新增实验代码等

2026-08-13 16:17:59 +08:00
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+# Agent 网络流量快速实验工程
+
+本工程用于快速完成以下闭环：
+
+1. 通过大模型生成任务行为画像（任务阶段、工具调用、拆分、消息大小、处理时间等）；
+2. 将行为画像扩展为结构化 Agent 事件日志；
+3. 从训练日志估计状态转移、持续时间、消息大小、超时和分支参数；
+4. 使用双层状态机、节点队列和离散事件调度进行仿真；
+5. 自动完成预测验证、基线对比、压力实验和重试放大实验；
+6. 输出 CSV、JSON 和 PNG 图表。
+
+## 快速开始
+
+当前环境已具备主要依赖，直接运行：
+
+```powershell
+cd agent_traffic_experiments
+python run_pipeline.py
+```
+
+默认使用 `local` 模式，不需要 API Key。结果写入 `outputs/latest/`。
+
+## 使用大模型生成行为画像
+
+本工程兼容 OpenAI 风格的 `/v1/chat/completions` 接口。先设置环境变量：
+
+```powershell
+$env:OPENAI_API_KEY="你的密钥"
+$env:OPENAI_BASE_URL="https://api.openai.com/v1"
+$env:OPENAI_MODEL="你要使用的模型名称"
+python run_pipeline.py --generator llm
+```
+
+也可以使用其他兼容服务，只需修改 `OPENAI_BASE_URL` 和 `OPENAI_MODEL`。
+
+大模型只负责生成少量、可解释的任务行为画像；本地生成器会校验画像并扩展成大量事件。这样比让大模型直接输出数万行日志更稳定、更便宜，也能保证消息 ID、父子关系、时间戳和流量统计一致。
+
+如果 LLM 请求失败，程序默认自动回退到本地画像。使用 `--no-fallback` 可禁止回退。
+
+## 常用命令
+
+只生成数据：
+
+```powershell
+python run_pipeline.py --steps generate
+```
+
+生成数据、估参并运行实验：
+
+```powershell
+python run_pipeline.py --steps generate,estimate,experiment
+```
+
+使用已有数据重新实验：
+
+```powershell
+python run_pipeline.py --steps estimate,experiment
+```
+
+指定配置和输出目录：
+
+```powershell
+python run_pipeline.py --config configs/default.yaml --output outputs/run_001
+```
+
+## 输出说明
+
+```text
+outputs/latest/
+  data/
+    behavior_profiles.json       # LLM或本地生成的任务画像
+    train_events.csv             # 参数估计数据
+    test_events.csv              # 留出验证数据
+    task_truth.csv               # 测试任务真实聚合值
+  parameters/
+    estimated_parameters.json    # 从训练日志估计的模型参数
+  results/
+    validation_metrics.csv       # 完整模型与基线误差
+    validation_predictions.csv   # 逐场景预测值和真实值
+    stress_results.csv           # 到达率压力实验
+    retry_results.csv            # 超时与重试放大实验
+    summary.json                 # 实验摘要
+  figures/
+    prediction_vs_truth.png
+    model_comparison.png
+    stress_curves.png
+    retry_heatmap.png
+```
+
+## 实验内容
+
+### E1 预测验证
+
+训练集用于估计参数，测试集作为“模拟实测值”。完整状态机模型预测测试场景的内部字节数、任务时延、消息数和工具调用数。
+
+### E2 基线对比
+
+- `static_mean`：固定平均流量放大倍数；
+- `no_context`：不区分任务类型和任务阶段；
+- `full_model`：按任务画像和估计参数运行状态机仿真。
+
+### E3 压力实验
+
+逐渐提高外部到达率，观察吞吐量、平均/P95延迟、队列峰值、失败率和流量放大系数。
+
+### E4 重试放大实验
+
+改变超时概率和最大重试次数，观察任务成功率、平均重试数和内部流量放大系数。
+
+## 数据字段
+
+事件日志主要字段包括：
+
+- `timestamp`、`task_id`、`message_id`、`parent_message_id`；
+- `task_type`、`task_phase`、`source`、`destination`；
+- `state_before`、`state_after`、`state_duration_ms`；
+- `message_type`、`message_size_bytes`；
+- `queue_length`、`success`、`retry_count`、`is_external`。
+
+## 注意事项
+
+- 当前数据是用于方法验证的模拟数据，不能冒充真实生产日志；
+- LLM 画像应在报告中标注模型名称、生成时间和提示词版本；
+- 正式参赛前，应尽量用少量真实 Agent 日志替换或校准模拟参数；
+- 所有随机实验都由配置中的随机种子控制，便于复现。
+
@@ -0,0 +1,37 @@
+seed: 20260813
+
+generation:
+  train_tasks_per_type: 260
+  test_tasks_per_type: 100
+  task_types:
+    - simple_qa
+    - tool_research
+    - collaborative_analysis
+  llm:
+    temperature: 0.5
+    timeout_seconds: 90
+    max_tokens: 3500
+
+network:
+  agent_count: 8
+  tool_count: 3
+  coordinator: agent_0
+  default_link_bandwidth_mbps: 20
+  default_link_delay_ms: 8
+  max_concurrency: 4
+  queue_capacity: 500
+
+simulation:
+  replications: 16
+  timeout_ms: 2200
+  max_retries: 2
+  retry_backoff_ms: 180
+
+experiments:
+  validation_tasks_per_scenario: 120
+  arrival_rates_per_second: [0.5, 1, 2, 4, 6, 8, 10, 12]
+  stress_duration_seconds: 180
+  stress_warmup_seconds: 20
+  timeout_probabilities: [0.0, 0.03, 0.08, 0.15, 0.25]
+  retry_limits: [0, 1, 2, 3, 5]
+
@@ -0,0 +1,37 @@
+seed: 20260813
+
+generation:
+  train_tasks_per_type: 260
+  test_tasks_per_type: 100
+  task_types:
+    - simple_qa
+    - tool_research
+    - collaborative_analysis
+  llm:
+    temperature: 0.5
+    timeout_seconds: 90
+    max_tokens: 3500
+
+network:
+  agent_count: 8
+  tool_count: 3
+  coordinator: agent_0
+  default_link_bandwidth_mbps: 20
+  default_link_delay_ms: 8
+  max_concurrency: 4
+  queue_capacity: 500
+
+simulation:
+  replications: 16
+  timeout_ms: 2200
+  max_retries: 2
+  retry_backoff_ms: 180
+
+experiments:
+  validation_tasks_per_scenario: 120
+  arrival_rates_per_second: [0.5, 1, 2, 4, 6, 8, 10, 12]
+  stress_duration_seconds: 180
+  stress_warmup_seconds: 20
+  timeout_probabilities: [0.0, 0.03, 0.08, 0.15, 0.25]
+  retry_limits: [0, 1, 2, 3, 5]
+
@@ -0,0 +1,52 @@
+{
+  "metadata": {
+    "generator": "local_builtin"
+  },
+  "profiles": [
+    {
+      "task_type": "simple_qa",
+      "description": "单节点即可完成的简短问答，少量情况下调用工具。",
+      "phase": "answering",
+      "tool_probability": 0.1,
+      "split_probability": 0.05,
+      "mean_subtasks": 1.2,
+      "timeout_probability": 0.02,
+      "failure_probability": 0.01,
+      "think_time_ms_mean": 420.0,
+      "think_time_cv": 0.55,
+      "request_size_bytes_mean": 1800.0,
+      "response_size_bytes_mean": 2600.0,
+      "message_size_cv": 0.45
+    },
+    {
+      "task_type": "tool_research",
+      "description": "需要搜索、数据库或工具结果的研究任务。",
+      "phase": "evidence_collection",
+      "tool_probability": 0.78,
+      "split_probability": 0.2,
+      "mean_subtasks": 1.8,
+      "timeout_probability": 0.07,
+      "failure_probability": 0.025,
+      "think_time_ms_mean": 900.0,
+      "think_time_cv": 0.75,
+      "request_size_bytes_mean": 3200.0,
+      "response_size_bytes_mean": 8500.0,
+      "message_size_cv": 0.7
+    },
+    {
+      "task_type": "collaborative_analysis",
+      "description": "协调多个执行 Agent 并汇总结果的复杂分析任务。",
+      "phase": "multi_agent_synthesis",
+      "tool_probability": 0.48,
+      "split_probability": 0.82,
+      "mean_subtasks": 3.4,
+      "timeout_probability": 0.09,
+      "failure_probability": 0.035,
+      "think_time_ms_mean": 1450.0,
+      "think_time_cv": 0.85,
+      "request_size_bytes_mean": 5200.0,
+      "response_size_bytes_mean": 11800.0,
+      "message_size_cv": 0.8
+    }
+  ]
+}
@@ -0,0 +1,301 @@
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@@ -0,0 +1,18 @@
+{
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@@ -0,0 +1,37 @@
+seed: 20260813
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+generation:
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+  test_tasks_per_type: 100
+  task_types:
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+    - tool_research
+    - collaborative_analysis
+  llm:
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+    timeout_seconds: 90
+    max_tokens: 3500
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+network:
+  agent_count: 8
+  tool_count: 3
+  coordinator: agent_0
+  default_link_bandwidth_mbps: 20
+  default_link_delay_ms: 8
+  max_concurrency: 4
+  queue_capacity: 500
+
+simulation:
+  replications: 16
+  timeout_ms: 2200
+  max_retries: 2
+  retry_backoff_ms: 180
+
+experiments:
+  validation_tasks_per_scenario: 120
+  arrival_rates_per_second: [0.5, 1, 2, 4, 6, 8, 10, 12]
+  stress_duration_seconds: 180
+  stress_warmup_seconds: 20
+  timeout_probabilities: [0.0, 0.03, 0.08, 0.15, 0.25]
+  retry_limits: [0, 1, 2, 3, 5]
+
@@ -0,0 +1,52 @@
+{
+  "metadata": {
+    "generator": "local_builtin"
+  },
+  "profiles": [
+    {
+      "task_type": "simple_qa",
+      "description": "单节点即可完成的简短问答，少量情况下调用工具。",
+      "phase": "answering",
+      "tool_probability": 0.1,
+      "split_probability": 0.05,
