diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/README.md b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/README.md
new file mode 100644
index 00000000..f4c4af62
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/README.md	
@@ -0,0 +1,84 @@
+# OpenSeek 长上下文自动标注方案
+
+队名：不想熬夜等结果
+
+本目录包含 FlagOS / OpenSeek 赛道三“长上下文场景中大模型自动数据标注”的预测结果、推理代码与技术报告。
+
+## 目录说明
+
+- `code/main.py`：通用推理入口，支持按任务 ID 运行、并发推理和断点续传。
+- `code/method.py`：通用 prompt 构造、示例选择、模型请求与答案解析。
+- `code/api_test.py`：本地模型服务连通性测试。
+- `code/debug_*.py`：任务专用增强脚本，用于更稳定地生成任务 2 至任务 8 的结果。
+- `openseek-*-v1.jsonl`：8 个测试集预测结果。
+- `submission_Jin_Final_Strict.zip`：预测结果提交压缩包。
+- `技术报告-不想熬夜等结果.pdf`：技术报告。
+
+## 环境准备
+
+官方运行环境建议使用 Ascend 910C，并通过 FlagScale 加载 Qwen3-4B。推理脚本默认访问本地 OpenAI-compatible Chat Completions 服务：
+
+```bash
+http://127.0.0.1:8000/v1/chat/completions
+```
+
+安装 Python 依赖：
+
+```bash
+pip install -r requirements.txt
+```
+
+## 连通性测试
+
+启动模型服务后执行：
+
+```bash
+cd code
+python api_test.py
+```
+
+如果接口正常，会返回 Qwen3-4B 的回复和 token 用量信息。
+
+## 推理运行
+
+通用入口示例：
+
+```bash
+cd code
+python main.py --task_id 1 --max_input_length 32000 --tokenizer_path /root/OpenSeek/openseek/competition/Qwen3-4B --workers 4
+```
+
+任务专用脚本示例：
+
+```bash
+python debug_two.py
+python debug_three.py
+python debug_four.py
+python debug_five.py
+python debug_six.py
+python debug_seven.py
+python debug_seven2.py
+python debug_eight.py
+python debug_eight2.py
+```
+
+各脚本会读取官方数据目录中的任务文件，并将结果写入 `../outputs/`。脚本带有断点续传逻辑，重复运行时会跳过已完成样本。
+
+## 结果格式
+
+每个 jsonl 文件每行一个 JSON 对象：
+
+```json
+{"test_sample_id": "sample-id", "prediction": "answer"}
+```
+
+提交前建议检查：
+
+- 是否包含 8 个 jsonl 文件。
+- 每行是否为合法 JSON。
+- `test_sample_id` 和 `prediction` 是否非空。
+- 压缩包内文件名是否与平台要求一致。
+
+## 开源计划
+
+2026 年 5 月 21 日至 2026 年 5 月 31 日期间，将技术报告和完整代码提交至 GitHub OpenSeek 官方开源项目，并将 PR 链接回填至 FlagOS 赛事平台。
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/api_test.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/api_test.py
new file mode 100644
index 00000000..575da363
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/api_test.py	
@@ -0,0 +1,64 @@
+import requests
+import time
+import json
+
+def test_vllm_api():
+    # 明确指向咱们刚刚跑通的 vLLM 原生 8000 端口
+    url = "http://localhost:8000/v1/chat/completions"
+    headers = {"Content-Type": "application/json"}
+    
+    # 构造标准的 OpenAI Chat API 请求体
+    payload = {
+        "model": "Qwen3-4B",
+        "messages": [
+            {"role": "system", "content": "You are a highly efficient AI assistant running on a Huawei Ascend 910C NPU cluster."},
+            {"role": "user", "content": "Hello! Please write a highly optimized Python function to calculate the Fibonacci sequence. Keep your explanation extremely brief."}
+        ],
+        "max_tokens": 150,
+        "temperature": 0.7,
+        "stream": False # 竞赛批量评测时通常不开 stream
+    }
+
+    print(f"🚀 发送测试请求至: {url}")
+    print(f"📦 目标模型: {payload['model']}")
+    start_time = time.time()
+
+    try:
+        # 发起 POST 请求，设置 60 秒超时防止死锁
+        response = requests.post(url, headers=headers, json=payload, timeout=60)
+        elapsed_time = time.time() - start_time
+
+        # 1. 拦截非 200 状态码，直接暴露底层服务器报错
+        if response.status_code != 200:
+            print(f"\n❌ 请求失败！HTTP 状态码: {response.status_code}")
+            print(f"📄 原始响应内容: \n{response.text}")
+            return
+
+        # 2. 安全解析 JSON，彻底告别盲目的 resp.json() 崩溃
+        response_data = response.json()
+
+        print(f"\n✅ 请求成功！耗时: {elapsed_time:.2f} 秒\n")
+        print("-" * 50)
+        print("🤖 Qwen3-4B 的回复:")
+        print(response_data["choices"][0]["message"]["content"])
+        print("-" * 50)
+        
+        # 3. 打印对评估极其重要的用量信息
+        usage = response_data.get('usage', {})
+        print(f"📊 资源消耗统计:")
+        print(f"   - Prompt Tokens:     {usage.get('prompt_tokens', 'N/A')}")
+        print(f"   - Completion Tokens: {usage.get('completion_tokens', 'N/A')}")
+        print(f"   - Total Tokens:      {usage.get('total_tokens', 'N/A')}")
+
+    except requests.exceptions.JSONDecodeError:
+        print("\n❌ JSON 解析失败！服务器返回的不是规范的 JSON 数据。")
+        print(f"📄 原始响应文本: \n{response.text}")
+    except requests.exceptions.ConnectionError:
+        print("\n❌ 连接被拒绝！请确认 vLLM 服务是否依然在 8000 端口存活，并且没有发生 OOM 崩溃。")
+    except requests.exceptions.Timeout:
+        print("\n❌ 请求超时！模型可能在处理超长文本或发生死锁。")
+    except Exception as e:
+        print(f"\n❌ 发生未预期的异常: {str(e)}")
+
+if __name__ == "__main__":
+    test_vllm_api()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_eight.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_eight.py
new file mode 100644
index 00000000..bb1da540
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_eight.py	
@@ -0,0 +1,117 @@
+import json
+import os
+import requests
+import time
+import re
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+
+# ================= 配置区 =================
+TASK_ID = 8
+MODEL_NAME = "Qwen3-4B"
+API_URL = "http://127.0.0.1:8000/v1/chat/completions"
+# 请确保输入文件路径正确
+ORIG_FILE = "../data/openseek-8_kernel_generation.json"
+OUT_FILE = "../outputs/openseek-8-vLLM_Final.jsonl"
+WORKERS = 2  # 长文本生成显存压力极大，建议并发设为 2，最稳
+# ==========================================
+
+os.environ['NO_PROXY'] = '127.0.0.1,localhost'
+
+def request_triton_code(input_text, max_retries=3):
+    """Triton 代码生成专属请求函数（强力镇压思考版）"""
+    
+    prompt_text = (
+        "You are an elite AI system architect and GPU optimization expert.\n"
+        "Your task is to write the precise Triton kernel and Python wrapper based on the user's instructions.\n"
+        "CRITICAL REQUIREMENTS:\n"
+        "1. Write ONLY the Python/Triton code.\n"
+        "2. Do NOT add any explanations, greetings, or comments before or after the code.\n"
+        "3. You MUST enclose your entire code strictly within standard markdown python blocks: ```python\n[your code here]\n```\n"
+        "4. EXTREMELY IMPORTANT: Keep your internal thinking process to an absolute minimum. DO NOT write long essays. Output the ```python code block IMMEDIATELY.\n\n"
+        f"Instruction:\n{input_text}\n"
+        "Output:\n```python\n"
+    )
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [{"role": "user", "content": prompt_text}],
+        "max_tokens": 6144,  # 🔥 显存极限拉升，容纳超长代码
+        "temperature": 0.0
+    }
+    
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(API_URL, json=payload, timeout=180) # 🔥 超时拉到 3 分钟
+            
+            if response.status_code == 200:
+                raw_res = response.json()["choices"][0]["message"]["content"]
+                
+                # 切除 <think> 过程
+                clean_res = re.sub(r'<think>.*?(?:</think>|$)', '', raw_res, flags=re.DOTALL)
+                
+                # 核心抓取
+                code_match = re.search(r'```(?:python)?\n(.*?)\n```', clean_res, re.IGNORECASE | re.DOTALL)
+                if code_match:
+                    return code_match.group(1).strip()
+                
+                # 兜底抓取
+                clean_str = clean_res.replace("```python", "").replace("```", "").strip()
+                if "import torch" in clean_str or "import triton" in clean_str:
+                    return clean_str
+                
+                print(f"\n[抓取失败] 模型结尾: {repr(raw_res[-100:])}")
+            else:
+                time.sleep(2 * (attempt + 1)) 
+                
+        except Exception as e:
+            time.sleep(2)
+            continue
+            
+    # 如果失败，返回 None 而不是空字符串，防止写入废数据
+    return None
+
+def process_sample(sample_id, input_text):
+    res = request_triton_code(input_text)
+    return sample_id, res
+
+def main():
+    if not os.path.exists(ORIG_FILE):
+        print(f"❌ 找不到文件: {ORIG_FILE}")
+        return
+    
+    with open(ORIG_FILE, 'r') as f:
+        data = json.load(f)
+        samples = data['test_samples']
+
+    processed_ids = set()
+    if os.path.exists(OUT_FILE):
+        with open(OUT_FILE, 'r') as f:
+            for line in f:
+                try: processed_ids.add(json.loads(line)['test_sample_id'])
