中国开源模型 12 天密集发布横评：Agentic 编程能力如何逼近西方前沿

# 基准测试成绩综合分析工具
# 用于多维度对比不同模型的真实能力，检测基准过拟合

import json
from dataclasses import dataclass, field
from typing import Dict, List

@dataclass
class BenchmarkResult:
    """单个模型在一个基准上的成绩"""
    model_name: str
    benchmark: str
    score: float  # 0-100
    sample_size: int = 0
    date: str = ""


class ModelComparisonEngine:
    """
    多维度模型对比分析引擎
    功能：跨基准对比、一致性分析、过拟合检测
    """
    
    def __init__(self):
        self.results: List[BenchmarkResult] = []
        self.benchmark_weights = {
            "MMLU": 0.20,
            "HumanEval": 0.15,
            "SWE-bench Verified": 0.25,
            "AgentBench (工具)": 0.15,
            "AgentBench (规划)": 0.15,
            "长上下文检索": 0.05,
            "多模态理解": 0.05,
        }
    
    def add_result(self, result: BenchmarkResult):
        self.results.append(result)
    
    def compute_overall_score(self, model_name: str) -> float:
        """计算模型的综合加权分数"""
        model_results = {r.benchmark: r.score for r in self.results if r.model_name == model_name}
        
        weighted_sum = 0.0
        weight_total = 0.0
        
        for benchmark, weight in self.benchmark_weights.items():
            if benchmark in model_results:
                weighted_sum += model_results[benchmark] * weight
                weight_total += weight
        
        if weight_total == 0:
            return 0.0
        
        return weighted_sum / weight_total
    
    def detect_overfitting(self, model_name: str) -> Dict[str, float]:
        """
        检测基准过拟合：如果某个基准的成绩
        显著高于其他基准的成绩，可能存在过拟合
        """
        model_results = {
            r.benchmark: r.score
            for r in self.results
            if r.model_name == model_name
        }
        
        if not model_results:
            return {}
        
        scores = list(model_results.values())
        mean_score = sum(scores) / len(scores)
        std_dev = (sum((s - mean_score)  2 for s in scores) / len(scores))  0.5
        
        overfitting_scores = {}
        for benchmark, score in model_results.items():
            # 如果某个基准成绩超过平均值 1.5 个标准差
            if std_dev > 0 and (score - mean_score) > 1.5 * std_dev:
                overfitting_scores[benchmark] = {
                    "score": score,
                    "mean": round(mean_score, 1),
                    "deviation": round((score - mean_score) / std_dev, 2),
                    "risk": "高" if (score - mean_score) > 2 * std_dev else "中"
                }
        
        return overfitting_scores
    
    def generate_comparison_report(self, model_names: List[str]) -> str:
        """生成对比分析报告"""
        report_lines = ["# 模型综合对比分析报告
"]
        
        # 综合分数排名
        report_lines.append("## 综合分数排名")
        scores = []
        for model in model_names:
            overall = self.compute_overall_score(model)
            scores.append((model, overall))
        
        scores.sort(key=lambda x: x[1], reverse=True)
        for rank, (model, score) in enumerate(scores, 1):
            report_lines.append(f"{rank}. {model}: {score:.1f}")
        
        # 过拟合检测
        report_lines.append("
## 过拟合风险检测")
        for model in model_names:
            overfit = self.detect_overfitting(model)
            if overfit:
                report_lines.append(f"\n{model} 存在过拟合风险:")
                for benchmark, info in overfit.items():
                    report_lines.append(
                        f"  - {benchmark}: {info['score']} "
                        f"(均值 {info['mean']}, 偏离 {info['deviation']}σ, "
                        f"风险等级: {info['risk']})"
                    )
            else:
                report_lines.append(f"\n{model}: 未检测到过拟合风险 ✓")
        
        return "\n".join(report_lines)


