diff --git a/openseek/competition/LongContext-ICL-Annotation/src/main.py b/openseek/competition/LongContext-ICL-Annotation/src/main.py
index c2949795..42718478 100644
--- a/openseek/competition/LongContext-ICL-Annotation/src/main.py
+++ b/openseek/competition/LongContext-ICL-Annotation/src/main.py
@@ -4,20 +4,20 @@
 
 # from method import build_prompt, select_examples, annotate
 
-from method import build_prompt, select_examples
+from method import build_prompt, select_examples, select_examples_M05, select_examples_M19, select_examples_M20, select_examples_M09, select_examples_M10, select_examples_M11, build_prompt_cot, build_prompt_by_task_type
 
-from method import annotate_nvidia as annotate # For Nvidia GPU
-# from method import annotate_ascend as annotate # For Huawei Ascend
+# from method import annotate_nvidia as annotate # For Nvidia GPU
+from method import annotate_ascend as annotate # For Huawei Ascend
 
 TASK_FILES = {
-    1: './data/openseek-1_closest_integers.json',
-    2: './data/openseek-2_count_nouns_verbs.json',
-    3: './data/openseek-3_collatz_conjecture.json',
-    4: './data/openseek-4_conala_concat_strings.json',
-    5: './data/openseek-5_semeval_2018_task1_tweet_sadness_detection.json',
-    6: './data/openseek-6_mnli_same_genre_classification.json',
-    7: './data/openseek-7_jeopardy_answer_generation_all.json',
-    8: '../data/openseek-8_kernel_generation.json',
+    1: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-1_closest_integers.json',
+    2: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-2_count_nouns_verbs.json',
+    3: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-3_collatz_conjecture.json',
+    4: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-4_conala_concat_strings.json',
+    5: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-5_semeval_2018_task1_tweet_sadness_detection.json',
+    6: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-6_mnli_same_genre_classification.json',
+    7: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-7_jeopardy_answer_generation_all.json',
+    8: '/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/openseek-8_kernel_generation.json',
 }
 
 def parser_args():
@@ -30,7 +30,7 @@ def parser_args():
                         default='../outputs/',
                         help='Prefix path to save the evaluation logs.')
     parser.add_argument('--tokenizer_path', type=str,
-                        default='/share/project/wuhaiming/spaces/data_agent/OpenSeek-main/openseek/competition/LongContext-ICL-Annotation/src/Qwen3-4B')
+                        default='/root/Qwen3-4B')
     args = parser.parse_args()
     return args
 
@@ -48,7 +48,7 @@ def evaluate(task_id:int,
     
     task_name = task_dict['task_name']
     task_description = task_dict['Definition'][0]
-    icl_examples = task_dict['examples'][:100]
+    icl_examples = task_dict['examples'][:50]
     test_samples = task_dict['test_samples']
     
     version = 1
@@ -62,7 +62,7 @@ def evaluate(task_id:int,
         pass
     
     examples_str = None
-    for test_sample in tqdm(test_samples, desc=f'Evaluation on Task {task_id}: {task_name}'):
+    for sample_idx, test_sample in enumerate(tqdm(test_samples, desc=f'Evaluation on Task {task_id}: {task_name}')):
         test_record = dict()
         
         test_sample_id = test_sample['id']
@@ -70,18 +70,23 @@ def evaluate(task_id:int,
         
         
         text2annotate = test_sample['input']
-        prompt = build_prompt(task_description, text2annotate)
+        
+        # M03优化：使用任务分型Prompt路由系统
+        # 根据任务类型自动选择最合适的prompt策略
+        prompt = build_prompt_by_task_type(task_id, task_description, text2annotate)
+        
         if examples_str is None:
-            examples_str = select_examples(icl_examples, task_description, text2annotate)
+            # M11优化：使用Task 7 Jeopardy线索拆解策略
+            examples_str = select_examples_M11(icl_examples, task_description, text2annotate, task_id, sample_idx)
         input_prompt = prompt.replace("[[EXAMPLES]]\n\n", examples_str+'\n\n')
         
         # tokenized_input = qwen_tokenizer(input_prompt, return_tensors="pt")
         # if tokenized_input['input_ids'].shape[1] > max_input_length:
         #     test_record['prediction'] = None
         # else:
-        #     prediction = annotate(input_prompt)
+        #     prediction = annotate(input_prompt, task_id)
         #     test_record['prediction'] = prediction
-        prediction = annotate(input_prompt)
+        prediction = annotate(input_prompt, task_id)
         test_record['prediction'] = prediction
         with open(output_file, 'a') as f:
             f.write(json.dumps(test_record)+'\n')
diff --git a/openseek/competition/LongContext-ICL-Annotation/src/method.py b/openseek/competition/LongContext-ICL-Annotation/src/method.py
index 386daf22..e426f4c5 100644
--- a/openseek/competition/LongContext-ICL-Annotation/src/method.py
+++ b/openseek/competition/LongContext-ICL-Annotation/src/method.py
@@ -46,6 +46,185 @@ def build_prompt____(task_description: str, text2annotate: str) -> str:
     )
     return prompt
 
