diff --git a/model/LMConfig.py b/model/LMConfig.py
index 7dd1f4e..8eb8a62 100644
--- a/model/LMConfig.py
+++ b/model/LMConfig.py
@@ -9,13 +9,13 @@ class LMConfig(PretrainedConfig):
             self,
             dim: int = 512,
             n_layers: int = 8,
-            n_heads: int = 32,
+            n_heads: int = 16,
             n_kv_heads: int = 8,
             vocab_size: int = 6400,
             hidden_dim: int = None,
             multiple_of: int = 64,
             norm_eps: float = 1e-5,
-            max_seq_len: int = 8192,
+            max_seq_len: int = 512,
             rope_theta: int = 1e6,
             dropout: float = 0.0,
             flash_attn: bool = True,
@@ -38,8 +38,8 @@ class LMConfig(PretrainedConfig):
             seq_aux: bool = True,
             norm_topk_prob: bool = True,
             ####################################################
-            knowledge_num: int = 64*64,
-            knowledge_length: int = 8,
+            knowledge_num: int = 1024*1024,
+            knowledge_length: int = 16,
             knowledge_dim: int = 128,
             ####################################################
             # EMA update related configurations (inspired by VQ-VAE)
diff --git a/model/model_memory_1_4_10.py b/model/model_memory_1_4_10.py
new file mode 100644
index 0000000..b40483b
--- /dev/null
+++ b/model/model_memory_1_4_10.py
@@ -0,0 +1,930 @@
+import math
+import struct
+import inspect
+import time
+
+from .LMConfig import LMConfig
+from typing import Any, Optional, Tuple, List, Union
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        return self.weight * self._norm(x.float()).type_as(x)
+
+
+def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return pos_cis
+
+
+def apply_rotary_emb(xq, xk, pos_cis):
+    def unite_shape(pos_cis, x):
+        ndim = x.ndim
+        assert 0 <= 1 < ndim
+        assert pos_cis.shape == (x.shape[1], x.shape[-1])
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return pos_cis.view(*shape)
+
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    pos_cis = unite_shape(pos_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
+    bs, slen, n_kv_heads, head_dim = x.shape
+    if n_rep == 1:
+        return x
+    return (
+        x[:, :, :, None, :]
+        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
+        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
+    )
+
+
+class Attention(nn.Module):
+    """Self attention module without KV cache"""
+    def __init__(self, args: LMConfig):
+        super().__init__()
+        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
+        assert args.n_heads % self.n_kv_heads == 0
+        self.n_local_heads = args.n_heads
+        self.n_local_kv_heads = self.n_kv_heads
+        self.n_rep = self.n_local_heads // self.n_local_kv_heads
+        self.head_dim = args.dim // args.n_heads
+        self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
+        self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
+        self.attn_dropout = nn.Dropout(args.dropout)
+        self.resid_dropout = nn.Dropout(args.dropout)
+        self.dropout = args.dropout
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
+        # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
+        mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
+        mask = torch.triu(mask, diagonal=1)
+        self.register_buffer("mask", mask, persistent=False)
+
+    def forward(self, x: torch.Tensor, pos_cis: torch.Tensor):
+        """Forward pass without KV cache"""
+        bsz, seq_len, _ = x.shape
+        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
+        xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim)
+        xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
+
+        xq, xk = apply_rotary_emb(xq, xk, pos_cis)
+        
+        # 注意：完全去除了KV cache相关代码
+        
+        xq, xk, xv = (
+            xq.transpose(1, 2),
+            repeat_kv(xk, self.n_rep).transpose(1, 2),
+            repeat_kv(xv, self.n_rep).transpose(1, 2)
+        )
+        if self.flash and seq_len != 1:
+            dropout_p = self.dropout if self.training else 0.0
+            output = F.scaled_dot_product_attention(
+                xq, xk, xv,
+                attn_mask=None,
+                dropout_p=dropout_p,
+                is_causal=True
+            )
+        else:
+            scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
+            scores += self.mask[:, :, :seq_len, :seq_len]
+            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
+            scores = self.attn_dropout(scores)
+            output = scores @ xv
+
+        output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
+        output = self.resid_dropout(self.wo(output))
+        return output
+
+
+class MemoryGate(nn.Module):
+    """Product Key Memory-based gate mechanism for memory selection with Gumbel-Softmax"""
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.dim = config.dim
+        self.knowledge_num = config.knowledge_num
+        self.knowledge_dim = config.knowledge_dim
+        self.num_candidates = getattr(config, 'num_candidates', 32)  # Generate 32 candidates
