto Repair Specialist\" role has been removed).\n    2.  **Job Type:** They have checked the box for \"Full Time\".\n*   **Result:** The job listings have been narrowed down from 5 to 2. Both remaining jobs are \"Customer Specialist\" roles, which are full-time positions.\n*   **User's Goal:** To find a **full-time, sales-related** position. They are no longer interested in part-time roles or other job categories like auto repair.\n\n### **Step 3: Re-sorting for Evaluation (Image 3)**\n\n*   **Action:** The user has changed the sorting criteria for the search results. In the previous images, the results were sorted by **\"Most relevant\"**. In this final image, they are sorted by **\"Most recent\"**. This is evident because the order of the two jobs has flipped.\n*   **Result:** The Springfield, Missouri position is now listed first, whereas the Springfield, Illinois position was first in the previous view.\n*   **User's Goal:** The user is now closely evaluating the two final job options. By changing the sort order, they are trying to view the results from a different perspective, perhaps to see which job was posted more recently. This"}
+{"id": "19f2ca13-87e2-48e2-8302-8507689d68c6.jpg_40", "model_answer": "Of course. Here is a detailed analysis of the structural and content-level changes across the three webpages to predict user intent.\n\n### Executive Summary\n\nThe three images depict a clear, linear user journey on the Drugs.com Pill Identifier tool. There are **no structural changes** to the webpage layout itself; all changes are at the **content level**, reflecting a user progressively entering information into a form. The user's intent is to **identify a specific, unknown pill** by providing its physical characteristics. Their actions show a desire to narrow the search for the most accurate"}
+{"id": "78d773dd-a767-49c0-bd5e-f823d55b577a.jpg_41", "model_answer": "Of course. Here is a detailed analysis of the structural and content-level changes across the three webpages to predict user intent.\n\n### Summary of User Intent\n\nThe sequence of images shows a user transitioning from a **broad, exploratory intent** to a **specific, high-intent, transactional intent**. The user begins by browsing general offers on the homepage and progressively refines their search criteria, signaling a clear interest in booking a cruise to a specific destination (Alaska).\n\n---\n\n### Detailed Step-by-Step Analysis\n\nHere is a breakdown of the changes and the corresponding user intent at each stage:\n\n#### **Image 1: Initial Exploration & Awareness**\n\n*   **Structural State:** This is the default homepage view. The primary interactive element is the main search widget with four blank fields: \"SAIL TO,\" \"SAIL FROM,\" \"DATES,\" and \"DURATION.\" The page is designed to capture attention with large, promotional banners.\n*   **Content Focus:** The content is broad and promotional, designed to appeal to a wide audience.\n    *   **Primary Offer:** \"SAVE WITH LOW DEPOSITS TOGETHER\" (40% off, $50 reduced deposit). This targets price-sensitive users or those looking for a good deal.\n    *   **Secondary Offer:** \"CARNIVAL VENEZIA™ ON SALE NOW!\" This highlights a specific ship or itinerary, appealing to users interested in new experiences.\n    *   **Tertiary Offer:** \"Financing Powered by uplift.\" This addresses budget concerns and aims to lower the barrier to booking.\n*   **Predicted User Intent:** At this stage, the user's intent is **exploratory and undefined**. They have landed on the Carnival homepage and are in the *awareness* or *early consideration* phase of their journey. They are likely asking themselves:\n    *   \"What kind of vacations does Carnival offer?\"\n    *   \"Are there any good deals right now?\"\n    *   \"Should I consider a cruise for my next trip?\"\n\n---\n\n#### **Image 2: Active Consideration & Narrowing Options**\n\n*   **Structural Change:** The user has clicked on the \"SAIL TO\" dropdown menu. A modal or dropdown layer now appears"}

Result/Single_stage_justchoice_5w_90-VisualWebBench_Action_Ground_103.json

@@ -0,0 +1,107 @@
+{
+  "metrics": {
+    "total": 103,
+    "correct": 89,
+    "accuracy": 86.40776699029125
+  },
+  "errors": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_15.png",
+      "ground_truth": "E",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_19.png",
+      "ground_truth": "F",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_26.png",
+      "ground_truth": "G",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "None"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_36.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_45.png",
+      "ground_truth": "A",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "None"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_55.png",
+      "ground_truth": "A",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "None"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_57.png",
+      "ground_truth": "D",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_67.png",
+      "ground_truth": "G",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_70.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_74.png",
+      "ground_truth": "C",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_83.png",
+      "ground_truth": "G",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_87.png",
+      "ground_truth": "E",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_89.png",
+      "ground_truth": "F",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_97.png",
+      "ground_truth": "B",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    }
+  ]
+}

Result/Single_stage_justchoice_90-VisualWebBench_Action_Ground_103.json

@@ -0,0 +1,107 @@
+{
+  "metrics": {
+    "total": 103,
+    "correct": 89,
+    "accuracy": 86.40776699029125
+  },
+  "errors": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_13.png",
+      "ground_truth": "H",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "None"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_15.png",
+      "ground_truth": "E",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_26.png",
+      "ground_truth": "G",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "None"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_36.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_45.png",
+      "ground_truth": "A",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "None"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_55.png",
+      "ground_truth": "A",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "None"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_57.png",
+      "ground_truth": "D",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_60.png",
+      "ground_truth": "H",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "None"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_63.png",
+      "ground_truth": "E",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_67.png",
+      "ground_truth": "G",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_74.png",
+      "ground_truth": "C",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_83.png",
+      "ground_truth": "G",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_89.png",
+      "ground_truth": "F",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_97.png",
+      "ground_truth": "B",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    }
+  ]
+}

Result/Stage_1_memory_2500_test-Element-Attribute_100.json

@@ -0,0 +1,94 @@
+{
+  "metrics": {
+    "total": 100,
+    "role_accuracy": 99.0,
+    "name_accuracy": 95.0,
+    "full_accuracy": 94.0
+  },
+  "errors": [
+    {
+      "image": "airbnb_lvl6_img_shot1_de4b9a.png",
+      "ground_truth": {
+        "role": "link",
+        "name": "BenalmádenaBeach house rentals"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "MarbellaBeachfront rentals"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    },
+    {
+      "image": "airbnb_lvl6_img_shot1_d8b969.png",
+      "ground_truth": {
+        "role": "searchbox",
+        "name": "query"
+      },
+      "prediction": {
+        "role": "combobox",
+        "name": "query"
+      },
+      "match_role": false,
+      "match_name": true,
+      "match_all": false
+    },
+    {
+      "image": "airbnb_lvl6_img_shot2_bd877f_dc9536.png",
+      "ground_truth": {
+        "role": "link",
+        "name": "Kid-friendly state parksCheck out these family-friendly hikes"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "Kid-friendly state parksCheck out these family-friendly"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    },
+    {
+      "image": "airbnb_lvl6_img_shot2_9268e1_e602ea.png",
+      "ground_truth": {
+        "role": "link",
+        "name": "Hosting responsibly"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "Find a co‑host"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    },
+    {
+      "image": "airbnb_lvl6_img_shot3_e8f44f_41dc22_126e8f.png",
+      "ground_truth": {
+        "role": "link",
+        "name": "60"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "50"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    },
+    {
+      "image": "airbnb_lvl6_img_shot3_e8f44f_41dc22_295a6d.png",
+      "ground_truth": {
+        "role": "link",
+        "name": "6"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "9"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    }
+  ]
+}

Result/Stage_1_memory_test-Next_Page_Prediction_100.json

@@ -0,0 +1,114 @@
+{
+  "metrics": {
+    "total": 100,
+    "correct": 85,
+    "accuracy": 85.0
+  },
+  "errors": [
+    {
+      "image": "/code/Data/MultiUI/v0.6_5M/task_data_vv4_50K_9_curated_v2/action_prediction/annotated_images/04881563_techgameworld.com_sony-srs-nb10-the-wea_start15936_annotated33.png",
+      "ground_truth": "E",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_10_curated_v2/action_prediction/annotated_images/07070789_www.freedomfirm.org_post_torn-in-two_start0_annotated12.png",
+      "ground_truth": "C",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_3_curated_v2/action_prediction/annotated_images/03051407_localsportsnews.co.uk_indoor-tennis-is-_start0_annotated32.png",
+      "ground_truth": "D",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_4_curated_v2/action_prediction/annotated_images/04819294_swimlessonssandiego.com_expect-toddler-_start640_annotated10.png",
+      "ground_truth": "H",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_8_curated_v2/action_prediction/annotated_images/05530739_wheelapex.com_how-to-make-car-tires-shi_start0_annotated4.png",
+      "ground_truth": "G",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_2_curated_v2/action_prediction/annotated_images/01089039_clarearts.ie_subpages_burren-college-of_start0_annotated3.png",
+      "ground_truth": "E",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.6_5M/task_data_vv4_50K_3_curated_v2/action_prediction/annotated_images/00864338_buyersagent-sydney.com.au_the-great-rid_start0_annotated0.png",
+      "ground_truth": "A",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_5_curated_v2/action_prediction/annotated_images/08369499_www.oil-painting-portraits.com_en_back-_start0_annotated3.png",
+      "ground_truth": "E",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.6_5M/task_data_vv4_50K_8_curated_v2/action_prediction/annotated_images/01425069_desertmeadows.laveenschools.org_2022_09_start640_annotated33.png",
+      "ground_truth": "E",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_6_curated_v2/action_prediction/annotated_images/06324591_www.centrefornewcomers.ca_post_request-_start0_annotated0.png",
+      "ground_truth": "H",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_6_curated_v2/action_prediction/annotated_images/03706268_onestopcarpetcleaning.ca_locations_carp_start0_annotated3.png",
+      "ground_truth": "G",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_4_curated_v2/action_prediction/annotated_images/00499738_barewealthadvisors.com_tag_goals_start0_annotated4.png",
+      "ground_truth": "D",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_8_curated_v2/action_prediction/annotated_images/09299542_www.terrible-lies.com_2009_11_08_saturd_start0_annotated6.png",
+      "ground_truth": "H",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_6_curated_v2/action_prediction/annotated_images/01356644_dancoulter.mla.bcndpcaucus.ca_news_chil_start0_annotated13.png",
+      "ground_truth": "H",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_4_curated_v2/action_prediction/annotated_images/00668540_blog.handcuffwarehouse.com_2008_01_03_h_start0_annotated16.png",
+      "ground_truth": "C",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    }
+  ]
+}

Result/Stage_Multi_Exploration_301-VisualWebBench_Action_Ground_103_repeat.json

@@ -0,0 +1,93 @@
+{
+  "metrics": {
+    "total": 103,
+    "correct": 91,
+    "accuracy": 88.3495145631068
+  },
+  "errors": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_12.png",
+      "ground_truth": "A",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_15.png",
+      "ground_truth": "E",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_34.png",
+      "ground_truth": "A",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_36.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_57.png",
+      "ground_truth": "D",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_67.png",
+      "ground_truth": "G",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_81.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_83.png",
+      "ground_truth": "G",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_87.png",
+      "ground_truth": "E",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_89.png",
+      "ground_truth": "F",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_93.png",
+      "ground_truth": "E",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_97.png",
+      "ground_truth": "B",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    }
+  ]
+}

Result/Stage_Multi_Exploration_all_withoutCoT_151-VisualWebBench_Action_Ground_103_repeat.json

@@ -0,0 +1,79 @@
+{
+  "metrics": {
+    "total": 103,
+    "correct": 93,
+    "accuracy": 90.29126213592234
+  },
+  "errors": [
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+      "ground_truth": "E",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_36.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_70.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_74.png",
+      "ground_truth": "C",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_81.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_83.png",
+      "ground_truth": "G",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_87.png",
+      "ground_truth": "E",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_89.png",
+      "ground_truth": "F",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_93.png",
+      "ground_truth": "E",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_97.png",
+      "ground_truth": "B",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    }
+  ]
+}