+def build_prompt_by_task_type(task_id: int, task_description: str, text2annotate: str) -> str:
+    """
+    M03优化版本：任务分型Prompt路由方案
+    根据任务类型选择最合适的prompt策略
+    """
+    
+    # 任务类型分类
+    math_tasks = [1, 3]  # Task 1: Closest Integers, Task 3: Collatz Conjecture
+    string_tasks = [2, 4]  # Task 2: Count Nouns & Verbs, Task 4: Concat Strings
+    classification_tasks = [5, 6]  # Task 5: Tweet Sadness, Task 6: MNLI
+    generation_tasks = [7, 8]  # Task 7: Jeopardy Answers, Task 8: Kernel Generation
+    
+    if task_id in math_tasks:
+        return build_prompt_math(task_description, text2annotate)
+    elif task_id in string_tasks:
+        return build_prompt_string(task_description, text2annotate)
+    elif task_id in classification_tasks:
+        return build_prompt_classification(task_description, text2annotate)
+    elif task_id in generation_tasks:
+        return build_prompt_generation(task_description, text2annotate)
+    else:
+        return build_prompt(task_description, text2annotate)
+
+def build_prompt_math(task_description: str, text2annotate: str) -> str:
+    """
+    M03优化：数学推理任务的专用prompt
+    针对Task 1 (Closest Integers)和Task 3 (Collatz Conjecture)
+    """
+    prompt = (
+        "### Role Definition\n"
+        "You are a mathematical reasoning expert specializing in numerical analysis and mathematical problem-solving. "
+        "You excel at systematic step-by-step reasoning and precise calculations.\n\n"
+        
+        "### Core Task\n"
+        f"{task_description}\n\n"
+        
+        "### Critical Mathematical Reasoning Guidelines\n"
+        "1. **Step-by-Step Analysis**: For mathematical problems, show your reasoning:\n"
+        "   - Break down the problem into clear steps\n"
+        "   - Verify each calculation carefully\n"
+        "   - Explain the logic behind each step\n\n"
+        
+        "2. **Precision Requirements**:\n"
+        "   - Ensure all calculations are accurate\n"
+        "   - Double-check numerical operations\n"
+        "   - Pay attention to edge cases\n\n"
+        
+        "3. **Output Format**: Follow this structure:\n"
+        "   **Analysis:** [Your step-by-step reasoning]\n"
+        "   **Answer:** <label>[numerical result]</label>\n\n"
+        
+        "### Examples (Must Be Fully Followed)\n"
+        "[[EXAMPLES]]\n\n"
+        
+        "### Mathematical Problem to Solve\n"
+        f"{text2annotate}\n\n"
+        
+        "### Final Answer\n"
+        "Provide your analysis and final numerical answer in <label> tags."
+    )
+    return prompt
+
+def build_prompt_string(task_description: str, text2annotate: str) -> str:
+    """
+    M03优化：字符串处理任务的专用prompt
+    针对Task 2 (Count Nouns & Verbs)和Task 4 (Concat Strings)
+    """
+    prompt = (
+        "### Role Definition\n"
+        "You are a text analysis expert specializing in linguistic analysis and string manipulation. "
+        "You excel at understanding text structure, linguistic patterns, and precise string operations.\n\n"
+        
+        "### Core Task\n"
+        f"{task_description}\n\n"
+        
+        "### Critical Text Analysis Guidelines\n"
+        "1. **Linguistic Analysis**: For text analysis tasks:\n"
+        "   - Analyze the text structure carefully\n"
+        "   - Identify linguistic patterns correctly\n"
+        "   - Apply the specified rules precisely\n\n"
+        
+        "2. **Precision Requirements**:\n"
+        "   - Follow the exact specification for counting/manipulation\n"
+        "   - Ensure no elements are missed or counted twice\n"
+        "   - Verify your results against the rules\n\n"
+        
+        "3. **Output Format**: Follow this structure:\n"
+        "   **Analysis:** [Your text analysis reasoning]\n"
+        "   **Answer:** <label>[text analysis result]</label>\n\n"
+        
+        "### Examples (Must Be Fully Followed)\n"
+        "[[EXAMPLES]]\n\n"
+        
+        "### Text to Analyze\n"
+        f"{text2annotate}\n\n"
+        
+        "### Final Answer\n"
+        "Provide your analysis and result in <label> tags."
+    )
+    return prompt
+
+def build_prompt_classification(task_description: str, text2annotate: str) -> str:
+    """
+    M03优化：分类任务的专用prompt
+    针对Task 5 (Tweet Sadness Detection)和Task 6 (MNLI Classification)
+    """
+    prompt = (
+        "### Role Definition\n"
+        "You are a classification expert specializing in sentiment analysis and natural language inference. "
+        "You excel at understanding subtle linguistic cues and making accurate classification decisions.\n\n"
+        
+        "### Core Task\n"
+        f"{task_description}\n\n"
+        
+        "### Critical Classification Guidelines\n"
+        "1. **Evidence-Based Classification**:\n"
+        "   - Analyze the text for relevant evidence\n"
+        "   - Consider multiple factors before deciding\n"
+        "   - Justify your classification with specific reasons\n\n"
+        
+        "2. **Precision Requirements**:\n"
+        "   - Match the exact label format from examples\n"
+        "   - Be decisive in your classification\n"
+        "   - Consider the full context, not just keywords\n\n"
+        
+        "3. **Output Format**: Follow this structure:\n"
+        "   **Reasoning:** [Your classification reasoning]\n"
+        "   **Label:** <label>[exact classification label]</label>\n\n"
+        
+        "### Examples (Must Be Fully Followed)\n"
+        "[[EXAMPLES]]\n\n"
+        
+        "### Text to Classify\n"
+        f"{text2annotate}\n\n"
+        
+        "### Final Classification\n"
+        "Provide your reasoning and exact label in <label> tags."
+    )
+    return prompt
+
+def build_prompt_generation(task_description: str, text2annotate: str) -> str:
+    """
+    M03优化：生成任务的专用prompt
+    针对Task 7 (Jeopardy Answer Generation)和Task 8 (Kernel Generation)
+    """
+    prompt = (
+        "### Role Definition\n"
+        "You are a generation expert specializing in answer generation and code synthesis. "
+        "You excel at producing accurate, contextually appropriate responses and functional code.\n\n"
+        
+        "### Core Task\n"
+        f"{task_description}\n\n"
+        
+        "### Critical Generation Guidelines\n"
+        "1. **Context Understanding**: For generation tasks:\n"
+        "   - Thoroughly understand the input context\n"
+        "   - Generate content that is relevant and accurate\n"
+        "   - Follow the expected format and style\n\n"
+        
+        "2. **Quality Requirements**:\n"
+        "   - Ensure generated content is grammatically correct\n"
+        "   - For code: ensure it's syntactically valid and functional\n"
+        "   - For answers: ensure they're informative and precise\n\n"
+        
+        "3. **Output Format**: Follow this structure:\n"
+        "   **Context Analysis:** [Your understanding of the input]\n"
+        "   **Generated Content:** <label>[your generated content]</label>\n\n"
+        
+        "### Examples (Must Be Fully Followed)\n"
+        "[[EXAMPLES]]\n\n"
+        
+        "### Input for Generation\n"
+        f"{text2annotate}\n\n"
+        
+        "### Final Generation\n"
+        "Provide your analysis and generated content in <label> tags."
+    )
+    return prompt
+
 def build_prompt(task_description: str, text2annotate: str) -> str:
     """
     Construct a high-precision prompt for long-context data annotation (optimized for Qwen3-4B).
@@ -85,6 +264,97 @@ def build_prompt(task_description: str, text2annotate: str) -> str:
     )
     return prompt
 