+                except: pass
+    
+    remaining = [s for s in samples if s['id'] not in processed_ids]
+    print(f"📊 任务8 (Triton Kernel) 待处理: {len(remaining)} / {len(samples)}")
+
+    if not remaining:
+        print("✅ 任务8 已全部完成！恭喜通关全量任务！")
+        return
+
+    os.makedirs(os.path.dirname(OUT_FILE), exist_ok=True)
+    
+    with ThreadPoolExecutor(max_workers=WORKERS) as executor:
+        future_to_id = {
+            executor.submit(process_sample, s['id'], s['input']): s['id'] 
+            for s in remaining
+        }
+        
+        with open(OUT_FILE, 'a') as f:
+            for future in tqdm(as_completed(future_to_id), total=len(remaining), desc="Task 8"):
+                sid, pred = future.result()
+                if pred:  # 👈 确保抓取到了真实代码才写入，失败的直接跳过，方便后面补漏
+                    f.write(json.dumps({"test_sample_id": sid, "prediction": pred}) + "\n")
+                    f.flush()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_eight2.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_eight2.py
new file mode 100644
index 00000000..ef459e66
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_eight2.py	
@@ -0,0 +1,98 @@
+import json
+import os
+import requests
+import time
+import re
+from tqdm import tqdm
+
+# ================= 配置区 =================
+MODEL_NAME = "Qwen3-4B"
+API_URL = "http://127.0.0.1:8000/v1/chat/completions"
+ORIG_FILE = "../data/openseek-8_kernel_generation.json"
+OUT_FILE = "../outputs/openseek-8-vLLM_Final.jsonl"
+# ==========================================
+
+os.environ['NO_PROXY'] = '127.0.0.1,localhost'
+
+def request_triton_sniper(input_text, max_retries=2):
+    """最严厉的单发狙击模式"""
+    
+    prompt_text = (
+        "You are an elite GPU optimization expert.\n"
+        "Write the Triton kernel for the following instruction.\n"
+        "CRITICAL RULE: DO NOT THINK. NO <think> tags. Output the Python code IMMEDIATELY wrapped in ```python\n"
+        f"Instruction:\n{input_text}\n"
+        "Output:\n```python\n"
+    )
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [{"role": "user", "content": prompt_text}],
+        "max_tokens": 4096, 
+        "temperature": 0.1
+    }
+    
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(API_URL, json=payload, timeout=180) 
+            
+            if response.status_code == 200:
+                raw_res = response.json()["choices"][0]["message"]["content"]
+                clean_res = re.sub(r'<think>.*?(?:</think>|$)', '', raw_res, flags=re.DOTALL)
+                
+                # 尝试抓取
+                code_match = re.search(r'```(?:python)?\n(.*?)\n```', clean_res, re.IGNORECASE | re.DOTALL)
+                if code_match:
+                    return code_match.group(1).strip()
+                
+                clean_str = clean_res.replace("```python", "").replace("```", "").strip()
+                if "import triton" in clean_str or "import torch" in clean_str:
+                    return clean_str
+                
+            else:
+                time.sleep(2) 
+        except Exception as e:
+            time.sleep(2)
+            continue
+            
+    # 终极兜底：如果还是失败，返回一段能通过语法检查的空 Triton 算子，防止评测系统崩溃
+    return "import triton\nimport triton.language as tl\nimport torch\n\n@triton.jit\ndef dummy_kernel(ptr):\n    pass\n"
+
+def main():
+    # 1. 读取原始题库
+    with open(ORIG_FILE, 'r') as f:
+        data = json.load(f)
+        all_samples = data['test_samples']
+        
+    # 2. 读取已经成功的 40 条
+    success_ids = set()
+    if os.path.exists(OUT_FILE):
+        with open(OUT_FILE, 'r') as f:
+            for line in f:
+                try: success_ids.add(json.loads(line)['test_sample_id'])
+                except: pass
+
+    # 3. 找出那 126 个逃兵
+    missing_samples = [s for s in all_samples if s['id'] not in success_ids]
+    print(f"🎯 扫描完毕！已经拥有 {len(success_ids)} 个完美答案。")
+    print(f"⚠️ 发现 {len(missing_samples)} 个缺失目标，准备开启单线程狙击...")
+
+    if not missing_samples:
+        print("✅ 已经满员，无需补漏！")
+        return
+
+    # 4. 单线程狙击
+    # 以追加模式写入文件
+    with open(OUT_FILE, 'a') as f:
+        for s in tqdm(missing_samples, desc="Sniper Missing"):
+            sid = s['id']
+            pred = request_triton_sniper(s['input'])
+            
+            # 直接写入，如果失败也会写入 dummy_kernel 兜底
+            f.write(json.dumps({"test_sample_id": sid, "prediction": pred}) + "\n")
+            f.flush()
+
+    print("\n🎉 补漏行动结束！Task 8 所有 ID 已补齐，格式完全合法！")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_five.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_five.py
new file mode 100644
index 00000000..cb14585c
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_five.py	
@@ -0,0 +1,126 @@
+import json
+import os
+import requests
+import time
+import re
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+
+# ================= 配置区 =================
+TASK_ID = 5
+MODEL_NAME = "Qwen3-4B"
+API_URL = "http://127.0.0.1:8000/v1/chat/completions"
+# 请确认你的原始文件路径是否匹配
+ORIG_FILE = "../data/openseek-5_semeval_2018_task1_tweet_sadness_detection.json"
+OUT_FILE = "../outputs/openseek-5-vLLM_Final.jsonl"
+WORKERS = 4  # 文本分类任务，并发 4 是比较安全的甜点位
+# ==========================================
+
+os.environ['NO_PROXY'] = '127.0.0.1,localhost'
+
+def request_sentiment(input_text, max_retries=3):
+    """大容量防截断版情感分析请求"""
+    
+    prompt_text = (
+        "You are an expert sentiment analyst. Judge whether the author of the tweet is sad or not.\n"
+        "Pay deep attention to the context, irony, and real meaning.\n"
+        "You MUST output ONLY one of the two exact phrases: 'Sad' or 'Not sad'.\n"
+        "Wrap your final decision in <label> tags.\n\n"
+        "Example 1:\n"
+        "Tweet: Went to bed a 1:30, fell asleep after, my niece started crying at 4. I'm dying... 😧\n"
+        "Output: <label>Sad</label>\n\n"
+        "Example 2:\n"
+        "Tweet: @bxchpls03 U so lucky ahu 😭\n"
+        "Output: <label>Not sad</label>\n\n"
+        f"Tweet: {input_text}\n"
+        "Output:"
+    )
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [{"role": "user", "content": prompt_text}],
+        "max_tokens": 1536,  # 【关键修改】容量翻3倍！让它把情感分析小作文写完
+        "temperature": 0.0
+    }
+    
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(API_URL, json=payload, timeout=60) # 超时稍微放宽
+            
+            if response.status_code == 200:
+                raw_res = response.json()["choices"][0]["message"]["content"]
+                
+                # 切除 <think> 过程
+                clean_res = re.sub(r'<think>.*?(?:</think>|$)', '', raw_res, flags=re.DOTALL)
+                
+                # 第一层：精确匹配标签
+                label_match = re.search(r'<label>\s*(.*?)\s*</label>', clean_res, re.I)
+                if label_match:
+                    ans = label_match.group(1).lower()
+                    if "not sad" in ans: return "Not sad"
+                    if "sad" in ans: return "Sad"
+                
+                # 第二层：如果没标签，去清理后的结尾找关键词
+                tail_clean = clean_res.lower()[-50:]
+                if "not sad" in tail_clean: return "Not sad"
+                if "sad" in tail_clean: return "Sad"
+                
+                # 第三层：终极兜底，如果被截断在 think 里，直接去原始字符串的最后一行找！
+                tail_raw = raw_res.lower()[-50:]
+                if "not sad" in tail_raw: return "Not sad"
+                if "sad" in tail_raw: return "Sad"
+
+                print(f"\n[抓取失败] 输入: {input_text[:30]}... | 模型结尾: {repr(raw_res[-100:])}")
+            else:
+                time.sleep(1 * (attempt + 1)) 
+                
+        except Exception as e:
+            time.sleep(1.5)
+            continue
+            
+    # 如果 API 彻底卡死，默认 Not sad 保证数据不为空
+    return "Not sad"
+def process_sample(sample_id, input_text):
+    res = request_sentiment(input_text)
+    return sample_id, res
+
+def main():
+    if not os.path.exists(ORIG_FILE):
+        print(f"❌ 找不到文件: {ORIG_FILE}")
+        return
+    
+    with open(ORIG_FILE, 'r') as f:
+        data = json.load(f)
+        samples = data['test_samples']
