diff --git a/MSGCoOp/scripts/base2new_all_unc.sh b/MSGCoOp/scripts/base2new_all_unc.sh
new file mode 100755
index 0000000..1bf7812
--- /dev/null
+++ b/MSGCoOp/scripts/base2new_all_unc.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+
+TRAINER=$1
+KG_WEIGHT=$2
+MP_WEIGHT=$3
+UNC_TEMPERATURE=$4
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} ucf101 ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} ucf101 ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} eurosat ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} eurosat ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} oxford_pets ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} oxford_pets ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} food101 ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} food101 ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} oxford_flowers ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} oxford_flowers ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} dtd ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} dtd ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} caltech101 ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} caltech101 ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} fgvc_aircraft ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} fgvc_aircraft ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} stanford_cars ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} stanford_cars ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} sun397 ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} sun397 ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_train_unc.sh ${TRAINER} imagenet ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
+CUDA_VISIBLE_DEVICES=0 bash scripts/base2new_test_unc.sh ${TRAINER} imagenet ${KG_WEIGHT} ${MP_WEIGHT} ${UNC_TEMPERATURE}
diff --git a/MSGCoOp/scripts/base2new_test_unc.sh b/MSGCoOp/scripts/base2new_test_unc.sh
new file mode 100755
index 0000000..be6d5fb
--- /dev/null
+++ b/MSGCoOp/scripts/base2new_test_unc.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+
+# custom config
+DATA=~/Datasets/CoOp
+TRAINER=$1
+DATASET=$2
+N_PROMPTS=4
+KG_WEIGHT=$3
+MP_WEIGHT=$4
+UNC_TEMPERATURE=$5
+#CFG=rn50_ep100  # config file
+CFG=vit_b16_ep100_ctxv1
+CTP=end  # class token position (end or middle)
+NCTX=4  # number of context tokens
+SHOTS=16  # number of shots (1, 2, 4, 8, 16)
+CSC=False  # class-specific context (False or True)
+
+LOADEP=100
+SUB=new
+
+for SEED in 1 2 3
+do
+    COMMON_DIR=${DATASET}/shots_${SHOTS}_${KG_WEIGHT}_unc${UNC_TEMPERATURE}/${TRAINER}/${CFG}/seed${SEED}
+    MODEL_DIR=output/base2new/train_base/${COMMON_DIR}
+    DIR=output/base2new/test_${SUB}/${COMMON_DIR}
+
+
+    if [ -d "$DIR" ]; then
+        echo "Results are available in ${DIR}. Skip this job"
+    else
+        echo "Run this job and save the output to ${DIR}"
+        python train.py \
+        --root ${DATA} \
+        --seed ${SEED} \
+        --trainer ${TRAINER} \
+        --dataset-config-file configs/datasets/${DATASET}.yaml \
+        --config-file configs/trainers/${TRAINER}/${CFG}.yaml \
+        --output-dir ${DIR} \
+        --model-dir ${MODEL_DIR} \
+        --load-epoch ${LOADEP} \
+        --eval-only \
+	    TRAINER.COOP.N_PROMPTS ${N_PROMPTS} \
+        TRAINER.COOP.N_CTX ${NCTX} \
+        TRAINER.COOP.CSC ${CSC} \
+        TRAINER.COOP.CLASS_TOKEN_POSITION ${CTP} \
+        DATASET.NUM_SHOTS ${SHOTS} \
+        DATASET.SUBSAMPLE_CLASSES ${SUB} \
+        TRAINER.COOP.UNC_ENABLED True \
+        TRAINER.COOP.UNC_TEMPERATURE ${UNC_TEMPERATURE}
+    fi
+done
diff --git a/MSGCoOp/scripts/base2new_train_unc.sh b/MSGCoOp/scripts/base2new_train_unc.sh
new file mode 100755
index 0000000..ef6f65e
--- /dev/null
+++ b/MSGCoOp/scripts/base2new_train_unc.sh
@@ -0,0 +1,43 @@
+#!/bin/bash
+
+# custom config
+DATA=~/Datasets/CoOp
+TRAINER=$1
+DATASET=$2
+KG_WEIGHT=$3
+MP_WEIGHT=$4
+UNC_TEMPERATURE=$5
+N_PROMPTS=4
+#CFG=rn50_ep100  # config file
+CFG=vit_b16_ep100_ctxv1
+CTP=end  # class token position (end or middle)
+NCTX=4  # number of context tokens
+SHOTS=16  # number of shots (1, 2, 4, 8, 16)
+CSC=False  # class-specific context (False or True)
+
+for SEED in 1 2 3
+do
+    DIR=output/base2new/train_base/${DATASET}/shots_${SHOTS}_${KG_WEIGHT}_unc${UNC_TEMPERATURE}/${TRAINER}/${CFG}/seed${SEED}
+    if [ -d "$DIR" ]; then
+        echo "Results are available in ${DIR}. Skip this job"
+    else
+        echo "Run this job and save the output to ${DIR}"
+        python train.py \
+        --root ${DATA} \
+        --seed ${SEED} \
+        --trainer ${TRAINER} \
+        --dataset-config-file configs/datasets/${DATASET}.yaml \
+        --config-file configs/trainers/${TRAINER}/${CFG}.yaml \
+        --output-dir ${DIR} \
+        TRAINER.COOP.N_CTX ${NCTX} \
+        TRAINER.COOP.CSC ${CSC} \
+        TRAINER.COOP.W ${KG_WEIGHT} \
+        TRAINER.COOP.CLASS_TOKEN_POSITION ${CTP} \
+        DATASET.NUM_SHOTS ${SHOTS} \
+        DATASET.SUBSAMPLE_CLASSES base \
+	    TRAINER.COOP.N_PROMPTS ${N_PROMPTS} \
+        TRAINER.COOP.DIV_WEIGHT ${MP_WEIGHT} \
+        TRAINER.COOP.UNC_ENABLED True \
+        TRAINER.COOP.UNC_TEMPERATURE ${UNC_TEMPERATURE}
+    fi
+done
diff --git a/MSGCoOp/train.py b/MSGCoOp/train.py
index 1258a76..6cbff82 100644
--- a/MSGCoOp/train.py
+++ b/MSGCoOp/train.py
@@ -105,6 +105,10 @@ def extend_cfg(cfg):
     cfg.TRAINER.COCOOP.PREC = "fp16"  # fp16, fp32, amp
     cfg.TRAINER.COOP.DIV_WEIGHT = 0.1
     cfg.TRAINER.COOP.N_PROMPTS = 3
+    
+    # 不确定性集成配置
+    cfg.TRAINER.COOP.UNC_ENABLED = False  # 是否启用基于熵的不确定性集成
+    cfg.TRAINER.COOP.UNC_TEMPERATURE = 1.0  # 控制权重分布的平滑度
 
