前沿技术与发展趋势

摘要

上下文工程正在快速演进，本文展望Automatic Context Management、Context Routing、Hierarchical Context、Memory-augmented LM、State Space Models（如Mamba、RWKV）等前沿技术，分析LLM上下文处理的未来发展方向。

关键词速览

术语	英文	说明
ACM	Automatic Context Management	自动上下文管理
Context Routing	Context Routing	上下文路由
Hierarchical Context	Hierarchical Context	分层上下文
Memory LM	Memory-Augmented LM	记忆增强语言模型
SSM	State Space Models	状态空间模型
Mamba	Mamba	选择性状态空间模型
RWKV	RWKV	循环GPT
Linear Attention	Linear Attention	线性注意力
Mixture of Experts	Mixture of Experts	专家混合
Long Range Arena	Long Range Arena	长程竞技场

一、Automatic Context Management

1.1 ACM概述

自动上下文管理（ACM）是让LLM自动决定如何处理上下文的范式：

传统方式：
用户 ──► 手动设计prompt ──► LLM ──► 固定上下文

ACM方式：
用户 ──► ACM系统自动决策 ──► LLM
         │
         ├── 决定包含什么
         ├── 决定如何组织
         ├── 决定压缩程度
         └── 决定存储位置

1.2 ACM核心组件

class AutomaticContextManager:
    """自动上下文管理器"""
    
    def __init__(
        self,
        llm_client,
        memory_store,
        embedding_model
    ):
        self.llm = llm_client
        self.memory = memory_store
        self.embedding = embedding_model
    
    def manage_context(
        self,
        user_input: str,
        session_id: str
    ) -> dict:
        """
        自动管理上下文
        """
        # 1. 分析任务类型
        task_analysis = self._analyze_task(user_input)
        
        # 2. 决定上下文策略
        strategy = self._decide_strategy(task_analysis)
        
        # 3. 收集相关上下文
        contexts = self._collect_contexts(user_input, strategy)
        
        # 4. 决定是否压缩
        if self._needs_compression(contexts, strategy):
            contexts = self._compress_contexts(contexts, strategy)
        
        # 5. 决定是否存储
        if strategy.get('should_remember'):
            self._store_in_memory(user_input, contexts, session_id)
        
        return {
            'contexts': contexts,
            'strategy': strategy,
            'task_type': task_analysis['type'],
            'compression_applied': self._needs_compression(contexts, strategy)
        }
    
    def _analyze_task(self, user_input: str) -> dict:
        """分析任务类型"""
        prompt = f"""分析以下用户输入，确定其任务类型和上下文需求。
 
输入：{user_input}
 
分析维度：
1. 任务类型：factual（事实查询）/creative（创意）/reasoning（推理）/conversation（对话）
2. 上下文依赖度：高/中/低
3. 需要的历史深度：短期/中期/长期
4. 是否需要专业知识
 
输出JSON格式："""
        
        result = self.llm.generate(prompt)
        # 解析JSON
        return json.loads(result)
    
    def _decide_strategy(self, task_analysis: dict) -> dict:
        """决定处理策略"""
        strategies = {
            'factual': {
                'context_window': 'focused',  # 聚焦在相关事实上
                'compression': 'aggressive',
                'should_remember': True,
                'retrieval_weight': 0.8
            },
            'creative': {
                'context_window': 'broad',  # 更宽泛的上下文
                'compression': 'light',
                'should_remember': False,
                'retrieval_weight': 0.3
            },
            'reasoning': {
                'context_window': 'complete',  # 完整上下文
                'compression': 'minimal',
                'should_remember': True,
                'retrieval_weight': 0.5
            },
            'conversation': {
                'context_window': 'recent',  # 最近对话
                'compression': 'moderate',
                'should_remember': True,
                'retrieval_weight': 0.2
            }
        }
        
        task_type = task_analysis.get('type', 'conversation')
        return strategies.get(task_type, strategies['conversation'])

1.3 自适应压缩

class AdaptiveCompressor:
    """自适应压缩器"""
    
    def __init__(self, llm_client):
        self.llm = llm_client
        self.compression_models = {
            'light': LightCompressor(),
            'moderate': ModerateCompressor(),
            'aggressive': AggressiveCompressor()
        }
    
    def compress_adaptive(
        self,
        contexts: List[str],
        task_type: str,
        available_tokens: int
    ) -> List[str]:
        """根据任务自适应压缩"""
        # 选择压缩级别
        if task_type == 'factual':
            compression_level = 'aggressive'
        elif task_type == 'reasoning':
            compression_level = 'light'
        else:
            compression_level = 'moderate'
        
        compressor = self.compression_models[compression_level]
        