+      "mean_subtasks": 1.2,
+      "timeout_probability": 0.02,
+      "failure_probability": 0.01,
+      "think_time_ms_mean": 420.0,
+      "think_time_cv": 0.55,
+      "request_size_bytes_mean": 1800.0,
+      "response_size_bytes_mean": 2600.0,
+      "message_size_cv": 0.45
+    },
+    {
+      "task_type": "tool_research",
+      "description": "需要搜索、数据库或工具结果的研究任务。",
+      "phase": "evidence_collection",
+      "tool_probability": 0.78,
+      "split_probability": 0.2,
+      "mean_subtasks": 1.8,
+      "timeout_probability": 0.07,
+      "failure_probability": 0.025,
+      "think_time_ms_mean": 900.0,
+      "think_time_cv": 0.75,
+      "request_size_bytes_mean": 3200.0,
+      "response_size_bytes_mean": 8500.0,
+      "message_size_cv": 0.7
+    },
+    {
+      "task_type": "collaborative_analysis",
+      "description": "协调多个执行 Agent 并汇总结果的复杂分析任务。",
+      "phase": "multi_agent_synthesis",
+      "tool_probability": 0.48,
+      "split_probability": 0.82,
+      "mean_subtasks": 3.4,
+      "timeout_probability": 0.09,
+      "failure_probability": 0.035,
+      "think_time_ms_mean": 1450.0,
+      "think_time_cv": 0.85,
+      "request_size_bytes_mean": 5200.0,
+      "response_size_bytes_mean": 11800.0,
+      "message_size_cv": 0.8
+    }
+  ]
+}
@@ -0,0 +1,301 @@
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+      "p50": 8.0,
+      "p95": 1323.2897999999977,
+      "count": 5597
+    },
+    "message_size_bytes": {
+      "mean": 5570.409505092013,
+      "std": 6586.248171004335,
+      "cv": 1.1823633729232519,
+      "p50": 3158.0,
+      "p95": 18393.199999999997,
+      "count": 5597
+    }
+  },
+  "task_types": {
+    "collaborative_analysis": {
+      "phase": "multi_agent_synthesis",
+      "task_count": 260,
+      "tool_probability": 0.4806201550387597,
+      "split_probability": 0.8192307692307692,
+      "mean_subtasks": 2.976923076923077,
+      "retry_probability": 0.12470588235294118,
+      "mean_retries_if_any": 1.1627906976744187,
+      "failure_probability": 0.007692307692307693,
+      "think_time_ms": {
+        "mean": 1012.4040078828829,
+        "std": 819.189229985454,
+        "cv": 0.8091524960460444,
+        "p50": 802.0150000000001,
+        "p95": 2609.3335999999995,
+        "count": 888
+      },
+      "external_size_bytes": {
+        "mean": 4103.692307692308,
+        "std": 3820.340621837784,
+        "cv": 0.9309520147688984,
+        "p50": 3039.5,
+        "p95": 11107.699999999995,
+        "count": 260
+      },
+      "request_size_bytes": {
+        "mean": 3936.623019182652,
+        "std": 3891.679972776879,
+        "cv": 0.9885833501997089,
+        "p50": 2796.0,
+        "p95": 10800.3,
+        "count": 1199
+      },
+      "response_size_bytes": {
+        "mean": 11829.127142857144,
+        "std": 8952.816088861513,
+        "cv": 0.7568450301312002,
+        "p50": 9558.5,
+        "p95": 29122.249999999993,
+        "count": 1400
+      },
+      "message_count_per_task": {
+        "mean": 11.01923076923077,
+        "std": 5.376910696585285,
+        "cv": 0.48795699166218987,
+        "p50": 10.0,
+        "p95": 21.049999999999983,
+        "count": 260
+      }
+    },
+    "simple_qa": {
+      "phase": "answering",
+      "task_count": 260,
+      "tool_probability": 0.13740458015267176,
+      "split_probability": 0.03461538461538462,
+      "mean_subtasks": 1.0076923076923077,
+      "retry_probability": 0.02702702702702703,
+      "mean_retries_if_any": 1.0,
+      "failure_probability": 0.0,
+      "think_time_ms": {
+        "mean": 315.74304316546767,
+        "std": 193.59344702605458,
+        "cv": 0.6131360649634342,
+        "p50": 262.062,
+        "p95": 693.3605,
+        "count": 556
+      },
+      "external_size_bytes": {
+        "mean": 1285.7846153846153,
+        "std": 562.841233039538,
+        "cv": 0.43774145864327046,
+        "p50": 1167.5,
+        "p95": 2332.1,
+        "count": 260
+      },
+      "request_size_bytes": {
+        "mean": 1646.314381270903,
+        "std": 819.0778288985601,
+        "cv": 0.4975221247027301,
+        "p50": 1537.0,
+        "p95": 3065.4999999999995,
+        "count": 299
+      },
+      "response_size_bytes": {
+        "mean": 1909.388888888889,
+        "std": 1208.6473367395993,
+        "cv": 0.6330021839830309,
+        "p50": 1577.5,
+        "p95": 4346.099999999999,
+        "count": 558
+      },
+      "message_count_per_task": {
+        "mean": 4.296153846153846,
+        "std": 0.7184281823915581,
+        "cv": 0.16722589742328123,
+        "p50": 4.0,
+        "p95": 6.0,
+        "count": 260
+      }
+    },
+    "tool_research": {
+      "phase": "evidence_collection",
+      "task_count": 260,
+      "tool_probability": 0.7631578947368421,
+      "split_probability": 0.2076923076923077,
+      "mean_subtasks": 1.1692307692307693,
+      "retry_probability": 0.09019607843137255,
+      "mean_retries_if_any": 1.0952380952380953,
+      "failure_probability": 0.0038461538461538464,
+      "think_time_ms": {
+        "mean": 660.7411426666666,
+        "std": 548.0475005635645,
+        "cv": 0.8294435826286146,
+        "p50": 513.7415,
+        "p95": 1615.4994999999997,
+        "count": 750
+      },
+      "external_size_bytes": {
+        "mean": 2591.223076923077,
+        "std": 1938.9205968801086,
+        "cv": 0.7482646377101818,
+        "p50": 2023.0,
+        "p95": 6498.299999999999,
+        "count": 260
+      },
+      "request_size_bytes": {
+        "mean": 2178.8425760286227,
+        "std": 1869.411620553448,
+        "cv": 0.8579837942954215,
+        "p50": 1586.0,
+        "p95": 6085.500000000001,
+        "count": 559
+      },
+      "response_size_bytes": {
+        "mean": 6327.029003783102,
+        "std": 4973.701881543845,
+        "cv": 0.7861038535732859,
+        "p50": 4987.0,
+        "p95": 15569.8,
+        "count": 793
+      },
+      "message_count_per_task": {
+        "mean": 6.211538461538462,
+        "std": 2.097035805988451,
+        "cv": 0.33760328765139147,
+        "p50": 6.0,
+        "p95": 11.0,
+        "count": 260
+      }
+    }
+  }
+}
@@ -0,0 +1,26 @@
+timeout_probability,max_retries,success_rate,mean_retries,mean_amplification,mean_latency_ms
+0.0,0,1.0,0.0,9.014727352307537,1829.8204232372348
+0.0,1,1.0,0.0,9.017931822157774,1786.1398565415025
+0.0,2,1.0,0.0,9.497944538818517,1655.013246029438
+0.0,3,1.0,0.0,8.981130309004985,1716.8465797602803
+0.0,5,1.0,0.0,9.540066677486474,1716.7430554392593
+0.03,0,0.9666666666666667,0.0,9.063950194066456,1669.7925665017842
+0.03,1,0.9958333333333333,0.016666666666666666,9.063781276545864,1657.0420076151597
+0.03,2,1.0,0.016666666666666666,9.014566351856745,1691.3863870856096
+0.03,3,1.0,0.020833333333333332,9.637237082920953,1721.830506046892
+0.03,5,1.0,0.0125,9.649347558637414,1718.5997383083138
+0.08,0,0.9041666666666667,0.0,8.25627307377185,1635.0229558657152
+0.08,1,1.0,0.05416666666666667,9.289215941037927,1787.9821751080794
+0.08,2,1.0,0.06666666666666667,9.431797013914702,1916.4375526605215
+0.08,3,1.0,0.058333333333333334,9.050271941109205,1898.8272546658047
+0.08,5,1.0,0.07916666666666666,9.741006123762736,1970.2545335768014
+0.15,0,0.8416666666666667,0.0,8.164931539209656,1610.0725491672833
