Remove duplicate test docs

Delete redundant and pure-English test documentation so the docs set has one clear Chinese source of truth for standards, scenarios, model I/O, and industry mapping.

Constraint: The user asked to delete unnecessary duplicate docs and pure-English docs.

Rejected: Keeping parallel English and article-derived matrices | they duplicate the current Chinese standard reports and confuse the active S07 FastAPI evidence.

Confidence: high

Scope-risk: narrow

Directive: Keep future Agent test documentation in docs/ and prefer Chinese source-of-truth reports over parallel duplicate matrices.

Tested: .venv/bin/python -B -m unittest discover -s tests; .venv/bin/python -B -m py_compile swarm_minimal/*.py examples/*.py tests/*.py; git diff --check; duplicate-doc reference scan; docs secret pattern scan.

Not-tested: Live S07 matrix was not rerun because this change only deletes duplicate docs and updates indexes.

Co-authored-by: OmX <omx@oh-my-codex.dev>

README.md

@@ -23,9 +23,8 @@
 主要报告入口都放在 `docs/`，完整索引见 `docs/README.md`：
 
 - `docs/AGENT_SWARM_QUALITY_STANDARD.zh-CN.md`：Agent / 蜂群 Agent 质量标准、测试标准来源、S07 外部 GitHub 代码任务分配和接手机制。
-- `docs/ACADEMIC_STANDARD_TEST_REPORT.zh-CN.md`：测试场景、测试过程、测试结果、算法清单和马尔可夫过程判定。
+- `docs/ACADEMIC_STANDARD_TEST_REPORT.zh-CN.md`：S01-S08 测试场景、测试过程、测试结果、算法清单和马尔可夫过程判定。
 - `docs/MODEL_AGNET_IO_REPORT.zh-CN.md`：每个模型 / Agnet 的任务、输入、输出、评分和交接过程。
-- `docs/STANDARD_TEST_MATRIX.md`：S01-S08 标准矩阵。
 - `docs/INDUSTRY_STANDARD_AGNET_TEST_PLAN.md`：行业参考框架到本项目标准的映射。
 