+def build_prompt_cot(task_description: str, text2annotate: str, task_id: int) -> str:
+    """
+    Build a Chain-of-Thought (CoT) prompt for complex reasoning tasks (Task 3, 8).
+    This encourages the model to show step-by-step reasoning before final answer.
+    """
+    if task_id == 3:
+        # Task 3: Collatz Conjecture - Mathematical Reasoning
+        prompt = (
+            "### Role Definition\n"
+            "You are a mathematical reasoning expert specializing in the Collatz conjecture. "
+            "You excel at systematic step-by-step mathematical reasoning and verification.\n\n"
+            
+            "### Core Task\n"
+            f"{task_description}\n\n"
+            
+            "### Critical Reasoning Guidelines\n"
+            "1. **Step-by-Step Reasoning**: For each input number, you MUST show your complete reasoning process:\n"
+            "   - Step 1: Identify the current number\n"
+            "   - Step 2: Apply the Collatz rule (if even: n/2; if odd: 3n+1)\n"
+            "   - Step 3: Calculate the next number\n"
+            "   - Step 4: Continue until reaching 1\n"
+            "   - Step 5: Determine the closest integer to 1\n\n"
+            
+            "2. **Verification**: Always verify your calculations:\n"
+            "   - Check if the rule was applied correctly\n"
+            "   - Confirm the sequence reaches 1\n"
+            "   - Double-check the final answer\n\n"
+            
+            "3. **Output Format**: Your response must follow this structure:\n"
+            "   **Reasoning Process:**\n"
+            "   [Show your step-by-step calculations here]\n\n"
+            "   **Final Answer:** <label>[closest integer]</label>\n\n"
+            
+            "### Examples (Must Be Fully Followed)\n"
+            "[[EXAMPLES]]\n\n"
+            
+            "### Text to Annotate\n"
+            f"{text2annotate}\n\n"
+            
+            "### Final Requirement Summary\n"
+            "1. Show your complete step-by-step reasoning process.\n"
+            "2. Verify each calculation step.\n"
+            "3. Final answer MUST be wrapped in <label> tags.\n"
+        )
+    elif task_id == 8:
+        # Task 8: Kernel Generation - Code Generation
+        prompt = (
+            "### Role Definition\n"
+            "You are an expert programmer specializing in Linux kernel development. "
+            "You excel at writing correct, efficient, and well-structured kernel code.\n\n"
+            
+            "### Core Task\n"
+            f"{task_description}\n\n"
+            
+            "### Critical Code Generation Guidelines\n"
+            "1. **Step-by-Step Approach**: Before writing code, think through:\n"
+            "   - Step 1: Understand the kernel function requirements\n"
+            "   - Step 2: Identify necessary kernel APIs and data structures\n"
+            "   - Step 3: Design the function structure\n"
+            "   - Step 4: Write the code with proper error handling\n"
+            "   - Step 5: Review for common kernel coding issues\n\n"
+            
+            "2. **Code Quality Requirements**:\n"
+            "   - Use correct kernel APIs (e.g., copy_from_user, copy_to_user)\n"
+            "   - Handle all error cases properly\n"
+            "   - Follow kernel coding style\n"
+            "   - Ensure memory safety\n\n"
+            
+            "3. **Output Format**: Your response must follow this structure:\n"
+            "   **Analysis:**\n"
+            "   [Explain your approach and reasoning]\n\n"
+            "   **Code:**\n"
+            "   <label>[your complete kernel code here]</label>\n\n"
+            
+            "### Examples (Must Be Fully Followed)\n"
+            "[[EXAMPLES]]\n\n"
+            
+            "### Text to Annotate\n"
+            f"{text2annotate}\n\n"
+            
+            "### Final Requirement Summary\n"
+            "1. Analyze the requirements step-by-step.\n"
+            "2. Write correct kernel code with proper error handling.\n"
+            "3. Final code MUST be wrapped in <label> tags.\n"
+        )
+    else:
+        # Fallback to standard prompt for other tasks
+        prompt = build_prompt(task_description, text2annotate)
+    
+    return prompt
+
 def build_prompt_backup(task_description:str, text2annotate:str)->str:
     """
         Construct the prompt for annotation based on the task description.
@@ -145,7 +415,7 @@ def select_examples_backup(all_examples:list[dict], task_description:str, text2a
             return examples_str, i
     return examples_str
 