+0.15,1,0.9833333333333333,0.10833333333333334,9.088215821284614,1938.8382504596384
+0.15,2,1.0,0.1,9.261076832398844,1941.657277871303
+0.15,3,0.9958333333333333,0.13333333333333333,11.372866350773522,2027.2488180006546
+0.15,5,1.0,0.13333333333333333,8.152159646681561,2064.0974036821353
+0.25,0,0.7916666666666666,0.0,7.82021056651408,1652.063945123461
+0.25,1,0.9541666666666667,0.17916666666666667,8.424662742312817,2194.736913880946
+0.25,2,0.9916666666666667,0.2875,9.275168718803195,2475.475754982108
+0.25,3,1.0,0.2833333333333333,8.808713566195896,2429.5919980366966
+0.25,5,1.0,0.1875,9.459047702518982,2044.5400456955706
@@ -0,0 +1,9 @@
+arrival_rate,offered_tasks,completed_tasks,throughput_per_second,mean_delay_ms,p95_delay_ms,queue_peak,drop_rate,success_rate,mean_amplification
+0.5,87,79,0.49375,5690.601711699437,12756.456777674,2,0.0,1.0,32.68982726898488
+1.0,178,154,0.9625,28654.713037581158,50223.56851458479,35,0.0,1.0,35.61819225706701
+2.0,354,315,1.96875,159265.0479860076,259462.94208402873,216,0.0,1.0,27.685762197623816
+4.0,735,553,3.45625,360462.8938916123,611043.0735286651,500,0.1251700680272109,0.9981916817359855,28.819579849617657
+6.0,1140,538,3.3625,406978.074020204,627164.1820327837,500,0.4280701754385965,1.0,30.664346517813648
+8.0,1419,478,2.9875,439078.312694735,605565.2288237411,500,0.5419309372797745,0.997907949790795,28.477068296105013
+10.0,1736,459,2.86875,462378.903316947,623226.2655493785,500,0.6215437788018433,0.9978213507625272,29.655131218317656
+12.0,2196,407,2.54375,503595.3336070842,641094.366587491,500,0.7108378870673953,1.0,29.595658555137497
@@ -0,0 +1,18 @@
+{
+  "best_model_by_internal_bytes_mape": {
+    "model": "full_model",
+    "metric": "internal_bytes",
+    "mae": 9852.85421644164,
+    "rmse": 11957.784318614104,
+    "mape_percent": 27.25831155178904
+  },
+  "best_model_by_latency_mape": {
+    "model": "full_model",
+    "metric": "latency_ms",
+    "mae": 1677.2186263904505,
+    "rmse": 2068.228406399321,
+    "mape_percent": 38.01609858429338
+  },
+  "max_stress_arrival_rate": 12.0,
+  "max_observed_p95_delay_ms": 641094.366587491
+}
@@ -0,0 +1,16 @@
+model,metric,mae,rmse,mape_percent
+full_model,internal_bytes,9852.85421644164,11957.784318614104,27.25831155178904
+full_model,latency_ms,1677.2186263904505,2068.228406399321,38.01609858429338
+full_model,message_count,0.43277777777777793,0.5471686380984558,5.855207621776822
+full_model,tool_calls,0.11166666666666662,0.14041275134666625,14.427527090058401
+full_model,amplification,4.305062499013978,5.243570352907988,22.67666226433828
+no_context,internal_bytes,28387.670805477606,38228.5095522755,119.54177279524332
+no_context,latency_ms,2907.231795727213,3869.904048337353,62.66674153347929
+no_context,message_count,2.485555555555556,3.1931712438565825,38.768826743972944
+no_context,tool_calls,0.6505555555555556,0.7056603159321639,196.9584307828373
+no_context,amplification,10.922275115304535,13.789521598016519,70.65863904754978
+static_mean,internal_bytes,29834.93333333333,32709.783175317236,230.39064998346973
+static_mean,latency_ms,2757.6886622222223,3058.6901465213614,119.58343818448623
+static_mean,message_count,2.5155555555555558,2.7855500155066126,48.03496553983882
+static_mean,tool_calls,0.5444444444444444,0.6355225321645873,264.95059144468667
+static_mean,amplification,10.71876769134868,12.026846682885607,111.10365289444462
@@ -0,0 +1,10 @@
+task_type,model,actual_internal_bytes,predicted_internal_bytes,actual_latency_ms,predicted_latency_ms,actual_message_count,predicted_message_count,actual_tool_calls,predicted_tool_calls,actual_amplification,predicted_amplification
+collaborative_analysis,static_mean,82782.45,38030.049999999996,8428.771760000001,4292.238766666668,9.88,6.1066666666666665,1.66,0.9266666666666667,33.798140646040636,17.719989109017618
+collaborative_analysis,no_context,82782.45,18429.737056529975,8428.771760000001,1933.7549094389146,9.88,4.658333333333333,1.66,0.6333333333333333,33.798140646040636,10.99100144733211
+collaborative_analysis,full_model,82782.45,65408.811967296744,8428.771760000001,5132.429177599434,9.88,9.033333333333333,1.66,1.4333333333333333,33.798140646040636,25.68477275651994
+simple_qa,static_mean,5514.23,38030.049999999996,1128.43623,4292.238766666668,3.24,6.1066666666666665,0.11,0.9266666666666667,4.875984197964249,17.719989109017618
+simple_qa,no_context,5514.23,19556.05971301258,1128.43623,1878.3339705388123,3.24,5.0,0.11,0.65,4.875984197964249,10.442958092513376
+simple_qa,full_model,5514.23,4564.059252059445,1128.43623,740.9752102854878,3.24,3.2666666666666666,0.11,0.13333333333333333,4.875984197964249,4.07657616682107
+tool_research,static_mean,25793.47,38030.049999999996,3319.5083100000024,4292.238766666668,5.2,6.1066666666666665,1.01,0.9266666666666667,14.485842483047964,17.719989109017618
+tool_research,no_context,25793.47,19025.000240049787,3319.5083100000024,1842.727513918263,5.2,4.725,1.01,0.625,14.485842483047964,10.09313023039201
+tool_research,full_model,25793.47,14558.716131318892,3319.5083100000024,1971.65603294373,5.2,4.775,1.01,0.925,14.485842483047964,10.483430906669906
@@ -0,0 +1,6 @@
+numpy>=1.20
+pandas>=1.2
+matplotlib>=3.3
+PyYAML>=5.4
+requests>=2.25
+
@@ -0,0 +1,11 @@
+$ErrorActionPreference = 'Stop'
+$scriptDir = Split-Path -Parent $MyInvocation.MyCommand.Path
+Set-Location $scriptDir
+
+if (-not $env:OPENAI_API_KEY) {
+    throw '请先设置 OPENAI_API_KEY。也可以直接运行 run_local.ps1 使用本地画像。'
+}
+if (-not $env:OPENAI_MODEL) {
+    throw '请先设置 OPENAI_MODEL。'
+}
+python run_pipeline.py --generator llm
@@ -0,0 +1,4 @@
+$ErrorActionPreference = 'Stop'
+$scriptDir = Split-Path -Parent $MyInvocation.MyCommand.Path
+Set-Location $scriptDir
+python run_pipeline.py
@@ -0,0 +1,77 @@
+import argparse
+import shutil
+from pathlib import Path
+
+import pandas as pd
+
+from src.common import ensure_dirs, load_config, load_json, save_json, seeded_rng
+from src.data_generator import aggregate_task_truth, generate_event_log, generate_profiles
+from src.experiments import make_figures, retry_experiment, stress_experiment, validation_experiment
+from src.parameter_estimation import estimate_parameters
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Agent network traffic data generation and experiments")
+    parser.add_argument("--config", default="configs/default.yaml")
+    parser.add_argument("--output", default="outputs/latest")
+    parser.add_argument("--generator", choices=["local", "llm"], default="local")
+    parser.add_argument("--steps", default="generate,estimate,experiment")
+    parser.add_argument("--no-fallback", action="store_true")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    root = Path(__file__).resolve().parent
+    config_path = (root / args.config).resolve() if not Path(args.config).is_absolute() else Path(args.config)
+    output_path = (root / args.output).resolve() if not Path(args.output).is_absolute() else Path(args.output)
+    cfg = load_config(config_path)
+    dirs = ensure_dirs(output_path)
+    shutil.copy2(config_path, output_path / "config_used.yaml")
+    steps = {x.strip() for x in args.steps.split(",") if x.strip()}
+    rng = seeded_rng(int(cfg["seed"]))
+
+    if "generate" in steps:
+        profiles, metadata = generate_profiles(
+            args.generator, cfg["generation"]["task_types"], cfg["generation"]["llm"],