+        self.num_selected = getattr(config, 'num_selected', 1)  # Select 1 best from candidates
+        
+        # 确保知识库数量是完全平方数
+        assert int(self.knowledge_num ** 0.5) ** 2 == self.knowledge_num, \
+            f"knowledge_num ({self.knowledge_num}) must be a perfect square for product key memory"
+        
+        self.num_keys = int(self.knowledge_num ** 0.5)
+        
+        # 查询投影：将输入维度映射到knowledge_dim * 2（用于两个product key）
+        self.gate_proj = nn.Linear(self.dim, self.knowledge_dim, bias=False)
+        
+        # Product Key Memory: 两个独立的键集合
+        self.keys = nn.Parameter(torch.randn(2, self.num_keys, self.knowledge_dim // 2))
+        
+        self.dropout = nn.Dropout(config.dropout)
+        
+    def forward(self, x: torch.Tensor):
+        """
+        Args:
+            x: [batch_size, seq_len, dim]
+        Returns:
+            memory_indices: [batch_size, seq_len, num_selected]
+            memory_scores: [batch_size, seq_len, num_selected]
+            balance_loss: 平衡损失（KL散度 + 基尼系数）
+            stats: 监控统计信息字典
+        """
+        bsz, seq_len, _ = x.shape
+        
+        # 生成查询向量
+        queries = self.gate_proj(x)  # [batch, seq_len, knowledge_dim]
+        
+        # 分割为两部分用于product key
+        q1 = queries[:, :, :self.knowledge_dim // 2]  # [batch, seq_len, knowledge_dim // 2]
+        q2 = queries[:, :, self.knowledge_dim // 2:]  # [batch, seq_len, knowledge_dim // 2]
+        
+        # 计算与两个键集合的相似度
+        scores_1 = torch.einsum('bsd,kd->bsk', q1, self.keys[0])  # [batch, seq_len, num_keys]
+        scores_2 = torch.einsum('bsd,kd->bsk', q2, self.keys[1])  # [batch, seq_len, num_keys]
+        
+        # 获取top-k candidates (now using num_candidates instead of num_selected)
+        topk_scores_1, topk_indices_1 = scores_1.topk(self.num_candidates, dim=-1)
+        topk_scores_2, topk_indices_2 = scores_2.topk(self.num_candidates, dim=-1)
+        
+        # 组合product key的结果
+        combined_scores = topk_scores_1.unsqueeze(-1) + topk_scores_2.unsqueeze(-2)  # [batch, seq_len, num_candidates, num_candidates]
+        combined_indices = topk_indices_1.unsqueeze(-1) * self.num_keys + topk_indices_2.unsqueeze(-2)  # [batch, seq_len, num_candidates, num_candidates]
+        
+        # 展平并选择最终的top-k candidates
+        combined_scores = combined_scores.view(bsz, seq_len, -1)
+        combined_indices = combined_indices.view(bsz, seq_len, -1)
+        
+        candidate_scores, candidate_pk_indices = combined_scores.topk(self.num_candidates, dim=-1)
+        candidate_indices = combined_indices.gather(-1, candidate_pk_indices)  # [batch, seq_len, num_candidates]
+        
+        # 归一化候选分数
+        candidate_scores = F.softmax(candidate_scores, dim=-1)
+        candidate_scores = self.dropout(candidate_scores)
+        
+        # 返回候选项用于后续的相似度选择
+        # 注意：这里返回候选项，在MiniMindBlock中进行相似度选择和多样性损失计算
+        return candidate_indices, candidate_scores, None, {}
+    
+    def _compute_balance_loss_and_stats(self, memory_indices, memory_scores):
+        """
+        计算平衡损失和监控统计信息
+        
+        Args:
+            memory_indices: [batch_size, seq_len, num_selected]
+            memory_scores: [batch_size, seq_len, num_selected]
+        
+        Returns:
+            balance_loss: 标量张量
+            stats: 统计信息字典
+        """
+        bsz, seq_len, num_selected = memory_indices.shape
+        device = memory_indices.device
+        
+        # 1. 计算记忆选择分布
+        # 将所有选择的记忆索引展平
+        flat_indices = memory_indices.view(-1)  # [batch_size * seq_len * num_selected]
+        
+        # 统计每个记忆条目被选中的次数
+        memory_counts = torch.zeros(self.knowledge_num, device=device)
+        memory_counts.scatter_add_(0, flat_indices, torch.ones_like(flat_indices, dtype=torch.float))
+        
+        # 计算选择概率分布
+        total_selections = bsz * seq_len * num_selected
+        memory_probs = memory_counts / total_selections
+        
+        # 2. 计算KL散度损失（与均匀分布的KL散度）
+        uniform_prob = 1.0 / self.knowledge_num
+        # 避免log(0)的问题
+        memory_probs_safe = memory_probs + 1e-10
+        kl_loss = F.kl_div(
+            torch.log(memory_probs_safe),
+            torch.full_like(memory_probs, uniform_prob),
+            reduction='sum'
+        )
+        
+        # 3. 计算基尼系数损失（衡量分布不平等程度）
+        sorted_probs, _ = torch.sort(memory_probs)
+        n = self.knowledge_num
+        index = torch.arange(1, n + 1, device=device, dtype=torch.float)
+        gini_coeff = (2 * torch.sum(index * sorted_probs) / (n * torch.sum(sorted_probs))) - (n + 1) / n
+        gini_loss = gini_coeff  # 基尼系数越大，分布越不均匀
+        
+        # 4. 组合平衡损失
+        balance_loss = 0.5 * kl_loss + 0.5 * gini_loss
+        
+        # 5. 计算监控统计信息
+        with torch.no_grad():
+            # 记忆覆盖率：被选中的记忆条目占总数的比例
+            coverage_rate = (memory_counts > 0).float().mean().item()
+            
+            # 热点记忆：选择次数前10%的记忆条目
+            top10_threshold = torch.quantile(memory_counts, 0.9)
+            hot_memories = (memory_counts >= top10_threshold).sum().item()
+            
+            # 死记忆：从未被选中的记忆条目
+            dead_memories = (memory_counts == 0).sum().item()
+            
+            # 记忆选择方差（衡量不平衡程度）
+            selection_variance = memory_counts.var().item()