Result/Stage_Multi_Exploration_all_withoutCoT_151-VisualWebBench_Action_Prediction_281.json

@@ -0,0 +1,100 @@
+{
+  "metrics": {
+    "total": 281,
+    "correct": 268,
+    "accuracy": 95.37366548042705
+  },
+  "errors": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_16.png",
+      "ground_truth": "H",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_23.png",
+      "ground_truth": "E",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_24.png",
+      "ground_truth": "H",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_25.png",
+      "ground_truth": "F",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_90.png",
+      "ground_truth": "C",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_174.png",
+      "ground_truth": "G",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_177.png",
+      "ground_truth": "C",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_183.png",
+      "ground_truth": "F",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_201.png",
+      "ground_truth": "C",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_202.png",
+      "ground_truth": "D",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_203.png",
+      "ground_truth": "E",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_215.png",
+      "ground_truth": "G",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_260.png",
+      "ground_truth": "E",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    }
+  ]
+}

Result/Stage_memory_full_1500-Element-Attribute_100.json

@@ -0,0 +1,80 @@
+{
+  "metrics": {
+    "total": 100,
+    "role_accuracy": 100.0,
+    "name_accuracy": 95.0,
+    "full_accuracy": 95.0
+  },
+  "errors": [
+    {
+      "image": "airbnb_lvl6_img_shot1_de4b9a.png",
+      "ground_truth": {
+        "role": "link",
+        "name": "BenalmádenaBeach house rentals"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "MarbellaBeachfront rentals"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    },
+    {
+      "image": "airbnb_lvl6_img_shot1_713f5f.png",
+      "ground_truth": {
+        "role": "tab",
+        "name": "Arts & culture"
+      },
+      "prediction": {
+        "role": "tab",
+        "name": "Outdoors"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    },
+    {
+      "image": "airbnb_lvl6_img_shot2_3a609f_49cafc.png",
+      "ground_truth": {
+        "role": "button",
+        "name": "Check-in3/16/2025Checkout3/21/2025"
+      },
+      "prediction": {
+        "role": "button",
+        "name": "Check-in3/16/2025"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    },
+    {
+      "image": "airbnb_lvl6_img_shot2_9268e1_e602ea.png",
+      "ground_truth": {
+        "role": "link",
+        "name": "Hosting responsibly"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "Find a co‑host"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    },
+    {
+      "image": "airbnb_lvl6_img_shot3_e8f44f_41dc22_295a6d.png",
+      "ground_truth": {
+        "role": "link",
+        "name": "6"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "9"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    }
+  ]
+}

Result/Stage_memory_full_1500-Next_Page_Prediction_100.json

@@ -0,0 +1,107 @@
+{
+  "metrics": {
+    "total": 100,
+    "correct": 86,
+    "accuracy": 86.0
+  },
+  "errors": [
+    {
+      "image": "/code/Data/MultiUI/v0.6_5M/task_data_vv4_50K_9_curated_v2/action_prediction/annotated_images/04881563_techgameworld.com_sony-srs-nb10-the-wea_start15936_annotated33.png",
+      "ground_truth": "E",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_10_curated_v2/action_prediction/annotated_images/07070789_www.freedomfirm.org_post_torn-in-two_start0_annotated12.png",
+      "ground_truth": "C",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_3_curated_v2/action_prediction/annotated_images/03051407_localsportsnews.co.uk_indoor-tennis-is-_start0_annotated32.png",
+      "ground_truth": "D",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_4_curated_v2/action_prediction/annotated_images/04819294_swimlessonssandiego.com_expect-toddler-_start640_annotated10.png",
+      "ground_truth": "H",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_8_curated_v2/action_prediction/annotated_images/05530739_wheelapex.com_how-to-make-car-tires-shi_start0_annotated4.png",
+      "ground_truth": "G",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_9_curated_v2/action_prediction/annotated_images/05998622_www.basecamptreknepal.com_how-hard-is-i_start9960_annotated19.png",
+      "ground_truth": "F",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.6_5M/task_data_vv4_50K_6_curated_v2/action_prediction/annotated_images/05076589_thesalonproject.com_3cp-hair-color_start0_annotated1.png",
+      "ground_truth": "G",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.6_5M/task_data_vv4_50K_3_curated_v2/action_prediction/annotated_images/00864338_buyersagent-sydney.com.au_the-great-rid_start0_annotated0.png",
+      "ground_truth": "A",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_5_curated_v2/action_prediction/annotated_images/08369499_www.oil-painting-portraits.com_en_back-_start0_annotated3.png",
+      "ground_truth": "E",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_8_curated_v2/action_prediction/annotated_images/00325530_apps.apple.com_us_app_scanbizcards-lite_start0_annotated1.png",
+      "ground_truth": "H",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_6_curated_v2/action_prediction/annotated_images/06324591_www.centrefornewcomers.ca_post_request-_start0_annotated0.png",
+      "ground_truth": "H",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_6_curated_v2/action_prediction/annotated_images/03706268_onestopcarpetcleaning.ca_locations_carp_start0_annotated3.png",
+      "ground_truth": "G",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_4_curated_v2/action_prediction/annotated_images/00499738_barewealthadvisors.com_tag_goals_start0_annotated4.png",
+      "ground_truth": "D",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_6_curated_v2/action_prediction/annotated_images/01356644_dancoulter.mla.bcndpcaucus.ca_news_chil_start0_annotated13.png",
+      "ground_truth": "H",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    }
+  ]
+}

Result/Stage_memory_full_500-Next_Page_Prediction_100.json

@@ -0,0 +1,121 @@
+{
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Result/Stage_memory_lowlr_2epoch-Element-Attribute_100.json

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Result/Stage_memory_lowlr_2epoch-Next_Page_Prediction_2000.json

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Result/Stage_memory_lowlr_2epoch-VisualWebBench_Action_Ground_103.json

@@ -0,0 +1,471 @@
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+    },
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+    },
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+    },
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+    },
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+    },
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+      "raw_model_output": "B"
+    },
+    {
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+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
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+    },
+    {
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+      "raw_model_output": "E"
+    },
+    {
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+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "c"
+    },
+    {
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+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_36.png",
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+      "raw_model_output": "C"
+    },
+    {
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+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
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+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
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+      "match": false,
+      "raw_model_output": "H"
+    },
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+    },
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+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_49.png",
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+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_50.png",
+      "ground_truth": "F",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "Glasses"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_51.png",
+      "ground_truth": "C",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_52.png",
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+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_53.png",
+      "ground_truth": "E",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_55.png",
+      "ground_truth": "A",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_57.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
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+      "ground_truth": "H",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_60.png",
+      "ground_truth": "H",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_62.png",
+      "ground_truth": "G",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_66.png",
+      "ground_truth": "A",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_67.png",
+      "ground_truth": "G",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_69.png",
+      "ground_truth": "F",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_70.png",
+      "ground_truth": "D",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_71.png",
+      "ground_truth": "A",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_72.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_74.png",
+      "ground_truth": "C",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_75.png",
+      "ground_truth": "A",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_76.png",
+      "ground_truth": "F",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_77.png",
+      "ground_truth": "D",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_78.png",
+      "ground_truth": "D",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_81.png",
+      "ground_truth": "D",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_82.png",
+      "ground_truth": "A",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_83.png",
+      "ground_truth": "G",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_84.png",
+      "ground_truth": "D",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_85.png",
+      "ground_truth": "D",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "HORSES"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_86.png",
+      "ground_truth": "G",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_89.png",
+      "ground_truth": "F",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_90.png",
+      "ground_truth": "E",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_91.png",
+      "ground_truth": "H",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_92.png",
+      "ground_truth": "A",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_93.png",
+      "ground_truth": "E",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_94.png",
+      "ground_truth": "C",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_95.png",
+      "ground_truth": "D",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_99.png",
+      "ground_truth": "D",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_100.png",
+      "ground_truth": "E",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_103.png",
+      "ground_truth": "G",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    }
+  ]
+}

Result/Stage_understanding_200-VisualWebBench_Action_Prediction_281.json

@@ -0,0 +1,100 @@
+{
+  "metrics": {
+    "total": 281,
+    "correct": 268,
+    "accuracy": 95.37366548042705
+  },
+  "errors": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_16.png",
+      "ground_truth": "H",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_23.png",
+      "ground_truth": "E",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_24.png",
+      "ground_truth": "H",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_25.png",
+      "ground_truth": "F",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_46.png",
+      "ground_truth": "F",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_90.png",
+      "ground_truth": "C",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_174.png",
+      "ground_truth": "G",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_177.png",
+      "ground_truth": "C",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_183.png",
+      "ground_truth": "F",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_198.png",
+      "ground_truth": "A",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_202.png",
+      "ground_truth": "D",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_203.png",
+      "ground_truth": "E",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_260.png",
+      "ground_truth": "E",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    }
+  ]
+}

Result/Stage_understanding_400-Element-Attribute_100.json

@@ -0,0 +1,38 @@
+{
+  "metrics": {
+    "total": 100,
+    "role_accuracy": 99.0,
+    "name_accuracy": 99.0,
+    "full_accuracy": 98.0
+  },
+  "errors": [
+    {
+      "image": "airbnb_lvl6_img_shot1_de4b9a.png",
+      "ground_truth": {
+        "role": "link",
+        "name": "BenalmádenaBeach house rentals"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "Beach"
+      },
+      "match_role": true,
+      "match_name": false,
+      "match_all": false
+    },
+    {
+      "image": "airbnb_lvl6_img_shot1_713f5f.png",
+      "ground_truth": {
+        "role": "tab",
+        "name": "Arts & culture"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "Arts & culture"
+      },
+      "match_role": false,
+      "match_name": true,
+      "match_all": false
+    }
+  ]
+}