+            allow_fallback=not args.no_fallback,
+        )
+        save_json({"metadata": metadata, "profiles": profiles}, dirs["data"] / "behavior_profiles.json")
+        train = generate_event_log(profiles, int(cfg["generation"]["train_tasks_per_type"]), rng, cfg["network"])
+        test = generate_event_log(profiles, int(cfg["generation"]["test_tasks_per_type"]), rng, cfg["network"])
+        train.to_csv(dirs["data"] / "train_events.csv", index=False, encoding="utf-8-sig")
+        test.to_csv(dirs["data"] / "test_events.csv", index=False, encoding="utf-8-sig")
+        aggregate_task_truth(test).to_csv(dirs["data"] / "task_truth.csv", index=False, encoding="utf-8-sig")
+        print("[generate] profiles=%d, train_events=%d, test_events=%d" % (len(profiles), len(train), len(test)))
+
+    if "estimate" in steps:
+        train = pd.read_csv(dirs["data"] / "train_events.csv")
+        params = estimate_parameters(train)
+        save_json(params, dirs["parameters"] / "estimated_parameters.json")
+        print("[estimate] task_types=%s" % ",".join(params["task_types"].keys()))
+
+    if "experiment" in steps:
+        params = load_json(dirs["parameters"] / "estimated_parameters.json")
+        truth = pd.read_csv(dirs["data"] / "task_truth.csv")
+        predictions, metrics = validation_experiment(truth, params, cfg, rng)
+        stress = stress_experiment(params, cfg, rng)
+        retry = retry_experiment(params, cfg, rng)
+        predictions.to_csv(dirs["results"] / "validation_predictions.csv", index=False, encoding="utf-8-sig")
+        metrics.to_csv(dirs["results"] / "validation_metrics.csv", index=False, encoding="utf-8-sig")
+        stress.to_csv(dirs["results"] / "stress_results.csv", index=False, encoding="utf-8-sig")
+        retry.to_csv(dirs["results"] / "retry_results.csv", index=False, encoding="utf-8-sig")
+        make_figures(predictions, metrics, stress, retry, dirs["figures"])
+        summary = {
+            "best_model_by_internal_bytes_mape": metrics[metrics["metric"] == "internal_bytes"].sort_values("mape_percent").iloc[0].to_dict(),
+            "best_model_by_latency_mape": metrics[metrics["metric"] == "latency_ms"].sort_values("mape_percent").iloc[0].to_dict(),
+            "max_stress_arrival_rate": float(stress["arrival_rate"].max()),
+            "max_observed_p95_delay_ms": float(stress["p95_delay_ms"].max()),
+        }
+        save_json(summary, dirs["results"] / "summary.json")
+        print("[experiment] results written to %s" % output_path)
+        print(metrics[metrics["metric"].isin(["internal_bytes", "latency_ms"])][["model", "metric", "mape_percent"]].to_string(index=False))
+
+
+if __name__ == "__main__":
+    main()
+
@@ -0,0 +1,2 @@
+"""Agent traffic modeling experiment package."""
+
@@ -0,0 +1,61 @@
+import json
+import math
+import os
+import random
+from pathlib import Path
+
+import numpy as np
+import yaml
+
+
+def load_config(path):
+    with open(path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+
+def ensure_dirs(root):
+    root = Path(root)
+    dirs = {name: root / name for name in ("data", "parameters", "results", "figures")}
+    for directory in dirs.values():
+        directory.mkdir(parents=True, exist_ok=True)
+    return dirs
+
+
+def save_json(data, path):
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+
+def load_json(path):
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def seeded_rng(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    return np.random.default_rng(seed)
+
+
+def clamp(value, low, high):
+    return max(low, min(high, value))
+
+
+def lognormal_params_from_mean_cv(mean, cv):
+    mean = max(float(mean), 1e-6)
+    cv = max(float(cv), 1e-6)
+    sigma2 = math.log(cv * cv + 1.0)
+    sigma = math.sqrt(sigma2)
+    mu = math.log(mean) - sigma2 / 2.0
+    return mu, sigma
+
+
+def sample_lognormal(rng, mean, cv, minimum=1.0):
+    mu, sigma = lognormal_params_from_mean_cv(mean, cv)
+    return max(minimum, float(rng.lognormal(mu, sigma)))
+
+
+def env(name, default=None):
+    value = os.environ.get(name)
+    return default if value in (None, "") else value
+
@@ -0,0 +1,308 @@
+import json
+import re
+import uuid
+from copy import deepcopy
+
+import pandas as pd
+import requests
+
+from .common import clamp, sample_lognormal
+
+
+DEFAULT_PROFILES = [
+    {
+        "task_type": "simple_qa",
+        "description": "单节点即可完成的简短问答，少量情况下调用工具。",
+        "phase": "answering",
+        "tool_probability": 0.10,
+        "split_probability": 0.05,
+        "mean_subtasks": 1.2,
+        "timeout_probability": 0.02,
+        "failure_probability": 0.01,
+        "think_time_ms_mean": 420,
+        "think_time_cv": 0.55,
+        "request_size_bytes_mean": 1800,
+        "response_size_bytes_mean": 2600,
+        "message_size_cv": 0.45,
+    },
+    {
+        "task_type": "tool_research",
+        "description": "需要搜索、数据库或工具结果的研究任务。",
+        "phase": "evidence_collection",
+        "tool_probability": 0.78,
+        "split_probability": 0.20,
+        "mean_subtasks": 1.8,
+        "timeout_probability": 0.07,
+        "failure_probability": 0.025,
+        "think_time_ms_mean": 900,
+        "think_time_cv": 0.75,
+        "request_size_bytes_mean": 3200,
+        "response_size_bytes_mean": 8500,
+        "message_size_cv": 0.70,
+    },
+    {
+        "task_type": "collaborative_analysis",
+        "description": "协调多个执行 Agent 并汇总结果的复杂分析任务。",
+        "phase": "multi_agent_synthesis",
+        "tool_probability": 0.48,
+        "split_probability": 0.82,
+        "mean_subtasks": 3.4,
+        "timeout_probability": 0.09,
+        "failure_probability": 0.035,
+        "think_time_ms_mean": 1450,
+        "think_time_cv": 0.85,
+        "request_size_bytes_mean": 5200,
+        "response_size_bytes_mean": 11800,
+        "message_size_cv": 0.80,
+    },
+]
+
+
+PROFILE_KEYS = {
+    "task_type", "description", "phase", "tool_probability", "split_probability",
+    "mean_subtasks", "timeout_probability", "failure_probability",
+    "think_time_ms_mean", "think_time_cv", "request_size_bytes_mean",
+    "response_size_bytes_mean", "message_size_cv",
+}
+
+
+def _extract_json(text):
+    text = text.strip()
+    fenced = re.search(r"```(?:json)?\s*(.*?)```", text, flags=re.S | re.I)
+    if fenced:
+        text = fenced.group(1).strip()
+    start = min([i for i in (text.find("["), text.find("{")) if i >= 0] or [0])
+    text = text[start:]
+    if text.startswith("{"):
+        obj = json.loads(text)
+        return obj.get("profiles", obj)
+    return json.loads(text)
+
+
+def _validate_profiles(profiles, requested_types):
+    if not isinstance(profiles, list):
+        raise ValueError("LLM output must be a JSON list")
+    validated = []
+    by_type = {p["task_type"]: p for p in DEFAULT_PROFILES}
+    for raw in profiles:
+        if not isinstance(raw, dict) or "task_type" not in raw:
+            continue
+        base = deepcopy(by_type.get(raw["task_type"], DEFAULT_PROFILES[0]))
+        for key in PROFILE_KEYS:
+            if key in raw:
+                base[key] = raw[key]
+        for key in ("tool_probability", "split_probability", "timeout_probability", "failure_probability"):
+            base[key] = clamp(float(base[key]), 0.0, 0.95)