+            
+            stats = {
+                'gini_coefficient': gini_coeff.item(),
+                'kl_divergence': kl_loss.item(),
+                'coverage_rate': coverage_rate,
+                'hot_memories': hot_memories,
+                'dead_memories': dead_memories,
+                'selection_variance': selection_variance,
+                'max_selections': memory_counts.max().item(),
+                'min_selections': memory_counts.min().item(),
+            }
+        
+        return balance_loss, stats
+
+
+class GatedMemoryFusion(nn.Module):
+    """Gated MLP fusion for concatenated h_attn and selected memories"""
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.dim = config.dim
+        self.knowledge_dim = config.knowledge_dim
+        self.num_selected = getattr(config, 'num_selected', 1)  # Now we select 1 best memory
+        
+        # 输入维度：dim (h_attn) + num_selected * dim (选中的记忆，现在只有1个)
+        # 实验1.4.9：修改为只选择1个最佳记忆
+        concat_dim = self.dim + self.num_selected * self.dim
+        
+        # 类似SwiGLU的门控MLP结构
+        self.gate_proj = nn.Linear(concat_dim, self.dim, bias=False)
+        self.up_proj = nn.Linear(concat_dim, self.dim, bias=False)
+        self.down_proj = nn.Linear(self.dim, self.dim, bias=False)
+        
+        self.dropout = nn.Dropout(config.dropout)
+        
+    def forward(self, h_attn: torch.Tensor, selected_memory: torch.Tensor):
+        """
+        Args:
+            h_attn: [batch_size, seq_len, dim] - Self attention output
+            selected_memory: [batch_size, seq_len, dim] - Selected single best memory
+        Returns:
+            output: [batch_size, seq_len, dim]
+        """
+        bsz, seq_len, _ = h_attn.shape
+        
+        # 拼接h_attn和最佳记忆
+        concat_input = torch.cat([h_attn, selected_memory], dim=-1)  # [batch, seq_len, dim + dim]
+        
+        # 门控MLP处理（类似SwiGLU）
+        gate = F.silu(self.gate_proj(concat_input))  # [batch, seq_len, dim]
+        up = self.up_proj(concat_input)              # [batch, seq_len, dim]
+        fusion_output = gate * up                    # Element-wise multiplication
+        
+        # 输出投影
+        output = self.down_proj(fusion_output)       # [batch, seq_len, dim]
+        output = self.dropout(output)
+        
+        return output
+
+
+class MiniMindBlock(nn.Module):
+    """Transformer block with memory-based cross attention instead of FFN"""
+    def __init__(self, layer_id: int, config: LMConfig):
+        super().__init__()
+        self.config = config  # 保存config引用
+        self.n_heads = config.n_heads
+        self.dim = config.dim
+        self.head_dim = config.dim // config.n_heads
+        self.attention = Attention(config)
+        
+        self.layer_id = layer_id
+        self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.memory_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        
+        # 记忆相关模块
+        self.memory_gate = MemoryGate(config)
+        self.gated_memory_fusion = GatedMemoryFusion(config)
+        
+        # Gumbel-Softmax参数
+        self.gumbel_temperature = getattr(config, 'gumbel_temperature', 1.0)
+
+        # self.attentionpool = nn.Linear(config.dim, 1)
+    
+    def gumbel_softmax_selection(self, similarity_scores, temperature=1.0, hard=True):
+        """
+        使用Gumbel-Softmax进行可微分的离散选择
+        
+        Args:
+            similarity_scores: [batch_size, seq_len, num_candidates] - 相似度分数
+            temperature: Gumbel-Softmax温度参数
+            hard: 是否使用硬选择（one-hot）
+        
+        Returns:
+            selection_weights: [batch_size, seq_len, num_candidates] - 选择权重
+            selected_indices: [batch_size, seq_len] - 选中的索引（用于统计）
+        """
+        # 添加Gumbel噪声
+        gumbel_noise = -torch.log(-torch.log(torch.rand_like(similarity_scores) + 1e-20) + 1e-20)
+        logits = (similarity_scores + gumbel_noise) / temperature
+        
+        # Softmax
+        soft_weights = F.softmax(logits, dim=-1)
+        
+        if hard:
+            # 硬选择：创建one-hot向量
+            _, max_indices = soft_weights.max(dim=-1, keepdim=True)
+            hard_weights = torch.zeros_like(soft_weights).scatter_(-1, max_indices, 1.0)
+            # 使用straight-through estimator
+            selection_weights = hard_weights - soft_weights.detach() + soft_weights
+            selected_indices = max_indices.squeeze(-1)  # [batch_size, seq_len]
+        else:
+            # 软选择
+            selection_weights = soft_weights
+            selected_indices = torch.argmax(soft_weights, dim=-1)
+            
+        return selection_weights, selected_indices
+    
+    def compute_diversity_loss(self, candidate_memories):
+        """
+        计算候选集内部多样性损失（鼓励候选项之间的差异性）
+        
+        Args:
+            candidate_memories: [batch_size, seq_len, num_candidates, dim]
+        
+        Returns:
+            diversity_loss: 标量张量
+        """
+        bsz, seq_len, num_candidates, dim = candidate_memories.shape
+        
+        # 计算候选项之间的相似度矩阵
+        # 归一化候选记忆用于计算余弦相似度
+        normalized_memories = F.normalize(candidate_memories, p=2, dim=-1)  # [batch, seq_len, num_candidates, dim]
+        
+        # 计算相似度矩阵: [batch, seq_len, num_candidates, num_candidates]
