diff --git a/openseek/competition/LongContext-ICL-Annotation/src/README.md b/openseek/competition/LongContext-ICL-Annotation/src/README.md
new file mode 100644
index 00000000..d3ec761a
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/src/README.md
@@ -0,0 +1,159 @@
+# 长上下文ICL自动数据标注方案
+
+本方案基于Qwen3-4B大语言模型，采用In-Context Learning (ICL) 范式完成8个数据集的自动标注任务。
+
+## 环境要求
+
+- Python >= 3.8
+- Huawei Ascend 910C GPU
+- vLLM推理引擎 (支持Ascend)
+
+## 安装依赖
+
+```bash
+pip install -r requirements.txt
+```
+
+## 模型准备
+
+下载Qwen3-4B模型到指定路径（默认为 `/root/Qwen3-4B`）：
+
+- HuggingFace: https://huggingface.co/Qwen/Qwen3-4B
+- ModelScope: https://modelscope.cn/models/Qwen/Qwen3-4B
+
+## 启动vLLM服务
+
+在使用本方案前，需要先启动vLLM推理服务：
+
+```bash
+# 使用vLLM Ascend版本启动服务
+vllm serve /root/Qwen3-4B \
+  --port 9010 \
+  --dtype auto \
+  --max-model-len 131072 \
+  --tensor-parallel-size 1 \
+  --gpu-memory-utilization 0.95 \
+  --block-size 16 \
+  --trust-remote-code
+```
+
+服务启动后，API地址为：`http://localhost:9010/v1/`
+
+## 数据准备
+
+确保数据集已放置在正确路径：
+
+```
+/root/OpenSeek/openseek/competition/LongContext-ICL-Annotation/data/
+```
+
+## 运行标注任务
+
+### 单个任务运行
+
+```bash
+python3 main.py \
+  --task_id 1 \
+  --tokenizer_path /root/Qwen3-4B \
+  --log_path_prefix ./outputs/ \
+  --max_input_length 128000
+```
+
+参数说明：
+- `--task_id`: 任务ID (1-8)
+- `--tokenizer_path`: Qwen3-4B模型路径
+- `log_path_prefix`: 输出文件路径前缀
+- `--max_input_length`: 最大输入长度（默认128000）
+
+### 批量运行所有任务
+
+```bash
+#!/bin/bash
+OUTPUT_DIR="./outputs"
+TOKENIZER_PATH="/root/Qwen3-4B"
+
+for task_id in 1 2 3 4 5 6 7 8; do
+    python3 main.py \
+        --task_id $task_id \
+        --log_path_prefix $OUTPUT_DIR/ \
+        --tokenizer_path $TOKENIZER_PATH
+done
+```
+
+### 并行运行（4个任务同时）
+
+```bash
+#!/bin/bash
+OUTPUT_DIR="./outputs"
+TOKENIZER_PATH="/root/Qwen3-4B"
+CONCURRENT=4
+
+for task_id in 1 2 3 4 5 6 7 8; do
+    python3 main.py \
+        --task_id $task_id \
+        --log_path_prefix $OUTPUT_DIR/ \
+        --tokenizer_path $TOKENIZER_PATH > task${task_id}.log 2>&1 &
+    
+    if [ $((task_id % $CONCURRENT)) -eq 0 ]; then
+        wait
+    fi
+done
+wait
+```
+
+## 输出结果
+
+每个任务会生成一个 `.jsonl` 文件，格式如下：
+
+```json
+{"test_sample_id": "1", "prediction": "Good Review"}
+{"test_sample_id": "2", "prediction": "Bad Review"}
+```
+
+## 方案特点
+
+1. **长上下文支持**：使用50个ICL示例，通过YARN RoPE scaling支持最长131,072 tokens
+2. **提示工程**：优化的prompt设计，确保输出格式准确（使用<label>标签）
+3. **高效推理**：基于vLLM引擎，支持OpenAI兼容API
+4. **Ascend优化**：针对华为Ascend NPU进行了专门优化
+
+## 技术方案说明
+
+### 1. 提示策略设计
+- 结构化prompt模板，明确角色定义和任务规则
+- 使用`<label>`标签确保输出格式统一
+- 鼓励内部推理过程，但只输出最终结果
+
+### 2. 上下文构造
+- 动态选择ICL示例，基于token长度控制上下文大小
+- 使用Qwen3-4B原生tokenizer精确计算token数量
+- 最多使用50个示例，平衡性能与上下文长度
+
+### 3. 一致性保证
+- 统一的prompt模板确保不同任务的输出格式一致
+- 多轮对话场景下，使用相同的系统提示和示例选择策略
+- 可扩展的架构设计，便于添加新任务
+
+## 测试API连接
+
+可以使用 `api_test.py` 测试vLLM服务是否正常运行：
+
+```bash
+python3 api_test.py
+```
+
+## 故障排除
+
+1. **内存不足**：减少 `--max-model-len` 或 `--gpu-memory-utilization`
+2. **推理速度慢**：增加 `--tensor-parallel-size`（如果有多张GPU）
+3. **输出格式错误**：检查prompt中的<label>标签使用是否正确
+
+## 版本信息
+
+- Qwen3-4B
+- vLLM 0.13.0+ascend
+- Python 3.11.13
+
+## 联系方式
+
+如有问题，请通过比赛官方渠道联系。
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/src/main.py b/openseek/competition/LongContext-ICL-Annotation/src/main.py
index c2949795..42197674 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, build_prompt_cot
 