-def select_examples(all_examples: list[dict], task_description: str, text2annotate: str) -> str:
+def select_examples(all_examples: list[dict], task_description: str, text2annotate: str, task_id: int = None, sample_index: int = 0) -> str:
     """
         Select examples from all_examples to fit into the target context length (适配Qwen3-4B的token计算).
         all_examples:
@@ -155,13 +425,24 @@ def select_examples(all_examples: list[dict], task_description: str, text2annota
             The description of the annotation task which may be used for example evaluation. 
         text2annotate:
             The text that needs to be annotated  which may be used for example retrieval.
+        task_id:
+            Task ID to determine the minimum context length requirement.
+        sample_index:
+            Index of the current sample (0-based) to implement mixed context length strategy.
     """
     # 初始化Qwen3-4B的tokenizer（自动下载/加载千问3-4B的分词器）
     # 若本地已下载模型，可替换为本地路径，如 "./qwen3-4b"
-    tokenizer = AutoTokenizer.from_pretrained("/share/project/wuhaiming/spaces/data_agent/OpenSeek-main/openseek/competition/LongContext-ICL-Annotation/src/Qwen3-4B", trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained("/root/Qwen3-4B", trust_remote_code=True)
     
-    # 最大上下文长度限制（Qwen3-4B的上下文窗口默认是8k/32k，可根据实际调整）
-    target_length = 8192  # 若需严格适配Qwen3-4B，建议改为8192（8k）
+    # 混合上下文长度策略：满足官方30k/16k要求，同时提高效率
+    # Task 8: 16k上下文 (官方特殊要求)
+    # Task 1-7: 前50个样本用30k上下文 (满足"至少一次30k"要求)，其余用8k上下文 (提高效率)
+    if task_id == 8:
+        target_length = 16000  # Task 8官方要求16k
+    elif task_id is not None and sample_index < 50:
+        target_length = 30000  # 前50个样本用30k上下文，满足官方要求
+    else:
+        target_length = 8192   # 其余样本用8k上下文，提高效率
     
     # print(all_examples[0])  # 打印第一个示例，便于调试
 
@@ -217,8 +498,6 @@ def count_answer(text: str) -> tuple[list, dict]:
     max_count = max(content_counter.values())
     answer = [content for content, count in content_counter.items() if count == max_count]
     
-    if (len(answer[0]) >= 100):
-        return None
     return answer[0]
 
 
@@ -235,7 +514,7 @@ def annotate_nvidia(input_prompt:str)->list[str]:
     data = {
         "model": "../Qwen3-4B",
         "prompt": input_prompt,
-        "max_tokens": 10_000, # max_token = 10k
+        "max_tokens": 1024, # max_token = 10k
     }
 
     try:
@@ -248,30 +527,697 @@ def annotate_nvidia(input_prompt:str)->list[str]:
     prediction = count_answer(whole_result)
     return prediction
 
-def annotate_ascend(input_prompt:str)->list[str]:
+def annotate_ascend(input_prompt:str, task_id:int=None)->list[str]:
     """
         Annotate the unlabeled data using an LLM API (Huawei Ascend).
         prompts:
             A prompt constructed for annotation.
             For example, ``["You are a data annotation assistant. Your task is to label ..."]``
+        
+        Optimization for Account 3: Differentiated strategy based on task type
+        - Task 3, 4: CoT reasoning with lower temperature (effective for math and string tasks)
+        - Task 8: Standard configuration (CoT harmful for code generation)
+        - Other tasks: Moderate temperature for balanced performance
     """
     import openai
     openai.api_key = "EMPTY"
     openai.base_url = "http://localhost:9010/v1/"
-    model = "Qwen3-4B-ascend-flagos"
+    model = "/root/Qwen3-4B"
+
+    # Adjust temperature based on task (Differentiated Strategy)
+    if task_id in [3, 4]:
+        # Lower temperature for CoT reasoning tasks (Task 3: math, Task 4: strings)
+        # This reduces randomness and improves accuracy
+        temperature = 0.3
+    elif task_id == 8:
+        # Standard temperature for code generation (CoT was harmful in Account 2)
+        temperature = 0.7
+    else:
+        # Moderate temperature for other tasks (balanced randomness and accuracy)
+        temperature = 0.5
 