# 使用示例：分析四大中国开源模型
engine = ModelComparisonEngine()

models_data = [
    ("GLM-5.1", "MMLU", 91.2),
    ("GLM-5.1", "HumanEval", 91.5),
    ("GLM-5.1", "SWE-bench Verified", 52.1),
    ("GLM-5.1", "AgentBench (工具)", 82.4),
    ("GLM-5.1", "AgentBench (规划)", 65.2),
    ("DeepSeek V4", "MMLU", 90.1),
    ("DeepSeek V4", "HumanEval", 92.0),
    ("DeepSeek V4", "SWE-bench Verified", 54.8),
    ("DeepSeek V4", "AgentBench (工具)", 81.3),
    ("DeepSeek V4", "AgentBench (规划)", 67.0),
]

for model, benchmark, score in models_data:
    engine.add_result(BenchmarkResult(model, benchmark, score))

report = engine.generate_comparison_report(["GLM-5.1", "DeepSeek V4"])
print(report)

# 课程学习训练策略：从易到难的结构化训练
import torch
from torch.utils.data import DataLoader, Dataset
from typing import List, Tuple
import random

class CurriculumLearningTrainer:
    """
    课程学习训练器：按难度递增组织训练数据
    核心思想：先学简单概念，再学复杂推理
    """
    
    def __init__(self, model, optimizer, device):
        self.model = model
        self.optimizer = optimizer
        self.device = device
        self.current_difficulty = 0  # 当前课程阶段 (0-4)
        
    def build_curriculum(self, dataset: Dataset) -> List[List[int]]:
        """
        构建课程：将数据集按难度分层
        阶段 0: 基础语言理解 (简单问答)
        阶段 1: 逻辑推理 (多步推理)
        阶段 2: 代码理解与生成
        阶段 3: 复杂多模态推理
        阶段 4: 开放域创造与辩论
        """
        curriculum = []
        
        # 每个阶段的数据索引和采样权重
        stage_configs = [
            {"difficulty_range": (0, 0.2), "batch_size": 512, "epochs": 2},
            {"difficulty_range": (0.2, 0.4), "batch_size": 384, "epochs": 3},
            {"difficulty_range": (0.4, 0.6), "batch_size": 256, "epochs": 3},
            {"difficulty_range": (0.6, 0.8), "batch_size": 192, "epochs": 2},
            {"difficulty_range": (0.8, 1.0), "batch_size": 128, "epochs": 2},
        ]
        
        for stage_id, config in enumerate(stage_configs):
            low, high = config["difficulty_range"]
            # 选择难度在范围内的数据样本
            stage_indices = [
                i for i, sample in enumerate(dataset)
                if low <= sample.difficulty < high
            ]
            curriculum.append({
                "indices": stage_indices,
                "batch_size": config["batch_size"],
                "epochs": config["epochs"],
                "stage_id": stage_id,
            })
        
        return curriculum
    
    def train_with_curriculum(self, curriculum: List[dict], dataset: Dataset):
        """
        按课程计划执行训练
        """
        for stage in curriculum:
            print(f"开始课程阶段 {stage['stage_id']}: "
                  f"{len(stage['indices'])} 个样本, "
                  f"batch_size={stage['batch_size']}, "
                  f"epochs={stage['epochs']}")
            
            # 创建该阶段的数据加载器
            stage_dataset = torch.utils.data.Subset(dataset, stage['indices'])
            loader = DataLoader(
                stage_dataset,
                batch_size=stage['batch_size'],
                shuffle=True,
                num_workers=4
            )
            
            for epoch in range(stage['epochs']):
                total_loss = 0
                for batch in loader:
                    self.optimizer.zero_grad()
                    
                    # 前向传播
                    inputs = batch['input_ids'].to(self.device)
                    labels = batch['labels'].to(self.device)
                    
                    outputs = self.model(inputs, labels=labels)
                    loss = outputs.loss
                    
                    # 反向传播
                    loss.backward()
                    self.optimizer.step()
                    
                    total_loss += loss.item()
                
                avg_loss = total_loss / len(loader)
                print(f"  Epoch {epoch+1}/{stage['epochs']}, "
                      f"Loss: {avg_loss:.4f}")
            
            # 阶段结束后评估当前能力
            self.evaluate_current_ability()
    
    def evaluate_current_ability(self):
        """
        评估当前模型在各个能力维度上的表现
        用于决定是否进入下一阶段或需要回退
        """
        # 在实际实现中，这里会运行一组验证基准
        # 如果某个维度的表现退化，则调整训练策略
        pass