+        similarity_matrix = torch.matmul(normalized_memories, normalized_memories.transpose(-2, -1))
+        
+        # 移除对角线（自相似度=1）
+        mask = torch.eye(num_candidates, device=candidate_memories.device).bool()
+        mask = mask.unsqueeze(0).unsqueeze(0).expand(bsz, seq_len, -1, -1)
+        
+        # 计算非对角线元素的平均相似度（希望越小越好，表示越多样）
+        off_diagonal_similarities = similarity_matrix.masked_select(~mask)
+        avg_similarity = off_diagonal_similarities.mean()
+        
+        # 多样性损失：相似度越高，损失越大
+        diversity_loss = avg_similarity
+        
+        return diversity_loss
+
+    def forward(self, x, pos_cis, memory_bank, tok_embeddings, collect_ema_stats=False):
+        """
+        实验1.4.9: Gumbel-Softmax + 多样性损失 + 可微分相似度损失
+        
+        Args:
+            x: [batch_size, seq_len, dim]
+            pos_cis: positional encoding
+            memory_bank: [knowledge_num, knowledge_length] - shared memory bank with token IDs
+            tok_embeddings: token embedding layer
+            collect_ema_stats: 是否收集EMA更新统计信息
+        
+        Returns:
+            out: [batch_size, seq_len, dim]
+            balance_loss: 该层的平衡损失 (从候选项计算)
+            similarity_loss: 相似度损失 (可微分)
+            diversity_loss: 多样性损失
+            layer_stats: 该层的监控统计信息
+            ema_stats: EMA更新统计信息（如果collect_ema_stats=True）
+            cosine_stats: 查找向量与候选记忆条目的余弦相似度统计信息
+        """
+        # Self attention
+        h_attn = self.attention(self.attention_norm(x), pos_cis)
+        h = x + h_attn
+        
+        # 使用h_attn作为门控和交叉注意力的输入（核心：self attention的输出）
+        h_for_memory = self.memory_norm(h_attn)
+        
+        # 🔥 新架构：生成32个候选项
+        candidate_indices, candidate_scores, _, _ = self.memory_gate(h_for_memory)
+        # candidate_indices: [batch, seq_len, num_candidates]
+        # candidate_scores: [batch, seq_len, num_candidates]
+        
+        bsz, seq_len, num_candidates = candidate_indices.shape
+        
+        # 解码候选token_ids为特征向量
+        candidate_indices_flat = candidate_indices.view(-1)  # [batch * seq_len * num_candidates]
+        candidate_token_ids = memory_bank[candidate_indices_flat]  # [batch * seq_len * num_candidates, knowledge_length]
+        
+        # 解码为embeddings并池化
+        candidate_embeddings = tok_embeddings(candidate_token_ids)  # [batch * seq_len * num_candidates, knowledge_length, dim]
+        candidate_memories = candidate_embeddings.mean(dim=1)  # [batch * seq_len * num_candidates, dim]
+        candidate_memories = candidate_memories.view(bsz, seq_len, num_candidates, self.dim)  # [batch, seq_len, num_candidates, dim]
+        
+        # 🔥 核心改进: 计算可微分的相似度分数 (移除no_grad)
+        h_expanded = h_for_memory.unsqueeze(2).expand(-1, -1, num_candidates, -1)  # [batch, seq_len, num_candidates, dim]
+        similarity_scores = F.cosine_similarity(h_expanded, candidate_memories, dim=-1)  # [batch, seq_len, num_candidates]
+        
+        # 🔥 使用Gumbel-Softmax选择最佳候选项
+        selection_weights, selected_indices = self.gumbel_softmax_selection(
+            similarity_scores, 
+            temperature=self.gumbel_temperature,
+            hard=True
+        )  # selection_weights: [batch, seq_len, num_candidates], selected_indices: [batch, seq_len]
+        
+        # 🔥 计算相似度损失 (现在是可微分的!)
+        # 相似度损失：希望选中的记忆与查询向量相似度尽可能高
+        selected_similarities = (similarity_scores * selection_weights).sum(dim=-1)  # [batch, seq_len]
+        similarity_loss = -selected_similarities.mean()  # 负号：相似度越高，损失越小
+        
+        # 🔥 计算候选集多样性损失
+        diversity_loss = self.compute_diversity_loss(candidate_memories)
+        
+        # 🔥 使用selection_weights进行加权选择最终记忆
+        selected_memory = (candidate_memories * selection_weights.unsqueeze(-1)).sum(dim=2)  # [batch, seq_len, dim]
+        
+        # 门控MLP融合：只融合选中的单个最佳记忆
+        memory_output = self.gated_memory_fusion(h_for_memory, selected_memory)
+        
+        # 残差连接
+        out = h + memory_output
+        
+        # 🔥 计算平衡损失和统计信息 (基于候选项的选择分布)
+        balance_loss, layer_stats = self._compute_candidate_balance_stats(candidate_indices, selection_weights)
+        
+        # 🔥 计算详细的相似度统计信息
+        cosine_stats = {
+            'similarity_scores': similarity_scores,                                    # [batch, seq_len, num_candidates]
+            'selected_similarities': selected_similarities,                           # [batch, seq_len]
+            'avg_similarity': similarity_scores.mean().item(),                        # 平均相似度
+            'max_similarity': similarity_scores.max().item(),                         # 最大相似度
+            'min_similarity': similarity_scores.min().item(),                         # 最小相似度
+            'selected_avg_similarity': selected_similarities.mean().item(),           # 选中记忆的平均相似度
+            'selection_entropy': -torch.sum(selection_weights * torch.log(selection_weights + 1e-10), dim=-1).mean().item()  # 选择熵
+        }
+        
+        # 收集EMA更新统计信息（现在基于选中的记忆）
+        ema_stats = None
+        if collect_ema_stats and self.training:
+            # 扩展选中的索引以匹配EMA更新的期望格式
+            selected_memory_indices = candidate_indices.gather(2, selected_indices.unsqueeze(-1))  # [batch, seq_len, 1]
+            ema_stats = {