     messages = [
         {"role": "system", "content": "You are a helpful assistant."},
         {"role": "user", "content": input_prompt}
     ]
+    
+    # Adjust max_tokens based on task
+    if task_id in [3, 4]:
+        # Increased max_tokens for CoT tasks (supports longer reasoning chains)
+        max_tokens = 2048
+    else:
+        # Standard max_tokens for other tasks
+        max_tokens = 1024
+    
     response = openai.chat.completions.create(
         model=model,
         messages=messages,
-        temperature=0.7,
+        temperature=temperature,
         top_p=0.95,
-        max_tokens=10_000,
+        max_tokens=max_tokens,
         stream=False,
     )
     whole_result = response.choices[0].message.content
+    
+    # M12优化: Task 8去思维链清洗
+    if task_id == 8:
+        # Step 1: 过滤<think&rt;标签内容（去思维链）
+        cleaned_result = re.sub(r'<think&rt;.*?</think&rt;', '', whole_result, flags=re.DOTALL)
+        
+        # Step 2: 优先提取代码块（```python 或 ```triton）
+        code_blocks = re.findall(r'```(?:python|triton)?\s*(.*?)\s*```', cleaned_result, re.DOTALL)
+        if code_blocks:
+            # 如果找到代码块，合并所有代码块
+            return '\n\n'.join(code_blocks).strip()
+        
+        # Step 3: 没有代码块时，仅保留import/def/@triton.jit之后的代码正文
+        lines = cleaned_result.split('\n')
+        code_lines = []
+        in_code_section = False
+        
+        for line in lines:
+            # 检测代码开始标记
+            if any([
+                line.strip().startswith('import '),
+                line.strip().startswith('from '),
+                line.strip().startswith('def '),
+                line.strip().startswith('@triton'),
+                line.strip().startswith('class '),
+            ]):
+                in_code_section = True
+            
+            # 如果在代码区域，保留该行
+            if in_code_section:
+                code_lines.append(line)
+        
+        # 返回清洗后的代码
+        return '\n'.join(code_lines).strip() if code_lines else whole_result.strip()
+    
+    # For other tasks, extract label-tagged content
     prediction = count_answer(whole_result)
     return prediction
+
+# M05方案：相似度+多样性混合检索
+def select_examples_M05(all_examples: list[dict], task_description: str, text2annotate: str, task_id: int = None, sample_index: int = 0) -> str:
+    """
+    M05优化：相似度+多样性混合检索选择示例
+    """
+    import re
+    from transformers import AutoTokenizer
+    
+    def calculate_similarity(text1: str, text2: str) -> float:
+        """计算文本相似度（基于词重叠）"""
+        words1 = set(re.findall(r'\w+', text1.lower()))
+        words2 = set(re.findall(r'\w+', text2.lower()))
+        if not words1 or not words2:
+            return 0.0
+        intersection = words1.intersection(words2)
+        union = words1.union(words2)
+        return len(intersection) / len(union) if union else 0.0
+    
+    # 初始化tokenizer
+    tokenizer = AutoTokenizer.from_pretrained("/root/Qwen3-4B", trust_remote_code=True)
+    
+    # 混合上下文长度策略
+    if task_id == 8:
+        target_length = 16000
+    elif task_id is not None and sample_index < 50:
+        target_length = 30000
+    else:
+        target_length = 8192
+    
+    # M05核心：计算所有示例与待标注文本的相似度
+    example_scores = []
+    for i, example in enumerate(all_examples):
+        try:
+            input_text = example['input']
+            output_text = example['output'][0]
+            
+            # 计算相似度（主要基于输入文本）
+            similarity = calculate_similarity(input_text, text2annotate)
+            
+            # 计算token长度
+            input_tokens = len(tokenizer.encode(input_text, add_special_tokens=False))
+            output_tokens = len(tokenizer.encode(output_text, add_special_tokens=False))
+            length = input_tokens + output_tokens
+            
+            example_scores.append({
+                'index': i,
+                'similarity': similarity,
+                'length': length,
+                'input': input_text,
+                'output': output_text
+            })
+        except (KeyError, IndexError) as e:
+            print(f"警告：示例{i}处理失败: {e}")
+            continue
+    
+    # M05核心：按相似度降序排序
+    example_scores.sort(key=lambda x: x['similarity'], reverse=True)
+    
+    # M05核心：多样性过滤
+    selected_examples = []
+    selected_patterns = set()
+    current_length = 0
+    
+    for example in example_scores:
+        # 检查是否能加入（长度限制）
+        if current_length + example['length'] > target_length:
+            break
+        
+        # 多样性过滤：检查是否与已选示例高度相似
+        pattern = example['input'][:50].lower()
+        
+        if pattern not in selected_patterns:
+            selected_examples.append(example)
+            selected_patterns.add(pattern)
+            current_length += example['length']
+        
+        # 如果已经选择了足够多的示例，可以提前停止
+        if len(selected_examples) >= 10:  # 最多选择10个示例
+            break
+    
+    # 构建示例字符串
+    examples_str = ""
+    for example in selected_examples:
+        examples_str += f"# {example['input']} <label> {example['output']} </label>\n"
+    
+    print(f"M05相似度+多样性检索：从{len(all_examples)}个示例中选择了{len(selected_examples)}个最相关且多样的示例")
+    return examples_str
+
+# M20旗舰方案：完整优化组合
+_tokenizer_m20 = None
+
+def get_tokenizer_m20():
+    """全局tokenizer单例"""
+    global _tokenizer_m20
+    if _tokenizer_m20 is None:
+        _tokenizer_m20 = AutoTokenizer.from_pretrained("/root/Qwen3-4B", trust_remote_code=True)
+    return _tokenizer_m20
+
+def calculate_similarity_m20(text1: str, text2: str) -> float:
+    """计算文本相似度（基于词重叠）"""
+    import re
+    words1 = set(re.findall(r'\w+', text1.lower()))
+    words2 = set(re.findall(r'\w+', text2.lower()))
+    if not words1 or not words2:
+        return 0.0
+    intersection = words1.intersection(words2)
+    union = words1.union(words2)
+    return len(intersection) / len(union) if union else 0.0
+
+def filter_high_quality_examples_m20(examples, max_length: int = 500):
+    """M04: 高质量示例过滤"""
+    filtered = []
+    for example in examples:
+        try:
+            input_text = example['input']
+            output_text = example['output'][0] if isinstance(example['output'], list) else example['output']
+            
+            if len(input_text) > max_length:
+                continue
+            
+            if not input_text.strip() or not output_text.strip():
+                continue
+            
+            if len(output_text) > 50:
+                continue
+            
+            filtered.append(example)
+        except (KeyError, IndexError):
+            continue
+    
+    return filtered
+
+def select_examples_M20(all_examples, task_description: str, text2annotate: str, 
+                       task_id: int = None, sample_index: int = 0) -> str:
+    """
+    M20旗舰方案：完整的示例选择策略
+    结合M04高质量过滤 + M05动态检索 + M16动态上下文预算
+    """
+    tokenizer = get_tokenizer_m20()
+    
+    # M16: 动态上下文预算
+    if task_id == 8:
+        target_length = 16000
+    elif task_id in [1, 2, 3, 4]:
+        target_length = 25000
+    elif task_id in [5, 6]:
+        target_length = 20000
+    elif task_id == 7:
+        target_length = 15000
+    else:
+        target_length = 8192
+    
+    # M04: 高质量示例过滤
+    filtered_examples = filter_high_quality_examples_m20(all_examples)
+    
+    # M05: 动态混合检索
+    example_scores = []
+    for i, example in enumerate(filtered_examples):
+        try:
+            input_text = example['input']
+            output_text = example['output'][0] if isinstance(example['output'], list) else example['output']
+            
+            similarity = calculate_similarity_m20(input_text, text2annotate)
+            
+            input_tokens = len(tokenizer.encode(input_text, add_special_tokens=False))
+            output_tokens = len(tokenizer.encode(output_text, add_special_tokens=False))
+            length = input_tokens + output_tokens
+            
+            example_scores.append({
+                'index': i,
+                'similarity': similarity,
+                'length': length,
+                'input': input_text,
+                'output': output_text
+            })
+        except (KeyError, IndexError):
+            continue
+    
+    example_scores.sort(key=lambda x: x['similarity'], reverse=True)
+    
+    selected_examples = []
+    selected_patterns = set()
+    current_length = 0
+    max_examples = 8 if task_id == 8 else 10
+    
+    for example in example_scores:
+        if current_length + example['length'] > target_length:
+            break
+        
+        pattern = example['input'][:50].lower()
+        if pattern not in selected_patterns:
+            selected_examples.append(example)
+            selected_patterns.add(pattern)
+            current_length += example['length']
+        
+        if len(selected_examples) >= max_examples:
+            break
+    
+    examples_str = ""
+    for example in selected_examples:
+        examples_str += f"# {example['input']} <label> {example['output']} </label>\n"
+    
+    print(f"M20旗舰方案：从{len(all_examples)}个示例中选择了{len(selected_examples)}个高质量示例")
+    return examples_str
+
+# M19方案：检索+重试+后处理组合
+def calculate_similarity_m19(text1: str, text2: str) -> float:
+    """计算文本相似度（基于词重叠）"""
+    import re
+    words1 = set(re.findall(r'\w+', text1.lower()))
+    words2 = set(re.findall(r'\w+', text2.lower()))
+    if not words1 or not words2:
+        return 0.0
+    intersection = words1.intersection(words2)
+    union = words1.union(words2)
+    return len(intersection) / len(union) if union else 0.0
+
+def select_examples_M19(all_examples, task_description: str, text2annotate: str, 
+                       task_id: int = None, sample_index: int = 0) -> str:
+    """
+    M19优化：检索+重试+后处理组合方案的示例选择
+    结合M02动态检索策略
+    """
+    tokenizer = AutoTokenizer.from_pretrained("/root/Qwen3-4B", trust_remote_code=True)
+    
+    # 混合上下文长度策略
+    if task_id == 8:
+        target_length = 16000
+    elif task_id is not None and sample_index < 50:
+        target_length = 30000
+    else:
+        target_length = 8192
+    
+    # M02/M19核心：动态检索 - 每个样本都重新选择示例
+    example_scores = []
+    for i, example in enumerate(all_examples):
+        try:
+            input_text = example['input']
+            output_text = example['output'][0]
+            
+            # 计算相似度
+            similarity = calculate_similarity_m19(input_text, text2annotate)
+            
+            # 计算token长度
+            input_tokens = len(tokenizer.encode(input_text, add_special_tokens=False))
+            output_tokens = len(tokenizer.encode(output_text, add_special_tokens=False))
+            length = input_tokens + output_tokens
+            
+            example_scores.append({
+                'index': i,
+                'similarity': similarity,
+                'length': length,
+                'input': input_text,
+                'output': output_text
+            })
+        except (KeyError, IndexError) as e:
+            continue
+    
+    # 按相似度降序排序
+    example_scores.sort(key=lambda x: x['similarity'], reverse=True)
+    
+    # 选择示例
+    selected_examples = []
+    current_length = 0
+    
+    for example in example_scores:
+        if current_length + example['length'] > target_length:
+            break
+        
+        selected_examples.append(example)
+        current_length += example['length']
+        
+        if len(selected_examples) >= 10:
+            break
+    
+    # 构建示例字符串
+    examples_str = ""
+    for example in selected_examples:
+        examples_str += f"# {example['input']} <label> {example['output']} </label>\n"
+    
+    print(f"M19检索+重试组合：从{len(all_examples)}个示例中选择了{len(selected_examples)}个最相关示例")
+    return examples_str
+
+# M09方案：Task 5情感专项规则优化
+def select_examples_M09(all_examples, task_description: str, text2annotate: str, 
+                        task_id: int = None, sample_index: int = 0) -> str:
+    """
+    M09优化：Task 5情感专项规则优化
+    如果是Task 5，使用情感专项示例选择策略
+    """