+        base["mean_subtasks"] = clamp(float(base["mean_subtasks"]), 1.0, 8.0)
+        for key in ("think_time_ms_mean", "request_size_bytes_mean", "response_size_bytes_mean"):
+            base[key] = max(float(base[key]), 1.0)
+        for key in ("think_time_cv", "message_size_cv"):
+            base[key] = clamp(float(base[key]), 0.05, 2.5)
+        validated.append(base)
+    found = {p["task_type"] for p in validated}
+    for task_type in requested_types:
+        if task_type not in found:
+            validated.append(deepcopy(by_type.get(task_type, DEFAULT_PROFILES[0])))
+    return [p for p in validated if p["task_type"] in requested_types]
+
+
+def generate_profiles(mode, task_types, llm_config, allow_fallback=True):
+    if mode == "local":
+        return _validate_profiles(DEFAULT_PROFILES, task_types), {"generator": "local_builtin"}
+
+    import os
+    api_key = os.environ.get("OPENAI_API_KEY")
+    base_url = os.environ.get("OPENAI_BASE_URL", "https://api.openai.com/v1").rstrip("/")
+    model = os.environ.get("OPENAI_MODEL", "")
+    if not api_key or not model:
+        if allow_fallback:
+            return _validate_profiles(DEFAULT_PROFILES, task_types), {
+                "generator": "local_fallback", "reason": "OPENAI_API_KEY or OPENAI_MODEL missing"
+            }
+        raise RuntimeError("LLM mode requires OPENAI_API_KEY and OPENAI_MODEL")
+
+    prompt = f"""你是多智能体网络仿真实验的数据设计专家。请为以下任务类型生成行为画像：
+{json.dumps(task_types, ensure_ascii=False)}
+
+仅输出 JSON 数组，每个对象必须包含这些字段：
+task_type, description, phase, tool_probability, split_probability, mean_subtasks,
+timeout_probability, failure_probability, think_time_ms_mean, think_time_cv,
+request_size_bytes_mean, response_size_bytes_mean, message_size_cv。
+
+约束：概率在0到0.95之间；mean_subtasks在1到8之间；时间单位毫秒；消息大小单位字节；
+不同任务类型应体现从简单问答、工具密集到多Agent协作的明显差异。画像将用于科学仿真，数值应合理且可解释。"""
+    payload = {
+        "model": model,
+        "messages": [
+            {"role": "system", "content": "Return valid JSON only. Do not include markdown commentary."},
+            {"role": "user", "content": prompt},
+        ],
+        "temperature": float(llm_config.get("temperature", 0.5)),
+        "max_tokens": int(llm_config.get("max_tokens", 3500)),
+    }
+    try:
+        response = requests.post(
+            base_url + "/chat/completions",
+            headers={"Authorization": "Bearer " + api_key, "Content-Type": "application/json"},
+            json=payload,
+            timeout=float(llm_config.get("timeout_seconds", 90)),
+        )
+        response.raise_for_status()
+        body = response.json()
+        content = body["choices"][0]["message"]["content"]
+        profiles = _validate_profiles(_extract_json(content), task_types)
+        return profiles, {"generator": "llm", "model": model, "base_url": base_url}
+    except Exception as exc:
+        if not allow_fallback:
+            raise
+        return _validate_profiles(DEFAULT_PROFILES, task_types), {
+            "generator": "local_fallback", "reason": str(exc), "requested_model": model
+        }
+
+
+def _event(task_id, msg_id, parent_id, timestamp, task_type, phase, source, destination,
+           state_before, state_after, duration, message_type, message_size, queue_length,
+           success=True, retry_count=0, is_external=False):
+    return {
+        "timestamp": round(float(timestamp), 6),
+        "task_id": task_id,
+        "message_id": msg_id,
+        "parent_message_id": parent_id or "",
+        "task_type": task_type,
+        "task_phase": phase,
+        "source": source,
+        "destination": destination,
+        "state_before": state_before,
+        "state_after": state_after,
+        "state_duration_ms": round(float(duration), 3),
+        "message_type": message_type,
+        "message_size_bytes": int(max(0, round(message_size))),
+        "queue_length": int(max(0, queue_length)),
+        "success": bool(success),
+        "retry_count": int(retry_count),
+        "is_external": bool(is_external),
+    }
+
+
+def generate_event_log(profiles, tasks_per_type, rng, network):
+    events = []
+    agent_count = int(network["agent_count"])
+    tool_count = int(network["tool_count"])
+    coordinator = network.get("coordinator", "agent_0")
+    clock = 0.0
+    for profile in profiles:
+        for index in range(tasks_per_type):
+            task_id = "%s_%05d" % (profile["task_type"], index)
+            clock += float(rng.exponential(0.7))
+            t = clock
+            external_size = sample_lognormal(rng, profile["request_size_bytes_mean"] * 0.75,
+                                              profile["message_size_cv"])
+            root_id = uuid.uuid4().hex[:16]
+            think = sample_lognormal(rng, profile["think_time_ms_mean"], profile["think_time_cv"])
+            queue = int(rng.poisson(0.5 + 2.0 * profile["split_probability"]))
+            events.append(_event(task_id, root_id, "", t, profile["task_type"], profile["phase"],
+                                 "external", coordinator, "Idle", "Think", think,
+                                 "external_task", external_size, queue, is_external=True))
+            t += think / 1000.0
+            parent = root_id
+            split = rng.random() < profile["split_probability"]
+            subtask_count = max(1, int(rng.poisson(max(profile["mean_subtasks"] - 1.0, 0.01)) + 1)) if split else 1
+            subtask_count = min(subtask_count, 8)
+            result_sizes = []
+            all_success = True
+            for sub_index in range(subtask_count):
+                worker = "agent_%d" % (1 + ((index + sub_index) % max(1, agent_count - 1)))
+                req_id = uuid.uuid4().hex[:16]
+                req_size = sample_lognormal(rng, profile["request_size_bytes_mean"], profile["message_size_cv"])
+                state = "Split" if split else "CallAgent"
+                events.append(_event(task_id, req_id, parent, t, profile["task_type"], profile["phase"],
+                                     coordinator, worker, "Think", state, 5.0, "agent_request",
+                                     req_size, queue))
+                worker_think = sample_lognormal(rng, profile["think_time_ms_mean"], profile["think_time_cv"])
+                t += (8.0 + worker_think) / 1000.0
+                tool_used = rng.random() < profile["tool_probability"]
+                retries = 0
+                success = True
+                tool_response_size = 0.0
+                if tool_used:
+                    tool = "tool_%d" % ((index + sub_index) % max(1, tool_count))
+                    tool_req_parent = req_id
+                    while True:
+                        tool_req_id = uuid.uuid4().hex[:16]
+                        tool_req_size = sample_lognormal(rng, profile["request_size_bytes_mean"] * 0.35,
+                                                         profile["message_size_cv"])
+                        events.append(_event(task_id, tool_req_id, tool_req_parent, t,
+                                             profile["task_type"], profile["phase"], worker, tool,
+                                             "Think" if retries == 0 else "Retry", "CallTool", 8.0,
+                                             "tool_request", tool_req_size, queue, retry_count=retries))
+                        t += 0.008
