推理越强幻觉越重：2026工具调用六基准实测与四层修复全账

层级 | 描述 | 前沿模型准确率
L1   | 单工具单参数调用    | 92-96%
L2   | 单工具多参数调用    | 85-90%
L3   | 多工具并行调用      | 55-68%
L4   | 多工具串行依赖链    | 35-48%
L5   | 条件分支+错误恢复    | 18-28%

L1: get_weather(city="Beijing")  → 92% 准确率
L2: get_weather(city="Beijing", unit="metric", lang="zh")  → 85%
L3: get_weather("Beijing") + get_flights("Beijing", "Shanghai")  → 55%
L4: get_weather("Beijing") → parse → suggest_clothing(result)  → 35%
L5: get_weather → IF rain THEN get_umbrella_prices ELSE suggest_park  → 18%

# 实验设置示意（论文 Table 2 简化）
models = {
    "Base (无RL)": {"reasoning_score": 62, "tool_hallucination": 3.2%},
    "RL-tuned (轻量)": {"reasoning_score": 78, "tool_hallucination": 5.1%},
    "RL-tuned (深度)": {"reasoning_score": 89, "tool_hallucination": 8.7%},
    "RL-tuned (极致)": {"reasoning_score": 94, "tool_hallucination": 12.3%},
}
# 推理分数每提升10分，工具幻觉率上升约1.5个百分点

# 工具列表中定义的是：
tools = [{"name": "get_user_preferences", ...}]

# 模型实际调用：
{"function": "get_user_preferences_v2",  # 添加了版本号
 "arguments": {"user_id": "12345"}}

# 用户问："帮我订明天去上海的机票"
# 工具列表中只有 search_flights, get_weather
# 模型调用：
{"function": "book_flight",  # 不存在！只有search没有book
 "arguments": {"from": "Beijing", "to": "Shanghai", "date": "2026-06-12"}}

# 工具定义要求 enum: ["economy", "business", "first"]
# 模型调用：
{"function": "search_flights",
 "arguments": {"class": "premium_economy"}}  # 不在enum中

Step 1: 调用 get_company_revenue("AAPL") → 返回幻觉数据 "$385B"
Step 2: 基于幻觉数据调用 compare_with_peers("$385B") → 虚假比较
Step 3: 基于虚假比较生成投资建议 → 完全不可靠

from openai import OpenAI
client = OpenAI()

tools = [{
    "type": "function",
    "function": {
        "name": "search_hotels",
        "description": "Search for hotels in a given city",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {"type": "string"},
                "max_price": {"type": "number"},
                "amenities": {
                    "type": "array",
                    "items": {"type": "string", "enum": ["wifi","pool","gym","spa"]}
                }
            },
            "required": ["city"]
        },
        "strict": True  # GPT-5.5 新增：严格Schema模式
    }
}]

response = client.chat.completions.create(
    model="gpt-5.5",
    messages=[{"role": "user", "content": "Find me a hotel in Tokyo with a pool"}],
    tools=tools
)

import anthropic
client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=1024,
    tools=[{
        "name": "search_hotels",
        "description": "Search for hotels in a given city",
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {"type": "string"},
                "max_price": {"type": "number"}
            },
            "required": ["city"]
        }
    }],
    messages=[{"role": "user", "content": "Find me a hotel in Tokyo with a pool"}]
)

# Claude返回 tool_use content block
for block in response.content:
    if block.type == "tool_use":
        print(f"Tool: {block.name}, Args: {block.input}")

import google.generativeai as genai

# Gemini支持在工具调用中传入图片参数
response = genai.GenerativeModel('gemini-2.5-pro').generate_content(
    "What's in this image and find similar products",
    tools=[{
        "function_declarations": [{
            "name": "search_products",
            "description": "Search for products matching a description",
            "parameters": {
                "type": "OBJECT",
                "properties": {
                    "query": {"type": "STRING"},
                    "image_url": {"type": "STRING"}  # Gemini独有：支持图片参数
                },
                "required": ["query"]
            }
        }]
    }]
)

# 使用Pydantic严格校验工具输出
from pydantic import BaseModel, Field, validator

class HotelSearchResult(BaseModel):
    name: str = Field(..., min_length=1, max_length=200)
    price: float = Field(..., gt=0)
    city: str = Field(..., pattern=r"^[A-Za-z\s]+$")

    @validator('name')
    def no_fabricated_name(cls, v):
        # 拒绝模型幻觉生成的酒店名
        if any(kw in v.lower() for kw in ['luxury', 'grand', 'premium']):
            if not known_hotel(v):  # 对照已知列表
                raise ValueError(f"疑似幻觉酒店名: {v}")
        return v

class ToolCallGuard:
    """拦截幽灵工具调用的运行时护栏"""

    ALLOWED_TOOLS = {"search_flights", "get_weather", "book_hotel"}

    def intercept(self, tool_call):
        # 检查1：工具名是否在允许列表中
        if tool_call.name not in self.ALLOWED_TOOLS:
            return self.reject(f"幽灵调用: {tool_call.name} 不在工具列表中")

        # 检查2：参数是否符合Schema
        schema = self.get_schema(tool_call.name)
        errors = validate_json(tool_call.arguments, schema)
        if errors:
            return self.reject(f"参数违规: {errors}")

        # 检查3：是否为NFC场景（不该调用工具）
        if self.is_nfc_context(tool_call):
            return self.reject("当前上下文不应触发工具调用")

        return tool_call  # 放行

用户请求 → [推理模型：分析意图、规划步骤]
         → [标准模型：严格按计划执行工具调用]
         → [推理模型：整合结果、生成回复]

async def execute_with_verification(tool_call, executor):
    """执行工具调用并验证结果真实性"""
    result = await executor.run(tool_call)

    # 交叉验证：对关键工具调用用第二个模型验证
    if tool_call.critical:
        verification = await second_model.verify(
            f"这个工具调用结果是否合理？\n"
            f"调用: {tool_call}\n结果: {result}"
        )
        if verification.suspicious:
            # 触发人工审核或降级为文本回复
            return await human_fallback(tool_call, result)

    return result

graph TB
    subgraph 用户层
        U[用户请求]
    end

    subgraph 路由层
        R{意图路由器}
        R1[NFC判断: 直接回答]
        R2[工具调用: 进入管线]
    end

    subgraph 推理层
        P[推理模型<br/>规划工具调用步骤]
    end

    subgraph 执行层
        G[工具调用护栏<br/>名称/参数/NFC检查]
        E1[OpenAI Structured Outputs]
        E2[Anthropic tool_use Block]
        E3[Google Gemini functionCall]
        E4[MCP Server调用]
    end

    subgraph 验证层
        V[后验证<br/>交叉检查+幻觉检测]
        H[人工降级]
    end

    U --> R
    R -->|无需工具| R1
    R -->|需要工具| P
    P --> G
    G -->|通过| E1
    G -->|通过| E2
    G -->|通过| E3
    G -->|通过| E4
    G -->|拒绝| H
    E1 --> V
    E2 --> V
    E3 --> V
    E4 --> V
    V -->|可疑| H
    V -->|确认| U