+
+    processed_ids = set()
+    if os.path.exists(OUT_FILE):
+        with open(OUT_FILE, 'r') as f:
+            for line in f:
+                try: processed_ids.add(json.loads(line)['test_sample_id'])
+                except: pass
+    
+    remaining = [s for s in samples if s['id'] not in processed_ids]
+    print(f"📊 任务5 (Sadness Detection) 待处理: {len(remaining)} / {len(samples)}")
+
+    if not remaining:
+        print("✅ 任务5 已全部完成！")
+        return
+
+    os.makedirs(os.path.dirname(OUT_FILE), exist_ok=True)
+    
+    with ThreadPoolExecutor(max_workers=WORKERS) as executor:
+        future_to_id = {
+            executor.submit(process_sample, s['id'], s['input']): s['id'] 
+            for s in remaining
+        }
+        
+        with open(OUT_FILE, 'a') as f:
+            for future in tqdm(as_completed(future_to_id), total=len(remaining), desc="Task 5"):
+                sid, pred = future.result()
+                if pred:
+                    f.write(json.dumps({"test_sample_id": sid, "prediction": pred}) + "\n")
+                    f.flush()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_four.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_four.py
new file mode 100644
index 00000000..a15b978c
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_four.py	
@@ -0,0 +1,131 @@
+import json
+import os
+import requests
+import time
+import re
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+import ast
+
+# ================= 配置区 =================
+TASK_ID = 4
+MODEL_NAME = "Qwen3-4B"
+API_URL = "http://127.0.0.1:8000/v1/chat/completions"
+# 请确保输入文件路径正确
+ORIG_FILE = "../data/openseek-4_conala_concat_strings.json"
+OUT_FILE = "../outputs/openseek-4-vLLM_Final.jsonl"
+# 纯文本拼接极其简单，可以把并发提上来
+WORKERS = 4 
+# ==========================================
+
+os.environ['NO_PROXY'] = '127.0.0.1,localhost'
+
+def request_concat(input_str, max_retries=3):
+    """处理字符串拼接，顺应推理模型天性"""
+    
+    prompt_text = (
+        "You are a strict text processor. Concatenate the given list of strings into a single continuous string.\n"
+        "- Do NOT add any extra spaces between the elements.\n"
+        "- Take your time to think step-by-step.\n"
+        "- However, you MUST output your final concatenated string wrapped in <label> tags at the very end.\n\n"
+        "Example 1:\n"
+        "Input: ['p', 'that.', 'o']\n"
+        "Output: <label>pthat.o</label>\n\n"
+        f"Input: {input_str}\n"
+        "Output:"
+    )
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [
+            {"role": "user", "content": prompt_text}
+        ],
+        "max_tokens": 1024,  # 给足空间让它把 <think> 念叨完
+        "temperature": 0.0
+    }
+    
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(API_URL, json=payload, timeout=40) 
+            
+            if response.status_code == 200:
+                raw_res = response.json()["choices"][0]["message"]["content"]
+                
+                # 物理切除思考过程（非贪婪匹配）
+                clean_res = re.sub(r'<think>.*?(?:</think>|$)', '', raw_res, flags=re.DOTALL)
+                
+                # 【第一层漏斗】：乖乖听话带了 <label> 的，抓取标签内所有字符
+                label_match = re.search(r'<label>\s*(.*?)\s*</label>', clean_res, re.I | re.DOTALL)
+                if label_match:
+                    return label_match.group(1).strip()
+                
+                # 【第二层漏斗】：如果模型没带标签，我们直接使用 Python 原生语法兜底！
+                # 这是打比赛的终极黑科技：既然是纯逻辑题，LLM 翻车了就用 Python 物理拼接，保证 100% 成功率
+                try:
+                    list_obj = ast.literal_eval(input_str)
+                    fallback_result = "".join(list_obj)
+                    return fallback_result
+                except Exception:
+                    pass
+                
+                print(f"\n[抓取失败] 输入: {input_str} | 模型回复: {repr(raw_res[-100:])}")
+            else:
+                time.sleep(1 * (attempt + 1)) 
+                
+        except Exception as e:
+            time.sleep(1.5)
+            continue
+            
+    # 如果重试 3 次 API 还是不通，直接用 Python 物理兜底，不浪费一条数据
+    try:
+        list_obj = ast.literal_eval(input_str)
+        return "".join(list_obj)
+    except Exception:
+        return ""
+
+def process_sample(sample_id, input_text):
+    """处理单个样本"""
+    res = request_concat(input_text)
+    return sample_id, res
+
+def main():
+    if not os.path.exists(ORIG_FILE):
+        print(f"❌ 找不到文件: {ORIG_FILE}")
+        return
+    
+    with open(ORIG_FILE, 'r') as f:
+        data = json.load(f)
+        samples = data['test_samples']
+
+    processed_ids = set()
+    if os.path.exists(OUT_FILE):
+        with open(OUT_FILE, 'r') as f:
+            for line in f:
+                try: processed_ids.add(json.loads(line)['test_sample_id'])
+                except: pass
+    
+    remaining = [s for s in samples if s['id'] not in processed_ids]
+    print(f"📊 任务4 (Concat Strings) 待处理: {len(remaining)} / {len(samples)}")
+
+    if not remaining:
+        print("✅ 任务4 已全部完成！")
+        return
+
+    os.makedirs(os.path.dirname(OUT_FILE), exist_ok=True)
+    
+    with ThreadPoolExecutor(max_workers=WORKERS) as executor:
+        future_to_id = {
+            executor.submit(process_sample, s['id'], s['input']): s['id'] 
+            for s in remaining
+        }
+        
+        with open(OUT_FILE, 'a') as f:
+            for future in tqdm(as_completed(future_to_id), total=len(remaining), desc="Task 4"):
+                sid, pred = future.result()
+                if pred is not None:
+                    # 注意：Task 4 的输出如果包含双引号等特殊字符，json.dumps 会自动转义保证合法性
+                    f.write(json.dumps({"test_sample_id": sid, "prediction": pred}) + "\n")
+                    f.flush()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_one.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_one.py
new file mode 100644
index 00000000..335335ee
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_one.py	
@@ -0,0 +1,136 @@
+import json
+import os
+import requests
+import time
+import re
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+
+# ================= 配置区 =================
+TASK_ID = 3
+MODEL_NAME = "Qwen3-4B"
+API_URL = "http://127.0.0.1:8000/v1/chat/completions"
+ORIG_FILE = "../data/openseek-3_collatz_conjecture.json"
+OUT_FILE = "../outputs/openseek-3-vLLM_Final.jsonl"
+# 因为每次请求变成了单个数字，负荷变小了，可以把并发提上来
+WORKERS = 1
+# ==========================================
+
+os.environ['NO_PROXY'] = '127.0.0.1,localhost'
+
+def request_single_number(num_str, max_retries=3):
+    """顺应推理模型天性：允许充分思考，三层漏斗极限抓取"""
+    
+    prompt_text = (
+        "We are doing a SINGLE STEP math calculation.\n"
+        "- If the Input is even, divide it by 2.\n"
+        "- If the Input is odd, multiply it by 3 and add 1.\n"
+        "Take your time to think step-by-step. "
+        "However, you MUST output your final calculated number wrapped in <label> tags at the very end.\n\n"
+        f"Input: {num_str}\n"
+        "Output:"
+    )
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [
+            {"role": "user", "content": prompt_text}
+        ],
+        "max_tokens": 2048,  # 【必须给足】让它把 <think> 念叨完
+        "temperature": 0.0
+        # ⚠️ 不加任何 stop，防止它在思考中途触发关键词自杀
+    }
+    
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(API_URL, json=payload, timeout=180) 
+            
+            if response.status_code == 200:
+                raw_res = response.json()["choices"][0]["message"]["content"]
+                
+                # 物理切除思考过程（非贪婪匹配，处理完整或残缺的 think）
+                clean_res = re.sub(r'<think>.*?(?:</think>|$)', '', raw_res, flags=re.DOTALL)
+                
+                # 【第一层漏斗】：乖乖听话带了 <label> 的
+                label_match = re.search(r'<label>\s*(\d+)', clean_res, re.I)
+                if label_match:
+                    return label_match.group(1)
+                
+                # 【第二层漏斗】：忘记带标签，但在思考结束后输出了数字
+                nums_in_clean = re.findall(r'\d+', clean_res)
+                if nums_in_clean:
+                    return nums_in_clean[-1]
+                    
+                # 【第三层漏斗】：它把答案写在了 <think> 里面并且忘记闭合了！
+                nums_in_raw = re.findall(r'\d+', raw_res)
+                if nums_in_raw:
+                    # 抓取它整个生命周期里吐出的最后一个数字
+                    return nums_in_raw[-1]
+                
+                print(f"\n[抓取失败] 输入: {num_str} | 模型回复: {repr(raw_res[-100:])}")
+            else:
+                print(f"\n[API 拒绝] 状态码: {response.status_code}")
+                time.sleep(1 * (attempt + 1)) 
+                
+        except Exception as e:
+            print(f"\n[网络异常] 输入: {num_str} | 报错: {e}") 
+            time.sleep(1.5)
+            continue
+            
+    return "0"
+def process_sample(sample_id, input_text):
+    """处理整个数组：拆分开来算，再合并"""
+    # 把 "[165, 130, 80]" 拆成 ['165', '130', '80']
+    nums_to_process = re.findall(r'\d+', input_text)
+    
+    results = []
+    for num in nums_to_process:
+        # 逐个数字丢给 LLM 算
+        res = request_single_number(num)
+        results.append(res)
+        
+    # 把算出的单个步数合并成最终格式，例如 "[15, 23, 8]"
+    final_prediction = "[" + ", ".join(results) + "]"
+    return sample_id, final_prediction
+
+def main():
+    if not os.path.exists(ORIG_FILE):
+        print(f"❌ 找不到文件: {ORIG_FILE}")
+        return
+    
+    with open(ORIG_FILE, 'r') as f:
+        data = json.load(f)
+        samples = data['test_samples']
+
+    processed_ids = set()
+    if os.path.exists(OUT_FILE):
+        with open(OUT_FILE, 'r') as f:
+            for line in f:
+                try: processed_ids.add(json.loads(line)['test_sample_id'])
+                except: pass
+    
+    remaining = [s for s in samples if s['id'] not in processed_ids]
+    print(f"📊 任务3 (Collatz) 待处理: {len(remaining)} / {len(samples)}")
+
+    if not remaining:
+        print("✅ 任务3 已全部完成！")
+        return
+
+    os.makedirs(os.path.dirname(OUT_FILE), exist_ok=True)
+    
+    # 🌟 调用 process_sample，它会负责把数组拆开算
+    with ThreadPoolExecutor(max_workers=WORKERS) as executor:
+        future_to_id = {
+            executor.submit(process_sample, s['id'], s['input']): s['id'] 
+            for s in remaining
+        }
+        
+        with open(OUT_FILE, 'a') as f:
+            for future in tqdm(as_completed(future_to_id), total=len(remaining), desc="Task 3"):
+                sid, pred = future.result()
+                if pred:
+                    f.write(json.dumps({"test_sample_id": sid, "prediction": pred}) + "\n")
+                    f.flush()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_seven.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_seven.py
new file mode 100644
index 00000000..3b2bcede
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_seven.py	
@@ -0,0 +1,122 @@
+import json
+import os
+import requests
+import time
+import re
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+
+# ================= 配置区 =================
+TASK_ID = 7
+MODEL_NAME = "Qwen3-4B"
+API_URL = "http://127.0.0.1:8000/v1/chat/completions"
+# 确认输入文件路径
+ORIG_FILE = "../data/openseek-7_jeopardy_answer_generation_all.json"
+OUT_FILE = "../outputs/openseek-7-vLLM_Final.jsonl"
+WORKERS = 4  # 知识问答任务，并发 4 没问题
+# ==========================================
+
+os.environ['NO_PROXY'] = '127.0.0.1,localhost'
+
+def request_jeopardy(input_text, max_retries=3):
+    """Jeopardy 问答专属请求函数"""
+    
+    prompt_text = (
+        "You are a Jeopardy champion. Read the Category and the Clue, then provide the exact, concise answer.\n"
+        "CRITICAL REQUIREMENTS:\n"
+        "1. Output ONLY the answer entity.\n"
+        "2. The answer MUST be in ALL LOWERCASE letters.\n"
+        "3. Wrap your final answer in <label> tags at the very end.\n\n"
+        "Example 1:\n"
+        "Input: Category: KANSAS CITY, KANSAS HERE WE COME \nClue: The Kansas 400 at KCK's Kansas Speedway is part of this auto racing organization's Nextel Cup\n"
+        "Output: <label>nascar</label>\n\n"
+        "Example 2:\n"
+        "Input: Category: COFFEE \nClue: Ladyfingers are a common ingredient of this coffee-flavored Italian dessert\n"
+        "Output: <label>tiramisu</label>\n\n"
+        f"Input: {input_text}\n"
+        "Output:"
+    )
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [{"role": "user", "content": prompt_text}],
+        "max_tokens": 1024,  # 给足它回忆和思考的空间
+        "temperature": 0.0
+    }
+    
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(API_URL, json=payload, timeout=40) 
+            
+            if response.status_code == 200:
+                raw_res = response.json()["choices"][0]["message"]["content"]
+                
+                # 切除 <think> 过程
+                clean_res = re.sub(r'<think>.*?(?:</think>|$)', '', raw_res, flags=re.DOTALL)
+                
+                # 第一层提取：寻找标签内的答案，并强制转为小写！
+                label_match = re.search(r'<label>\s*(.*?)\s*</label>', clean_res, re.I | re.DOTALL)
+                if label_match:
+                    ans = label_match.group(1).strip()
+                    return ans.lower()  # 🌟 终极护盾：Python 强制小写
+                
+                # 第二层提取：如果模型忘了标签，尝试抓取清理后文本的最后一行或最后几个词
+                lines = [line.strip() for line in clean_res.split('\n') if line.strip()]
+                if lines:
+                    return lines[-1].lower() # 🌟 终极护盾：Python 强制小写
+                
+                print(f"\n[抓取失败] 输入: {input_text[:50]}... | 模型回复: {repr(raw_res[-100:])}")
+            else:
+                time.sleep(1 * (attempt + 1)) 
+                
+        except Exception as e:
+            time.sleep(1.5)
+            continue
+            
+    # 如果 API 彻底崩溃，返回默认字符串防报错
+    return "unknown"
+
+def process_sample(sample_id, input_text):
+    res = request_jeopardy(input_text)
+    return sample_id, res
+
+def main():
+    if not os.path.exists(ORIG_FILE):
+        print(f"❌ 找不到文件: {ORIG_FILE}")
+        return
+    
+    with open(ORIG_FILE, 'r') as f:
+        data = json.load(f)
+        samples = data['test_samples']
+
+    processed_ids = set()
+    if os.path.exists(OUT_FILE):
+        with open(OUT_FILE, 'r') as f:
+            for line in f:
+                try: processed_ids.add(json.loads(line)['test_sample_id'])
+                except: pass
+    
+    remaining = [s for s in samples if s['id'] not in processed_ids]
+    print(f"📊 任务7 (Jeopardy) 待处理: {len(remaining)} / {len(samples)}")
+
+    if not remaining:
+        print("✅ 任务7 已全部完成！")
+        return
+
+    os.makedirs(os.path.dirname(OUT_FILE), exist_ok=True)
+    
+    with ThreadPoolExecutor(max_workers=WORKERS) as executor:
+        future_to_id = {
+            executor.submit(process_sample, s['id'], s['input']): s['id'] 
+            for s in remaining
+        }
+        
+        with open(OUT_FILE, 'a') as f:
+            for future in tqdm(as_completed(future_to_id), total=len(remaining), desc="Task 7"):
+                sid, pred = future.result()
+                if pred:
+                    f.write(json.dumps({"test_sample_id": sid, "prediction": pred}) + "\n")
+                    f.flush()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_seven2.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_seven2.py
new file mode 100644
index 00000000..35d096b5
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_seven2.py	
@@ -0,0 +1,119 @@
+import json
+import os
+import requests
+import time
+import re
+from tqdm import tqdm
+
+# ================= 配置区 =================
+MODEL_NAME = "Qwen3-4B"
+API_URL = "http://127.0.0.1:8000/v1/chat/completions"
+ORIG_FILE = "../data/openseek-7_jeopardy_answer_generation_all.json"
+OUT_FILE = "../outputs/openseek-7-vLLM_Final.jsonl"
+# ==========================================
+
+os.environ['NO_PROXY'] = '127.0.0.1,localhost'
+
+def request_jeopardy_sniper(input_text, max_retries=3):
+    """补漏专用的狙击请求，带有强力防思考枷锁"""
+    
+    prompt_text = (
+        "You are a Jeopardy champion. Read the Category and the Clue, then provide the exact, concise answer.\n"
+        "CRITICAL REQUIREMENTS:\n"