+                        timed_out = rng.random() < profile["timeout_probability"]
+                        failed = rng.random() < profile["failure_probability"]
+                        if not timed_out and not failed:
+                            tool_response_size = sample_lognormal(
+                                rng, profile["response_size_bytes_mean"] * 0.72,
+                                profile["message_size_cv"])
+                            response_id = uuid.uuid4().hex[:16]
+                            tool_ms = sample_lognormal(rng, profile["think_time_ms_mean"] * 0.65,
+                                                       profile["think_time_cv"])
+                            t += tool_ms / 1000.0
+                            events.append(_event(task_id, response_id, tool_req_id, t,
+                                                 profile["task_type"], profile["phase"], tool, worker,
+                                                 "CallTool", "Think", tool_ms, "tool_response",
+                                                 tool_response_size, queue, retry_count=retries))
+                            break
+                        if retries >= 2:
+                            success = False
+                            fail_id = uuid.uuid4().hex[:16]
+                            events.append(_event(task_id, fail_id, tool_req_id, t + 2.2,
+                                                 profile["task_type"], profile["phase"], worker, coordinator,
+                                                 "Wait", "Failed", 2200.0, "error", 600, queue,
+                                                 success=False, retry_count=retries))
+                            t += 2.2
+                            break
+                        retries += 1
+                        t += 2.2 + 0.18 * retries
+                result_id = uuid.uuid4().hex[:16]
+                result_size = sample_lognormal(rng, profile["response_size_bytes_mean"],
+                                                profile["message_size_cv"]) + tool_response_size * 0.15
+                events.append(_event(task_id, result_id, req_id, t, profile["task_type"], profile["phase"],
+                                     worker, coordinator, "Think", "Send" if success else "Failed", 5.0,
+                                     "agent_response" if success else "error", result_size if success else 600,
+                                     queue, success=success, retry_count=retries))
+                result_sizes.append(result_size if success else 600)
+                all_success = all_success and success
+                t += 0.005
+            final_id = uuid.uuid4().hex[:16]
+            final_size = max(800.0, sum(result_sizes) * 0.38)
+            final_think = sample_lognormal(rng, profile["think_time_ms_mean"] * 0.55,
+                                           profile["think_time_cv"])
+            t += final_think / 1000.0
+            events.append(_event(task_id, final_id, parent, t, profile["task_type"], profile["phase"],
+                                 coordinator, "external", "Think", "Send" if all_success else "Failed",
+                                 final_think, "final_response" if all_success else "error", final_size,
+                                 queue, success=all_success))
+    return pd.DataFrame(events).sort_values(["timestamp", "task_id"]).reset_index(drop=True)
+
+
+def aggregate_task_truth(events):
+    rows = []
+    for task_id, group in events.groupby("task_id"):
+        group = group.sort_values("timestamp")
+        external = group[group["is_external"]]
+        internal = group[~group["is_external"]]
+        input_bytes = int(external["message_size_bytes"].sum())
+        rows.append({
+            "task_id": task_id,
+            "task_type": group["task_type"].iloc[0],
+            "task_phase": group["task_phase"].iloc[0],
+            "latency_ms": max(0.0, (group["timestamp"].max() - group["timestamp"].min()) * 1000.0),
+            "internal_bytes": int(internal["message_size_bytes"].sum()),
+            "input_bytes": input_bytes,
+            "amplification": float(internal["message_size_bytes"].sum()) / max(input_bytes, 1),
+            "message_count": int(len(internal)),
+            "tool_calls": int((group["message_type"] == "tool_request").sum()),
+            "retries": int(group["retry_count"].max()),
+            "success": bool(group["success"].all()),
+        })
+    return pd.DataFrame(rows)
+
@@ -0,0 +1,152 @@
+import json
+from pathlib import Path
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from .simulator import simulate_queue_stress, simulate_task_batch
+
+
+def _metrics(actual, predicted):
+    actual = np.asarray(actual, dtype=float)
+    predicted = np.asarray(predicted, dtype=float)
+    return {
+        "mae": float(np.mean(np.abs(predicted - actual))),
+        "rmse": float(np.sqrt(np.mean((predicted - actual) ** 2))),
+        "mape_percent": float(np.mean(np.abs(predicted - actual) / np.maximum(np.abs(actual), 1e-9)) * 100.0),
+    }
+
+
+def validation_experiment(truth, params, cfg, rng):
+    scenario_truth = truth.groupby("task_type").agg(
+        internal_bytes=("internal_bytes", "mean"),
+        latency_ms=("latency_ms", "mean"),
+        message_count=("message_count", "mean"),
+        tool_calls=("tool_calls", "mean"),
+        amplification=("amplification", "mean"),
+    ).reset_index()
+    global_internal = float(scenario_truth["internal_bytes"].mean())
+    global_latency = float(scenario_truth["latency_ms"].mean())
+    rows = []
+    n = int(cfg["experiments"]["validation_tasks_per_scenario"])
+    for _, actual in scenario_truth.iterrows():
+        task_type = actual["task_type"]
+        for model in ("static_mean", "no_context", "full_model"):
+            if model == "static_mean":
+                prediction = {
+                    "internal_bytes": global_internal,
+                    "latency_ms": global_latency,
+                    "message_count": float(scenario_truth["message_count"].mean()),
+                    "tool_calls": float(scenario_truth["tool_calls"].mean()),
+                    "amplification": float(scenario_truth["amplification"].mean()),
+                }
+            else:
+                samples = pd.DataFrame(simulate_task_batch(
+                    task_type, n, params, rng, max_retries=cfg["simulation"]["max_retries"],
+                    include_context=(model == "full_model"),
+                ))
+                prediction = {key: float(samples[key].mean()) for key in
+                              ("internal_bytes", "latency_ms", "message_count", "tool_calls", "amplification")}
+            row = {"task_type": task_type, "model": model}
+            for metric, pred in prediction.items():
+                row["actual_" + metric] = float(actual[metric])
+                row["predicted_" + metric] = pred
+            rows.append(row)
+    predictions = pd.DataFrame(rows)
+    metric_rows = []
+    for model, group in predictions.groupby("model"):
+        for metric in ("internal_bytes", "latency_ms", "message_count", "tool_calls", "amplification"):
+            values = _metrics(group["actual_" + metric], group["predicted_" + metric])
+            metric_rows.append({"model": model, "metric": metric, **values})