 目标是先把蜂群的四个共享资源跑通：

docs/JUEJIN_SWARM_ARTICLE_TEST_STANDARD.zh-CN.md

@@ -1,139 +0,0 @@
-# 基于掘金《Agent 蜂群模式（Swarm）》的测试标准
-
-依据文章：[Agent 蜂群模式（Swarm）](https://juejin.cn/post/7603575399255949352)
-
-本文档把该文章中的蜂群定义、核心特征、执行流程、通信机制、系统组件和监控指标，转换成 `swarm-minimal` 的测试标准。后续判断“像不像蜂群”，以本文档为主；LangGraph Swarm 只作为 handoff 实现参考，不作为蜂群标准本身。
-
-## 1. 文章核心定义
-
-文章对 Agent 蜂群模式的核心定义可以归纳为：
-
-```text
-每个 Agent 仅依据局部环境信息和简单规则进行决策，
-通过信息素、消息广播或状态共享等方式间接通信，
-最终在整体层面涌现出复杂、高效的智能行为。
-```
-
-因此，测试不能只看“多个模型被调用”或“最终有一个结果”。必须证明：
-
-1. Agent 有局部感知。
-2. Agent 有自主决策。
-3. Agent 通过共享环境间接协作。
-4. 单个 Agent 失败不破坏整体结果。
-5. 多个局部行为能产生整体收敛。
-6. 系统能观测收敛过程和关键指标。
-
-## 2. 文章核心特征到测试项
-
-| 编号 | 文章特征 | 测试标准 | 当前状态 |
-| --- | --- | --- | --- |
-| J-SW-01 | 去中心化 | Task Orchestrator 只能注入初始任务，不能逐步命令每个 Agent 做什么；Agent 必须通过任务池和共享环境自主 claim/decide。 | 部分满足：当前 `SwarmCoordinator` 仍是中心循环，需继续弱化。 |
-| J-SW-02 | 自组织 | Agent 根据任务池、信息素、共享状态自主形成执行顺序，而不是固定链路。 | 部分满足：已有 pheromone claim 测试，但 live 路径仍偏顺序。 |
-| J-SW-03 | 涌现性 | 多个局部弱信号聚合后，群体结果强于任一单个局部判断。 | 已有确定性测试 B02/C02。 |
-| J-SW-04 | 鲁棒性 | 单个 Agent 出错后，其他 Agent 继续完成，整体仍可收敛。 | 已有确定性测试 B01/C01。 |
-| J-SW-05 | 可扩展性 | Agent 数量应能在 3-7 个范围内增减，任务池和环境结构不变。 | 待补规模测试。 |
-| J-SW-06 | 隐式协作 | Agent 不通过中央指令直接串联，而通过信息素、共享状态、消息流协作。 | 部分满足：PostgreSQL/Redis/Blob live 闭环已有，隐式协作测试还需加强。 |
-
-## 3. 文章执行流程到测试项
-
-文章给出的执行循环是：
-
-```text
-① 初始化 -> ② 感知 -> ③ 决策 -> ④ 交互与更新 -> ⑤ 收敛判断
-未收敛则继续迭代，已收敛则输出结果。
-```
-
-对应测试标准：
-
-| 阶段 | 测试标准 | 当前状态 |
-| --- | --- | --- |
-| 初始化 | 创建 N 个 Agent、初始任务池、信息素地图、共享状态和终止条件。 | 已满足基础形态。 |
-| 感知 | 每个 Agent 读取局部环境：可用任务、信息素、共享状态、上一轮摘要。 | 部分满足：代码有 shared_state 传入，但缺少显式 `perceive()` 抽象。 |
-| 决策 | Agent 基于感知结果选择任务、候选答案或 handoff 对象。 | 部分满足：claim 基于 capability/pheromone；缺少策略对象。 |
-| 交互更新 | Agent 完成动作后更新任务、信息素、共享状态、事件流。 | 已在 PG/Redis/Blob live 路径中实现基础闭环。 |
-| 收敛判断 | 不能只是“取最高分”；应有阈值、margin、多轮或任务完成率判定。 | 已新增本地多轮共识收敛；尚未接入 Azure live 主路径。 |
-
-## 4. 三种通信机制到测试项
-
-文章列出的通信机制：
-
-1. 信息素通信（Stigmergy）
-2. 广播通信（Broadcast）
-3. 交接通信（Handoff）
-
-对应标准：
-
-| 机制 | 合格标准 | 当前状态 |
-| --- | --- | --- |
-| 信息素通信 | Agent 修改环境分数，后续 Agent 感知并调整选择概率或 claim 顺序。 | 已有 B03/C03 确定性测试；Redis sorted set 已承担 live score。 |
-| 广播通信 | Agent 的状态变化进入消息流，其他 Agent 可观察。 | Redis Stream 记录事件，但尚未做“其他 Agent 消费广播后改变决策”的测试。 |
-| 交接通信 | Agent A 把上下文交给 Agent B，B 基于 payload 继续执行。 | 已有 B04 测试；但按文章它只是蜂群通信方式之一，不是唯一核心。 |
-
-## 5. 系统组件到测试项
-
-文章架构组件：
-
-| 组件 | 文章职责 | 当前实现 / 缺口 |
-| --- | --- | --- |