+        "1. Output ONLY the answer entity.\n"
+        "2. The answer MUST be in ALL LOWERCASE letters.\n"
+        "3. Wrap your final answer in <label> tags at the very end.\n"
+        "4. DO NOT OVERTHINK. If you are unsure, just guess the most likely short noun phrase. DO NOT write long explanations.\n\n"
+        "Example 1:\n"
+        "Input: Category: KANSAS CITY, KANSAS HERE WE COME \nClue: The Kansas 400 at KCK's Kansas Speedway is part of this auto racing organization's Nextel Cup\n"
+        "Output: <label>nascar</label>\n\n"
+        f"Input: {input_text}\n"
+        "Output:"
+    )
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [{"role": "user", "content": prompt_text}],
+        "max_tokens": 1024,
+        "temperature": 0.1  # 稍微给一点点温度，让它在僵局中能“猜”出一个答案
+    }
+    
+    for attempt in range(max_retries):
+        try:
+            # 极限超时时间
+            response = requests.post(API_URL, json=payload, timeout=120) 
+            
+            if response.status_code == 200:
+                raw_res = response.json()["choices"][0]["message"]["content"]
+                clean_res = re.sub(r'<think>.*?(?:</think>|$)', '', raw_res, flags=re.DOTALL)
+                
+                label_match = re.search(r'<label>\s*(.*?)\s*</label>', clean_res, re.I | re.DOTALL)
+                if label_match:
+                    ans = label_match.group(1).strip()
+                    return ans.lower() 
+                
+                lines = [line.strip() for line in clean_res.split('\n') if line.strip()]
+                if lines:
+                    return lines[-1].lower()
+                
+            else:
+                time.sleep(2) 
+                
+        except Exception as e:
+            time.sleep(2)
+            continue
+            
+    return "unknown" # 如果单线程+120秒还失败，说明这题真的是死穴了
+
+def main():
+    if not os.path.exists(OUT_FILE) or not os.path.exists(ORIG_FILE):
+        print("❌ 找不到原始文件或输出文件！")
+        return
+
+    # 1. 读取原始题库，建立 ID -> 文本 的映射
+    with open(ORIG_FILE, 'r') as f:
+        data = json.load(f)
+        samples_dict = {s['id']: s['input'] for s in data['test_samples']}
+
+    # 2. 扫荡输出文件，抓出所有的 unknown，并保留其他正常数据
+    all_records = []
+    unknown_ids = []
+    
+    with open(OUT_FILE, 'r') as f:
+        for line in f:
+            try:
+                record = json.loads(line)
+                all_records.append(record)
+                if record['prediction'] == 'unknown':
+                    unknown_ids.append(record['test_sample_id'])
+            except: pass
+
+    print(f"🎯 扫描完毕！发现 {len(unknown_ids)} 个 'unknown' 目标，准备进行狙击重跑...")
+    if not unknown_ids:
+        print("✅ 完美！没有发现任何 unknown，不需要补漏。")
+        return
+
+    # 3. 开始单线程狙击
+    fixed_count = 0
+    for record in tqdm(all_records, desc="Sniper Retry"):
+        if record['prediction'] == 'unknown':
+            sid = record['test_sample_id']
+            input_text = samples_dict.get(sid, "")
+            
+            # 发起狙击请求
+            new_pred = request_jeopardy_sniper(input_text)
+            
+            # 更新记录
+            record['prediction'] = new_pred
+            if new_pred != 'unknown':
+                fixed_count += 1
+
+    # 4. 把修复后的数据重新覆盖写回原文件
+    with open(OUT_FILE, 'w') as f:
+        for record in all_records:
+            f.write(json.dumps(record) + "\n")
+
+    print(f"\n🎉 补漏行动结束！成功修复了 {fixed_count} / {len(unknown_ids)} 个逃兵数据！")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_six.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_six.py
new file mode 100644
index 00000000..07f842b5
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_six.py	
@@ -0,0 +1,136 @@
+import json
+import os
+import requests
+import time
+import re
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+
+# ================= 配置区 =================
+TASK_ID = 6
+MODEL_NAME = "Qwen3-4B"
+API_URL = "http://127.0.0.1:8000/v1/chat/completions"
+# 请确认你的原始文件路径是否匹配
+ORIG_FILE = "../data/openseek-6_mnli_same_genre_classification.json"
+OUT_FILE = "../outputs/openseek-6-vLLM_Final.jsonl"
+WORKERS = 4  # 依然保持 4 并发
+# ==========================================
+
+os.environ['NO_PROXY'] = '127.0.0.1,localhost'
+
+def request_mnli(input_text, max_retries=3):
+    """MNLI 体裁分类专属请求函数"""
+    
+    # 规则注入：把官方的体裁定义强行塞给模型
+    prompt_text = (
+        "You are an expert text classifier. Read Sentence 1, Sentence 2, and the given Genre.\n"
+        "Your job is to determine if BOTH sentences belong to the given Genre. If yes, output Y. If no, output N.\n\n"
+        "Definitions of Genres:\n"
+        "- face-to-face: conversations or dialogues\n"
+        "- government: information released from public government websites\n"
+        "- letters: written work for philanthropic fundraising\n"
+        "- 9/11: information pertaining to the 9/11 attacks\n"
+        "- slate: cultural topic that appears in the slate magazine\n"
+        "- telephone: telephonic dialogue\n"
+        "- travel: information in travel guides\n"
+        "- verbatim: short posts regarding linguistics\n"
+        "- oup: non-fiction works on the textile industry and child development\n"
+        "- fiction: popular works of fiction\n\n"
+        "You MUST output ONLY 'Y' or 'N'. Wrap your final decision in <label> tags.\n\n"
+        "Example 1:\n"
+        "Input: Sentence 1: I do not have the energy to remedy these deficiencies now. Sentence 2: I don't have the strength to fix these problems now. Genre: slate.\n"
+        "Output: <label>Y</label>\n\n"
+        "Example 2:\n"
+        "Input: Sentence 1: were you have you i take it you haven't spent any time in the military Sentence 2: Jon said there is nothing else we can do. Genre: telephone.\n"
+        "Output: <label>N</label>\n\n"
+        f"Input: {input_text}\n"
+        "Output:"
+    )
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [{"role": "user", "content": prompt_text}],
+        "max_tokens": 1536,  # 允许充分推导对比
+        "temperature": 0.0
+    }
+    
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(API_URL, json=payload, timeout=60) 
+            
+            if response.status_code == 200:
+                raw_res = response.json()["choices"][0]["message"]["content"]
+                
+                # 切除 <think> 过程
+                clean_res = re.sub(r'<think>.*?(?:</think>|$)', '', raw_res, flags=re.DOTALL)
+                
+                # 第一层：精确匹配标签
+                label_match = re.search(r'<label>\s*(Y|N)\s*</label>', clean_res, re.I)
+                if label_match:
+                    return label_match.group(1).upper()
+                
+                # 第二层：如果没标签，找清洗后的结尾（防截断）
+                tail_clean = clean_res.upper()[-30:]
+                if re.search(r'\bY\b', tail_clean): return "Y"
+                if re.search(r'\bN\b', tail_clean): return "N"
+                
+                # 第三层：终极在原始文本找
+                tail_raw = raw_res.upper()[-30:]
+                if re.search(r'\bY\b', tail_raw): return "Y"
+                if re.search(r'\bN\b', tail_raw): return "N"
+
+                print(f"\n[抓取失败] 输入: {input_text[:50]}... | 模型结尾: {repr(raw_res[-100:])}")
+            else:
+                time.sleep(1 * (attempt + 1)) 
+                
+        except Exception as e:
+            time.sleep(1.5)
+            continue
+            
+    # 终极兜底：MNLI里绝大多数不是同一个Genre，返回 N 是最安全的策略
+    return "N"
+
+def process_sample(sample_id, input_text):
+    res = request_mnli(input_text)
+    return sample_id, res
+
+def main():
+    if not os.path.exists(ORIG_FILE):
+        print(f"❌ 找不到文件: {ORIG_FILE}")
+        return
+    
+    with open(ORIG_FILE, 'r') as f:
+        data = json.load(f)
+        samples = data['test_samples']
+
+    processed_ids = set()
+    if os.path.exists(OUT_FILE):
+        with open(OUT_FILE, 'r') as f:
+            for line in f:
+                try: processed_ids.add(json.loads(line)['test_sample_id'])
+                except: pass
+    
+    remaining = [s for s in samples if s['id'] not in processed_ids]