+                'memory_indices': selected_memory_indices,          # [batch, seq_len, 1]
+                'memory_scores': torch.ones_like(selected_memory_indices.float()),   # [batch, seq_len, 1] - 选中的权重为1
+                'h_for_memory': h_for_memory,                       # [batch, seq_len, dim]
+                'selected_memory': selected_memory.unsqueeze(2),    # [batch, seq_len, 1, dim]
+            }
+        
+        if collect_ema_stats:
+            return out, balance_loss, similarity_loss, diversity_loss, layer_stats, ema_stats, cosine_stats
+        else:
+            return out, balance_loss, similarity_loss, diversity_loss, layer_stats, cosine_stats
+    
+    def _compute_candidate_balance_stats(self, candidate_indices, selection_weights):
+        """
+        计算基于候选项选择的平衡损失和统计信息
+        
+        Args:
+            candidate_indices: [batch_size, seq_len, num_candidates]
+            selection_weights: [batch_size, seq_len, num_candidates] - Gumbel-Softmax权重
+        
+        Returns:
+            balance_loss: 标量张量
+            stats: 统计信息字典
+        """
+        bsz, seq_len, num_candidates = candidate_indices.shape
+        device = candidate_indices.device
+        
+        # 使用加权统计每个记忆条目被选中的概率
+        flat_indices = candidate_indices.view(-1)  # [batch * seq_len * num_candidates]
+        flat_weights = selection_weights.view(-1)  # [batch * seq_len * num_candidates]
+        
+        # 统计每个记忆条目被选中的加权次数
+        memory_counts = torch.zeros(self.config.knowledge_num, device=device)
+        memory_counts.scatter_add_(0, flat_indices, flat_weights)
+        
+        # 计算选择概率分布
+        total_selections = memory_counts.sum()
+        memory_probs = memory_counts / (total_selections + 1e-10)
+        
+        # 计算KL散度损失（与均匀分布的KL散度）
+        uniform_prob = 1.0 / self.config.knowledge_num
+        memory_probs_safe = memory_probs + 1e-10
+        kl_loss = F.kl_div(
+            torch.log(memory_probs_safe),
+            torch.full_like(memory_probs, uniform_prob),
+            reduction='sum'
+        )
+        
+        # 计算基尼系数损失
+        sorted_probs, _ = torch.sort(memory_probs)
+        n = self.config.knowledge_num
+        index = torch.arange(1, n + 1, device=device, dtype=torch.float)
+        gini_coeff = (2 * torch.sum(index * sorted_probs) / (n * torch.sum(sorted_probs))) - (n + 1) / n
+        gini_loss = gini_coeff
+        
+        # 组合平衡损失
+        balance_loss = 0.5 * kl_loss + 0.5 * gini_loss
+        
+        # 计算统计信息
+        with torch.no_grad():
+            coverage_rate = (memory_counts > 0.01).float().mean().item()  # 被选中概率>1%的记忆比例
+            top10_threshold = torch.quantile(memory_counts, 0.9)
+            hot_memories = (memory_counts >= top10_threshold).sum().item()
+            dead_memories = (memory_counts < 0.01).sum().item()  # 几乎从未被选中的记忆
+            selection_variance = memory_counts.var().item()
+            
+            stats = {
+                'gini_coefficient': gini_coeff.item(),
+                'kl_divergence': kl_loss.item(),
+                'coverage_rate': coverage_rate,
+                'hot_memories': hot_memories,
+                'dead_memories': dead_memories,
+                'selection_variance': selection_variance,
+                'max_selections': memory_counts.max().item(),
+                'min_selections': memory_counts.min().item(),
+            }
+        
+        return balance_loss, stats
+
+
+class MiniMindLM(PreTrainedModel):
+    config_class = LMConfig
+
+    def __init__(self, params: LMConfig = None):
+        self.params = params
+        super().__init__(self.params)
+        self.vocab_size, self.n_layers = params.vocab_size, params.n_layers
+        self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
+        self.dropout = nn.Dropout(params.dropout)
+        self.layers = nn.ModuleList([MiniMindBlock(l, params) for l in range(self.n_layers)])
+        self.norm = RMSNorm(params.dim, eps=params.norm_eps)
+        self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
+        self.tok_embeddings.weight = self.output.weight
+        self.register_buffer("pos_cis",
+                             precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta),
+                             persistent=False)
+        
+        # 初始化共享记忆库 - 实验1.4.6：存储token_id而非特征向量
+        # VQ-VAE风格：memory_bank作为codebook，使用EMA更新而非梯度更新
+        if params.use_ema_update:
+            self.memory_bank = nn.Parameter(
+                torch.randint(0, params.vocab_size, (params.knowledge_num, params.knowledge_length)),
+                requires_grad=False  # 禁用梯度更新，使用EMA更新
+            )
+        else:
+            self.memory_bank = nn.Parameter(
+                torch.randint(0, params.vocab_size, (params.knowledge_num, params.knowledge_length)),
+                requires_grad=True   # 传统梯度更新
+            )
+        
+        # EMA更新相关缓冲区
+        if params.use_ema_update:
+            # 记录每个memory条目的更新统计
+            self.register_buffer('ema_update_count', torch.zeros(params.knowledge_num), persistent=False)
+            # 注意：现在memory_bank存储token_id，但EMA在特征空间进行，所以不需要sum_buffer了
+            # self.register_buffer('ema_sum_buffer', torch.zeros_like(self.memory_bank), persistent=False)