Result/Stage_understanding_400-VisualWebBench_HeadingOCR_46.json

@@ -0,0 +1,239 @@
+{
+  "metrics": {
+    "rouge_1": 72.72840257148793,
+    "rouge_2": 71.6071424810023,
+    "rouge_l": 72.70252265496914
+  },
+  "results": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_1.png",
+      "ground_truth": "Discover, Appreciate, & Understand the Animal World!",
+      "prediction": "Discover, Appreciate, & Understand the Animal World!"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_2.png",
+      "ground_truth": "Publish your poetry online",
+      "prediction": "Publish your poetry online"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_3.png",
+      "ground_truth": "Book your next fishing trip",
+      "prediction": "Book your next fishing trip"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_4.png",
+      "ground_truth": "THE GOLD STANDARD IN\nOnline Invitations & Greeting Cards",
+      "prediction": "The Gold Standard in\nOnline Invitations & Greeting Cards"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_5.png",
+      "ground_truth": "Car rentals from trusted, local hosts",
+      "prediction": "Find your drive"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_6.png",
+      "ground_truth": "Local, trusted pet care",
+      "prediction": "Local, trusted pet care\nBook 5-star Pet Caregivers near you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_7.png",
+      "ground_truth": "How to Be a Caregiver for Someone With Arthritis",
+      "prediction": "How to Be a Caregiver for Someone With Arthritis"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_8.png",
+      "ground_truth": "Find your outdoors",
+      "prediction": "Find your outdoors"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_9.png",
+      "ground_truth": "Find senior living near you",
+      "prediction": "Find senior living near you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_10.png",
+      "ground_truth": "Gold and Silver Popular Categories",
+      "prediction": "Gold and Silver Popular Categories"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_11.png",
+      "ground_truth": "Basketball Stats and History Statistics, scores, and history for the NBA, ABA, WNBA, and top European competition.",
+      "prediction": "Every NBA & Every WNBA Player"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_12.png",
+      "ground_truth": "“I’m Definitely Going To Show What I’m Made Of,” Says Fundora Ahead Of Tszyu Clash",
+      "prediction": "“I’m Definitely Going To Show What I’m Made Of,” Says Fundora Ahead Of Tszyu Clash"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_13.png",
+      "ground_truth": "Free Online Calculators",
+      "prediction": "Free Online Calculators"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_14.png",
+      "ground_truth": "Crate and Barrel",
+      "prediction": "Save 10% off full-price items*"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_15.png",
+      "ground_truth": "Interested in unions & collective bargaining?",
+      "prediction": "Featured Stories"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_16.png",
+      "ground_truth": "For Your Home",
+      "prediction": "How can we help you?"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_17.png",
+      "ground_truth": "Easily find scholarships that fit you",
+      "prediction": "Easily find scholarships that fit you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_18.png",
+      "ground_truth": "#1 Provider in Online\nTraffic School, Defensive Driving & Drivers Ed",
+      "prediction": "#1 Provider in Online\nTraffic School, Defensive Driving & Drivers Ed"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_19.png",
+      "ground_truth": "Discover Land for Sale, All Across America",
+      "prediction": "Discover Land for Sale, All Across America"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_20.png",
+      "ground_truth": "Find Used Farm Equipment",
+      "prediction": "Find Used Farm Equipment"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_21.png",
+      "ground_truth": "Massachusetts Institute of Technology",
+      "prediction": "Human sweat contains a protein that may protect some people against Lyme disease, scientists report. “We think there are real implications here for a preventative and possibly a therapeutic based on this protein,” Michal Caspi Tal says."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_22.png",
+      "ground_truth": "Beyond the Guidebook",
+      "prediction": "Beyond the Guidebook"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_23.png",
+      "ground_truth": "Discover your family story",
+      "prediction": "Discover your family story"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_24.png",
+      "ground_truth": "Welcome to MyModivcare!",
+      "prediction": "Welcome to MyModivcare!"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_25.png",
+      "ground_truth": "Earth just had its warmest February on record",
+      "prediction": "Earth just had its warmest February on record >"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_26.png",
+      "ground_truth": "Find your table for any occasion",
+      "prediction": "Find your table for any occasion"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_27.png",
+      "ground_truth": "Online invitations to celebrate all of life’s moments",
+      "prediction": "Online invitations to celebrate all of life’s moments"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_28.png",
+      "ground_truth": "Your Climate Credit is coming",
+      "prediction": "Your Climate Credit is coming"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_29.png",
+      "ground_truth": "Football Stats and History The complete source for current and historical NFL, AFL, and AAFC players, teams, scores and leaders.",
+      "prediction": "Every Pro Football Player"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_30.png",
+      "ground_truth": "Welcome to Progressive Insurance®",
+      "prediction": "Welcome to Progressive Insurance®"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_31.png",
+      "ground_truth": "Loving pet care in your neighborhood™",
+      "prediction": "Loving pet care in your neighborhood™\nBook trusted sitters and dog walkers."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_32.png",
+      "ground_truth": "Securing your today and tomorrow",
+      "prediction": "What should I do if I get a call claiming there is a problem with my Social Security number or account?"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_33.png",
+      "ground_truth": "Providing expertise, innovation and world-class care",
+      "prediction": "Lulu’s Story"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_34.png",
+      "ground_truth": "Secretary Blinken’s Travel",
+      "prediction": "Secretary Blinken’s Travel"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_35.png",
+      "ground_truth": "Welcome to Texas.gov",
+      "prediction": "Welcome to Texas.gov"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_36.png",
+      "ground_truth": "Your wedding team and everything in between",
+      "prediction": "Your wedding team and everything in between"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_37.png",
+      "ground_truth": "TEXAS STATE UNIVERSITY",
+      "prediction": "NEXT MOVES US FORWARD"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_38.png",
+      "ground_truth": "USAA has insurance, banking and retirement solutions.",
+      "prediction": "USAA has insurance, banking and retirement solutions."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_39.png",
+      "ground_truth": "FIND THE VALVOLINE™ SOLUTION THAT'S RIGHT FOR YOU",
+      "prediction": "FIND THE VALVOLINE™ SOLUTION THAT’S RIGHT FOR YOU"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_40.png",
+      "ground_truth": "VIPLeague | Live Sports Streaming",
+      "prediction": "VIPLeague | Live Sports Streaming"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_41.png",
+      "ground_truth": "Periods of Snow Across the Great Lakes and Northeast; Elevated Fire Weather Conditions in the Ohio Valley and Mid-Atlantic",
+      "prediction": "Periods of Snow Across the Great Lakes and Northeast; Elevated Fire Weather Conditions in the Ohio Valley and Mid-Atlantic"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_42.png",
+      "ground_truth": "Find people, contact info & background checks",
+      "prediction": "Find people, contact info & background checks"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_43.png",
+      "ground_truth": "Welcome to wikiHow, the most trusted how-to site on the internet.",
+      "prediction": "Welcome to wikiHow, the most trusted how-to site on the internet."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_44.png",
+      "ground_truth": "Wikipedia\nThe Free Encyclopedia",
+      "prediction": "WIKIPEDIA\nThe Free Encyclopedia"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_45.png",
+      "ground_truth": "World History Encyclopedia",
+      "prediction": "The Railways in the British Industrial Revolution"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_46.png",
+      "ground_truth": "Central and Western, People's Republic of China",
+      "prediction": "Central and Western, People's Republic of China"
+    }
+  ]
+}

Result/Stage_understanding_only_new-VisualWebBench_Action_Prediction_281.json

@@ -0,0 +1,107 @@
+{
+  "metrics": {
+    "total": 281,
+    "correct": 267,
+    "accuracy": 95.01779359430606
+  },
+  "errors": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_12.png",
+      "ground_truth": "B",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_16.png",
+      "ground_truth": "H",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_23.png",
+      "ground_truth": "E",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_24.png",
+      "ground_truth": "H",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_25.png",
+      "ground_truth": "F",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_46.png",
+      "ground_truth": "F",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_55.png",
+      "ground_truth": "C",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_90.png",
+      "ground_truth": "C",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_174.png",
+      "ground_truth": "G",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_177.png",
+      "ground_truth": "C",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_183.png",
+      "ground_truth": "F",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_198.png",
+      "ground_truth": "A",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_202.png",
+      "ground_truth": "D",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_prediction_203.png",
+      "ground_truth": "E",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    }
+  ]
+}

Result/Stage_understanding_shot_prompt_300-VisualWebBench_HeadingOCR_46.json

@@ -0,0 +1,239 @@
+{
+  "metrics": {
+    "rouge_1": 70.4708774937448,
+    "rouge_2": 68.98150725462237,
+    "rouge_l": 70.37253380948448
+  },
+  "results": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_1.png",
+      "ground_truth": "Discover, Appreciate, & Understand the Animal World!",
+      "prediction": "Discover, Appreciate, & Understand the Animal World!"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_2.png",
+      "ground_truth": "Publish your poetry online",
+      "prediction": "Publish your poetry online"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_3.png",
+      "ground_truth": "Book your next fishing trip",
+      "prediction": "Book your next fishing trip"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_4.png",
+      "ground_truth": "THE GOLD STANDARD IN\nOnline Invitations & Greeting Cards",
+      "prediction": "The Gold Standard in\nOnline Invitations & Greeting Cards"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_5.png",
+      "ground_truth": "Car rentals from trusted, local hosts",
+      "prediction": "Find your drive"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_6.png",
+      "ground_truth": "Local, trusted pet care",
+      "prediction": "Local, trusted pet care\nBook 5-star Pet Caregivers near you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_7.png",
+      "ground_truth": "How to Be a Caregiver for Someone With Arthritis",
+      "prediction": "How to Be a Caregiver for Someone With Arthritis"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_8.png",
+      "ground_truth": "Find your outdoors",
+      "prediction": "Find your outdoors"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_9.png",
+      "ground_truth": "Find senior living near you",
+      "prediction": "Find senior living near you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_10.png",
+      "ground_truth": "Gold and Silver Popular Categories",
+      "prediction": "Gold and Silver Popular Categories"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_11.png",
+      "ground_truth": "Basketball Stats and History Statistics, scores, and history for the NBA, ABA, WNBA, and top European competition.",
+      "prediction": "Every NBA & Every WNBA Player"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_12.png",
+      "ground_truth": "“I’m Definitely Going To Show What I’m Made Of,” Says Fundora Ahead Of Tszyu Clash",
+      "prediction": "“I’m Definitely Going To Show What I’m Made Of,” Says Fundora Ahead Of Tszyu Clash"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_13.png",
+      "ground_truth": "Free Online Calculators",
+      "prediction": "Free Online Calculators"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_14.png",
+      "ground_truth": "Crate and Barrel",
+      "prediction": "Save 10% off full-price items*"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_15.png",
+      "ground_truth": "Interested in unions & collective bargaining?",
+      "prediction": "Featured Stories"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_16.png",
+      "ground_truth": "For Your Home",
+      "prediction": "How can we help you?"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_17.png",
+      "ground_truth": "Easily find scholarships that fit you",
+      "prediction": "Easily find scholarships that fit you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_18.png",
+      "ground_truth": "#1 Provider in Online\nTraffic School, Defensive Driving & Drivers Ed",
+      "prediction": "#1 Provider in Online\nTraffic School, Defensive Driving & Drivers Ed"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_19.png",
+      "ground_truth": "Discover Land for Sale, All Across America",
+      "prediction": "Discover Land for Sale, All Across America"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_20.png",
+      "ground_truth": "Find Used Farm Equipment",
+      "prediction": "Find Used Farm Equipment"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_21.png",
+      "ground_truth": "Massachusetts Institute of Technology",
+      "prediction": "Human sweat contains a protein that may protect some people against Lyme disease, scientists report. “We think there are real implications here for a preventative and possibly a therapeutic based on this protein,” Michal Caspi Tal says."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_22.png",
+      "ground_truth": "Beyond the Guidebook",
+      "prediction": "Beyond the Guidebook\n306,306 Routes Shared by Climbers Like You"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_23.png",
+      "ground_truth": "Discover your family story",
+      "prediction": "Discover your family story"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_24.png",
+      "ground_truth": "Welcome to MyModivcare!",
+      "prediction": "Welcome to MyModivcare!"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_25.png",
+      "ground_truth": "Earth just had its warmest February on record",
+      "prediction": "Earth just had its warmest February on record >"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_26.png",
+      "ground_truth": "Find your table for any occasion",
+      "prediction": "Find your table for any occasion"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_27.png",
+      "ground_truth": "Online invitations to celebrate all of life’s moments",
+      "prediction": "Online invitations to celebrate all of life’s moments"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_28.png",
+      "ground_truth": "Your Climate Credit is coming",
+      "prediction": "Your Climate Credit is coming"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_29.png",
+      "ground_truth": "Football Stats and History The complete source for current and historical NFL, AFL, and AAFC players, teams, scores and leaders.",
+      "prediction": "Every Pro Football Player"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_30.png",
+      "ground_truth": "Welcome to Progressive Insurance®",
+      "prediction": "Welcome to Progressive Insurance®"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_31.png",
+      "ground_truth": "Loving pet care in your neighborhood™",
+      "prediction": "Loving pet care in your neighborhood™\nBook trusted sitters and dog walkers."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_32.png",
+      "ground_truth": "Securing your today and tomorrow",
+      "prediction": "What should I do if I get a call claiming there is a problem with my Social Security number or account?"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_33.png",
+      "ground_truth": "Providing expertise, innovation and world-class care",
+      "prediction": "Lulu’s Story"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_34.png",
+      "ground_truth": "Secretary Blinken’s Travel",
+      "prediction": "Secretary Blinken’s Travel"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_35.png",
+      "ground_truth": "Welcome to Texas.gov",
+      "prediction": "Welcome to Texas.gov"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_36.png",
+      "ground_truth": "Your wedding team and everything in between",
+      "prediction": "Your wedding team and everything in between"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_37.png",
+      "ground_truth": "TEXAS STATE UNIVERSITY",
+      "prediction": "FINANCIAL AID DEADLINES"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_38.png",
+      "ground_truth": "USAA has insurance, banking and retirement solutions.",
+      "prediction": "USAA has insurance, banking and retirement solutions.\nHere are some of the ways we serve the military, veterans, and their eligible family members."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_39.png",
+      "ground_truth": "FIND THE VALVOLINE™ SOLUTION THAT'S RIGHT FOR YOU",
+      "prediction": "FIND THE VALVOLINE™ SOLUTION THAT’S RIGHT FOR YOU"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_40.png",
+      "ground_truth": "VIPLeague | Live Sports Streaming",
+      "prediction": "VIPLeague | Live Sports Streaming"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_41.png",
+      "ground_truth": "Periods of Snow Across the Great Lakes and Northeast; Elevated Fire Weather Conditions in the Ohio Valley and Mid-Atlantic",
+      "prediction": "Periods of Snow Across the Great Lakes and Northeast; Elevated Fire Weather Conditions in the Ohio Valley and Mid-Atlantic"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_42.png",
+      "ground_truth": "Find people, contact info & background checks",
+      "prediction": "Find people, contact info & background checks"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_43.png",
+      "ground_truth": "Welcome to wikiHow, the most trusted how-to site on the internet.",
+      "prediction": "Welcome to wikiHow, the most trusted how-to site on the internet."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_44.png",
+      "ground_truth": "Wikipedia\nThe Free Encyclopedia",
+      "prediction": "WIKIPEDIA\nThe Free Encyclopedia"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_45.png",
+      "ground_truth": "World History Encyclopedia",
+      "prediction": "The Railways in the British Industrial Revolution"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_46.png",
+      "ground_truth": "Central and Western, People's Republic of China",
+      "prediction": "Central and Western, People's Republic of China"
+    }
+  ]
+}