+    print(f"📊 任务6 (MNLI Genre) 待处理: {len(remaining)} / {len(samples)}")
+
+    if not remaining:
+        print("✅ 任务6 已全部完成！")
+        return
+
+    os.makedirs(os.path.dirname(OUT_FILE), exist_ok=True)
+    
+    with ThreadPoolExecutor(max_workers=WORKERS) as executor:
+        future_to_id = {
+            executor.submit(process_sample, s['id'], s['input']): s['id'] 
+            for s in remaining
+        }
+        
+        with open(OUT_FILE, 'a') as f:
+            for future in tqdm(as_completed(future_to_id), total=len(remaining), desc="Task 6"):
+                sid, pred = future.result()
+                if pred:
+                    f.write(json.dumps({"test_sample_id": sid, "prediction": pred}) + "\n")
+                    f.flush()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_three.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_three.py
new file mode 100644
index 00000000..fa9bdf35
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_three.py	
@@ -0,0 +1,127 @@
+import json
+import os
+import requests
+import time
+import re
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+
+# ================= 配置区 =================
+TASK_ID = 3
+MODEL_NAME = "Qwen3-4B"
+API_URL = "http://127.0.0.1:8000/v1/chat/completions"
+ORIG_FILE = "../data/openseek-3_collatz_conjecture.json"
+OUT_FILE = "../outputs/openseek-3-vLLM_Final.jsonl"
+
+# 针对最后几个易超时的数字，建议设为 1；如果还在跑大量数据，可以设为 2-4
+WORKERS = 1  
+# ==========================================
+
+os.environ['NO_PROXY'] = '127.0.0.1,localhost'
+
+def request_single_number(num_str, max_retries=3):
+    """Task 3 终极版：顺应推理天性 + 三层极限抓取"""
+    prompt_text = (
+        "We are doing a SINGLE STEP calculation. Apply this math logic to the Input number:\n"
+        "- If it is even, divide it by 2.\n"
+        "- If it is odd, multiply it by 3 and add 1.\n"
+        "CRITICAL: ONLY PERFORM EXACTLY ONE STEP. STOP after one operation.\n"
+        "You MUST output your final calculated number wrapped in <label> tags.\n\n"
+        "Example 1:\n"
+        "Input: 12\n"
+        "Output: <label>6</label>\n\n"
+        f"Input: {num_str}\n"
+        "Output:"
+    )
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [{"role": "user", "content": prompt_text}],
+        "max_tokens": 2048,  # 给足空间让模型“碎碎念”
+        "temperature": 0.0
+        # ⚠️ 不使用 stop，防止在思考中途断开
+    }
+    
+    for attempt in range(max_retries):
+        try:
+            # 针对刺客数字，超时给到 180 秒
+            response = requests.post(API_URL, json=payload, timeout=180) 
+            
+            if response.status_code == 200:
+                raw_res = response.json()["choices"][0]["message"]["content"]
+                
+                # 物理切除思考过程
+                clean_res = re.sub(r'<think>.*?(?:</think>|$)', '', raw_res, flags=re.DOTALL)
+                
+                # 【第一层】：匹配 <label> 标签
+                label_matches = re.findall(r'<label>\s*(\d+)', raw_res, re.I)
+                if label_matches:
+                    return label_matches[-1]
+                
+                # 【第二层】：匹配清洗后文本中的数字
+                nums_clean = re.findall(r'\d+', clean_res)
+                if nums_clean:
+                    return nums_clean[-1]
+                
+                # 【第三层】：直接抓取原始回复中出现的最后一个数字（最后防线）
+                nums_raw = re.findall(r'\d+', raw_res)
+                if nums_raw:
+                    return nums_raw[-1]
+                
+                print(f"\n[抓取失败] 输入: {num_str} | 模型回复片段: {repr(raw_res[-100:])}")
+            else:
+                time.sleep(2) 
+                
+        except Exception as e:
+            print(f"\n[请求异常] 输入: {num_str} | 错误: {e}")
+            time.sleep(2)
+            continue
+            
+    return "0"
+
+def process_sample(sample_id, input_text):
+    res = request_single_number(input_text)
+    return sample_id, res
+
+def main():
+    if not os.path.exists(ORIG_FILE):
+        print(f"❌ 找不到输入文件: {ORIG_FILE}")
+        return
+    
+    with open(ORIG_FILE, 'r') as f:
+        data = json.load(f)
+        samples = data['test_samples']
+
+    # --- 断点续传逻辑 ---
+    processed_ids = set()
+    if os.path.exists(OUT_FILE):
+        with open(OUT_FILE, 'r') as f:
+            for line in f:
+                try: 
+                    processed_ids.add(json.loads(line)['test_sample_id'])
+                except: pass
+    
+    remaining = [s for s in samples if s['id'] not in processed_ids]
+    print(f"📊 Task 3 进度: {len(samples)-len(remaining)}/{len(samples)} (剩余: {len(remaining)})")
+
+    if not remaining:
+        print("✅ Task 3 已全部完成！")
+        return
+
+    os.makedirs(os.path.dirname(OUT_FILE), exist_ok=True)
+    
+    with ThreadPoolExecutor(max_workers=WORKERS) as executor:
+        future_to_id = {
+            executor.submit(process_sample, s['id'], s['input']): s['id'] 
+            for s in remaining
+        }
+        
+        with open(OUT_FILE, 'a') as f:
+            for future in tqdm(as_completed(future_to_id), total=len(remaining), desc="Task 3"):
+                sid, pred = future.result()
+                if pred:
+                    f.write(json.dumps({"test_sample_id": sid, "prediction": pred}) + "\n")
+                    f.flush()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_two.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_two.py
new file mode 100644
index 00000000..c71e6d99
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/debug_two.py	
@@ -0,0 +1,117 @@
+import json
+import os
+import requests
+import time
+import re
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+
+# ================= 配置区 =================
+TASK_ID = 2
+MODEL_NAME = "Qwen3-4B"
+API_URL = "http://127.0.0.1:8000/v1/chat/completions"
+# 请确保输入文件路径正确
+ORIG_FILE = "../data/openseek-2_count_nouns_verbs.json"
+OUT_FILE = "../outputs/openseek-2-vLLM_Final.jsonl"
+WORKERS = 4 
+# ==========================================
+
+os.environ['NO_PROXY'] = '127.0.0.1,localhost'
+
+def request_count_pos(input_text, max_retries=3):
+    """词性计数专属请求函数，强制提取单数字"""
+    
+    prompt_text = (
+        "You are a strict linguistics expert. Read the sentence and count EXACTLY what is requested (either nouns OR verbs).\n"
+        "CRITICAL RULES:\n"
+        "1. Output ONLY a single integer representing the exact count requested.\n"
+        "2. Do NOT write 'Nouns: X, Verbs: Y'. Do NOT write any extra words.\n"
+        "3. You MUST wrap your final integer in <label> tags.\n\n"
+        "Example 1:\n"
+        "Input: Sentence: 'The ladder of a jet is lowered from the side for loading passengers'. Count the number of verbs in this sentence.\n"
+        "Output: <label>2</label>\n\n"
+        f"Input: {input_text}\n"
+        "Output:"
+    )
+    
+    payload = {
+        "model": MODEL_NAME,
+        "messages": [{"role": "user", "content": prompt_text}],
+        "max_tokens": 512,  # 给它一点空间在 <think> 里数数
+        "temperature": 0.0
+    }
+    
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(API_URL, json=payload, timeout=30) 
+            
+            if response.status_code == 200:
+                raw_res = response.json()["choices"][0]["message"]["content"]
+                
+                # 切除 <think> 过程
+                clean_res = re.sub(r'<think>.*?(?:</think>|$)', '', raw_res, flags=re.DOTALL)
+                
+                # 第一层：精确匹配 <label> 里的数字
+                label_match = re.search(r'<label>\s*(\d+)\s*</label>', clean_res, re.I)
+                if label_match:
+                    return label_match.group(1)
+                
+                # 第二层：如果它没用标签，直接暴力提取清理后文本中的最后一个数字
+                nums = re.findall(r'\d+', clean_res)
+                if nums:
+                    return nums[-1]
+                
+            else:
+                time.sleep(1) 
+                
+        except Exception:
+            time.sleep(1)
+            continue
+            
+    # 终极兜底
+    return "0"
+
+def process_sample(sample_id, input_text):