-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
@@ -70,7 +70,16 @@ def evaluate(task_id:int,
         
         
         text2annotate = test_sample['input']
-        prompt = build_prompt(task_description, text2annotate)
+        
+        # Use CoT prompt for Task 3 and 4, standard prompt for others (Account 3 strategy)
+        # Task 3: Collatz conjecture (math reasoning) - CoT helps
+        # Task 4: String concatenation - CoT significantly helped in Account 2 (+29.2%)
+        # Task 8: Kernel generation - CoT was harmful in Account 2 (6.0% -> 0.6%)
+        if task_id in [3, 4]:
+            prompt = build_prompt_cot(task_description, text2annotate, task_id)
+        else:
+            prompt = build_prompt(task_description, text2annotate)
+        
         if examples_str is None:
             examples_str = select_examples(icl_examples, task_description, text2annotate)
         input_prompt = prompt.replace("[[EXAMPLES]]\n\n", examples_str+'\n\n')
@@ -79,9 +88,9 @@ def evaluate(task_id:int,
         # 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..cd2e1e1f 100644
--- a/openseek/competition/LongContext-ICL-Annotation/src/method.py
+++ b/openseek/competition/LongContext-ICL-Annotation/src/method.py
@@ -85,6 +85,97 @@ 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.
@@ -145,57 +236,102 @@ 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:
+# M15优化：全局缓存机制
+_tokenizer_cache = None
+_example_length_cache = {}
+
+def get_tokenizer():
+    """M15优化：获取缓存的tokenizer实例"""
+    global _tokenizer_cache
+    if _tokenizer_cache is None:
+        _tokenizer_cache = AutoTokenizer.from_pretrained("/root/Qwen3-4B", trust_remote_code=True)
+    return _tokenizer_cache
+
+def compute_example_length(example: dict, tokenizer) -> int:
     """
-        Select examples from all_examples to fit into the target context length (适配Qwen3-4B的token计算).
-        all_examples:
-            A list of examples, where each example is a dict with keys 'input' and 'output' (no 'length' needed).
-            For example, ``{"input": "The material is good and looks great.", "output": "Good Review"}``,
-        task_description:
-            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.
+    M15优化：计算单个示例的token长度，带缓存
     """
-    # 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)
+    # 使用示例的唯一标识作为缓存键
+    example_key = f"{example['input'][:100]}_{example['output'][0] if isinstance(example['output'], list) else example['output']}"
     
-    # 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）
+    if example_key in _example_length_cache:
+        return _example_length_cache[example_key]
     
-    # print(all_examples[0])  # 打印第一个示例，便于调试
+    # 计算长度
+    input_text = example['input']
+    output_text = example['output'][0] if isinstance(example['output'], list) else example['output']
+    
+    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
+    
+    # 缓存结果
+    _example_length_cache[example_key] = length
+    
+    return length
 