        # 压缩每个上下文
        compressed = []
        for ctx in contexts:
            ctx_tokens = self._estimate_tokens(ctx)
            
            if ctx_tokens <= available_tokens:
                compressed.append(ctx)
            else:
                target_ratio = available_tokens / ctx_tokens
                compressed.append(compressor.compress(ctx, target_ratio))
        
        return compressed

二、Context Routing

2.1 Context Routing概念

Context Routing根据内容类型将信息路由到不同的处理路径：

class ContextRouter:
    """上下文路由器"""
    
    def __init__(self):
        self.routing_rules = {
            'factual': {
                'path': 'retrieval',  # 知识库检索
                'compression': 'high',
                'storage': 'knowledge_base'
            },
            'personal': {
                'path': 'memory',  # 个人记忆
                'compression': 'low',
                'storage': 'user_profile'
            },
            'session': {
                'path': 'window',  # 短期窗口
                'compression': 'minimal',
                'storage': 'session_cache'
            },
            'system': {
                'path': 'instruction',  # 指令处理
                'compression': 'none',
                'storage': 'system_prompt'
            }
        }
    
    def route(self, content: str, content_type: str = None) -> dict:
        """路由内容"""
        # 自动检测内容类型
        if content_type is None:
            content_type = self._detect_content_type(content)
        
        routing = self.routing_rules.get(
            content_type,
            self.routing_rules['session']
        )
        
        return {
            'content': content,
            'type': content_type,
            'path': routing['path'],
            'compression': routing['compression'],
            'storage': routing['storage']
        }
    
    def _detect_content_type(self, content: str) -> str:
        """检测内容类型"""
        # 简化实现
        if any(kw in content for kw in ['记住', '我的名字', '偏好']):
            return 'personal'
        elif any(kw in content for kw in ['什么是', '定义', '如何']):
            return 'factual'
        elif '系统' in content or '你是一个' in content:
            return 'system'
        else:
            return 'session'

2.2 动态上下文选择

class DynamicContextSelector:
    """动态上下文选择器"""
    
    def __init__(
        self,
        embedding_model,
        attention_scorer
    ):
        self.embedding = embedding_model
        self.attention_scorer = attention_scorer
    
    def select(
        self,
        query: str,
        candidates: List[Dict],
        budget_tokens: int,
        strategy: str = "weighted"
    ) -> List[Dict]:
        """
        动态选择上下文
        
        strategy: 'greedy', 'weighted', 'diverse'
        """
        if strategy == "greedy":
            return self._greedy_select(query, candidates, budget_tokens)
        elif strategy == "weighted":
            return self._weighted_select(query, candidates, budget_tokens)
        else:
            return self._diverse_select(query, candidates, budget_tokens)
    
    def _greedy_select(
        self,
        query: str,
        candidates: List[Dict],
        budget_tokens: int
    ) -> List[Dict]:
        """贪心选择"""
        query_emb = self.embedding.encode(query)
        
        # 按相关性排序
        scored = []
        for c in candidates:
            c_emb = self.embedding.encode(c['content'])
            score = self._cosine_similarity(query_emb, c_emb)
            scored.append((score, c))
        
        scored.sort(key=lambda x: x[0], reverse=True)
        
        # 贪心选择
        selected = []
        current_tokens = 0
        
        for score, c in scored:
            c_tokens = c.get('token_count', self._estimate_tokens(c['content']))
            
            if current_tokens + c_tokens <= budget_tokens:
                selected.append(c)
                current_tokens += c_tokens
        
        return selected
    
    def _weighted_select(
        self,
        query: str,
        candidates: List[Dict],
        budget_tokens: int
    ) -> List[Dict]:
        """加权选择：考虑相关性和重要性"""
        query_emb = self.embedding.encode(query)
        
        scored = []
        for c in candidates:
            relevance = self._cosine_similarity(
                query_emb,
                self.embedding.encode(c['content'])
            )
            
            importance = c.get('importance', 0.5)
            