+    return predictions, pd.DataFrame(metric_rows)
+
+
+def stress_experiment(params, cfg, rng):
+    task_type = "collaborative_analysis" if "collaborative_analysis" in params["task_types"] else list(params["task_types"])[-1]
+    rows = []
+    for rate in cfg["experiments"]["arrival_rates_per_second"]:
+        rows.append(simulate_queue_stress(
+            float(rate), cfg["experiments"]["stress_duration_seconds"],
+            cfg["experiments"]["stress_warmup_seconds"], task_type, params, rng,
+            max_concurrency=cfg["network"]["max_concurrency"],
+            queue_capacity=cfg["network"]["queue_capacity"],
+            max_retries=cfg["simulation"]["max_retries"],
+        ))
+    return pd.DataFrame(rows)
+
+
+def retry_experiment(params, cfg, rng):
+    task_type = "tool_research" if "tool_research" in params["task_types"] else list(params["task_types"])[0]
+    rows = []
+    for probability in cfg["experiments"]["timeout_probabilities"]:
+        for retry_limit in cfg["experiments"]["retry_limits"]:
+            samples = pd.DataFrame(simulate_task_batch(
+                task_type, 240, params, rng, timeout_probability=float(probability),
+                max_retries=int(retry_limit), include_context=True,
+            ))
+            rows.append({
+                "timeout_probability": probability,
+                "max_retries": retry_limit,
+                "success_rate": float(samples["success"].mean()),
+                "mean_retries": float(samples["retries"].mean()),
+                "mean_amplification": float(samples["amplification"].mean()),
+                "mean_latency_ms": float(samples["latency_ms"].mean()),
+            })
+    return pd.DataFrame(rows)
+
+
+def make_figures(predictions, metrics, stress, retry, figure_dir):
+    figure_dir = Path(figure_dir)
+    plt.rcParams["font.sans-serif"] = ["Microsoft YaHei", "SimHei", "Arial Unicode MS", "DejaVu Sans"]
+    plt.rcParams["axes.unicode_minus"] = False
+
+    full = predictions[predictions["model"] == "full_model"]
+    fig, axes = plt.subplots(1, 2, figsize=(11, 4.5))
+    for ax, metric, label in [(axes[0], "internal_bytes", "内部字节数"), (axes[1], "latency_ms", "任务延迟(ms)")]:
+        x, y = full["actual_" + metric], full["predicted_" + metric]
+        ax.scatter(x, y, s=65, color="#2E74B5")
+        lo, hi = min(x.min(), y.min()), max(x.max(), y.max())
+        ax.plot([lo, hi], [lo, hi], "--", color="#777777")
+        for _, row in full.iterrows():
+            ax.annotate(row["task_type"], (row["actual_" + metric], row["predicted_" + metric]), fontsize=8)
+        ax.set_xlabel("模拟实测值"); ax.set_ylabel("模型预测值"); ax.set_title(label)
+        ax.grid(alpha=0.25)
+    fig.tight_layout(); fig.savefig(figure_dir / "prediction_vs_truth.png", dpi=180); plt.close(fig)
+
+    subset = metrics[metrics["metric"].isin(["internal_bytes", "latency_ms"])]
+    pivot = subset.pivot(index="model", columns="metric", values="mape_percent")
+    pivot = pivot.rename(
+        index={"full_model": "完整模型", "no_context": "无上下文模型", "static_mean": "静态均值模型"},
+        columns={"internal_bytes": "内部字节数", "latency_ms": "任务延迟"},
+    )
+    fig, ax = plt.subplots(figsize=(8, 4.6)); pivot.plot(kind="bar", ax=ax, color=["#2E74B5", "#70AD47"])
+    ax.set_ylabel("MAPE (%)"); ax.set_xlabel("模型"); ax.set_title("完整模型与基线对比"); ax.grid(axis="y", alpha=0.25)
+    ax.legend(title="指标")
+    fig.tight_layout(); fig.savefig(figure_dir / "model_comparison.png", dpi=180); plt.close(fig)
+
+    fig, axes = plt.subplots(2, 2, figsize=(11, 7.5))
+    axes[0, 0].plot(stress["arrival_rate"], stress["throughput_per_second"], marker="o")
+    axes[0, 0].set_title("到达率—吞吐量")
+    axes[0, 1].plot(stress["arrival_rate"], stress["p95_delay_ms"], marker="o", color="#C55A11")
+    axes[0, 1].set_title("到达率—P95延迟")
+    axes[1, 0].plot(stress["arrival_rate"], stress["queue_peak"], marker="o", color="#A5A5A5")
+    axes[1, 0].set_title("到达率—峰值队列")
+    axes[1, 1].plot(stress["arrival_rate"], stress["drop_rate"], marker="o", color="#C00000")
+    axes[1, 1].set_title("到达率—丢弃率")
+    for ax in axes.flat: ax.grid(alpha=0.25); ax.set_xlabel("任务/秒")
+    fig.tight_layout(); fig.savefig(figure_dir / "stress_curves.png", dpi=180); plt.close(fig)
+
+    pivot = retry.pivot(index="timeout_probability", columns="max_retries", values="mean_amplification")
+    fig, ax = plt.subplots(figsize=(8, 5)); image = ax.imshow(pivot.values, aspect="auto", cmap="YlOrRd")
+    ax.set_xticks(range(len(pivot.columns)))
+    ax.set_xticklabels([str(x) for x in pivot.columns])
+    ax.set_yticks(range(len(pivot.index)))
+    ax.set_yticklabels([str(x) for x in pivot.index])
+    ax.set_xlabel("最大重试次数"); ax.set_ylabel("超时概率"); ax.set_title("重试导致的流量放大")
+    for i in range(pivot.shape[0]):
+        for j in range(pivot.shape[1]): ax.text(j, i, "%.2f" % pivot.iloc[i, j], ha="center", va="center", fontsize=8)
+    fig.colorbar(image, ax=ax, label="平均流量放大系数")
+    fig.tight_layout(); fig.savefig(figure_dir / "retry_heatmap.png", dpi=180); plt.close(fig)
@@ -0,0 +1,67 @@
+from collections import defaultdict
+
+import numpy as np
+
+
+def _stats(series, default=1.0):
+    values = np.asarray(series, dtype=float)
+    values = values[np.isfinite(values)]
+    if len(values) == 0:
+        return {"mean": default, "std": 0.0, "cv": 0.1, "p50": default, "p95": default, "count": 0}
+    mean = float(np.mean(values))
+    std = float(np.std(values))
+    return {
+        "mean": mean,
+        "std": std,
+        "cv": max(0.05, std / max(mean, 1e-9)),
+        "p50": float(np.quantile(values, 0.50)),
+        "p95": float(np.quantile(values, 0.95)),
+        "count": int(len(values)),
+    }
+
+
+def estimate_parameters(events):
+    parameters = {"global": {}, "task_types": {}}
+    transitions = events.groupby(["state_before", "state_after"]).size().to_dict()
+    totals = events.groupby("state_before").size().to_dict()
+    parameters["global"]["transition_probabilities"] = {
+        "%s->%s" % key: float((count + 1.0) / (totals[key[0]] + 8.0))
+        for key, count in transitions.items()
+    }
+    parameters["global"]["duration_ms"] = _stats(events["state_duration_ms"])
+    parameters["global"]["message_size_bytes"] = _stats(events["message_size_bytes"])
+
+    for task_type, group in events.groupby("task_type"):
+        tasks = group.groupby("task_id")
+        task_count = max(1, group["task_id"].nunique())
+        initial_tool_requests = group[(group["message_type"] == "tool_request") & (group["retry_count"] == 0)]
+        agent_requests = group[group["message_type"] == "agent_request"]
+        split_tasks = tasks["state_after"].apply(lambda s: (s == "Split").any())
+        retry_tasks = tasks["retry_count"].max()
+        tool_requests = group[group["message_type"] == "tool_request"]
+        success_tasks = tasks["success"].all()
+        external_sizes = group[group["is_external"]]["message_size_bytes"]
+        request_sizes = group[group["message_type"].isin(["agent_request", "tool_request"])]["message_size_bytes"]
+        response_sizes = group[group["message_type"].isin(["agent_response", "tool_response", "final_response"])]["message_size_bytes"]
+        # 5-8ms values represent message-send bookkeeping, not reasoning/service time.