+def precompute_all_examples_length(all_examples: list[dict]):
+    """
+    M15优化：预先计算所有示例的长度
+    """
+    global _example_length_cache
+    tokenizer = get_tokenizer()
+    
+    for i, example in enumerate(all_examples):
+        try:
+            compute_example_length(example, tokenizer)
+        except (KeyError, IndexError) as e:
+            print(f"警告：示例{i}长度计算失败，跳过")
+            continue
+
+def select_examples(all_examples: list[dict], task_description: str, text2annotate: str) -> str:
+    """
+    M15优化版本：示例缓存与长度预计算方案
+    通过预先计算和缓存示例长度，提高运行效率
+    
+    Parameters:
+        all_examples: 所有示例列表，每个示例包含'input'和'output'键
+        task_description: 任务描述
+        text2annotate: 待标注文本
+    """
+    # M15优化：预先计算所有示例长度（仅第一次调用时执行）
+    if not _example_length_cache:
+        print("M15优化：开始预计算所有示例长度...")
+        precompute_all_examples_length(all_examples)
+        print(f"M15优化：长度预计算完成，已缓存{len(_example_length_cache)}个示例")
+    
+    # 获取缓存的tokenizer
+    tokenizer = get_tokenizer()
+    
+    # 最大上下文长度限制
+    target_length = 8192
+    
     examples_str, token_num = "", 0
-    # 遍历所有示例，基于Qwen3-4B的tokenizer计算token数
+    selected_count = 0
+    
+    # 遍历所有示例，使用缓存的长度信息
     for i, example in enumerate(all_examples):
         try:
-            # 提取input和output（兼容output是列表的情况）
             input_text = example['input']
-            output_text = example['output'][0]
+            output_text = example['output'][0] if isinstance(example['output'], list) else example['output']
             
-            # 核心：用Qwen3-4B的tokenizer计算input+output的token数（替代原length键）
-            # encode返回token id列表，len即为token数
-            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  # 等效原示例的length值
+            # M15优化：使用缓存的长度信息
+            length = compute_example_length(example, tokenizer)
             
-            # 校验当前示例是否能加入（总长度不超限制）
+            # 校验当前示例是否能加入
             if length + token_num <= target_length:
-                # 累加总token数：示例文本长度 + 格式符号的token数（<label>2 + </label>3 + \n1 + #1）
-                # 注：格式符号的token数是原代码约定，Qwen3-4B对这些符号的实际编码可能略有差异，若需精准可改为：
-                # symbol_tokens = len(tokenizer.encode(f"# <label> </label>\n", add_special_tokens=False))
-                # token_num += (length + symbol_tokens)
-                token_num += (length + 2 + 3 + 1 + 1)
-                # 拼接单个示例字符串
+                token_num += (length + 2 + 3 + 1 + 1)  # <label>2 + </label>3 + \n1 + #1
                 example_str = f"# {input_text} <label> {output_text} </label>\n"
                 examples_str += example_str
+                selected_count += 1
             else:
-                # 超过长度限制，返回已拼接的示例和已选数量
-                return examples_str
-        except KeyError as e:
-            print(f"警告：示例{i}缺少键{e}，跳过该示例")
+                # 超过长度限制，停止选择
+                break
+        except (KeyError, IndexError) as e:
+            print(f"警告：示例{i}缺少必要键或格式错误，跳过该示例")
             continue
-    # 遍历完所有示例且未超长度，返回完整拼接结果
+    
+    print(f"M15优化：从{len(all_examples)}个示例中选择了{selected_count}个示例（使用缓存长度信息）")
     return examples_str
 
 
@@ -217,8 +353,6 @@ 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 +369,7 @@ 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 +382,63 @@ 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
+    
+    # Special handling for Task 8 (code generation): return raw model output
+    # Task 8 generates Triton code without <label> tags
+    if task_id == 8:
+        return whole_result.strip()
+    
+    # For other tasks, extract label-tagged content
     prediction = count_answer(whole_result)
     return prediction
diff --git a/openseek/competition/LongContext-ICL-Annotation/src/requirements.txt b/openseek/competition/LongContext-ICL-Annotation/src/requirements.txt
new file mode 100644
index 00000000..87043eae
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/src/requirements.txt
@@ -0,0 +1,17 @@
+# Python依赖环境配置
+# 适用于 Huawei Ascend 910C 环境
+
+# 基础依赖
+numpy>=1.24.0
+torch>=2.8.0
+torch_npu>=2.8.0
+torchvision>=0.23.0
+
+# 模型与推理
+transformers>=4.57.0
+vllm_ascend>=0.13.0rc1
+
+# 工具库
+tqdm>=4.67.0
+requests>=2.32.0
+openai>=2.14.0
\ No newline at end of file
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