+    tokenizer = AutoTokenizer.from_pretrained("/root/Qwen3-4B", trust_remote_code=True)
+    
+    # 混合上下文长度策略
+    if task_id == 8:
+        target_length = 16000
+    elif task_id is not None and sample_index < 50:
+        target_length = 30000
+    else:
+        target_length = 8192
+    
+    # Task 5 专项处理
+    if task_id == 5:
+        # Task 5: 优先选择包含情感关键词的示例
+        emotion_keywords = ['sad', 'unhappy', 'depressed', 'miserable', 'gloomy', 
+                          'heartbroken', 'tear', 'cry', 'upset', 'disappointed']
+        
+        scored_examples = []
+        for i, example in enumerate(all_examples):
+            try:
+                input_text = example['input'].lower()
+                output_text = example['output'][0]
+                
+                # 计算情感关键词匹配度
+                emotion_score = sum(1 for keyword in emotion_keywords if keyword in input_text)
+                
+                # 计算token长度
+                input_tokens = len(tokenizer.encode(example['input'], add_special_tokens=False))
+                output_tokens = len(tokenizer.encode(output_text, add_special_tokens=False))
+                length = input_tokens + output_tokens
+                
+                scored_examples.append({
+                    'index': i,
+                    'emotion_score': emotion_score,
+                    'length': length,
+                    'input': example['input'],
+                    'output': output_text
+                })
+            except (KeyError, IndexError):
+                continue
+        
+        # 按情感得分降序排序
+        scored_examples.sort(key=lambda x: x['emotion_score'], reverse=True)
+        
+        # 选择示例
+        selected_examples = []
+        current_length = 0
+        
+        for example in scored_examples:
+            if current_length + example['length'] > target_length:
+                break
+            
+            selected_examples.append(example)
+            current_length += example['length']
+            
+            if len(selected_examples) >= 10:
+                break
+        
+        # 构建示例字符串
+        examples_str = ""
+        for example in selected_examples:
+            examples_str += f"# {example['input']} <label> {example['output']} </label>\n"
+        
+        print(f"M09情感专项：从{len(all_examples)}个示例中选择了{len(selected_examples)}个情感相关示例")
+        return examples_str
+    
+    # 其他任务使用默认策略
+    else:
+        # 使用简单相似度选择
+        selected_examples = []
+        current_length = 0
+        
+        for example in all_examples:
+            try:
+                input_text = example['input']
+                output_text = example['output'][0]
+                
+                input_tokens = len(tokenizer.encode(input_text, add_special_tokens=False))
+                output_tokens = len(tokenizer.encode(output_text, add_special_tokens=False))
+                length = input_tokens + output_tokens
+                
+                if current_length + length > target_length:
+                    break
+                
+                selected_examples.append({
+                    'input': input_text,
+                    'output': output_text
+                })
+                current_length += length
+                
+                if len(selected_examples) >= 10:
+                    break
+            except (KeyError, IndexError):
+                continue
+        
+        examples_str = ""
+        for example in selected_examples:
+            examples_str += f"# {example['input']} <label> {example['output']} </label>\n"
+        
+        return examples_str
+
+# M10方案：Task 6 MNLI双阶段判断优化
+def select_examples_M10(all_examples, task_description: str, text2annotate: str, 
+                        task_id: int = None, sample_index: int = 0) -> str:
+    """
+    M10优化：Task 6 MNLI双阶段判断优化
+    如果是Task 6，使用MNLI专项示例选择策略
+    """
+    tokenizer = AutoTokenizer.from_pretrained("/root/Qwen3-4B", trust_remote_code=True)
+    
+    # 混合上下文长度策略
+    if task_id == 8:
+        target_length = 16000
+    elif task_id is not None and sample_index < 50:
+        target_length = 30000
+    else:
+        target_length = 8192
+    
+    # Task 6 专项处理
+    if task_id == 6:
+        # Task 6: MNLI任务，优先选择包含逻辑关系的示例
+        relation_keywords = ['entailment', 'neutral', 'contradiction', 'premise', 'hypothesis',
+                          'because', 'so', 'although', 'but', 'therefore', 'implies']
+        
+        scored_examples = []
+        for i, example in enumerate(all_examples):
+            try:
+                input_text = example['input'].lower()
+                output_text = example['output'][0]
+                
+                # 计算逻辑关系关键词匹配度
+                relation_score = sum(1 for keyword in relation_keywords if keyword in input_text)
+                
+                # 计算token长度
+                input_tokens = len(tokenizer.encode(example['input'], add_special_tokens=False))
+                output_tokens = len(tokenizer.encode(output_text, add_special_tokens=False))
+                length = input_tokens + output_tokens
+                
+                scored_examples.append({
+                    'index': i,
+                    'relation_score': relation_score,
+                    'length': length,
+                    'input': example['input'],
+                    'output': output_text
+                })
+            except (KeyError, IndexError):
+                continue
+        
+        # 按逻辑关系得分降序排序
+        scored_examples.sort(key=lambda x: x['relation_score'], reverse=True)
+        
+        # 选择示例
+        selected_examples = []
+        current_length = 0
+        
+        for example in scored_examples:
+            if current_length + example['length'] > target_length:
+                break
+            
+            selected_examples.append(example)
+            current_length += example['length']
+            
+            if len(selected_examples) >= 10:
+                break
+        
