diff --git a/qwen3_6_scripts/verify_functional.py b/qwen3_6_scripts/verify_functional.py
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+++ b/qwen3_6_scripts/verify_functional.py
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+#!/usr/bin/env python3
+"""Functional verification script — mirrors CCCL's test design pattern.
+
+CCCL catch2_test_device_three_way_partition.cu verifies:
+  1. Empty input handling
+  2. Stability (CUB result == Thrust result)
+  3. Edge cases (empty first/second/unselected parts)
+  4. Large problem sizes
+
+We verify the same categories for vllm:
+  1. Empty/minimal input handling
+  2. Response correctness (HTTP 200, valid JSON, non-empty content)
+  3. Edge cases (long context, tool calls, reasoning split)
+  4. All chat_dataset_v0.json conversations
+
+Usage (after starting vllm server):
+  python3 verify_functional.py --endpoint http://localhost:8000
+  python3 verify_functional.py --endpoint http://localhost:8000 --quick
+"""
+
+import argparse
+import json
+import sys
+import time
+import requests
+from typing import List, Dict, Tuple
+
+
+def chat_completion(endpoint: str, messages: List[Dict], **kwargs) -> Dict:
+    """Send a chat completion request and return the response."""
+    url = f"{endpoint}/v1/chat/completions"
+    payload = {
+        "model": "llm",
+        "messages": messages,
+        "max_tokens": kwargs.get("max_tokens", 200),
+        "temperature": kwargs.get("temperature", 0.7),
+        "stream": False,
+    }
+    payload.update(kwargs)
+    resp = requests.post(url, json=payload, timeout=120)
+    return resp.status_code, resp.json() if resp.status_code == 200 else resp.text
+
+
+# ================================================================
+# Test cases — mirrors CCCL's categorized test structure
+# ================================================================
+
+def test_basic_chat(endpoint: str) -> Tuple[bool, str]:
+    """TC-01: Basic non-streaming chat returns HTTP 200 + valid content."""
+    code, data = chat_completion(endpoint, [
+        {"role": "user", "content": "你好"}
+    ], max_tokens=50)
+    if code != 200:
+        return False, f"HTTP {code}: {data}"
+    content = data["choices"][0]["message"]["content"]
+    if not content or len(content) < 2:
+        return False, f"Empty or too short content: '{content}'"
+    usage = data.get("usage", {})
+    if usage.get("completion_tokens", 0) <= 0:
+        return False, f"completion_tokens <= 0: {usage}"
+    return True, f"OK: {len(content)} chars, {usage.get('completion_tokens')} tokens"
+
+
+def test_finish_reason(endpoint: str) -> Tuple[bool, str]:
+    """TC-02: finish_reason is 'stop' or 'length'."""
+    code, data = chat_completion(endpoint, [
+        {"role": "user", "content": "说一个字"}
+    ], max_tokens=10)
+    if code != 200:
+        return False, f"HTTP {code}"
+    fr = data["choices"][0].get("finish_reason")
+    if fr not in ("stop", "length"):
+        return False, f"finish_reason='{fr}', expected stop/length"
+    return True, f"OK: finish_reason={fr}"
+
+
+def test_chinese_output(endpoint: str) -> Tuple[bool, str]:
+    """TC-03: Chinese content generation quality."""
+    code, data = chat_completion(endpoint, [
+        {"role": "user", "content": "请用一句话解释什么是GPU"}
+    ], max_tokens=100)
+    if code != 200:
+        return False, f"HTTP {code}"
+    content = data["choices"][0]["message"]["content"]
+    has_chinese = any('\u4e00' <= c <= '\u9fff' for c in content)
+    if not has_chinese:
+        return False, f"No Chinese characters in: '{content[:50]}'"
+    if len(content) < 10:
+        return False, f"Content too short: {len(content)} chars"
+    return True, f"OK: {len(content)} chars, Chinese present"
+
+
+def test_system_prompt(endpoint: str) -> Tuple[bool, str]:
+    """TC-04: System prompt controls output."""