Result/Stage_understanding_shot_prompt_400-Element-Attribute_100.json

@@ -0,0 +1,24 @@
+{
+  "metrics": {
+    "total": 100,
+    "role_accuracy": 99.0,
+    "name_accuracy": 100.0,
+    "full_accuracy": 99.0
+  },
+  "errors": [
+    {
+      "image": "airbnb_lvl6_img_shot1_713f5f.png",
+      "ground_truth": {
+        "role": "tab",
+        "name": "Arts & culture"
+      },
+      "prediction": {
+        "role": "link",
+        "name": "Arts & culture"
+      },
+      "match_role": false,
+      "match_name": true,
+      "match_all": false
+    }
+  ]
+}

Result/Test-Claude-Action_Prediction.json

@@ -0,0 +1,129 @@
+{
+  "metrics": {
+    "total": 32,
+    "correct": 20,
+    "accuracy": 62.5
+  },
+  "errors": [
+    {
+      "image": [
+        "/code/Data/gc/saw/github/images/111111555.png",
+        "/code/Data/gc/saw/github/images/111111564.png"
+      ],
+      "ground_truth": "H,G,I,E",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/github/images/111111321.png",
+        "/code/Data/gc/saw/github/images/111111331.png"
+      ],
+      "ground_truth": "J",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/github/images/111111487.png",
+        "/code/Data/gc/saw/github/images/111111496.png"
+      ],
+      "ground_truth": "I",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/ebay/images/111111202.png",
+        "/code/Data/gc/saw/ebay/images/111111206.png"
+      ],
+      "ground_truth": "D",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/ebay/images/111111136.png",
+        "/code/Data/gc/saw/ebay/images/111111138.png"
+      ],
+      "ground_truth": "B",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/ebay/images/111111242.png",
+        "/code/Data/gc/saw/ebay/images/111111248.png"
+      ],
+      "ground_truth": "F",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/_12306/images/111111235.png",
+        "/code/Data/gc/saw/_12306/images/111111241.png"
+      ],
+      "ground_truth": "F",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/github/images/111111757.png",
+        "/code/Data/gc/saw/github/images/111111766.png"
+      ],
+      "ground_truth": "I",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/github/images/111111124.png",
+        "/code/Data/gc/saw/github/images/111111131.png"
+      ],
+      "ground_truth": "J,G",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/_12306/images/111111246.png",
+        "/code/Data/gc/saw/_12306/images/111111253.png"
+      ],
+      "ground_truth": "G",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/bilibili/images/111111352.png",
+        "/code/Data/gc/saw/bilibili/images/111111355.png"
+      ],
+      "ground_truth": "C",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": [
+        "/code/Data/gc/saw/ebay/images/111111157.png",
+        "/code/Data/gc/saw/ebay/images/111111159.png"
+      ],
+      "ground_truth": "B",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    }
+  ]
+}

Result/Test-Claude-Next_Page_Prediction_100.json

@@ -0,0 +1,51 @@
+{
+  "metrics": {
+    "total": 93,
+    "correct": 87,
+    "accuracy": 93.54838709677419
+  },
+  "errors": [
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_4_curated_v2/action_prediction/annotated_images/04819294_swimlessonssandiego.com_expect-toddler-_start640_annotated10.png",
+      "ground_truth": "H",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_8_curated_v2/action_prediction/annotated_images/05530739_wheelapex.com_how-to-make-car-tires-shi_start0_annotated4.png",
+      "ground_truth": "G",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] Error calling Claude model: ThrottlingException: Too many requests, please wait before trying again."
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.6_5M/task_data_vv4_50K_3_curated_v2/action_prediction/annotated_images/00864338_buyersagent-sydney.com.au_the-great-rid_start0_annotated0.png",
+      "ground_truth": "A",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.6_5M/task_data_vv4_50K_11_curated_v2/action_prediction/annotated_images/09600879_www.umesnaa.org_alumni-spotlight---c-pa_start8964_annotated2.png",
+      "ground_truth": "C",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.6_5M/task_data_vv4_50K_12_curated_v2/action_prediction/annotated_images/00642886_blocknow.net_blockchain-is-one-of-the-m_start0_annotated4.png",
+      "ground_truth": "C",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/Data/MultiUI/v0.7_exclude_v0.6/task_data_vv4_50K_6_curated_v2/action_prediction/annotated_images/01356644_dancoulter.mla.bcndpcaucus.ca_news_chil_start0_annotated13.png",
+      "ground_truth": "H",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    }
+  ]
+}

Result/Test-CogReasoner-VisualWebBench_HeadingOCR_46.json

@@ -0,0 +1,239 @@
+{
+  "metrics": {
+    "rouge_1": 72.69137437751588,
+    "rouge_2": 71.26901214039609,
+    "rouge_l": 72.61214201671298
+  },
+  "results": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_1.png",
+      "ground_truth": "Discover, Appreciate, & Understand the Animal World!",
+      "prediction": "Discover, Appreciate, & Understand the Animal World!"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_2.png",
+      "ground_truth": "Publish your poetry online",
+      "prediction": "Publish your poetry online"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_3.png",
+      "ground_truth": "Book your next fishing trip",
+      "prediction": "Book your next fishing trip"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_4.png",
+      "ground_truth": "THE GOLD STANDARD IN\nOnline Invitations & Greeting Cards",
+      "prediction": "The Gold Standard in\nOnline Invitations & Greeting Cards"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_5.png",
+      "ground_truth": "Car rentals from trusted, local hosts",
+      "prediction": "Find your drive"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_6.png",
+      "ground_truth": "Local, trusted pet care",
+      "prediction": "Local, trusted pet care\nBook 5-star Pet Caregivers near you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_7.png",
+      "ground_truth": "How to Be a Caregiver for Someone With Arthritis",
+      "prediction": "How to Be a Caregiver for Someone With Arthritis"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_8.png",
+      "ground_truth": "Find your outdoors",
+      "prediction": "Find your outdoors"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_9.png",
+      "ground_truth": "Find senior living near you",
+      "prediction": "Find senior living near you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_10.png",
+      "ground_truth": "Gold and Silver Popular Categories",
+      "prediction": "Gold and Silver Popular Categories"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_11.png",
+      "ground_truth": "Basketball Stats and History Statistics, scores, and history for the NBA, ABA, WNBA, and top European competition.",
+      "prediction": "Every NBA & Every WNBA Player"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_12.png",
+      "ground_truth": "“I’m Definitely Going To Show What I’m Made Of,” Says Fundora Ahead Of Tszyu Clash",
+      "prediction": "“I’m Definitely Going To Show What I’m Made Of,” Says Fundora Ahead Of Tszyu Clash"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_13.png",
+      "ground_truth": "Free Online Calculators",
+      "prediction": "Free Online Calculators"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_14.png",
+      "ground_truth": "Crate and Barrel",
+      "prediction": "Save 10% off full-price items*"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_15.png",
+      "ground_truth": "Interested in unions & collective bargaining?",
+      "prediction": "U.S. DEPARTMENT OF LABOR"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_16.png",
+      "ground_truth": "For Your Home",
+      "prediction": "How can we help you?"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_17.png",
+      "ground_truth": "Easily find scholarships that fit you",
+      "prediction": "Easily find scholarships that fit you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_18.png",
+      "ground_truth": "#1 Provider in Online\nTraffic School, Defensive Driving & Drivers Ed",
+      "prediction": "#1 Provider in Online\nTraffic School, Defensive Driving & Drivers Ed"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_19.png",
+      "ground_truth": "Discover Land for Sale, All Across America",
+      "prediction": "Discover Land for Sale, All Across America"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_20.png",
+      "ground_truth": "Find Used Farm Equipment",
+      "prediction": "Find Used Farm Equipment"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_21.png",
+      "ground_truth": "Massachusetts Institute of Technology",
+      "prediction": "Human sweat contains a protein that may protect some people against Lyme disease, scientists report. “We think there are real implications here for a preventative and possibly a therapeutic based on this protein,” Michal Caspi Tal says."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_22.png",
+      "ground_truth": "Beyond the Guidebook",
+      "prediction": "Beyond the Guidebook\n306,306 Routes Shared by Climbers Like You"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_23.png",
+      "ground_truth": "Discover your family story",
+      "prediction": "Discover your family story"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_24.png",
+      "ground_truth": "Welcome to MyModivcare!",
+      "prediction": "Welcome to MyModivcare!"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_25.png",
+      "ground_truth": "Earth just had its warmest February on record",
+      "prediction": "Earth just had its warmest February on record ›"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_26.png",
+      "ground_truth": "Find your table for any occasion",
+      "prediction": "Find your table for any occasion"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_27.png",
+      "ground_truth": "Online invitations to celebrate all of life’s moments",
+      "prediction": "Online invitations to celebrate all of life’s moments"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_28.png",
+      "ground_truth": "Your Climate Credit is coming",
+      "prediction": "Your Climate Credit is coming"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_29.png",
+      "ground_truth": "Football Stats and History The complete source for current and historical NFL, AFL, and AAFC players, teams, scores and leaders.",
+      "prediction": "Every Pro Football Player"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_30.png",
+      "ground_truth": "Welcome to Progressive Insurance®",
+      "prediction": "Welcome to Progressive Insurance®"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_31.png",
+      "ground_truth": "Loving pet care in your neighborhood™",
+      "prediction": "Loving pet care in your neighborhood™\nBook trusted sitters and dog walkers."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_32.png",
+      "ground_truth": "Securing your today and tomorrow",
+      "prediction": "What should I do if I get a call claiming there is a problem with my Social Security number or account?"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_33.png",
+      "ground_truth": "Providing expertise, innovation and world-class care",
+      "prediction": "Lulu’s Story"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_34.png",
+      "ground_truth": "Secretary Blinken’s Travel",
+      "prediction": "Secretary Blinken’s Travel"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_35.png",
+      "ground_truth": "Welcome to Texas.gov",
+      "prediction": "Welcome to Texas.gov"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_36.png",
+      "ground_truth": "Your wedding team and everything in between",
+      "prediction": "Your wedding team and everything in between"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_37.png",
+      "ground_truth": "TEXAS STATE UNIVERSITY",
+      "prediction": "APPLY NOW FOR 2024"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_38.png",
+      "ground_truth": "USAA has insurance, banking and retirement solutions.",
+      "prediction": "USAA has insurance, banking and retirement solutions.\nHere are some of the ways we serve the military, veterans, and their eligible family members."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_39.png",
+      "ground_truth": "FIND THE VALVOLINE™ SOLUTION THAT'S RIGHT FOR YOU",
+      "prediction": "FIND THE VALVOLINE™ SOLUTION THAT’S RIGHT FOR YOU"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_40.png",
+      "ground_truth": "VIPLeague | Live Sports Streaming",
+      "prediction": "VIPLeague | Live Sports Streaming"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_41.png",
+      "ground_truth": "Periods of Snow Across the Great Lakes and Northeast; Elevated Fire Weather Conditions in the Ohio Valley and Mid-Atlantic",
+      "prediction": "Periods of Snow Across the Great Lakes and Northeast; Elevated Fire Weather Conditions in the Ohio Valley and Mid Atlantic"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_42.png",
+      "ground_truth": "Find people, contact info & background checks",
+      "prediction": "Find people, contact info & background checks"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_43.png",
+      "ground_truth": "Welcome to wikiHow, the most trusted how-to site on the internet.",
+      "prediction": "Welcome to wikiHow, the most trusted how-to site on the internet."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_44.png",
+      "ground_truth": "Wikipedia\nThe Free Encyclopedia",
+      "prediction": "The Free Encyclopedia"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_45.png",
+      "ground_truth": "World History Encyclopedia",
+      "prediction": "World History Encyclopedia"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_46.png",
+      "ground_truth": "Central and Western, People's Republic of China",
+      "prediction": "Central and Western, People's Republic of China"
+    }
+  ]
+}