+            # EMA更新频率计数器
+            self.register_buffer('ema_step_counter', torch.zeros(1, dtype=torch.long), persistent=False)
+        
+        # 记录上一步的记忆库状态，用于计算更新统计
+        self.register_buffer('prev_memory_bank', torch.zeros_like(self.memory_bank), persistent=False)
+        
+        # 🔥 新增: 冻结mask - 标记哪些memory_bank条目被冻结（不更新）
+        if params.freeze_ratio > 0.0:
+            freeze_num = int(params.knowledge_num * params.freeze_ratio)
+            freeze_mask = torch.zeros(params.knowledge_num, dtype=torch.bool)
+            # 固定冻结前面的条目
+            freeze_mask[:freeze_num] = True
+            self.register_buffer('freeze_mask', freeze_mask, persistent=False)
+            print(f"🔥 Memory bank freezing enabled: {freeze_num}/{params.knowledge_num} entries ({params.freeze_ratio*100:.1f}%) frozen", flush=True)
+            import sys; sys.stdout.flush()
+        else:
+            self.register_buffer('freeze_mask', torch.zeros(params.knowledge_num, dtype=torch.bool), persistent=False)
+            print(f"🔥 Memory bank freezing disabled: all entries can be updated", flush=True)
+            import sys; sys.stdout.flush()
+        
+        self.OUT = CausalLMOutputWithPast()
+    
+    def get_memory_update_stats(self):
+        """
+        计算记忆库更新统计信息
+        
+        Returns:
+            update_stats: 包含更新统计的字典
+        """
+        with torch.no_grad():
+            if hasattr(self, 'prev_memory_bank') and self.prev_memory_bank.numel() > 0:
+                # 计算L2距离变化
+                l2_distance = torch.norm(self.memory_bank - self.prev_memory_bank, p=2, dim=-1)
+                avg_l2_distance = l2_distance.mean().item()
+                max_l2_distance = l2_distance.max().item()
+                
+                # 计算余弦相似度
+                cos_sim = F.cosine_similarity(
+                    self.memory_bank.view(-1), 
+                    self.prev_memory_bank.view(-1), 
+                    dim=0
+                ).item()
+                
+                # 计算更新率（发生显著变化的记忆条目比例）
+                threshold = 0.01  # 更新阈值
+                updated_memories = (l2_distance > threshold).sum().item()
+                update_rate = updated_memories / self.memory_bank.size(0)
+                
+                update_stats = {
+                    'memory_avg_l2_change': avg_l2_distance,
+                    'memory_max_l2_change': max_l2_distance,
+                    'memory_cosine_similarity': cos_sim,
+                    'memory_update_rate': update_rate,
+                    'memory_updated_count': updated_memories
+                }
+            else:
+                # 第一次调用时的默认值
+                update_stats = {
+                    'memory_avg_l2_change': 0.0,
+                    'memory_max_l2_change': 0.0,
+                    'memory_cosine_similarity': 1.0,
+                    'memory_update_rate': 0.0,
+                    'memory_updated_count': 0
+                }
+            
+            # 更新prev_memory_bank
+            self.prev_memory_bank.copy_(self.memory_bank)
+            
+            return update_stats
+
+    def forward(self,
+                input_ids: Optional[torch.Tensor] = None,
+                **args):
+        """Forward pass without KV cache support"""
+        start_pos = args.get('start_pos', 0)
+        collect_ema_stats = args.get('collect_ema_stats', self.params.use_ema_update and self.training)
+        
+        h = self.dropout(self.tok_embeddings(input_ids))
+        pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)]
+        
+        # 收集所有层的损失和统计信息 - 实验1.4.9: 四损失系统
+        total_balance_loss = 0
+        total_similarity_loss = 0
+        total_diversity_loss = 0
+        all_layer_stats = {}
+        all_ema_stats = {}
+        all_cosine_stats = {}
+        
+        for layer_idx, layer in enumerate(self.layers):
+            if collect_ema_stats:
+                h, balance_loss, similarity_loss, diversity_loss, layer_stats, ema_stats, cosine_stats = layer(h, pos_cis, self.memory_bank, self.tok_embeddings, collect_ema_stats=True)
+                all_ema_stats[f'layer_{layer_idx}'] = ema_stats
+            else:
+                h, balance_loss, similarity_loss, diversity_loss, layer_stats, cosine_stats = layer(h, pos_cis, self.memory_bank, self.tok_embeddings, collect_ema_stats=False)
+            
+            # 累加四种损失
+            total_balance_loss += balance_loss
+            total_similarity_loss += similarity_loss
+            total_diversity_loss += diversity_loss
+            
+            # 为每层的统计信息添加前缀
+            for key, value in layer_stats.items():
+                all_layer_stats[f'layer_{layer_idx}_{key}'] = value
+            
+            # 为每层的余弦相似度统计信息添加前缀
+            for key, value in cosine_stats.items():
+                all_cosine_stats[f'layer_{layer_idx}_{key}'] = value
+
+        logits = self.output(self.norm(h))
+        
+        # 🔥 新的四损失结构
+        aux_loss = {
+            'balance_loss': total_balance_loss,
+            'similarity_loss': total_similarity_loss,
+            'diversity_loss': total_diversity_loss,
+        }
+        
+        self.OUT.__setitem__('last_hidden_state', h)
+        self.OUT.__setitem__('logits', logits)