+    code, data = chat_completion(endpoint, [
+        {"role": "system", "content": "无论用户说什么，你只能回复 FIXED_REPLY_42"},
+        {"role": "user", "content": "你好啊"}
+    ], max_tokens=50)
+    if code != 200:
+        return False, f"HTTP {code}"
+    content = data["choices"][0]["message"]["content"]
+    if "FIXED_REPLY_42" not in content:
+        return False, f"System prompt not followed: '{content[:80]}'"
+    return True, f"OK: contains FIXED_REPLY_42"
+
+
+def test_multi_turn_memory(endpoint: str) -> Tuple[bool, str]:
+    """TC-05: Multi-turn conversation memory."""
+    code, data = chat_completion(endpoint, [
+        {"role": "user", "content": "记住暗号：ALPHA_BRAVO"},
+        {"role": "assistant", "content": "好的，我记住了暗号ALPHA_BRAVO"},
+        {"role": "user", "content": "请说出之前的暗号"}
+    ], max_tokens=50)
+    if code != 200:
+        return False, f"HTTP {code}"
+    content = data["choices"][0]["message"]["content"]
+    if "ALPHA_BRAVO" not in content:
+        return False, f"Memory failed: '{content[:80]}'"
+    return True, f"OK: recalled ALPHA_BRAVO"
+
+
+def test_reasoning_separation(endpoint: str) -> Tuple[bool, str]:
+    """TC-06: reasoning_content and content are separated."""
+    code, data = chat_completion(endpoint, [
+        {"role": "user", "content": "逐步计算 17×23"}
+    ], max_tokens=500)
+    if code != 200:
+        return False, f"HTTP {code}"
+    msg = data["choices"][0]["message"]
+    content = msg.get("content", "")
+    reasoning = msg.get("reasoning_content", "")
+    if not content:
+        return False, "content is empty"
+    if "<think>" in content:
+        return False, f"content contains <think> tag"
+    # reasoning_content may or may not be present depending on model config
+    return True, f"OK: content={len(content)}c, reasoning={len(reasoning)}c"
+
+
+def test_tool_calling(endpoint: str) -> Tuple[bool, str]:
+    """TC-07: Tool calling returns valid tool_calls."""
+    code, data = chat_completion(endpoint, [
+        {"role": "user", "content": "北京今天天气怎么样"}
+    ], max_tokens=200, tools=[{
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "获取天气信息",
+            "parameters": {
+                "type": "object",
+                "properties": {"city": {"type": "string"}},
+                "required": ["city"]
+            }
+        }
+    }], tool_choice="required")
+    if code != 200:
+        return False, f"HTTP {code}: {data}"
+    msg = data["choices"][0]["message"]
+    tool_calls = msg.get("tool_calls", [])
+    if not tool_calls:
+        return False, "No tool_calls returned"
+    tc = tool_calls[0]
+    try:
+        args = json.loads(tc["function"]["arguments"])
+    except (json.JSONDecodeError, KeyError) as e:
+        return False, f"Invalid tool_calls: {e}"
+    return True, f"OK: {tc['function']['name']}({args})"
+
+
+def test_stop_sequence(endpoint: str) -> Tuple[bool, str]:
+    """TC-08: Stop sequence truncation."""
+    code, data = chat_completion(endpoint, [
+        {"role": "user", "content": "从1数到30"}
+    ], max_tokens=200, stop=["15"])
+    if code != 200:
+        return False, f"HTTP {code}"
+    content = data["choices"][0]["message"]["content"]
+    fr = data["choices"][0].get("finish_reason")
+    if "16" in content or "17" in content:
+        return False, f"Stop sequence not effective: '{content[:80]}'"
+    return True, f"OK: finish_reason={fr}, no '16' in output"
+
+
+def test_temperature_zero(endpoint: str) -> Tuple[bool, str]:
+    """TC-09: temperature=0 (greedy) works."""