# 合成数据质量控制管道
# 防止合成数据中的偏差传播到训练模型

class SyntheticDataQualityPipeline:
    """
    合成数据质量控制系统
    确保用强模型生成的训练数据不会引入系统性偏差
    """
    
    def __init__(self, quality_threshold=0.85):
        self.quality_threshold = quality_threshold
        self.bias_tracker = {}
    
    def generate_and_validate(
        self,
        prompt_template: str,
        generator_model,
        num_samples: int,
        validation_criteria: dict
    ) -> list:
        """
        生成合成数据并进行多层质量验证
        """
        raw_samples = []
        
        # 第一步：生成原始样本
        for i in range(num_samples):
            prompt = self._instantiate_template(prompt_template, i)
            response = generator_model.generate(prompt)
            raw_samples.append({
                "prompt": prompt,
                "response": response,
                "generation_id": f"gen_{i}"
            })
        
        # 第二步：多维度质量过滤
        validated_samples = []
        for sample in raw_samples:
            quality_scores = {}
            
            # 检查项 1: 事实准确性
            quality_scores["factual_accuracy"] = self._check_factual_accuracy(
                sample["response"]
            )
            
            # 检查项 2: 逻辑一致性
            quality_scores["logical_consistency"] = self._check_logical_consistency(
                sample["response"]
            )
            
            # 检查项 3: 偏见检测
            quality_scores["bias_score"] = self._detect_bias(
                sample["response"]
            )
            
            # 检查项 4: 多样性评估
            quality_scores["diversity_score"] = self._assess_diversity(
                sample["response"], validated_samples
            )
            
            # 综合质量分数
            overall_score = self._compute_overall_quality(quality_scores)
            
            if overall_score >= self.quality_threshold:
                sample["quality_scores"] = quality_scores
                sample["overall_quality"] = overall_score
                validated_samples.append(sample)
            else:
                # 记录被拒绝样本的特征，用于分析系统性偏差
                self._track_rejection_pattern(sample, quality_scores)
        
        acceptance_rate = len(validated_samples) / num_samples
        print(f"合成数据通过率: {acceptance_rate:.1%} "
              f"({len(validated_samples)}/{num_samples})")
        
        return validated_samples
    
    def _check_factual_accuracy(self, text: str) -> float:
        """事实准确性检查：与已知事实的一致性"""
        # 实际实现中会调用事实核查 API
        # 或对比权威知识库
        return 0.9
    
    def _check_logical_consistency(self, text: str) -> float:
        """逻辑一致性检查：内部逻辑是否自洽"""
        # 使用 NLI (Natural Language Inference) 模型
        # 检查文本中是否存在自相矛盾的陈述
        return 0.85
    
    def _detect_bias(self, text: str) -> float:
        """偏见检测：识别文本中的系统性偏见"""
        # 检测性别、种族、地域等维度的偏见
        bias_score = 0.1  # 低偏见 = 高分
        return 1.0 - bias_score
    
    def _assess_diversity(self, text: str, existing: list) -> float:
        """多样性评估：新样本是否与已有样本过于相似"""
        if not existing:
            return 1.0
        # 计算与已有样本的相似度
        # 如果过于相似，说明生成器陷入了模式重复
        return 0.8
    
    def _compute_overall_quality(self, scores: dict) -> float:
        """计算综合质量分数"""
        weights = {
            "factual_accuracy": 0.35,
            "logical_consistency": 0.30,
            "bias_score": 0.20,
            "diversity_score": 0.15,
        }
        return sum(scores[k] * weights[k] for k in weights)
    
    def _track_rejection_pattern(self, sample: dict, scores: dict):
        """记录被拒绝样本的模式，用于分析系统性偏差"""
        # 如果某个检查项的拒绝率异常高
        # 说明生成器在该维度上存在系统性问题
        for check, score in scores.items():
            if check not in self.bias_tracker:
                self.bias_tracker[check] = {"rejected": 0, "total": 0}
            self.bias_tracker[check]["total"] += 1
            if score < self.quality_threshold:
                self.bias_tracker[check]["rejected"] += 1