            # 综合分数
            combined_score = 0.7 * relevance + 0.3 * importance
            
            scored.append((combined_score, c))
        
        scored.sort(key=lambda x: x[0], reverse=True)
        
        selected = []
        current_tokens = 0
        
        for score, c in scored:
            c_tokens = c.get('token_count', self._estimate_tokens(c['content']))
            
            if current_tokens + c_tokens <= budget_tokens:
                selected.append(c)
                current_tokens += c_tokens
        
        return selected
    
    def _diverse_select(
        self,
        query: str,
        candidates: List[Dict],
        budget_tokens: int
    ) -> List[Dict]:
        """多样性选择"""
        # 先按相关性筛选top-k
        query_emb = self.embedding.encode(query)
        
        scored = []
        for c in candidates:
            relevance = self._cosine_similarity(
                query_emb,
                self.embedding.encode(c['content'])
            )
            scored.append((relevance, c))
        
        scored.sort(key=lambda x: x[0], reverse=True)
        top_k = scored[:min(20, len(scored))]
        
        # MMSR多样性选择
        selected = []
        current_tokens = 0
        
        for relevance, c in top_k:
            c_tokens = c.get('token_count', self._estimate_tokens(c['content']))
            
            if current_tokens + c_tokens > budget_tokens:
                continue
            
            # 检查与已选内容的多样性
            is_diverse = True
            for s in selected:
                sim = self._cosine_similarity(
                    self.embedding.encode(c['content']),
                    self.embedding.encode(s['content'])
                )
                if sim > 0.9:  # 太相似
                    is_diverse = False
                    break
            
            if is_diverse or len(selected) < 3:  # 至少选3个
                selected.append(c)
                current_tokens += c_tokens
        
        return selected
    
    @staticmethod
    def _cosine_similarity(a, b):
        dot = sum(x * y for x, y in zip(a, b))
        norm_a = sum(x * x for x in a) ** 0.5
        norm_b = sum(x * x for x in b) ** 0.5
        return dot / (norm_a * norm_b + 1e-8)
    
    @staticmethod
    def _estimate_tokens(text: str) -> int:
        chinese = sum(1 for c in text if '\u4e00' <= c <= '\u9fff')
        english = len(text.split()) - chinese
        return int(chinese * 0.5 + english * 0.25)

三、Hierarchical Context

3.1 分层上下文架构

┌─────────────────────────────────────────────────────────────────┐
│                    分层上下文架构                                 │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  L3: 全局层 (Global)                                            │
│      - 跨会话的知识                                             │
│      - 用户长期偏好                                             │
│      - 领域知识库                                               │
│                                                                 │
│  L2: 会话层 (Session)                                           │
│      - 当前会话的历史                                           │
│      - 任务上下文                                               │
│      - 临时信息                                                 │
│                                                                 │
│  L1: 实时层 (Real-time)                                          │
│      - 当前输入                                                 │
│      - 活跃焦点                                                 │
│      - 立即上下文                                               │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

3.2 实现代码

class HierarchicalContextManager:
    """分层上下文管理器"""
    
    def __init__(self):
        self.global_layer = GlobalContextStore()
        self.session_layer = SessionContextStore()
        self.realtime_layer = {}
    
    def get_context(
        self,
        query: str,
        session_id: str,
        config: dict = None
    ) -> str:
        """获取分层上下文"""
        if config is None:
            config = self._get_default_config()
        
        context_parts = []
        
        # 1. 实时层
        if config.get('include_realtime', True):
            realtime = self.realtime_layer.get(session_id, [])
            context_parts.extend(realtime)
        
        # 2. 会话层
        if config.get('include_session', True):
            session_context = self.session_layer.get(session_id)
            if session_context:
                context_parts.append(f"[会话上下文]\n{session_context}")
        
        # 3. 全局层
        if config.get('include_global', True):
            global_context = self.global_layer.get_relevant(query)
            if global_context:
                context_parts.append(f"[全局知识]\n{global_context}")
        
        return "\n\n".join(context_parts)
    
    def update_context(
        self,
        session_id: str,
        content: str,
        layer: str = 'session'
    ):
        """更新指定层"""
        if layer == 'session':
            self.session_layer.add(session_id, content)
        elif layer == 'global':
            self.global_layer.add(content)
        elif layer == 'realtime':
            if session_id not in self.realtime_layer:
                self.realtime_layer[session_id] = []
            self.realtime_layer[session_id].append(content)
    
    def _get_default_config(self) -> dict:
        """获取默认配置"""
        return {
            'include_realtime': True,
            'include_session': True,
            'include_global': True,
            'max_tokens': {
                'realtime': 2000,
                'session': 8000,
                'global': 5000
            }
        }