+    code, data = chat_completion(endpoint, [
+        {"role": "user", "content": "hi"}
+    ], max_tokens=20, temperature=0.0)
+    if code != 200:
+        return False, f"HTTP {code}: {data}"
+    return True, f"OK: greedy sampling works"
+
+
+def test_empty_messages_error(endpoint: str) -> Tuple[bool, str]:
+    """TC-10: Empty messages returns 4xx."""
+    url = f"{endpoint}/v1/chat/completions"
+    resp = requests.post(url, json={"model": "llm", "messages": []}, timeout=30)
+    if resp.status_code < 400:
+        return False, f"Expected 4xx, got {resp.status_code}"
+    return True, f"OK: HTTP {resp.status_code} for empty messages"
+
+
+def test_chat_dataset(endpoint: str) -> Tuple[bool, str]:
+    """TC-11: Run chat_dataset_v0.json conversations."""
+    try:
+        with open("chat_dataset_v0.json") as f:
+            dataset = json.load(f)
+    except FileNotFoundError:
+        # Try from script directory
+        import os
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+        with open(os.path.join(script_dir, "..", "chat_dataset_v0.json")) as f:
+            dataset = json.load(f)
+
+    total = 0
+    passed = 0
+    for conv in dataset:
+        system = conv.get("system_prompt", "You are a helpful assistant.")
+        messages = [{"role": "system", "content": system}]
+        for q in conv["user_questions"][:2]:  # First 2 turns only for speed
+            messages.append({"role": "user", "content": q})
+            code, data = chat_completion(endpoint, messages, max_tokens=300)
+            total += 1
+            if code == 200:
+                content = data["choices"][0]["message"]["content"]
+                if content and len(content) > 5:
+                    passed += 1
+                    messages.append({"role": "assistant", "content": content})
+                else:
+                    messages.append({"role": "assistant", "content": ""})
+            else:
+                messages.append({"role": "assistant", "content": ""})
+
+    if passed < total * 0.8:
+        return False, f"Only {passed}/{total} turns passed"
+    return True, f"OK: {passed}/{total} turns passed"
+
+
+# ================================================================
+# Runner
+# ================================================================
+
+ALL_TESTS = [
+    ("TC-01 Basic chat", test_basic_chat),
+    ("TC-02 Finish reason", test_finish_reason),
+    ("TC-03 Chinese output", test_chinese_output),
+    ("TC-04 System prompt", test_system_prompt),
+    ("TC-05 Multi-turn memory", test_multi_turn_memory),
+    ("TC-06 Reasoning separation", test_reasoning_separation),
+    ("TC-07 Tool calling", test_tool_calling),
+    ("TC-08 Stop sequence", test_stop_sequence),
+    ("TC-09 Temperature zero", test_temperature_zero),
+    ("TC-10 Empty messages error", test_empty_messages_error),
+    ("TC-11 Chat dataset", test_chat_dataset),
+]
+
+QUICK_TESTS = ALL_TESTS[:5]  # First 5 for quick validation
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--endpoint", default="http://localhost:8000")
+    parser.add_argument("--quick", action="store_true")
+    args = parser.parse_args()
+
+    tests = QUICK_TESTS if args.quick else ALL_TESTS
+    passed = 0
+    failed = 0
+
+    print(f"=== Functional Verification ({len(tests)} tests) ===")
+    print(f"Endpoint: {args.endpoint}\n")
+
+    for name, fn in tests:
+        try:
+            ok, msg = fn(args.endpoint)
+            status = "PASS" if ok else "FAIL"
+            if ok:
+                passed += 1
+            else:
+                failed += 1
+            print(f"  [{status}] {name}: {msg}")
+        except Exception as e:
+            failed += 1
+            print(f"  [ERROR] {name}: {type(e).__name__}: {e}")
+
+    print(f"\nResult: {passed}/{passed + failed} passed")
+    sys.exit(0 if failed == 0 else 1)
+
+
+if __name__ == "__main__":
+    main()