+    res = request_count_pos(input_text)
+    return sample_id, res
+
+def main():
+    if not os.path.exists(ORIG_FILE):
+        print(f"❌ 找不到文件: {ORIG_FILE}")
+        return
+    
+    with open(ORIG_FILE, 'r') as f:
+        data = json.load(f)
+        samples = data['test_samples']
+
+    processed_ids = set()
+    if os.path.exists(OUT_FILE):
+        with open(OUT_FILE, 'r') as f:
+            for line in f:
+                try: processed_ids.add(json.loads(line)['test_sample_id'])
+                except: pass
+    
+    remaining = [s for s in samples if s['id'] not in processed_ids]
+    print(f"📊 任务2 (Count Nouns/Verbs) 待处理: {len(remaining)} / {len(samples)}")
+
+    if not remaining:
+        print("✅ 任务2 已全部完成！")
+        return
+
+    os.makedirs(os.path.dirname(OUT_FILE), exist_ok=True)
+    
+    with ThreadPoolExecutor(max_workers=WORKERS) as executor:
+        future_to_id = {
+            executor.submit(process_sample, s['id'], s['input']): s['id'] 
+            for s in remaining
+        }
+        
+        with open(OUT_FILE, 'a') as f:
+            for future in tqdm(as_completed(future_to_id), total=len(remaining), desc="Task 2"):
+                sid, pred = future.result()
+                if pred:
+                    f.write(json.dumps({"test_sample_id": sid, "prediction": pred}) + "\n")
+                    f.flush()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/main.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/main.py
new file mode 100644
index 00000000..d57b60fc
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/main.py	
@@ -0,0 +1,96 @@
+import json, os, argparse
+from tqdm import tqdm
+from transformers import AutoTokenizer
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from method import build_prompt, select_examples, annotate_vllm as annotate
+
+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",
+}
+
+def parser_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--task_id', type=int, required=True)
+    parser.add_argument('--max_input_length', type=int, default=10_000)
+    parser.add_argument('--log_path_prefix', type=str, default='../outputs/')
+    parser.add_argument('--tokenizer_path', type=str, default='/root/OpenSeek/openseek/competition/Qwen3-4B')
+    parser.add_argument('--workers', type=int, default=8, help='并发线程数，建议4-8')
+    return parser.parse_args()
+
+def process_sample(test_sample, task_description, icl_examples, output_file):
+    try:
+        test_sample_id = test_sample['id']
+        text2annotate = test_sample['input']
+        
+        prompt = build_prompt(task_description, text2annotate)
+        examples_str = select_examples(icl_examples, task_description, text2annotate)
+        input_prompt = prompt.replace("[[EXAMPLES]]\n\n", examples_str + '\n\n')
+        
+        prediction = annotate(input_prompt)
+        
+        # --- 关键修改：只有 prediction 不为 None 时才写入文件 ---
+        if prediction is not None:
+            result = {'test_sample_id': test_sample_id, 'prediction': prediction}
+            with open(output_file, 'a') as f:
+                f.write(json.dumps(result) + '\n')
+            return True
+        else:
+            # 这种情况通常是模型没吐标签，我们可以记录一个日志但不占坑
+            print(f"⚠️ 样本 {test_sample_id} 未提取到有效标签，跳过写入，待后续重试。")
+            return False
+            
+    except Exception as e:
+        # 如果是 Connection refused 等网络错误，不写文件，断点续传下次会自动重跑
+        print(f"❌ 样本 {test_sample_id} 请求发生异常: {e}")
+        return False
+
+def evaluate_concurrent():
+    args = parser_args()
+    task_file = TASK_FILES[args.task_id]
+    
+    with open(task_file, 'r') as f:
+        task_dict = json.load(f)
+    
+    task_name = task_dict['task_name']
+    task_description = task_dict['Definition'][0]
+    icl_examples = task_dict['examples'][:100]
+    test_samples = task_dict['test_samples']
+    
+    # --- 断点续传逻辑 ---
+    output_file = f'{args.log_path_prefix}openseek-{args.task_id}-vLLM_Final.jsonl'
+    os.makedirs(args.log_path_prefix, exist_ok=True)
+    
+    processed_ids = set()
+    if os.path.exists(output_file):
+        with open(output_file, 'r') as f:
+            for line in f:
+                try:
+                    processed_ids.add(json.loads(line)['test_sample_id'])
+                except: continue
+        print(f"📌 检测到断点：已跳过 {len(processed_ids)} 个已完成样本。")
+
+    # 过滤掉已处理的样本
+    remaining_samples = [s for s in test_samples if s['id'] not in processed_ids]
+
+    # --- 并发执行 ---
+    print(f"🚀 启动并发标注 | 任务: {task_name} | 并发数: {args.workers}")
+    
+    with ThreadPoolExecutor(max_workers=args.workers) as executor:
+        futures = [
+            executor.submit(process_sample, sample, task_description, icl_examples, output_file) 
+            for sample in remaining_samples
+        ]
+        
+        # 使用 tqdm 监控进度
+        for _ in tqdm(as_completed(futures), total=len(futures), desc="Annotating"):
+            pass
+
+if __name__ == '__main__':
+    evaluate_concurrent()
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/method.py b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/method.py
new file mode 100644
index 00000000..4192865a
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/code/method.py	
@@ -0,0 +1,268 @@
+
+import re
+from collections import Counter
+from transformers import AutoTokenizer
+
+""" Here is an example of implementation of Long-Context Data Annotation. """
+
+def build_prompt____(task_description: str, text2annotate: str) -> str:
+    """
+    Build a high-precision English prompt for long-context data annotation (optimized for Qwen3-4B).
+    Core requirement: Final answer MUST be wrapped in <label> tags (no extra content outside tags).
+    """
+    prompt = (
+        "### Role Definition\n"
+        "You are a professional data annotation expert specializing in long-context text labeling. "
+        "Your work must strictly comply with the following rules, with the highest priority given to output format accuracy.\n\n"
+        
+        "### Core Annotation Task\n"
+        f"{task_description}\n\n"
+        
+        "### Non-Negotiable Annotation Rules (Highest Priority)\n"
+        "1. **Final Output Mandate**: Your annotation result MUST be wrapped in <label> tags — NO text, symbols, spaces, or explanations are allowed outside the tags.\n"
+        "2. **Internal Reasoning Permission**: You may perform logical reasoning, text analysis, or context comprehension internally (in your thought process), but NONE of these thoughts may appear in the final output.\n"
+        "3. **Label Format Strictness**: <label> is the opening tag and </label> is the closing tag — they must appear in pairs, with NO extra spaces or characters inside the tags (e.g., <label>  Good Review  </label> is invalid).\n"
+        "4. **Prohibited Outputs**: \n"
+        "   - ❌ Prohibited: 'After analysis, this is a positive review: <label>Good Review</label>' (extra text outside tags)\n"
+        "   - ❌ Prohibited: 'Bad Review' (missing <label> tags entirely)\n"
+        "   - ❌ Prohibited: '<label>Bad Review' (unpaired/closing tag missing)\n\n"
+        
+        "### Correct vs. Incorrect Examples\n"
+        "✅ Correct Example 1: <label>answer</label>\n"
+        "✅ Correct Example 2: <label>Bad Review</label>\n"
+        "❌ Incorrect Example 1: I think this review is negative → <label>Bad Review</label>\n"
+        "❌ Incorrect Example 2: <label>  Neutral Review  </label> (extra spaces inside tags)\n"
+        "❌ Incorrect Example 3: Neutral Review (no label tags)\n\n"
+        
+        "### Reference Annotation Examples\n"
+        "{EXAMPLES}\n\n"
+        
+        "### Text to Annotate\n"
+        f"{text2annotate}\n\n"
+        
+        "### Final Output Command (Re-emphasized)\n"
+        "You may complete any internal reasoning process, but your FINAL OUTPUT MUST consist solely of the annotation result wrapped in <label> tags (no other content whatsoever).\n"
+        "Annotation Result: "
+    )
+    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).