四、Memory-Augmented LM

4.1 记忆增强语言模型

class MemoryAugmentedLM:
    """记忆增强语言模型"""
    
    def __init__(
        self,
        llm_client,
        memory_store,
        controller
    ):
        self.llm = llm_client
        self.memory = memory_store
        self.controller = controller  # 记忆控制器
    
    def generate_with_memory(
        self,
        query: str,
        session_id: str
    ) -> str:
        """带记忆的生成"""
        # 1. 从记忆中检索相关内容
        relevant_memories = self.memory.retrieve(query, session_id)
        
        # 2. 决定读取哪些记忆
        selected_memories = self.controller.select_read(
            query,
            relevant_memories
        )
        
        # 3. 构建提示
        prompt = self._build_prompt(query, selected_memories)
        
        # 4. 生成
        response = self.llm.generate(prompt)
        
        # 5. 决定是否写入记忆
        if self.controller.should_write(response, session_id):
            self.memory.write(response, session_id)
        
        return response
    
    def learn_from_feedback(
        self,
        query: str,
        response: str,
        feedback: dict,
        session_id: str
    ):
        """从反馈中学习"""
        # 更新记忆权重
        self.memory.update_weights(
            query,
            response,
            feedback,
            session_id
        )
        
        # 如果反馈负面，修正相关记忆
        if feedback.get('rating', 0) < 3:
            self._revise_memory(query, response, feedback, session_id)

4.2 神经记忆模块

class NeuralMemoryModule:
    """神经记忆模块"""
    
    def __init__(self, embedding_dim: int = 768):
        self.embedding_dim = embedding_dim
        self.memory_keys = []  # 记忆的key向量
        self.memory_values = []  # 记忆的内容
        self.memory_strengths = []  # 记忆强度
    
    def write(
        self,
        key: str,
        value: str,
        strength: float = 1.0
    ):
        """写入记忆"""
        # 简化：直接存储
        self.memory_keys.append(key)
        self.memory_values.append(value)
        self.memory_strengths.append(strength)
    
    def read(self, query: str, top_k: int = 5) -> List[dict]:
        """读取记忆"""
        # 简化实现
        # 实际中应该用向量相似度
        results = []
        
        for i, key in enumerate(self.memory_keys):
            # 简单的关键词匹配
            overlap = len(set(key.split()) & set(query.split()))
            if overlap > 0:
                results.append({
                    'content': self.memory_values[i],
                    'strength': self.memory_strengths[i],
                    'relevance': overlap / len(key.split())
                })
        
        # 排序并返回top_k
        results.sort(key=lambda x: x['strength'] * x['relevance'], reverse=True)
        return results[:top_k]
    
    def update_strength(
        self,
        index: int,
        delta: float
    ):
        """更新记忆强度"""
        if 0 <= index < len(self.memory_strengths):
            self.memory_strengths[index] += delta
            # 限制范围
            self.memory_strengths[index] = max(0.1, min(2.0, self.memory_strengths[index]))

五、State Space Models

5.1 SSM基础

状态空间模型（SSM）是一种新型的序列建模架构，与Transformer相比具有线性复杂度的优势：

class StateSpaceModel:
    """
    状态空间模型基础
    
    SSM通过状态方程建模序列：
    x(t+1) = Ax(t) + Bu(t)
    y(t) = Cx(t) + Du(t)
    
    其中：
    - A: 状态转移矩阵
    - B: 输入矩阵
    - C: 输出矩阵
    - D: 直连矩阵
    """
    
    def __init__(
        self,
        state_dim: int,
        input_dim: int,
        output_dim: int
    ):
        self.state_dim = state_dim
        self.input_dim = input_dim
        self.output_dim = output_dim
        
        # 初始化参数
        self.A = np.random.randn(state_dim, state_dim) * 0.1
        self.B = np.random.randn(state_dim, input_dim) * 0.1
        self.C = np.random.randn(output_dim, state_dim) * 0.1
        self.D = np.zeros((output_dim, input_dim))
    
    def forward(self, u: np.ndarray) -> tuple:
        """
        SSM前向传播
        
        Args:
            u: 输入序列 [seq_len, input_dim]
        