+        think_durations = group[
+            (group["state_duration_ms"] > 20.0)
+            & (group["message_type"].isin(["external_task", "tool_response", "final_response"]))
+        ]["state_duration_ms"]
+        subtask_counts = tasks["message_type"].apply(lambda s: int((s == "agent_request").sum()))
+        parameters["task_types"][task_type] = {
+            "phase": str(group["task_phase"].mode().iloc[0]),
+            "task_count": task_count,
+            "tool_probability": float(len(initial_tool_requests) / max(len(agent_requests), 1)),
+            "split_probability": float(split_tasks.mean()),
+            "mean_subtasks": float(subtask_counts.mean()),
+            "retry_probability": float((tool_requests["retry_count"] > 0).sum() / max(len(tool_requests), 1)),
+            "mean_retries_if_any": float(retry_tasks[retry_tasks > 0].mean()) if (retry_tasks > 0).any() else 0.0,
+            "failure_probability": float((~success_tasks).mean()),
+            "think_time_ms": _stats(think_durations, 500.0),
+            "external_size_bytes": _stats(external_sizes, 1500.0),
+            "request_size_bytes": _stats(request_sizes, 2000.0),
+            "response_size_bytes": _stats(response_sizes, 4000.0),
+            "message_count_per_task": _stats(tasks.size(), 3.0),
+        }
+    return parameters
@@ -0,0 +1,176 @@
+import heapq
+import itertools
+import math
+from collections import defaultdict, deque
+
+import numpy as np
+
+from .common import sample_lognormal
+
+
+def simulate_task_batch(task_type, task_count, params, rng, timeout_probability=None,
+                        max_retries=2, include_context=True):
+    task_params = params["task_types"][task_type] if include_context else _globalized(params)
+    rows = []
+    for _ in range(task_count):
+        split = rng.random() < task_params["split_probability"]
+        subtasks = max(1, int(rng.poisson(max(task_params["mean_subtasks"] - 1.0, 0.01)) + 1)) if split else 1
+        subtasks = min(subtasks, 8)
+        input_size = sample_lognormal(rng, task_params["external_size_bytes"]["mean"],
+                                      task_params["external_size_bytes"]["cv"])
+        total_bytes = 0.0
+        messages = 0
+        tool_calls = 0
+        retries_total = 0
+        success = True
+        durations = []
+        coordinator_think = sample_lognormal(rng, task_params["think_time_ms"]["mean"],
+                                              task_params["think_time_ms"]["cv"])
+        durations.append(coordinator_think)
+        for _sub in range(subtasks):
+            tool = rng.random() < task_params["tool_probability"]
+            req = sample_lognormal(rng, task_params["request_size_bytes"]["mean"],
+                                   task_params["request_size_bytes"]["cv"])
+            total_bytes += req
+            messages += 1
+            worker_time = sample_lognormal(rng, task_params["think_time_ms"]["mean"],
+                                           task_params["think_time_ms"]["cv"])
+            durations.append(worker_time)
+            if tool:
+                p_timeout = task_params["retry_probability"] if timeout_probability is None else timeout_probability
+                attempts = 0
+                tool_success = False
+                while attempts <= max_retries:
+                    tool_calls += 1
+                    tool_req = sample_lognormal(rng, task_params["request_size_bytes"]["mean"] * 0.35,
+                                                task_params["request_size_bytes"]["cv"])
+                    total_bytes += tool_req
+                    messages += 1
+                    timed_out = rng.random() < p_timeout
+                    if not timed_out:
+                        tool_resp = sample_lognormal(rng, task_params["response_size_bytes"]["mean"] * 0.72,
+                                                     task_params["response_size_bytes"]["cv"])
+                        total_bytes += tool_resp
+                        messages += 1
+                        durations.append(sample_lognormal(rng, task_params["think_time_ms"]["mean"] * 0.65,
+                                                          task_params["think_time_ms"]["cv"]))
+                        tool_success = True
+                        break
+                    if attempts < max_retries:
+                        retries_total += 1
+                        durations.append(2200.0 + 180.0 * (attempts + 1))
+                    attempts += 1
+                success = success and tool_success
+            response = sample_lognormal(rng, task_params["response_size_bytes"]["mean"],
+                                        task_params["response_size_bytes"]["cv"])
+            total_bytes += response if success else 600.0
+            messages += 1
+        final_size = max(800.0, subtasks * task_params["response_size_bytes"]["mean"] * 0.38)
+        total_bytes += final_size
+        messages += 1
+        latency = sum(durations) + 8.0 * messages
+        rows.append({
+            "task_type": task_type,
+            "internal_bytes": total_bytes,
+            "input_bytes": input_size,
+            "amplification": total_bytes / max(input_size, 1.0),
+            "latency_ms": latency,
+            "message_count": messages,
+            "tool_calls": tool_calls,
+            "retries": retries_total,
+            "success": success,
+        })
+    return rows
+
+
+def _globalized(params):
+    entries = list(params["task_types"].values())
+    def mean(name):
+        return float(np.mean([e[name] for e in entries]))
+    def stat(name):
+        return {
+            "mean": float(np.mean([e[name]["mean"] for e in entries])),
+            "cv": float(np.mean([e[name]["cv"] for e in entries])),
+        }
+    return {
+        "tool_probability": mean("tool_probability"),
+        "split_probability": mean("split_probability"),
+        "mean_subtasks": mean("mean_subtasks"),
+        "retry_probability": mean("retry_probability"),
+        "failure_probability": mean("failure_probability"),
+        "think_time_ms": stat("think_time_ms"),
+        "external_size_bytes": stat("external_size_bytes"),
+        "request_size_bytes": stat("request_size_bytes"),
+        "response_size_bytes": stat("response_size_bytes"),
+    }
+
+
+def simulate_queue_stress(arrival_rate, duration_seconds, warmup_seconds, task_type, params,
+                          rng, max_concurrency=4, queue_capacity=500, timeout_probability=None,
+                          max_retries=2):
+    tasks = simulate_task_batch(
+        task_type, max(200, int(arrival_rate * duration_seconds * 1.5)), params, rng,
+        timeout_probability=timeout_probability, max_retries=max_retries,
+    )
+    event_queue = []
+    counter = itertools.count()
+    time = 0.0
+    task_index = 0
+    while time < duration_seconds:
+        time += float(rng.exponential(1.0 / max(arrival_rate, 1e-9)))
+        if time <= duration_seconds:
+            heapq.heappush(event_queue, (time, next(counter), "arrival", task_index))
+            task_index += 1
+
+    waiting = deque()
+    busy = 0
+    completed = []
+    dropped = 0
+    queue_peak = 0
+    arrival_times = {}
+    while event_queue:
+        now, _, event_type, idx = heapq.heappop(event_queue)
+        if event_type == "arrival":
+            arrival_times[idx] = now
+            if busy < max_concurrency:
+                busy += 1
+                task = tasks[idx % len(tasks)]
+                service = task["latency_ms"] / 1000.0
+                heapq.heappush(event_queue, (now + service, next(counter), "complete", idx))
+            elif len(waiting) < queue_capacity:
+                waiting.append(idx)
+                queue_peak = max(queue_peak, len(waiting))
+            else:
+                dropped += 1
+        else:
+            task = tasks[idx % len(tasks)]
+            if arrival_times[idx] >= warmup_seconds:
+                record = dict(task)
+                record["end_to_end_ms"] = (now - arrival_times[idx]) * 1000.0
+                completed.append(record)
+            busy -= 1
+            if waiting:
+                nxt = waiting.popleft()
+                busy += 1
+                task2 = tasks[nxt % len(tasks)]
+                heapq.heappush(event_queue, (now + task2["latency_ms"] / 1000.0,
+                                             next(counter), "complete", nxt))
+    offered = max(1, task_index)
+    if completed:
+        delays = np.array([x["end_to_end_ms"] for x in completed])
+        amps = np.array([x["amplification"] for x in completed])
+        success = np.array([x["success"] for x in completed], dtype=float)
+    else:
+        delays, amps, success = np.array([0.0]), np.array([0.0]), np.array([0.0])
+    return {
+        "arrival_rate": arrival_rate,
+        "offered_tasks": offered,
+        "completed_tasks": len(completed),
+        "throughput_per_second": len(completed) / max(duration_seconds - warmup_seconds, 1),
+        "mean_delay_ms": float(delays.mean()),
+        "p95_delay_ms": float(np.quantile(delays, 0.95)),
+        "queue_peak": queue_peak,
+        "drop_rate": dropped / offered,
+        "success_rate": float(success.mean()),
+        "mean_amplification": float(amps.mean()),
+    }
				`@@ -0,0 +1,2 @@`
				`"""Agent traffic modeling experiment package."""`