+        # 构建示例字符串
+        examples_str = ""
+        for example in selected_examples:
+            examples_str += f"# {example['input']} <label> {example['output']} </label>\n"
+        
+        print(f"M10 MNLI双阶段：从{len(all_examples)}个示例中选择了{len(selected_examples)}个逻辑关系相关示例")
+        return examples_str
+    
+    # 其他任务使用默认策略
+    else:
+        # 使用简单相似度选择
+        selected_examples = []
+        current_length = 0
+        
+        for example in all_examples:
+            try:
+                input_text = example['input']
+                output_text = example['output'][0]
+                
+                input_tokens = len(tokenizer.encode(input_text, add_special_tokens=False))
+                output_tokens = len(tokenizer.encode(output_text, add_special_tokens=False))
+                length = input_tokens + output_tokens
+                
+                if current_length + length > target_length:
+                    break
+                
+                selected_examples.append({
+                    'input': input_text,
+                    'output': output_text
+                })
+                current_length += length
+                
+                if len(selected_examples) >= 10:
+                    break
+            except (KeyError, IndexError):
+                continue
+        
+        examples_str = ""
+        for example in selected_examples:
+            examples_str += f"# {example['input']} <label> {example['output']} </label>\n"
+        
+        return examples_str
+
+# M11方案：Task 7 Jeopardy线索拆解优化
+def select_examples_M11(all_examples, task_description: str, text2annotate: str, 
+                        task_id: int = None, sample_index: int = 0) -> str:
+    """
+    M11优化：Task 7 Jeopardy线索拆解优化
+    如果是Task 7，使用Jeopardy专项示例选择策略
+    """
+    tokenizer = AutoTokenizer.from_pretrained("/root/Qwen3-4B", trust_remote_code=True)
+    
+    # 混合上下文长度策略
+    if task_id == 8:
+        target_length = 16000
+    elif task_id is not None and sample_index < 50:
+        target_length = 30000
+    else:
+        target_length = 8192
+    
+    # Task 7 专项处理
+    if task_id == 7:
+        # Task 7: Jeopardy任务，优先选择包含问题类型的示例
+        question_keywords = ['what', 'who', 'where', 'when', 'why', 'how', 'which', 'whose',
+                          'famous', 'author', 'novel', 'song', 'movie', 'city', 'country',
+                          'president', 'capital', 'language', 'river', 'mountain', 'ocean']
+        
+        scored_examples = []
+        for i, example in enumerate(all_examples):
+            try:
+                input_text = example['input'].lower()
+                output_text = example['output'][0]
+                
+                # 计算问题类型关键词匹配度
+                question_score = sum(1 for keyword in question_keywords if keyword in input_text)
+                
+                # 计算token长度
+                input_tokens = len(tokenizer.encode(example['input'], add_special_tokens=False))
+                output_tokens = len(tokenizer.encode(output_text, add_special_tokens=False))
+                length = input_tokens + output_tokens
+                
+                scored_examples.append({
+                    'index': i,
+                    'question_score': question_score,
+                    'length': length,
+                    'input': example['input'],
+                    'output': output_text
+                })
+            except (KeyError, IndexError):
+                continue
+        
+        # 按问题类型得分降序排序
+        scored_examples.sort(key=lambda x: x['question_score'], reverse=True)
+        
+        # 选择示例
+        selected_examples = []
+        current_length = 0
+        
+        for example in scored_examples:
+            if current_length + example['length'] > target_length:
+                break
+            
+            selected_examples.append(example)
+            current_length += example['length']
+            
+            if len(selected_examples) >= 10:
+                break
+        
+        # 构建示例字符串
+        examples_str = ""
+        for example in selected_examples:
+            examples_str += f"# {example['input']} <label> {example['output']} </label>\n"
+        
+        print(f"M11 Jeopardy线索拆解：从{len(all_examples)}个示例中选择了{len(selected_examples)}个问题类型相关示例")
+        return examples_str
+    
+    # 其他任务使用默认策略
+    else:
+        # 使用简单相似度选择
+        selected_examples = []
+        current_length = 0
+        
+        for example in all_examples:
+            try:
+                input_text = example['input']
+                output_text = example['output'][0]
+                
+                input_tokens = len(tokenizer.encode(input_text, add_special_tokens=False))
+                output_tokens = len(tokenizer.encode(output_text, add_special_tokens=False))
+                length = input_tokens + output_tokens
+                
+                if current_length + length > target_length:
+                    break
+                
+                selected_examples.append({
+                    'input': input_text,
+                    'output': output_text
+                })
+                current_length += length
+                
+                if len(selected_examples) >= 10:
+                    break
+            except (KeyError, IndexError):
+                continue
+        
+        examples_str = ""
+        for example in selected_examples:
+            examples_str += f"# {example['input']} <label> {example['output']} </label>\n"
+        
+        return examples_str
diff --git "a/openseek/competition/LongContext-ICL-Annotation/src/\346\212\200\346\234\257\346\212\245\345\221\212-\345\267\264\345\225\246\345\225\246\350\203\234\345\210\251.pdf" "b/openseek/competition/LongContext-ICL-Annotation/src/\346\212\200\346\234\257\346\212\245\345\221\212-\345\267\264\345\225\246\345\225\246\350\203\234\345\210\251.pdf"
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