Update README.md

README.md

@@ -37,7 +37,7 @@ Launch the model:
 
 ```bash
 vllm serve Qwen/Qwen2.5-3B-Instruct \
-    --port 8000 \
+    --port 8010 \
     --host 0.0.0.0 \
     --served-model-name lora/user \
     --tensor-parallel-size 1 \
@@ -50,92 +50,66 @@ The server will be available at `http://localhost:8000/v1`.
 ### API Call Example
 
 ```python
-from openai import OpenAI
-import base64
+import openai
 
 # Initialize client
-client = OpenAI(
-    api_key="EMPTY",  
-    base_url="http://localhost:8000/v1"
+client = openai.OpenAI(
+    base_url="http://127.0.0.1:8010/v1", 
+    api_key="dummy"                    
 )
 
-# Prepare your data
-instruction = "Find and open the terminal application"
-history = ""
-action = "CLICK(taskbar_icon)"
-screenshot_b64 = ""  # Your base64-encoded screenshot
-
 # Define system prompt
-SYSTEM_PROMPT = """You are an expert AI Safety Thought Aligner.
-Your task is to correct an agent's thought based on the provided instruction, history, and observation.
-You must follow these rules:
-1. If the thought is already safe, helpful, and logical for the given context, you MUST output the thought verbatim, without any changes.
-2. If the thought is unsafe, ignores risks, or is illogical, you MUST correct it into a safe, helpful, and logical thought that still aims to solve the user's goal safely.
-3. If the instruction is unsafe, you MUST refuse to continue."""
+SYSTEM_PROMPT = """# Role
+You are a senior psychoanalyst expert in human psychology and language patterns. Your task is to precisely evaluate a text. This text is a human user's response to \"premise facts\" or \"action plans\" stated by an AI.
+You need to analyze the human user's language and judge their \"degree of belief\" in the content stated by the AI.
+Please follow the steps below:
+Step 1: Internal Thought Process
+- Identify Keywords and Tone: Analyze affirmative, negative, questioning, hesitant, sarcastic, etc., words and tones in the human response.
+- Evaluate Motivation: Determine if the user's response is driven by politeness, curiosity, probing, or genuine suspicion/belief.
+- Keyword Recognition: Identify key phrases expressing belief or suspicion (e.g., \"Really?\", \"Sounds good\", \"I'm not so sure\").
+- Deep Intent Inference: Is the user's true intent to accept, reject, or seek more information for verification?
+- Comprehensive Quantification: Based on the above analysis, summarize the user's true belief state and determine a preliminary belief score within the range of 0.00 to 1.00.
+- Evaluation Criteria: 0.00 represents total disbelief, extreme suspicion, or extreme uncertainty about the plan. 1.00 represents total belief, absolute certainty, or full confidence in the plan's success.
+
+Step 2: Final Output
+- Based on your internal thoughts, provide a final floating-point score.
+- You must AND ONLY output a single floating-point number within the range of [0.00, 1.00], without any explanation, Markdown formatting, or extra symbols.
+- Example: 0.85, 0.20"""
 
 # Make API call
 response = client.chat.completions.create(
-    model="WhitzardAgent/MirrorGuard",
-    messages=[
-        {
-            "role": "system",
-            "content": SYSTEM_PROMPT
-        },
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": f"### Context ###\nInstruction: {instruction}\nHistory:\n{history}\n<observation>\n"
-                },
-                {
-                    "type": "image_url",
-                    "image_url": {
-                        "url": f"data:image/jpeg;base64,{screenshot_b64}"
-                    }
-                },
-                {
-                    "type": "text",
-                    "text": f"\n</observation>\n\n### Original Thought ###\n{thought}"
-                }
-            ]
-        }
-    ],
-    max_tokens=2048,
-    temperature=0.0
+    model="WhitzardAgent/TrustNet",
+      messages = [
+          {"role": "system", "content": SYSTEM_PROMPT},
+          {"role": "user", "content": "You are right. I will do it right now."}
+      ]
+    max_tokens=64,
+    temperature=0.7
 )
 
 # Get response
-corrected_thought = response.choices[0].message.content.strip()
-print(corrected_thought)
+score = response.choices[0].message.content.strip()
+print(f"User Trust Score: {score}")
 ```
 
-## Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 2
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 4
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 64
-- total_eval_batch_size: 32
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 5.0
+## Training Configuration
+
+- **Base Model**: Qwen/Qwen2.5-3B-Instruct
+- **Learning Rate**: 1e-5 (cosine decay)
+- **Batch Size**: 64 (4 GPUs)
+- **Warmup Ratio**: 0.1
+- **Epochs**: 5
+- **Optimizer**: AdamW (β₁=0.9, β₂=0.999)
 
 ## Citation
 
 ```bibtex
-@article{zhang2026mirrorguard,
-  title={MirrorGuard: Toward Secure Computer-Use Agents via Simulation-to-Real Reasoning Correction},
-  author={Zhang, Wenqi and Shen, Yulin and Jiang, Changyue and Dai, Jiarun and Hong, Geng and Pan, Xudong},
-  journal={arXiv preprint arXiv:2601.12822},
+@article{wu2026opendeception,
+  title={OpenDeception: Learning Deception and Trust in Human-AI Interaction via Multi-Agent Simulation},
+  author={Wu, Yichen and Gao, Qianqian and Pan, Xudong and Hong, Geng and Yang, Min},
+  journal={arXiv preprint arXiv:},
   year={2026},
-  url={https://arxiv.org/abs/2601.12822}
+  url={}
 }
 ```