Result/Test-Gemini-Action_Prediction.json

@@ -0,0 +1,59 @@
+{
+  "metrics": {
+    "total": 32,
+    "correct": 27,
+    "accuracy": 84.375
+  },
+  "errors": [
+    {
+      "images": [
+        "/code/Data/gc/saw/github/images/111111555.png",
+        "/code/Data/gc/saw/github/images/111111564.png"
+      ],
+      "ground_truth": "H,G,I,E",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "images": [
+        "/code/Data/gc/saw/github/images/111111284.png",
+        "/code/Data/gc/saw/github/images/111111285.png"
+      ],
+      "ground_truth": "A",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    },
+    {
+      "images": [
+        "/code/Data/gc/saw/_12306/images/111111235.png",
+        "/code/Data/gc/saw/_12306/images/111111241.png"
+      ],
+      "ground_truth": "F",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "images": [
+        "/code/Data/gc/saw/bilibili/images/111111202.png",
+        "/code/Data/gc/saw/bilibili/images/111111207.png"
+      ],
+      "ground_truth": "E",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "images": [
+        "/code/Data/gc/saw/ebay/images/111111218.png",
+        "/code/Data/gc/saw/ebay/images/111111220.png"
+      ],
+      "ground_truth": "B",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    }
+  ]
+}

Result/Test-Gemini-Popup_close.json

@@ -0,0 +1,552 @@
+{
+  "model_name": "Gemini",
+  "metrics": {
+    "total_processed": 58,
+    "correct": 56,
+    "accuracy": 96.55172413793103
+  },
+  "results": [
+    {
+      "images": [
+        "/code/sh/output/Google Search--general--00335--r01--step01_popup25.png"
+      ],
+      "ground_truth": "CLICK(41)",
+      "prediction": "CLICK(41)",
+      "match": true,
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+}

Result/Test-Gemini-VisualWebBench_Action_Ground_103.json

@@ -0,0 +1,79 @@
+{
+  "metrics": {
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+    "correct": 93,
+    "accuracy": 90.29126213592234
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+      "ground_truth": "E",
+      "prediction": "H",
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+      "raw_model_output": "H"
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+      "ground_truth": "H",
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+      "raw_model_output": "G"
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+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_9.png",
+      "ground_truth": "D",
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+      "match": false,
+      "raw_model_output": "B"
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+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_17.png",
+      "ground_truth": "A",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_19.png",
+      "ground_truth": "F",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_36.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_63.png",
+      "ground_truth": "E",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_83.png",
+      "ground_truth": "G",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_93.png",
+      "ground_truth": "E",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/action_ground_100.png",
+      "ground_truth": "E",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    }
+  ]
+}

Result/Test-Gemini-VisualWebBench_Element_Ground.json

@@ -0,0 +1,247 @@
+{
+  "metrics": {
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+    "correct": 379,
+    "accuracy": 91.76755447941889
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+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_23.png",
+      "ground_truth": "D",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_28.png",
+      "ground_truth": "A",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_30.png",
+      "ground_truth": "G",
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+      "match": false,
+      "raw_model_output": "E"
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+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_33.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_46.png",
+      "ground_truth": "C",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_52.png",
+      "ground_truth": "E",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_66.png",
+      "ground_truth": "C",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_72.png",
+      "ground_truth": "G",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_73.png",
+      "ground_truth": "E",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_80.png",
+      "ground_truth": "A",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_102.png",
+      "ground_truth": "A",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_130.png",
+      "ground_truth": "G",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_131.png",
+      "ground_truth": "D",
+      "prediction": "E",
+      "match": false,
+      "raw_model_output": "E"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_152.png",
+      "ground_truth": "H",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_153.png",
+      "ground_truth": "F",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "I"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_156.png",
+      "ground_truth": "G",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_160.png",
+      "ground_truth": "A",
+      "prediction": "D",
+      "match": false,
+      "raw_model_output": "D"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_167.png",
+      "ground_truth": "A",
+      "prediction": "B",
+      "match": false,
+      "raw_model_output": "B"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_183.png",
+      "ground_truth": "B",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_195.png",
+      "ground_truth": "E",
+      "prediction": "F",
+      "match": false,
+      "raw_model_output": "F"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_222.png",
+      "ground_truth": "C",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_224.png",
+      "ground_truth": "B",
+      "prediction": "A",
+      "match": false,
+      "raw_model_output": "A"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_295.png",
+      "ground_truth": "B",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_310.png",
+      "ground_truth": "F",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "I"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_311.png",
+      "ground_truth": "D",
+      "prediction": "C",
+      "match": false,
+      "raw_model_output": "C"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_341.png",
+      "ground_truth": "G",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_344.png",
+      "ground_truth": "B",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_355.png",
+      "ground_truth": "H",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_396.png",
+      "ground_truth": "A",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_403.png",
+      "ground_truth": "A",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_408.png",
+      "ground_truth": "A",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "[ERROR] 'NoneType' object has no attribute 'strip'"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_412.png",
+      "ground_truth": "D",
+      "prediction": "G",
+      "match": false,
+      "raw_model_output": "G"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/element_ground_413.png",
+      "ground_truth": "E",
+      "prediction": "H",
+      "match": false,
+      "raw_model_output": "H"
+    }
+  ]
+}

Result/Test-Gemini-VisualWebBench_HeadingOCR_46.json

@@ -0,0 +1,239 @@
+{
+  "metrics": {
+    "rouge_1": 70.77998049435837,
+    "rouge_2": 67.51369340282723,
+    "rouge_l": 70.77998049435837
+  },
+  "results": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_1.png",
+      "ground_truth": "Discover, Appreciate, & Understand the Animal World!",
+      "prediction": "Discover, Appreciate, & Understand the Animal World"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_2.png",
+      "ground_truth": "Publish your poetry online",
+      "prediction": "Publish your poetry online"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_3.png",
+      "ground_truth": "Book your next fishing trip",
+      "prediction": "Book your next fishing trip"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_4.png",
+      "ground_truth": "THE GOLD STANDARD IN\nOnline Invitations & Greeting Cards",
+      "prediction": "THE GOLD STANDARD IN\nOnline Invitations & Greeting Cards"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_5.png",
+      "ground_truth": "Car rentals from trusted, local hosts",
+      "prediction": "Find your drive\nCar rentals from trusted, local hosts"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_6.png",
+      "ground_truth": "Local, trusted pet care",
+      "prediction": "Local, trusted pet care\nBook 5-star Pet Caregivers near you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_7.png",
+      "ground_truth": "How to Be a Caregiver for Someone With Arthritis",
+      "prediction": "FAMILY CAREGIVING\n\nHow to Be a Caregiver for Someone With Arthritis\n\nTips to help reduce their pain and improve their lives"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_8.png",
+      "ground_truth": "Find your outdoors",
+      "prediction": "Find your outdoors"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_9.png",
+      "ground_truth": "Find senior living near you",
+      "prediction": "Find senior living near you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_10.png",
+      "ground_truth": "Gold and Silver Popular Categories",
+      "prediction": "Gold & Silver Eagle Coins on Sale\nUnlock Limited-Time Savings"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_11.png",
+      "ground_truth": "Basketball Stats and History Statistics, scores, and history for the NBA, ABA, WNBA, and top European competition.",
+      "prediction": "Every NBA & Every WNBA Player\nEvery NBA Team"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_12.png",
+      "ground_truth": "“I’m Definitely Going To Show What I’m Made Of,” Says Fundora Ahead Of Tszyu Clash",
+      "prediction": "“I’m Definitely Going To Show What I’m Made Of,” Says Fundora Ahead Of Tszyu Clash"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_13.png",
+      "ground_truth": "Free Online Calculators",
+      "prediction": "Free Online Calculators"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_14.png",
+      "ground_truth": "Crate and Barrel",
+      "prediction": "4 DAYS LEFT\nfree shipping sitewide"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_15.png",
+      "ground_truth": "Interested in unions & collective bargaining?",
+      "prediction": "U.S. DEPARTMENT OF LABOR"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_16.png",
+      "ground_truth": "For Your Home",
+      "prediction": "How can we help you?"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_17.png",
+      "ground_truth": "Easily find scholarships that fit you",
+      "prediction": "Easily find scholarships that fit you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_18.png",
+      "ground_truth": "#1 Provider in Online\nTraffic School, Defensive Driving & Drivers Ed",
+      "prediction": "#1 Provider in Online\nTraffic School, Defensive Driving & Drivers Ed"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_19.png",
+      "ground_truth": "Discover Land for Sale, All Across America",
+      "prediction": "Discover Land for Sale, All Across America"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_20.png",
+      "ground_truth": "Find Used Farm Equipment",
+      "prediction": "MACHINERY PETE THE MOST TRUSTED NAME IN FARM EQUIPMENT"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_21.png",
+      "ground_truth": "Massachusetts Institute of Technology",
+      "prediction": "Human sweat contains a protein that may protect some people against Lyme disease, scientists report. “We think there are real implications here for a preventative and possibly a therapeutic based on this protein,” Michal Caspi Tal says."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_22.png",
+      "ground_truth": "Beyond the Guidebook",
+      "prediction": "Beyond the Guidebook\n306,306 Routes Shared by Climbers Like You"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_23.png",
+      "ground_truth": "Discover your family story",
+      "prediction": "Discover your family story\n\nGrow your family tree, find new relatives, and explore billions of historical records with a 14-day FREE trial"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_24.png",
+      "ground_truth": "Welcome to MyModivcare!",
+      "prediction": "Welcome to MyModivcare!\nWe can get you where you need to go."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_25.png",
+      "ground_truth": "Earth just had its warmest February on record",
+      "prediction": "Earth just had its warmest February on record"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_26.png",
+      "ground_truth": "Find your table for any occasion",
+      "prediction": "Find your table for any occasion"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_27.png",
+      "ground_truth": "Online invitations to celebrate all of life’s moments",
+      "prediction": "Online invitations to celebrate all of life's moments"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_28.png",
+      "ground_truth": "Your Climate Credit is coming",
+      "prediction": "Your Climate Credit is coming\nIn support of a low-carbon future"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_29.png",
+      "ground_truth": "Football Stats and History The complete source for current and historical NFL, AFL, and AAFC players, teams, scores and leaders.",
+      "prediction": "Football Stats and History"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_30.png",
+      "ground_truth": "Welcome to Progressive Insurance®",
+      "prediction": "Welcome to Progressive Insurance®\nBetter insurance starts here"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_31.png",
+      "ground_truth": "Loving pet care in your neighborhood™",
+      "prediction": "Loving pet care in your neighborhood™\nBook trusted sitters and dog walkers."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_32.png",
+      "ground_truth": "Securing your today and tomorrow",
+      "prediction": "Securing your today and tomorrow"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_33.png",
+      "ground_truth": "Providing expertise, innovation and world-class care",
+      "prediction": "Providing expertise, innovation and world-class care"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_34.png",
+      "ground_truth": "Secretary Blinken’s Travel",
+      "prediction": "MARCH 19, 2024\n\nSecretary Blinken's Travel\n\nSecretary Blinken is on travel to Vienna, Seoul, Manila, Jeddah, and Cairo."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_35.png",
+      "ground_truth": "Welcome to Texas.gov",
+      "prediction": "Welcome to Texas.gov"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_36.png",
+      "ground_truth": "Your wedding team and everything in between",
+      "prediction": "Your wedding team and everything in between"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_37.png",
+      "ground_truth": "TEXAS STATE UNIVERSITY",
+      "prediction": "NEXT MOVES US FORWARD\n\nAPPLICATIONS OPEN FOR 2024"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_38.png",
+      "ground_truth": "USAA has insurance, banking and retirement solutions.",
+      "prediction": "USAA has insurance, banking and retirement solutions."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_39.png",
+      "ground_truth": "FIND THE VALVOLINE™ SOLUTION THAT'S RIGHT FOR YOU",
+      "prediction": "FIND THE VALVOLINE™ SOLUTION THAT'S RIGHT FOR YOU"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_40.png",
+      "ground_truth": "VIPLeague | Live Sports Streaming",
+      "prediction": "VIPLeague | Live Sports Streaming"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_41.png",
+      "ground_truth": "Periods of Snow Across the Great Lakes and Northeast; Elevated Fire Weather Conditions in the Ohio Valley and Mid-Atlantic",
+      "prediction": "Periods of Snow Across the Great Lakes and Northeast; Elevated Fire Weather Conditions in the Ohio Valley and Mid-Atlantic"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_42.png",
+      "ground_truth": "Find people, contact info & background checks",
+      "prediction": "Find people, contact info & background checks"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_43.png",
+      "ground_truth": "Welcome to wikiHow, the most trusted how-to site on the internet.",
+      "prediction": "Welcome to wikiHow, the most trusted how-to site on the internet."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_44.png",
+      "ground_truth": "Wikipedia\nThe Free Encyclopedia",
+      "prediction": "WIKIPEDIA\nThe Free Encyclopedia"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_45.png",
+      "ground_truth": "World History Encyclopedia",
+      "prediction": "World History Encyclopedia"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_46.png",
+      "ground_truth": "Central and Western, People's Republic of China",
+      "prediction": "Central and Western, People's Republic of China"
+    }
+  ]
+}