        Returns:
            y: 输出序列 [seq_len, output_dim]
            state: 最终状态 [state_dim]
        """
        seq_len = len(u)
        state = np.zeros(self.state_dim)
        outputs = []
        
        for t in range(seq_len):
            # 状态更新
            state = self.A @ state + self.B @ u[t]
            
            # 输出
            y = self.C @ state + self.D @ u[t]
            outputs.append(y)
        
        return np.array(outputs), state

5.2 Mamba架构

Mamba是选择性状态空间模型（Selective SSM），通过输入依赖的选择机制实现：

class MambaBlock:
    """
    Mamba选择机制
    
    核心改进：让A, B, C矩阵成为输入的函数，
    从而实现内容感知的序列建模
    """
    
    def __init__(
        self,
        d_model: int,
        d_state: int = 16,
        d_conv: int = 4,
        expand: int = 2
    ):
        self.d_model = d_model
        self.d_state = d_state
        self.d_conv = d_conv
        self.expand = expand
        
        d_inner = d_model * expand
        
        # 投影矩阵
        self.in_proj = nn.Linear(d_model, d_inner * 2)
        
        # 卷积
        self.conv = nn.Conv1d(
            d_inner,
            d_inner,
            d_conv,
            padding=d_conv - 1
        )
        
        # SSM参数投影
        self.x_proj = nn.Linear(d_inner, d_state * 2 + 1)
        self.dt_proj = nn.Linear(d_state, d_inner)
        
        # 输出投影
        self.out_proj = nn.Linear(d_inner, d_model)
        
        # A矩阵初始化
        self.A_log = nn.Parameter(torch.randn(d_state, d_model))
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Mamba前向传播
        """
        batch, seq_len, d = x.shape
        
        # 输入投影
        xz = self.in_proj(x)
        x_inner, z = xz.chunk(2, dim=-1)
        
        # 卷积
        x_conv = self.conv(x_inner.transpose(1, 2)).transpose(1, 2)
        x_conv = F.silu(x_conv)
        
        # SSM参数（选择机制）
        x_dbl = self.x_proj(x_conv)
        dt, B, C = x_dbl.split([self.d_state, self.d_state, self.d_state], dim=-1)
        dt = self.dt_proj(dt)
        
        # 选择性扫描
        y = self.selective_scan(
            x_conv,
            dt,
            self.A_log,
            B,
            C,
            z
        )
        
        # 输出
        y = y * F.silu(z)
        return self.out_proj(y)
    
    def selective_scan(
        self,
        u,
        dt,
        A,
        B,
        C,
        z
    ):
        """
        选择性扫描算法
        
        这是Mamba的核心：参数由输入决定
        """
        # 简化实现
        # 实际使用硬件感知的并行扫描
        batch, seq_len, d_inner = u.shape
        d_state = self.d_state
        
        # 离散化
        A_dis = torch.exp(torch.einsum('bd,dn->bdn', dt, A))
        B_dis = torch.einsum('bdn,bd->bdn', B, dt)
        
        # 扫描
        y = torch.zeros_like(u)
        h = torch.zeros(batch, d_state, device=u.device)
        
        for i in range(seq_len):
            h = A_dis[:, i] * h + B_dis[:, i] * u[:, i:i+1]
            y[:, i] = torch.einsum('bn,bn->b', h, C[:, i])
        
        return y

5.3 RWKV架构

RWKV（Receptance Weighted Key Value）是一种结合RNN和Transformer优点的架构：

class RWKVBlock:
    """
    RWKV块
    
    特点：
    - 线性注意力（O(n)复杂度）
    - 可以并行训练
    - 可以高效推理
    - 保留长期依赖
    """
    
    def __init__(
        self,
        d_model: int,
        d_ffn: int,
        layer_id: int,
        n_layers: int
    ):
        self.layer_id = layer_id
        self.n_layers = n_layers
        
        # 时间混合
        self.time_mix = TimeMixing(d_model)
        
        # 通道混合
        self.channel_mix = ChannelMixing(d_model, d_ffn)
    
    def forward(self, x: torch.Tensor, last_state=None) -> tuple:
        """RWKV前向传播"""
        # 时间混合（类似注意力）
        x, new_time_state = self.time_mix(x, last_state)
        
