GPT-5.5 技术路线图深度解析：从 Scaling Laws 到推理时计算的范式转移

# 自适应计算预算（ACB）的简化实现
# 展示核心思路，非 OpenAI 实际代码

import torch
from transformers import AutoModelForCausalLM

class AdaptiveComputeBudget:
    """自适应计算预算分配器"""
    
    def __init__(self, model: AutoModelForCausalLM):
        self.model = model
        self.confidence_threshold = 0.995
        self.max_thinking_tokens = 4096
    
    def estimate_difficulty(self, prompt: str) -> int:
        """
        评估问题难度（1-10）
        使用一次快速前向传播
        """
        # 简化的难度评估逻辑
        indicators = {
            "math_keywords": ["证明", "求解", "方程", "积分", "推导"],
            "reasoning_keywords": ["为什么", "分析", "比较", "评价"],
            "complexity_markers": ["步骤", "详细", "完整"],
        }
        
        score = 1  # 基础难度
        for category, keywords in indicators.items():
            for kw in keywords:
                if kw in prompt:
                    score += 1
        
        return min(score, 10)
    
    def compute_budget_for_difficulty(self, difficulty: int) -> int:
        """根据难度分配思考 token 预算"""
        # 指数增长：难度每增加 1，预算翻倍
        budget = 16 * (2 ** (difficulty - 1))
        return min(budget, self.max_thinking_tokens)
    
    def generate_with_acb(self, prompt: str) -> str:
        """使用自适应计算预算生成回答"""
        
        # Step 1: 评估难度
        difficulty = self.estimate_difficulty(prompt)
        budget = self.compute_budget_for_difficulty(difficulty)
        
        print(f"难度评估: {difficulty}/10")
        print(f"思考预算: {budget} tokens")
        
        # Step 2: 开始生成（带思考过程）
        thinking_tokens = []
        confidence = 0.0
        
        for i in range(budget):
            # 生成下一个 token
            next_token = self.model.generate_next_token(prompt + "".join(thinking_tokens))
            
            # 检查是否是「答案开始」标记
            if next_token == "<answer>":
                break
            
            thinking_tokens.append(next_token)
            
            # 每 32 个 token 评估一次置信度
            if (i + 1) % 32 == 0:
                confidence = self.estimate_confidence(
                    prompt, thinking_tokens
                )
                if confidence >= self.confidence_threshold:
                    print(f"提前终止: 置信度 {confidence:.3f} ≥ {self.confidence_threshold}")
                    break
            
            # 检查是否需要申请额外预算
            if i == budget - 1 and confidence < self.confidence_threshold:
                # 可以申请额外预算（最多 2 次）
                extra = self.request_extra_budget(difficulty)
                budget += extra
        
        # Step 3: 生成最终答案
        answer = self.model.generate_answer(
            prompt, "".join(thinking_tokens)
        )
        
        return answer
    
    def estimate_confidence(self, prompt, thinking) -> float:
        """评估当前推理的置信度"""
        # 简化版：基于模型对答案 token 的概率
        logits = self.model.get_logits(prompt + "".join(thinking))
        top_prob = torch.softmax(logits[-1], dim=-1).max().item()
        return top_prob
    
    def request_extra_budget(self, difficulty: int) -> int:
        """申请额外计算预算"""
        return min(1024, self.max_thinking_tokens // 4)

# 使用示例
# model = AutoModelForCausalLM.from_pretrained("gpt-5.5")
# acb = AdaptiveComputeBudget(model)
# result = acb.generate_with_acb("证明 √2 是无理数")