Result/Test-Qwen2.5-VL-7B-Popup_close.json

@@ -0,0 +1,55 @@
+{
+  "model_name": "Qwen2.5-VL-7B",
+  "metrics": {
+    "total_processed": 58,
+    "correct": 53,
+    "accuracy": 91.37931
+  },
+  "results": [
+    {
+      "images": [
+        "/code/sh/output/Google Search--general--00383--r01--step04_popup3.png"
+      ],
+      "ground_truth": "CLICK(81)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "Action: click 80"
+    },
+    {
+      "images": [
+        "/code/sh/output/Google Search--general--00427--r01--step04_popup28.png"
+      ],
+      "ground_truth": "CLICK(33)",
+      "prediction": "CLICK(36)",
+      "match": false,
+      "raw_model_output": "Action: click [36]"
+    },
+    {
+      "images": [
+        "/code/sh/output/Google Search--general--00124--r01--step06_popup6.png"
+      ],
+      "ground_truth": "CLICK(85)",
+      "prediction": "CLICK(84)",
+      "match": false,
+      "raw_model_output": "Action: Click [84]"
+    },
+    {
+      "images": [
+        "/code/sh/output/Google Search--general--00056--r01--step04_popup25.png"
+      ],
+      "ground_truth": "CLICK(81)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "Action: click 80"
+    },
+    {
+      "images": [
+        "/code/sh/output/Google Search--general--00225--r01--step03_popup28.png"
+      ],
+      "ground_truth": "CLICK(67)",
+      "prediction": "CLICK(70)",
+      "match": false,
+      "raw_model_output": "Action: click [70]"
+    }
+  ]
+}

Result/Test-Qwen2.5-VL-7B-VisualWebBench_HeadingOCR_46.json

@@ -0,0 +1,239 @@
+{
+  "metrics": {
+    "rouge_1": 71.7703889339315,
+    "rouge_2": 69.88612800574188,
+    "rouge_l": 71.69534917825345
+  },
+  "results": [
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_1.png",
+      "ground_truth": "Discover, Appreciate, & Understand the Animal World!",
+      "prediction": "Discover, Appreciate, & Understand the Animal World!"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_2.png",
+      "ground_truth": "Publish your poetry online",
+      "prediction": "Publish your poetry online"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_3.png",
+      "ground_truth": "Book your next fishing trip",
+      "prediction": "Book your next fishing trip"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_4.png",
+      "ground_truth": "THE GOLD STANDARD IN\nOnline Invitations & Greeting Cards",
+      "prediction": "The Gold Standard in Online Invitations & Greeting Cards"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_5.png",
+      "ground_truth": "Car rentals from trusted, local hosts",
+      "prediction": "Find your drive"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_6.png",
+      "ground_truth": "Local, trusted pet care",
+      "prediction": "Local, trusted pet care"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_7.png",
+      "ground_truth": "How to Be a Caregiver for Someone With Arthritis",
+      "prediction": "How to Be a Caregiver for Someone With Arthritis"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_8.png",
+      "ground_truth": "Find your outdoors",
+      "prediction": "Find your outdoors"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_9.png",
+      "ground_truth": "Find senior living near you",
+      "prediction": "Find senior living near you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_10.png",
+      "ground_truth": "Gold and Silver Popular Categories",
+      "prediction": "Gold and Silver Popular Categories"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_11.png",
+      "ground_truth": "Basketball Stats and History Statistics, scores, and history for the NBA, ABA, WNBA, and top European competition.",
+      "prediction": "Basketball Reference"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_12.png",
+      "ground_truth": "“I’m Definitely Going To Show What I’m Made Of,” Says Fundora Ahead Of Tszyu Clash",
+      "prediction": "\"‘I’m Definitely Going To Show What I’m Made Of,’ Says Fundora Ahead Of Tszyu Clash\""
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_13.png",
+      "ground_truth": "Free Online Calculators",
+      "prediction": "Free Online Calculators"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_14.png",
+      "ground_truth": "Crate and Barrel",
+      "prediction": "Crate&Barrel"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_15.png",
+      "ground_truth": "Interested in unions & collective bargaining?",
+      "prediction": "U.S. Department of Labor"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_16.png",
+      "ground_truth": "For Your Home",
+      "prediction": "For Your Home"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_17.png",
+      "ground_truth": "Easily find scholarships that fit you",
+      "prediction": "Easily find scholarships that fit you"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_18.png",
+      "ground_truth": "#1 Provider in Online\nTraffic School, Defensive Driving & Drivers Ed",
+      "prediction": "#1 Provider in Online Traffic School, Defensive Driving & Drivers Ed"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_19.png",
+      "ground_truth": "Discover Land for Sale, All Across America",
+      "prediction": "Discover Land for Sale, All Across America"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_20.png",
+      "ground_truth": "Find Used Farm Equipment",
+      "prediction": "THE MOST TRUSTED NAME IN FARM EQUIPMENT"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_21.png",
+      "ground_truth": "Massachusetts Institute of Technology",
+      "prediction": "Human sweat contains a protein that may protect some people against Lyme disease, scientists report."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_22.png",
+      "ground_truth": "Beyond the Guidebook",
+      "prediction": "Beyond the Guidebook"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_23.png",
+      "ground_truth": "Discover your family story",
+      "prediction": "Discover your family story"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_24.png",
+      "ground_truth": "Welcome to MyModivcare!",
+      "prediction": "Welcome to MyModivcare!"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_25.png",
+      "ground_truth": "Earth just had its warmest February on record",
+      "prediction": "Earth just had its warmest February on record >"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_26.png",
+      "ground_truth": "Find your table for any occasion",
+      "prediction": "Find your table for any occasion"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_27.png",
+      "ground_truth": "Online invitations to celebrate all of life’s moments",
+      "prediction": "Online invitations to celebrate all of life’s moments"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_28.png",
+      "ground_truth": "Your Climate Credit is coming",
+      "prediction": "Your Climate Credit is coming"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_29.png",
+      "ground_truth": "Football Stats and History The complete source for current and historical NFL, AFL, and AAFC players, teams, scores and leaders.",
+      "prediction": "Pro Football Reference"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_30.png",
+      "ground_truth": "Welcome to Progressive Insurance®",
+      "prediction": "Better insurance starts here"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_31.png",
+      "ground_truth": "Loving pet care in your neighborhood™",
+      "prediction": "Loving pet care in your neighborhood™"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_32.png",
+      "ground_truth": "Securing your today and tomorrow",
+      "prediction": "Social Security"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_33.png",
+      "ground_truth": "Providing expertise, innovation and world-class care",
+      "prediction": "Lulu's Story"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_34.png",
+      "ground_truth": "Secretary Blinken’s Travel",
+      "prediction": "Secretary Blinken’s Travel"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_35.png",
+      "ground_truth": "Welcome to Texas.gov",
+      "prediction": "Welcome to Texas.gov"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_36.png",
+      "ground_truth": "Your wedding team and everything in between",
+      "prediction": "Your wedding team and everything in between"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_37.png",
+      "ground_truth": "TEXAS STATE UNIVERSITY",
+      "prediction": "NEXT MOVES US FORWARD"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_38.png",
+      "ground_truth": "USAA has insurance, banking and retirement solutions.",
+      "prediction": "USAA has insurance, banking and retirement solutions."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_39.png",
+      "ground_truth": "FIND THE VALVOLINE™ SOLUTION THAT'S RIGHT FOR YOU",
+      "prediction": "FIND THE VALVOLINE™ SOLUTION THAT'S RIGHT FOR YOU"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_40.png",
+      "ground_truth": "VIPLeague | Live Sports Streaming",
+      "prediction": "VIPLeague | Live Sports Streaming"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_41.png",
+      "ground_truth": "Periods of Snow Across the Great Lakes and Northeast; Elevated Fire Weather Conditions in the Ohio Valley and Mid-Atlantic",
+      "prediction": "National Weather Service"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_42.png",
+      "ground_truth": "Find people, contact info & background checks",
+      "prediction": "Find people, contact info & background checks"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_43.png",
+      "ground_truth": "Welcome to wikiHow, the most trusted how-to site on the internet.",
+      "prediction": "Welcome to wikiHow, the most trusted how-to site on the internet."
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_44.png",
+      "ground_truth": "Wikipedia\nThe Free Encyclopedia",
+      "prediction": "Wikipedia"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_45.png",
+      "ground_truth": "World History Encyclopedia",
+      "prediction": "World History Encyclopedia"
+    },
+    {
+      "image": "/code/CogReasoner/Images/Benchmark/VisualWebBench/heading_ocr_46.png",
+      "ground_truth": "Central and Western, People's Republic of China",
+      "prediction": "Central and Western, People's Republic of China"
+    }
+  ]
+}