+        self.OUT.__setitem__('aux_loss', aux_loss)
+        self.OUT.__setitem__('layer_stats', all_layer_stats)  # 添加层级统计信息
+        self.OUT.__setitem__('ema_stats', all_ema_stats if collect_ema_stats else None)  # 添加EMA统计信息
+        self.OUT.__setitem__('cosine_stats', all_cosine_stats)  # 添加余弦相似度统计信息
+        self.OUT.__setitem__('past_key_values', None)  # 不支持KV cache
+        return self.OUT
+
+    @torch.inference_mode()
+    def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90,
+                 stream=False, rp=1., pad_token_id=0, num_return_sequences=1, **args):
+        """Generate without KV cache"""
+        # 流式生成
+        if stream:
+            return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args)
+
+        # 直接生成
+        generated = []
+        for i in range(input_ids.size(0)):
+            non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0)
+            for _ in range(num_return_sequences):
+                out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, **args)
+                tokens_list = [tokens[:, -1:] for tokens in out]
+                gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad
+                full_sequence = torch.cat([non_pad, gen], dim=-1)
+                generated.append(full_sequence)
+
+        max_length = max(seq.size(1) for seq in generated)
+        generated = [
+            torch.cat(
+                [seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)],
+                dim=-1)
+            for seq in generated
+        ]
+        output = torch.cat(generated, dim=0)
+        res = output.view(input_ids.size(0) * num_return_sequences, -1)
+        return res
+
+    def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **args):
+        """Stream generation without KV cache - regenerates full sequence each time"""
+        start = input_ids.shape[1]
+        while input_ids.shape[1] < start + max_new_tokens:
+            # 每次都重新计算整个序列（因为没有KV cache）
+            out = self(input_ids, **args)
+            logits = out.logits[:, -1, :]
+            
+            # 重复惩罚
+            logits[:, list(set(input_ids.tolist()[0]))] /= rp
+            logits /= (temperature + 1e-9)
+            
+            # Top-p采样
+            if top_p is not None and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
+                sorted_probs = F.softmax(sorted_logits, dim=-1)
+                cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = -float('Inf')
+                
+            input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
+            input_ids = torch.cat((input_ids, input_ids_next), dim=1)
+            yield input_ids[:, start:]
+            if input_ids_next.item() == eos_token_id:
+                break
+    
+    def apply_ema_update(self, ema_stats):
+        """
+        应用token-based EMA更新到memory_bank
+        实验1.4.6：批量化tensor操作优化版本
+        
+        Args:
+            ema_stats: 从forward pass收集的EMA统计信息，格式为：
+                {'layer_0': {'memory_indices': ..., 'h_for_memory': ...}, 'layer_1': ...}
+        """
+        if not self.params.use_ema_update:
+            return {}
+        
+        # 增加EMA步数计数器
+        self.ema_step_counter += 1
+        
+        # 检查是否需要进行EMA更新
+        if self.ema_step_counter % self.params.ema_update_freq != 0:
+            return {'ema_update_applied': False, 'reason': 'frequency_check_failed'}
+        
+        with torch.no_grad():
+            device = self.memory_bank.device
+            knowledge_num, knowledge_length = self.memory_bank.shape
+            dim = self.params.dim
+            
+            # 🚀 批量收集所有层的数据（避免字典操作）
+            all_indices = []
+            all_features = []
+            total_selections = 0
+            total_layers = 0
+            
+            # 收集所有层的EMA统计信息
+            for layer_ema_stats in ema_stats.values():
+                if layer_ema_stats is None:
+                    continue
+                    
+                total_layers += 1
+                memory_indices = layer_ema_stats['memory_indices']  # [batch, seq_len, num_selected]
+                h_for_memory = layer_ema_stats['h_for_memory']      # [batch, seq_len, dim]
+                
+                bsz, seq_len, num_selected = memory_indices.shape
+                total_selections += bsz * seq_len * num_selected
+                
+                # 展平索引和对应的h_for_memory
+                flat_indices = memory_indices.view(-1)  # [batch * seq_len * num_selected]
+                
+                # 为每个选择位置复制对应的h_for_memory
+                h_expanded = h_for_memory.unsqueeze(2).expand(-1, -1, num_selected, -1)  # [batch, seq_len, num_selected, dim]
+                flat_h = h_expanded.reshape(-1, dim)  # [batch * seq_len * num_selected, dim]
+                
+                all_indices.append(flat_indices)
+                all_features.append(flat_h)
+            
+            if not all_indices:
+                return {'ema_update_applied': False, 'reason': 'no_ema_stats'}
+                
+            # 🚀 合并所有数据
+            all_indices = torch.cat(all_indices, dim=0)  # [total_selections]
+            all_features = torch.cat(all_features, dim=0)  # [total_selections, dim]
+            