        # 通道混合（类似FFN）
        x = self.channel_mix(x)
        
        return x, {'time': new_time_state}
 
 
class TimeMixing(nn.Module):
    """RWKV时间混合"""
    
    def __init__(self, d_model: int):
        super().__init__()
        
        self.d_model = d_model
        
        # RWKV参数
        self.time_decay = nn.Parameter(torch.zeros(d_model))
        self.time_first = nn.Parameter(torch.zeros(d_model))
        
        # 投影
        self.key = nn.Linear(d_model, d_model)
        self.value = nn.Linear(d_model, d_model)
        self.receptance = nn.Linear(d_model, d_model)
        
        self.output = nn.Linear(d_model, d_model)
    
    def forward(self, x: torch.Tensor, state=None) -> tuple:
        """
        RWKV时间混合
        
        核心公式：
        wkv = sum_i(exp(-(i-t+1)*decay) * value_i) / sum_i(exp(-(i-t+1)*decay))
        rwkv = sigmoid(receptance) * wkv
        
        然后 output = rwkv @ output_weights
        """
        B, T, C = x.shape
        
        # 投影
        k = self.key(x)
        v = self.value(x)
        r = torch.sigmoid(self.receptance(x))
        
        # WKV计算（简化）
        wkv = self._compute_wkv(k, v)
        
        # 输出
        rwkv = r * wkv
        output = self.output(rwkv)
        
        return output, None  # state简化
    
    def _compute_wkv(self, k, v):
        """计算RWKV的wkv项"""
        # 简化实现
        # 实际使用数值稳定的并行算法
        B, T, C = k.shape
        
        wkv = torch.zeros_like(k)
        
        for t in range(T):
            w = torch.exp(self.time_decay)
            w_first = torch.exp(self.time_first)
            
            # 计算分母
            denom = w_first
            # 计算分子
            numer = w_first * v[:, t:t+1]
            
            for i in range(t):
                denom = 1 + w * denom
                numer = w * numer + w * v[:, i:i+1]
            
            wkv[:, t] = numer / denom
        
        return wkv

5.4 SSM vs Transformer对比

特性	Transformer	SSM (Mamba/RWKV)
时间复杂度	O(n²)	O(n)
空间复杂度	O(n²)	O(n)
长期依赖	有限（位置编码）	自然保持
并行训练	完全并行	几乎完全并行
推理速度	慢（完整注意力）	快（线性）
可解释性	注意力权重	状态向量
上下文学习	强	较弱（但增强中）
硬件效率	中等	高

六、未来展望

6.1 短期发展趋势（1-2年）

1. 上下文窗口继续扩大：百万token将成为常态
2. 更智能的压缩：基于语义的理解而非规则
3. 原生缓存支持：硬件和模型层面的缓存
4. 多模态上下文：文本、图像、视频的统一处理

6.2 中期发展趋势（3-5年）

1. 自适应上下文：模型自动学习最优上下文策略
2. 外部记忆整合：持久的、可学习的外部记忆系统
3. State Space融合：SSM与Transformer的深度融合
4. 个性化上下文：针对用户定制的上下文管理

6.3 长期愿景

┌─────────────────────────────────────────────────────────────────┐
│                     理想上下文系统                                │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  用户意图 ──► 智能理解 ──► 最优上下文 ──► 高效推理              │
│                                    │                            │
│                          ┌─────────┴─────────┐                  │
│                          │                   │                  │
│                    长期记忆              短期窗口                │
│                          │                   │                  │
│                    ┌─────┴─────┐        ┌───┴───┐               │
│                    │           │        │       │               │
│                  知识图谱    个人记忆   会话历史  实时信息         │
│                                                                 │
│  特点：上下文像人脑一样自然、高效、个性化                         │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

七、相关主题

• 上下文窗口深度解析
• Long Context Model详解
• 上下文压缩技术
• Memory-Augmented LM
• RAG上下文优化指南

八、参考文献

1. Gu, A., et al. (2021). Efficiently Modeling Long Sequences with Structured State Spaces. NeurIPS.
2. Dao, T., et al. (2023). Hungry Hungry Hippos: Towards Language Modeling with State Space Models. ICLR.
3. Peng, B., et al. (2023). RWKV: Reinventing RNNs for the Transformer Era. ACL.
4. Liu, H., et al. (2024). Mixture-of-Experts Meets Instruction Tuning. arXiv.
5. Anthropic (2024). Claude 3.5 System Card.