Result/Test-UI-TARs-Single_Step.json

@@ -0,0 +1,1252 @@
+{
+  "model_name": "UI-TARs",
+  "metrics": {
+    "total_processed": 62,
+    "correct": 21,
+    "accuracy": 0.3387096774193549
+  },
+  "results": [
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-ryanair--00004--r01--step01.png"
+      ],
+      "prompt": "Task: Accept the website's privacy and cookie policy.\n",
+      "ground_truth": "CLICK(66)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(712,593)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-ticketcenter--00013--r02--step01.png"
+      ],
+      "prompt": "Task: Go to the Sports section.\n",
+      "ground_truth": "CLICK(6)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(266,107)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Allrecipes--extend--00005--r02--step01.png"
+      ],
+      "prompt": "Task: Log in to your account.\n",
+      "ground_truth": "CLICK(7)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(952,51)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-agoda--00006--r02--step01.png"
+      ],
+      "prompt": "Task: Add a child to your search.\n",
+      "ground_truth": "CLICK(49)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(363,772)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Google Flights--extend--00008--r02--step01.png"
+      ],
+      "prompt": "Task: Sign in to your Google account.\n",
+      "ground_truth": "CLICK(13)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(919,40)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-mbta--00008--r01--step01.png"
+      ],
+      "prompt": "Task: Click on the bus route 22.\n",
+      "ground_truth": "CLICK(36)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(795,983)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-sports.yahoo--00007--r01--step01.png"
+      ],
+      "prompt": "Task: Open the Daily Fantasy page.\n",
+      "ground_truth": "CLICK(32)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(865,109)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-yelp--00016--r02--step01.png"
+      ],
+      "prompt": "Task: Sign up for a new account.\n",
+      "ground_truth": "CLICK(9)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(931,62)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-marriott--00004--r01--step01.png"
+      ],
+      "prompt": "Task: Click the \"Special Offers\" button.\n",
+      "ground_truth": "CLICK(10)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(316,106)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Google Flights-human-extend--00002--r01--step01.png"
+      ],
+      "prompt": "Task: Go to the Hotels section.\n",
+      "ground_truth": "CLICK(9)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(528,41)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-us.megabus--00001--r02--step01.png"
+      ],
+      "prompt": "Task: Decline the offer for exclusive deals.\n",
+      "ground_truth": "CLICK(28)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(499,713)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-last.fm--00013--r02--step01.png"
+      ],
+      "prompt": "Task: Sign up to Last.fm.\n",
+      "ground_truth": "CLICK(17)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(961,39)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-flightaware--00004--r01--step01.png"
+      ],
+      "prompt": "Task: Sign up for a FlightAware account.\n",
+      "ground_truth": "CLICK(15)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(932,80)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Huggingface-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Create a new account.\n",
+      "ground_truth": "CLICK(12)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(934,40)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-apple--00001--r02--step01.png"
+      ],
+      "prompt": "Task: View the technical specifications of the iPad Pro.\n",
+      "ground_truth": "CLICK(32)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(493,316)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-nps.gov--00000--r02--step01.png"
+      ],
+      "prompt": "Task: Open the dropdown menu to find a park by state.\n",
+      "ground_truth": "CLICK(13)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(239,740)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-thetrainline--00002--r02--step01.png"
+      ],
+      "prompt": "Task: Accept all cookies to continue browsing the site.\n",
+      "ground_truth": "CLICK(48)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(695,700)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/general_google_search_images/Google Search--general--00458--r01--step01.png"
+      ],
+      "prompt": "Task: Click on the \"Culture\" link.\n",
+      "ground_truth": "CLICK(19)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(499,853)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-espn--00014--r02--step01.png"
+      ],
+      "prompt": "Task: Search the website for content.\n",
+      "ground_truth": "CLICK(19)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(919,113)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Google Search-human-extend--00002--r01--step01.png"
+      ],
+      "prompt": "Task: Click the \"I'm Feeling Artistic\" button.\n",
+      "ground_truth": "CLICK(12)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(575,506)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Cambridge Dictionary-human-extend--00004--r01--step01.png"
+      ],
+      "prompt": "Task: Accept the website's cookies.\n",
+      "ground_truth": "CLICK(48)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(727,950)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-spothero--00014--r01--step01.png"
+      ],
+      "prompt": "Task: Change the search type to monthly parking.\n",
+      "ground_truth": "CLICK(8)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(487,430)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-ikea--00004--r01--step01.png"
+      ],
+      "prompt": "Task: Accept all cookies.\n",
+      "ground_truth": "CLICK(15)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(260,783)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-tvguide--00003--r02--step01.png"
+      ],
+      "prompt": "Task: Join or sign in to your account.\n",
+      "ground_truth": "CLICK(14)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(936,169)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-amazon--00001--r02--step01.png"
+      ],
+      "prompt": "Task: Go to the Today's Deals page.\n",
+      "ground_truth": "CLICK(23)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(729,105)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Amazon-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Try a different image.\n",
+      "ground_truth": "CLICK(5)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(566,547)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-rottentomatoes--00007--r01--step01.png"
+      ],
+      "prompt": "Task: Click on the NEWS link in the main navigation bar.\n",
+      "ground_truth": "CLICK(8)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(827,198)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-uniqlo--00016--r02--step01.png"
+      ],
+      "prompt": "Task: Go to the men's section.\n",
+      "ground_truth": "CLICK(9)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(442,44)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-store.steampowered--00016--r01--step01.png"
+      ],
+      "prompt": "Task: Go to the \"About\" page.\n",
+      "ground_truth": "CLICK(6)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(441,45)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-amtrak-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Allow all cookies.\n",
+      "ground_truth": "CLICK(58)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(926,916)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Google Map-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Sign in to your Google account.\n",
+      "ground_truth": "CLICK(11)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(939,41)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-eventbrite--00007--r01--step01.png"
+      ],
+      "prompt": "Task: Find events in the Dating category.\n",
+      "ground_truth": "CLICK(19)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(725,658)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-target--00008--r01--step01.png"
+      ],
+      "prompt": "Task: Click the red \"Sign in\" button.\n",
+      "ground_truth": "CLICK(25)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(592,869)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-enterprise--00007--r02--step01.png"
+      ],
+      "prompt": "Task: View, modify, or cancel your reservation.\n",
+      "ground_truth": "CLICK(23)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(502,412)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-imdb--00013--r02--step01.png"
+      ],
+      "prompt": "Task: Close the video player.\n",
+      "ground_truth": "CLICK(13)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(42,102)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/ArXiv--extend--00009--r01--step01.png"
+      ],
+      "prompt": "Task: Find recent articles in the General Relativity and Quantum Cosmology category.\n",
+      "ground_truth": "CLICK(80)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(326,711)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Wolfram Alpha-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Navigate to the Personal Finance section.\n",
+      "ground_truth": "CLICK(45)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(835,706)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Booking--extend--00012--r01--step01.png"
+      ],
+      "prompt": "Task: Dismiss the sign in information.\n",
+      "ground_truth": "CLICK(23)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(755,325)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Wolfram Alpha--extend--00006--r01--step01.png"
+      ],
+      "prompt": "Task: Click on the \"Words & Linguistics\" link.\n",
+      "ground_truth": "CLICK(40)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(613,889)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-ebay--00002--r02--step01.png"
+      ],
+      "prompt": "Task: Copy the coupon code \"DAZZLINGJEWEL\".\n",
+      "ground_truth": "CLICK(26)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(182,417)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Coursera--extend--00000--r01--step01.png"
+      ],
+      "prompt": "Task: Accept the use of cookies.\n",
+      "ground_truth": "CLICK(27)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(878,951)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-BBC News-human-extend--00000--r01--step01.png"
+      ],
+      "prompt": "Task: Watch the live stream of the news event.\n",
+      "ground_truth": "CLICK(36)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(86,513)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-booking--00010--r02--step01.png"
+      ],
+      "prompt": "Task: Click the \"Sign in or register\" button.\n",
+      "ground_truth": "CLICK(29)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(500,657)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Huggingface--extend--00000--r01--step01.png"
+      ],
+      "prompt": "Task: Create a new account by clicking the sign up button.\n",
+      "ground_truth": "CLICK(12)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(930,40)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/ESPN--extend--00005--r01--step01.png"
+      ],
+      "prompt": "Task: Follow the coverage of the 2024 MLB Draft.\n",
+      "ground_truth": "CLICK(4)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(276,17)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-resy--00017--r01--step01.png"
+      ],
+      "prompt": "Task: Sign up for Resy's email newsletter.\n",
+      "ground_truth": "CLICK(30)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(813,566)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Apple-human-extend--00001--r01--step01.png"
+      ],
+      "prompt": "Task: View the technical specifications for the iPhone 16 Pro.\n",
+      "ground_truth": "CLICK(33)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(263,28)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Booking-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Dismiss the sign in information.\n",
+      "ground_truth": "CLICK(23)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(755,325)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Apple--extend--00015--r02--step01.png"
+      ],
+      "prompt": "Task: Compare the iPad Pro with other models.\n",
+      "ground_truth": "CLICK(33)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(195,28)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-foxsports--00008--r02--step01.png"
+      ],
+      "prompt": "Task: Navigate to the National Basketball Association (NBA) page.\n",
+      "ground_truth": "CLICK(16)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(774,373)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-cvs--00011--r02--step01.png"
+      ],
+      "prompt": "Task: Close the \"Improving your site experience\" banner.\n",
+      "ground_truth": "CLICK(23)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(953,878)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Allrecipes-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Log in to your account.\n",
+      "ground_truth": "CLICK(7)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(952,51)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Coursera-human-extend--00000--r01--step01.png"
+      ],
+      "prompt": "Task: Accept all cookies to continue.\n",
+      "ground_truth": "CLICK(33)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(882,949)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-ESPN-human-extend--00000--r01--step01.png"
+      ],
+      "prompt": "Task: Read the news about the Man City hearing.\n",
+      "ground_truth": "CLICK(45)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(152,612)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/BBC News--extend--00017--r01--step01.png"
+      ],
+      "prompt": "Task: Watch the live video.\n",
+      "ground_truth": "CLICK(35)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(86,559)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-GitHub-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Sign in to your account.\n",
+      "ground_truth": "CLICK(17)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(829,125)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-koa--00008--r01--step01.png"
+      ],
+      "prompt": "Task: Go to the rewards program page.\n",
+      "ground_truth": "CLICK(18)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(704,69)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Google Map--extend--00018--r01--step01.png"
+      ],
+      "prompt": "Task: Zoom in on the map.\n",
+      "ground_truth": "CLICK(13)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(964,877)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-nyc--00002--r01--step01.png"
+      ],
+      "prompt": "Task: Find information on hotels.\n",
+      "ground_truth": "CLICK(8)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(309,155)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-discogs--00005--r02--step01.png"
+      ],
+      "prompt": "Task: Accept the cookies on this website.\n",
+      "ground_truth": "CLICK(25)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(733,927)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-soundcloud--00009--r01--step01.png"
+      ],
+      "prompt": "Task: Accept the website's cookies to continue.\n",
+      "ground_truth": "CLICK(42)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(767,873)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Cambridge Dictionary--extend--00008--r01--step01.png"
+      ],
+      "prompt": "Task: Accept the cookies.\n",
+      "ground_truth": "CLICK(48)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(725,950)"