     cfg.DATASET.SUBSAMPLE_CLASSES = "all"  # all, base or new
     """
diff --git a/MSGCoOp/trainers/msgcoop.py b/MSGCoOp/trainers/msgcoop.py
index 0047355..9e64dff 100644
--- a/MSGCoOp/trainers/msgcoop.py
+++ b/MSGCoOp/trainers/msgcoop.py
@@ -223,6 +223,41 @@ class Adapter(nn.Module):
         x = self.fc(x)
         return x
 
+class UncertaintyPromptIntegrator(nn.Module):
+    def __init__(self, temperature=1.0):
+        """
+        基于预测熵的不确定性加权集成器
+        
+        Args:
+            temperature: 控制权重分布的平滑度，值越大权重分布越平均
+        """
+        super().__init__()
+        self.temperature = temperature
+    
+    def forward(self, all_logits):
+        """
+        Args:
+            all_logits: [n_prompts, batch_size, n_classes]
+        
+        Returns:
+            integrated_logits: [batch_size, n_classes]
+            prompt_weights: [n_prompts, batch_size]
+            entropy: [n_prompts, batch_size]
+        """
+        n_prompts, batch_size, n_classes = all_logits.shape
+        
+        log_probs = F.log_softmax(all_logits, dim=-1)
+        probs = log_probs.exp()
+        
+        entropy = -(probs * log_probs).sum(dim=-1)
+        
+        temperature = max(self.temperature, 1e-8)
+        weights = F.softmax(-entropy / temperature, dim=0)
+        
+        integrated_logits = torch.einsum('pb,pbc->bc', weights, all_logits)
+        
+        return integrated_logits, weights, entropy
+
 class CustomCLIP(nn.Module):
     def __init__(self, cfg, classnames, clip_model):
         super().__init__()
@@ -236,6 +271,14 @@ class CustomCLIP(nn.Module):
         self.dtype = clip_model.dtype
         self.meta_net = self.prompt_learner.meta_net
         self.adapter = Adapter(512, 4).to(clip_model.dtype)
+        
+        self.use_uncertainty_integration = cfg.TRAINER.COOP.get('UNC_ENABLED', False)
+        self.unc_temperature = cfg.TRAINER.COOP.get('UNC_TEMPERATURE', 1.0)
+        
+        if self.use_uncertainty_integration:
+            self.unc_integrator = UncertaintyPromptIntegrator(
+                temperature=self.unc_temperature
+            )
 
     def compute_diversity_loss(self, text_features):
         if self.n_prompts == 1:
@@ -283,8 +326,15 @@ class CustomCLIP(nn.Module):
             text_features_i = text_features_i / text_features_i.norm(dim=-1, keepdim=True)
             logits_i = logit_scale * image_features @ text_features_i.t()
             all_logits.append(logits_i)
-
-        logits = torch.stack(all_logits).mean(dim=0)
+        
+        all_logits = torch.stack(all_logits)
+        
+        if self.use_uncertainty_integration:
+            logits, prompt_weights, entropy = self.unc_integrator(all_logits)
+            self.last_prompt_weights = prompt_weights.detach()
+            self.last_entropy = entropy.detach()
+        else:
+            logits = all_logits.mean(dim=0)
 
         return logits, score, diversity_loss
 
diff --git a/readme.md b/readme.md
index ce24d7d..9278432 100644
--- a/readme.md
+++ b/readme.md
@@ -1,5 +1,8 @@
 # MSGCoOp: Visual-Language Prompt Tuning with Knowledge-guided Context Optimization
 
+[![Paper](https://img.shields.io/badge/arXiv-Paper-brightgreen.svg)](https://arxiv.org/abs/2507.21786)
+[![Code](https://img.shields.io/badge/Code-GitHub-blueviolet.svg)](https://github.com/Rain-Bus/MSGCoOp)
+
 ## Overview of MSGCoOp
 
 We introduce **Multi-prompt Semantic-Guided Context Optimization (MSGCoOp)**, a novel framework that advances CLIP-based prompt tuning for few-shot learning. MSGCoOp addresses the challenge of generalizing to novel classes efficiently, without heavy architectural modifications or expensive computation.