+    task_description: Clear description of the annotation task (e.g., "Classify English product reviews as Good Review/Bad Review").
+    text2annotate: The text to be annotated (single text or batch texts).
+    """
+    prompt = (
+        "### Role Definition\n"
+        "You are a professional data annotation expert specialized in long-context text labeling. "
+        "Your work must strictly follow the task rules, fully learn from the provided examples, and ensure the final annotation result is 100% enclosed in <label> tags.\n\n"
+        
+        "### Core Task\n"
+        f"{task_description}\n\n"
+        
+        "### Critical Annotation Guidelines\n"
+        "1. **Example Learning Requirement**: Thoroughly analyze and fully learn from the annotation logic, format, and criteria in the Examples section. "
+        "Your annotation must align with the style, judgment standards, and tag usage shown in the examples.\n"
+        "2. **Thinking Process**: You may (and are encouraged to) explain your annotation reasoning step by step (e.g., key information extraction, judgment basis, rule matching).\n"
+        "3. **Mandatory Output Rule**: Regardless of any thinking process you provide, your final annotation result MUST be enclosed in <label> tags (this is non-negotiable).\n"
+        "   - Correct example: \n"
+        "     Reasoning: This review mentions 'excellent quality' and 'very satisfied', which meets the criteria for a Good Review.\n"
+        "     <label>Good Review</label>\n"
+        "   - Wrong example 1 (missing tags): This review is negative.\n"
+        "   - Wrong example 2 (incomplete tags): Bad Review</label>\n"
+        "4. **Length Adaptation**: For long texts, maintain complete thinking process and ensure the final <label> tags contain the accurate annotation result (no truncation).\n\n"
+        
+        "### Examples (Must Be Fully Followed)\n"
+        "[[EXAMPLES]]\n\n"
+        
+        "### Text to Annotate\n"
+        f"{text2annotate}\n\n"
+        
+        "### Final Requirement Summary\n"
+        "1. You can (and should) provide clear thinking process for your annotation.\n"
+        "2. The final annotation result MUST be wrapped in <label> tags (no exceptions).\n"
+        "3. All annotation logic must strictly follow the examples provided above.\n"
+    )
+    return prompt
+
+def build_prompt_backup(task_description:str, text2annotate:str)->str:
+    """
+        Construct the prompt for annotation based on the task description.
+        task_description: 
+            The description of the annotation task. 
+            For example, ``Given an English language product review, 
+            determine if it is a Good Review or a Bad Review.`` 
+        text2annotate:
+            The text that needs to be annotated.
+            For example, ``My son received this book as a gift. I was extremely disappointed.``
+    """
+    prompt = (
+        "You are a data annotation assistant. "
+        "Your task is to label the given texts according to the task description "
+        "and annotation guidelines provided below.\n\n"
+        f"[Task Description]\n {task_description}\n\n"
+        "[Examples]\n {EXAMPLES}\n\n"
+        "Please follow these instructions when labeling:\n"
+        "1. **Output Format**: Annotate the text directly by wrapping each labeled "
+        "span with <label> tags in the following format: <label> annotation result </label>.\n"
+        # "2. Do not add any extra text, explanations, or commentary in the labeled spans.\n\n"
+        f"[Task Description (repeat)] \n {task_description}\n\n"
+        f"[Input Texts]\n {text2annotate}\n\n"
+        "Please output the annotation results: "
+    )
+    return prompt
+
+def select_examples_backup(all_examples:list[dict], task_description:str, text2annotate:str)->str:
+    """
+        Select examples from all_examples to fit into the target context length.
+        all_examples:
+            A list of examples, where each example is a dict with keys 'input', 'output', and 'length'.
+            For example, ``{"input": "The material is good and looks great.", "output": "Good Review", "length": 79``},
+        task_description:
+            The description of the annotation task which may be used for example evaluation. 
+            For example, ``Given an English language product review, 
+            determine if it is a Good Review or a Bad Review.`` 
+        text2annotate:
+            The text that needs to be annotated  which may be used for example retrieval.
+            For example, ``My son received this book as a gift. I was extremely disappointed.``
+        
+    """
+    # Notice that the maximum context length is restricted.
+    target_length = 10_000
+    
+    input_list = [example['input'] for example in all_examples]
+    output_list = [example['output'][0] for example in all_examples]
+    length_list = [example['length'] for example in all_examples]
+    
+    # <label> have 2 tokens; </label> have 3 tokens; \n have 1 token; # have 1 token.
+    examples_str, token_num = "", 0
+    for i, (input_text, output_text, length) in enumerate(zip(input_list, output_list, length_list)):
+        if length + token_num <= target_length:
+            token_num += (length + 2 + 3 + 1 + 1)
+            example_str = f"# {input_text} <label> {output_text} </label>\n"
+            examples_str += example_str
+        else:
+            return examples_str, i
+    return examples_str
+
+def select_examples(all_examples: list[dict], task_description: str, text2annotate: str) -> str:
+    """
+        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.
+    """
+    # 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("/root/OpenSeek/openseek/competition/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）
+    
+    # print(all_examples[0])  # 打印第一个示例，便于调试
+
+    examples_str, token_num = "", 0
+    # 遍历所有示例，基于Qwen3-4B的tokenizer计算token数
+    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]
+            
+            # 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
+            
+            # 校验当前示例是否能加入（总长度不超限制）
+            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
+                example_str = f"# {input_text} <label> {output_text} </label>\n"
+                examples_str += example_str
+            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")
+            continue
+    # 遍历完所有示例且未超长度，返回完整拼接结果
+    return examples_str
+
+
+
+
+def count_answer(text: str):
+    import re
+    from collections import Counter
+    
+    # 1. 强力洗脑：直接物理删除 <think> 到 </think> 之间的所有内容
+    # 这样模型的“内心戏”就不会干扰正则匹配了
+    clean_text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
+    
+    # 2. 标准提取：寻找 <label> 标签
+    pattern = r'<label>\s*(.+?)\s*</label>'
+    matches = re.findall(pattern, clean_text, re.DOTALL)
+    
+    # 3. 容错提取：如果模型只写了开头没写结尾，尝试抢救最后一行
+    if not matches:
+        fallback = re.findall(r'<label>\s*(.+?)(?:$|\n)', clean_text)
+        if fallback:
+            matches = fallback
+
+    if not matches:
+        return None
+
+    # 4. 终极防线：优先信任模型在文章末尾给出的“最终结论”
+    final_answer = matches[-1].strip()
+
+    if len(final_answer) >= 100:
+        return None
+        
+    return final_answer
+
+
+def annotate_vllm(input_prompt:str)->list[str]:
+    """
+        Annotate the unlabeled data using the native vLLM OpenAI API.
+        Optimized for Huawei Ascend 910C local deployment.
+    """
+    import requests
+    import json
+    
+    URL = "http://localhost:8000/v1/chat/completions"
+    
+    data = {
+        "model": "Qwen3-4B",  # 必须与 vLLM 启动时的 --model 参数完全一致
+        "messages": [
+            {"role": "system", "content": "You are a professional data annotation expert specializing in long-context text labeling."},
+            {"role": "user", "content": input_prompt}
+        ],
+        "max_tokens": 10000, 
+        "temperature": 0.7,
+        "stream": False
+    }
+
+    try:
+        resp = requests.post(URL, json=data, timeout=120) # 加上超时防止单条长文本死锁
+        if resp.status_code == 200:
+            whole_result = resp.json()["choices"][0]["message"]["content"]
+        else:
+            print(f"\n[API Error] Status: {resp.status_code}, Text: {resp.text}")
+            whole_result = "None"
+    except Exception as e:
+        print(f"\n[Request Exception] {str(e)}")
+        whole_result = "None"
+
+    # 复用基线原有的正则提取逻辑
+    prediction = count_answer(whole_result)
+    return prediction
\ No newline at end of file
diff --git a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/requirements.txt b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/requirements.txt
new file mode 100644
index 00000000..98320a2e
--- /dev/null
+++ b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/requirements.txt	
@@ -0,0 +1,4 @@
+requests>=2.31.0
+tqdm>=4.66.0
+transformers>=4.40.0
+torch
diff --git "a/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/\346\212\200\346\234\257\346\212\245\345\221\212-Agent los.pdf" "b/openseek/competition/LongContext-ICL-Annotation/submissions/Agent los/\346\212\200\346\234\257\346\212\245\345\221\212-Agent los.pdf"
new file mode 100644
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