+            # 🚀 批量计算每个memory的平均特征（避免循环）
+            unique_indices, inverse_indices = torch.unique(all_indices, return_inverse=True)
+            
+            # 使用scatter_add批量聚合（确保数据类型一致）
+            aggregated_features = torch.zeros(unique_indices.size(0), dim, device=device, dtype=all_features.dtype)
+            count_per_memory = torch.zeros(unique_indices.size(0), device=device, dtype=all_features.dtype)
+            
+            aggregated_features.scatter_add_(0, inverse_indices.unsqueeze(1).expand(-1, dim), all_features)
+            count_per_memory.scatter_add_(0, inverse_indices, torch.ones_like(inverse_indices, dtype=all_features.dtype))
+            
+            # 计算平均值
+            avg_features = aggregated_features / count_per_memory.unsqueeze(1)  # [unique_count, dim]
+            
+            # 🚀 分批EMA更新（控制显存使用）
+            batch_size = 4096  # 每批处理4096个memory，控制显存
+            updated_memories = 0
+            
+            for i in range(0, unique_indices.size(0), batch_size):
+                end_i = min(i + batch_size, unique_indices.size(0))
+                batch_indices = unique_indices[i:end_i]
+                batch_avg_features = avg_features[i:end_i]
+                
+                # 当前批次的token解码
+                current_tokens_batch = self.memory_bank[batch_indices]  # [batch_size, knowledge_length]
+                current_embeddings_batch = self.tok_embeddings(current_tokens_batch.view(-1)).view(
+                    batch_indices.size(0), knowledge_length, dim)  # [batch_size, knowledge_length, dim]
+                
+                old_features_batch = current_embeddings_batch.view(batch_indices.size(0), -1)  # [batch_size, knowledge_length * dim]
+                expanded_new_features = batch_avg_features.repeat(1, knowledge_length)  # [batch_size, knowledge_length * dim]
+                
+                # EMA更新：new = γ * old + (1-γ) * new_avg
+                updated_features_batch = (
+                    self.params.ema_decay * old_features_batch + 
+                    (1 - self.params.ema_decay) * expanded_new_features
+                )
+                
+                # 分批编码为token_ids（关键：控制输出层的输入大小）
+                updated_reshaped = updated_features_batch.view(-1, dim)  # [batch_size * knowledge_length, dim]
+                logits_batch = self.output(updated_reshaped)  # [batch_size * knowledge_length, vocab_size]
+                new_token_ids_batch = torch.argmax(logits_batch, dim=-1).view(batch_indices.size(0), knowledge_length)
+                
+                # 🔥 新增: 应用冻结mask，只更新未冻结的条目
+                # 检查哪些batch_indices对应的条目没有被冻结
+                unfrozen_mask_batch = ~self.freeze_mask[batch_indices]  # [batch_size] - True表示未冻结
+                
+                # 只更新未冻结的条目
+                if unfrozen_mask_batch.any():
+                    unfrozen_indices = batch_indices[unfrozen_mask_batch]
+                    unfrozen_tokens = new_token_ids_batch[unfrozen_mask_batch]
+                    self.memory_bank[unfrozen_indices] = unfrozen_tokens
+                    updated_memories += unfrozen_indices.size(0)
+                else:
+                    # 如果这个batch中的所有条目都被冻结，则跳过更新
+                    pass
+            
+            update_ratio = updated_memories / knowledge_num
+            
+            # 🔥 新增: 计算冻结统计信息
+            frozen_count = self.freeze_mask.sum().item()
+            total_memories = knowledge_num
+            
+            update_stats = {
+                'ema_update_applied': True,
+                'ema_step': self.ema_step_counter.item(),
+                'total_selections': total_selections,
+                'total_layers': total_layers,
+                'updated_memories': updated_memories,
+                'update_ratio': update_ratio,
+                'frozen_memories': frozen_count,
+                'frozen_ratio': frozen_count / total_memories,
+                'ema_decay': self.params.ema_decay,
+                'selected_memory_coverage': updated_memories / knowledge_num,
+            }
+            
+            return update_stats
\ No newline at end of file
diff --git a/run_file/experiment_1_4_10.sh b/run_file/experiment_1_4_10.sh
index 0e6eb43..86bd057 100644
--- a/run_file/experiment_1_4_10.sh
+++ b/run_file/experiment_1_4_10.sh
@@ -40,8 +40,8 @@ LOG_FILE="$LOG_DIR/experiment.log"
 # ----------------------------------------------------------------------------
 # 🤖 硬件配置
 # ----------------------------------------------------------------------------
-CUDA_VISIBLE_DEVICES="0,1"
-NUM_PROCESSES="2"
+CUDA_VISIBLE_DEVICES="0,1,2,3"
+NUM_PROCESSES="4"
 MIXED_PRECISION="bf16"
 MAIN_PROCESS_PORT="29500"
 
@@ -58,7 +58,7 @@ USE_MOE="false"
 
 # 🔥 知识库配置（四损失系统优化）
 KNOWLEDGE_NUM="1048576"  # 1M entries
-KNOWLEDGE_LENGTH="16"    # 🔥 增加到16个token提升表达能力
+KNOWLEDGE_LENGTH="8"    # 🔥 增加到16个token提升表达能力
 KNOWLEDGE_DIM="128"      # 保留兼容性
 DISABLE_DB="false"
 
@@ -67,7 +67,7 @@ DISABLE_DB="false"
 # ----------------------------------------------------------------------------
 EPOCHS="3"
 EMBEDDING_EPOCH="2"
-BATCH_SIZE="64"          # 🔥 降低批次大小以适应更复杂的计算
+BATCH_SIZE="48"          # 🔥 降低批次大小以适应更复杂的计算
 ACCUMULATION_STEPS="8"  # 🔥 增加累积步数保持有效批次大小
 LEARNING_RATE="2e-4"   # 🔥 适度降低学习率提升稳定性
 DTYPE="bfloat16"