+    }
+  ],
+  "errors": [
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-ryanair--00004--r01--step01.png"
+      ],
+      "prompt": "Task: Accept the website's privacy and cookie policy.\n",
+      "ground_truth": "CLICK(66)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(712,593)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-ticketcenter--00013--r02--step01.png"
+      ],
+      "prompt": "Task: Go to the Sports section.\n",
+      "ground_truth": "CLICK(6)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(266,107)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Allrecipes--extend--00005--r02--step01.png"
+      ],
+      "prompt": "Task: Log in to your account.\n",
+      "ground_truth": "CLICK(7)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(952,51)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-agoda--00006--r02--step01.png"
+      ],
+      "prompt": "Task: Add a child to your search.\n",
+      "ground_truth": "CLICK(49)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(363,772)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Google Flights--extend--00008--r02--step01.png"
+      ],
+      "prompt": "Task: Sign in to your Google account.\n",
+      "ground_truth": "CLICK(13)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(919,40)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-mbta--00008--r01--step01.png"
+      ],
+      "prompt": "Task: Click on the bus route 22.\n",
+      "ground_truth": "CLICK(36)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(795,983)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-sports.yahoo--00007--r01--step01.png"
+      ],
+      "prompt": "Task: Open the Daily Fantasy page.\n",
+      "ground_truth": "CLICK(32)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(865,109)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-yelp--00016--r02--step01.png"
+      ],
+      "prompt": "Task: Sign up for a new account.\n",
+      "ground_truth": "CLICK(9)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(931,62)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-marriott--00004--r01--step01.png"
+      ],
+      "prompt": "Task: Click the \"Special Offers\" button.\n",
+      "ground_truth": "CLICK(10)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(316,106)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Google Flights-human-extend--00002--r01--step01.png"
+      ],
+      "prompt": "Task: Go to the Hotels section.\n",
+      "ground_truth": "CLICK(9)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(528,41)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-us.megabus--00001--r02--step01.png"
+      ],
+      "prompt": "Task: Decline the offer for exclusive deals.\n",
+      "ground_truth": "CLICK(28)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(499,713)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-last.fm--00013--r02--step01.png"
+      ],
+      "prompt": "Task: Sign up to Last.fm.\n",
+      "ground_truth": "CLICK(17)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(961,39)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-flightaware--00004--r01--step01.png"
+      ],
+      "prompt": "Task: Sign up for a FlightAware account.\n",
+      "ground_truth": "CLICK(15)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(932,80)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Huggingface-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Create a new account.\n",
+      "ground_truth": "CLICK(12)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(934,40)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-apple--00001--r02--step01.png"
+      ],
+      "prompt": "Task: View the technical specifications of the iPad Pro.\n",
+      "ground_truth": "CLICK(32)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(493,316)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-nps.gov--00000--r02--step01.png"
+      ],
+      "prompt": "Task: Open the dropdown menu to find a park by state.\n",
+      "ground_truth": "CLICK(13)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(239,740)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-thetrainline--00002--r02--step01.png"
+      ],
+      "prompt": "Task: Accept all cookies to continue browsing the site.\n",
+      "ground_truth": "CLICK(48)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(695,700)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/general_google_search_images/Google Search--general--00458--r01--step01.png"
+      ],
+      "prompt": "Task: Click on the \"Culture\" link.\n",
+      "ground_truth": "CLICK(19)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(499,853)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-espn--00014--r02--step01.png"
+      ],
+      "prompt": "Task: Search the website for content.\n",
+      "ground_truth": "CLICK(19)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(919,113)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Google Search-human-extend--00002--r01--step01.png"
+      ],
+      "prompt": "Task: Click the \"I'm Feeling Artistic\" button.\n",
+      "ground_truth": "CLICK(12)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(575,506)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Cambridge Dictionary-human-extend--00004--r01--step01.png"
+      ],
+      "prompt": "Task: Accept the website's cookies.\n",
+      "ground_truth": "CLICK(48)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(727,950)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-spothero--00014--r01--step01.png"
+      ],
+      "prompt": "Task: Change the search type to monthly parking.\n",
+      "ground_truth": "CLICK(8)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(487,430)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-ikea--00004--r01--step01.png"
+      ],
+      "prompt": "Task: Accept all cookies.\n",
+      "ground_truth": "CLICK(15)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(260,783)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-tvguide--00003--r02--step01.png"
+      ],
+      "prompt": "Task: Join or sign in to your account.\n",
+      "ground_truth": "CLICK(14)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(936,169)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-amazon--00001--r02--step01.png"
+      ],
+      "prompt": "Task: Go to the Today's Deals page.\n",
+      "ground_truth": "CLICK(23)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(729,105)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Amazon-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Try a different image.\n",
+      "ground_truth": "CLICK(5)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(566,547)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-rottentomatoes--00007--r01--step01.png"
+      ],
+      "prompt": "Task: Click on the NEWS link in the main navigation bar.\n",
+      "ground_truth": "CLICK(8)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(827,198)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-uniqlo--00016--r02--step01.png"
+      ],
+      "prompt": "Task: Go to the men's section.\n",
+      "ground_truth": "CLICK(9)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(442,44)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-store.steampowered--00016--r01--step01.png"
+      ],
+      "prompt": "Task: Go to the \"About\" page.\n",
+      "ground_truth": "CLICK(6)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(441,45)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-amtrak-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Allow all cookies.\n",
+      "ground_truth": "CLICK(58)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(926,916)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Google Map-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Sign in to your Google account.\n",
+      "ground_truth": "CLICK(11)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(939,41)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-eventbrite--00007--r01--step01.png"
+      ],
+      "prompt": "Task: Find events in the Dating category.\n",
+      "ground_truth": "CLICK(19)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(725,658)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-target--00008--r01--step01.png"
+      ],
+      "prompt": "Task: Click the red \"Sign in\" button.\n",
+      "ground_truth": "CLICK(25)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(592,869)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-enterprise--00007--r02--step01.png"
+      ],
+      "prompt": "Task: View, modify, or cancel your reservation.\n",
+      "ground_truth": "CLICK(23)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(502,412)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-imdb--00013--r02--step01.png"
+      ],
+      "prompt": "Task: Close the video player.\n",
+      "ground_truth": "CLICK(13)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(42,102)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/ArXiv--extend--00009--r01--step01.png"
+      ],
+      "prompt": "Task: Find recent articles in the General Relativity and Quantum Cosmology category.\n",
+      "ground_truth": "CLICK(80)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(326,711)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Wolfram Alpha-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Navigate to the Personal Finance section.\n",
+      "ground_truth": "CLICK(45)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(835,706)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Booking--extend--00012--r01--step01.png"
+      ],
+      "prompt": "Task: Dismiss the sign in information.\n",
+      "ground_truth": "CLICK(23)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(755,325)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Wolfram Alpha--extend--00006--r01--step01.png"
+      ],
+      "prompt": "Task: Click on the \"Words & Linguistics\" link.\n",
+      "ground_truth": "CLICK(40)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(613,889)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-ebay--00002--r02--step01.png"
+      ],
+      "prompt": "Task: Copy the coupon code \"DAZZLINGJEWEL\".\n",
+      "ground_truth": "CLICK(26)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(182,417)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Coursera--extend--00000--r01--step01.png"
+      ],
+      "prompt": "Task: Accept the use of cookies.\n",
+      "ground_truth": "CLICK(27)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(878,951)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-BBC News-human-extend--00000--r01--step01.png"
+      ],
+      "prompt": "Task: Watch the live stream of the news event.\n",
+      "ground_truth": "CLICK(36)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(86,513)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-booking--00010--r02--step01.png"
+      ],
+      "prompt": "Task: Click the \"Sign in or register\" button.\n",
+      "ground_truth": "CLICK(29)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(500,657)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Huggingface--extend--00000--r01--step01.png"
+      ],
+      "prompt": "Task: Create a new account by clicking the sign up button.\n",
+      "ground_truth": "CLICK(12)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(930,40)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/ESPN--extend--00005--r01--step01.png"
+      ],
+      "prompt": "Task: Follow the coverage of the 2024 MLB Draft.\n",
+      "ground_truth": "CLICK(4)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(276,17)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-resy--00017--r01--step01.png"
+      ],
+      "prompt": "Task: Sign up for Resy's email newsletter.\n",
+      "ground_truth": "CLICK(30)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(813,566)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Apple-human-extend--00001--r01--step01.png"
+      ],
+      "prompt": "Task: View the technical specifications for the iPhone 16 Pro.\n",
+      "ground_truth": "CLICK(33)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(263,28)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Booking-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Dismiss the sign in information.\n",
+      "ground_truth": "CLICK(23)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(755,325)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Apple--extend--00015--r02--step01.png"
+      ],
+      "prompt": "Task: Compare the iPad Pro with other models.\n",
+      "ground_truth": "CLICK(33)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(195,28)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-foxsports--00008--r02--step01.png"
+      ],
+      "prompt": "Task: Navigate to the National Basketball Association (NBA) page.\n",
+      "ground_truth": "CLICK(16)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(774,373)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-cvs--00011--r02--step01.png"
+      ],
+      "prompt": "Task: Close the \"Improving your site experience\" banner.\n",
+      "ground_truth": "CLICK(23)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(953,878)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Allrecipes-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Log in to your account.\n",
+      "ground_truth": "CLICK(7)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(952,51)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-Coursera-human-extend--00000--r01--step01.png"
+      ],
+      "prompt": "Task: Accept all cookies to continue.\n",
+      "ground_truth": "CLICK(33)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(882,949)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-ESPN-human-extend--00000--r01--step01.png"
+      ],
+      "prompt": "Task: Read the news about the Man City hearing.\n",
+      "ground_truth": "CLICK(45)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(152,612)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/BBC News--extend--00017--r01--step01.png"
+      ],
+      "prompt": "Task: Watch the live video.\n",
+      "ground_truth": "CLICK(35)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(86,559)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/WebVoyager-GitHub-human-extend--00003--r01--step01.png"
+      ],
+      "prompt": "Task: Sign in to your account.\n",
+      "ground_truth": "CLICK(17)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(829,125)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-koa--00008--r01--step01.png"
+      ],
+      "prompt": "Task: Go to the rewards program page.\n",
+      "ground_truth": "CLICK(18)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(704,69)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Google Map--extend--00018--r01--step01.png"
+      ],
+      "prompt": "Task: Zoom in on the map.\n",
+      "ground_truth": "CLICK(13)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(964,877)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-nyc--00002--r01--step01.png"
+      ],
+      "prompt": "Task: Find information on hotels.\n",
+      "ground_truth": "CLICK(8)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(309,155)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-discogs--00005--r02--step01.png"
+      ],
+      "prompt": "Task: Accept the cookies on this website.\n",
+      "ground_truth": "CLICK(25)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(733,927)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/mind2web_images/Mind2Web-soundcloud--00009--r01--step01.png"
+      ],
+      "prompt": "Task: Accept the website's cookies to continue.\n",
+      "ground_truth": "CLICK(42)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(767,873)"
+    },
+    {
+      "images": [
+        "/code/Data/Trajectories/OpenWebVoyager_images/webvoyager_images/Cambridge Dictionary--extend--00008--r01--step01.png"
+      ],
+      "prompt": "Task: Accept the cookies.\n",
+      "ground_truth": "CLICK(48)",
+      "prediction": null,
+      "match": false,
+      "raw_model_output": "(725,950)"
+    }
+  ]
+}