-| Agent Pool | 管理所有 Agent，每个 Agent 有状态、行为规则和通信接口。 | 目前是 `Agent` 列表；缺少状态生命周期和利用率统计。 |
-| Environment | 任务池、信息素地图、共享状态、约束条件。 | `InMemorySwarmStore` 和 `PostgresRedisBlobSwarmStore` 已有四资源模型。 |
-| Message Bus / Shared Memory | 间接通信。 | Redis Stream + PG shared_state 已有基础；消费侧测试不足。 |
-| Monitor / Logger | 状态监控、收敛指标、性能日志。 | 缺少统一 metrics 报告。 |
-| Task Orchestrator | 只注入初始任务，不参与调度，以保持去中心化。 | 当前 `SwarmCoordinator` 仍参与执行循环，需进一步改造。 |
-
-## 6. 文章监控指标到测试项
-
-文章列出的关键监控指标：
-
-| 指标 | 文章含义 | 测试标准 |
-| --- | --- | --- |
-| 收敛速度 | 达到目标质量所需迭代次数。 | 输出 `rounds_to_converge`，并验证小于最大轮数的 50% 或业务阈值。 |
-| Agent 利用率 | 每个 Agent 忙碌时间占比。 | 输出每个 Agent claim/complete 次数，目标 > 60% 仅适合压测，不适合小样本。 |
-| 任务完成率 | 成功完成任务比例。 | 小样本应为 100%；故障注入场景应证明整体仍收敛。 |
-| 平均响应时间 | 从任务提交到结果产出的时间。 | live 路径需要记录每个 task started/completed 时间。 |
-| 信息素分布 | 信息素浓度均匀程度，文章建议基尼系数 < 0.5。 | 待实现 pheromone Gini 计算和测试。 |
-
-## 7. 当前测试结论，按文章标准重述
-
-当前 `swarm-minimal` 已经证明：
-
-1. 有四类共享资源：任务池、信息素/得分、共享状态、结果收敛。
-2. 有真实 Azure-backed live 闭环：PostgreSQL、Redis、Blob、NewAPI。
-3. 有多模型调用和任务拆分。
-4. 有单 Agent 失败隔离的确定性测试。
-5. 有局部弱信号聚合为群体更优结果的涌现测试。
-6. 有信息素影响 claim 顺序的测试。
-7. 有 handoff 上下文连续性测试。
-8. 已新增本地多轮共识收敛，证明不是简单最高分选择。
-
-但按文章标准，当前还不能说是完整蜂群系统，因为仍有缺口：
-
-1. `SwarmCoordinator` 仍偏中心循环，不够“无单一控制节点”。
-2. live 路径还没有真正的并发多 worker 自主 claim。
-3. Agent 缺少显式 `perceive -> decide -> act` 生命周期。
-4. Redis Stream 目前更多是日志/事件，不是已验证的广播决策输入。
-5. 多轮共识收敛还没接入 Azure PG/Redis/Blob 主路径。
-6. 缺少 Agent 利用率、响应时间、收敛速度、信息素 Gini 的统一 metrics。
-7. 缺少 3-7 Agent 扩缩容下的稳定性测试。
-
-## 8. 后续实现优先级
-
-按文章标准，下一步不是再堆安全测试，而是改造蜂群运行时：
-
-1. 把 Agent 拆成 `perceive()`、`decide()`、`act()` 三段。
-2. 把 `SwarmCoordinator` 降级为只初始化任务和停止条件。
-3. 引入 worker loop：每个 Agent 自主从任务池 claim。
-4. 把 Redis Stream 的事件变成 Agent 可消费的广播输入。
-5. 把多轮共识收敛接到 Azure live store。
-6. 增加 metrics：收敛轮数、任务完成率、Agent 利用率、平均响应时间、信息素 Gini。
-7. 增加 3、5、7 个 Agent 的扩缩容测试。
-
-## 9. 严谨结论
-
-```text
-当前原型已经覆盖文章中蜂群模式的部分关键性质：
-鲁棒性、涌现性、信息素间接协作、共享环境和 handoff。
-
-但它还不是完整意义上的文章式蜂群系统：
-去中心化、自组织 worker loop、广播驱动决策、完整监控指标和 live 多轮收敛仍需补齐。
-```

docs/README.md

@@ -3,14 +3,9 @@
 ## Agent / 蜂群 Agent 测试
 
 - `AGENT_SWARM_QUALITY_STANDARD.zh-CN.md`：AQS / SW-AQS v1 主标准，说明 Agent 和蜂群 Agent 的质量项、S07 任务分配、模型交接和当前结论。
-- `ACADEMIC_STANDARD_TEST_REPORT.zh-CN.md`：测试场景、测试过程、测试结果、算法清单和马尔可夫过程判定。
+- `ACADEMIC_STANDARD_TEST_REPORT.zh-CN.md`：S01-S08 测试场景、测试过程、测试结果、算法清单和马尔可夫过程判定。
 - `MODEL_AGNET_IO_REPORT.zh-CN.md`：从 live run 导出的模型 / Agnet 任务、输入、输出、评分和交接过程。
-- `STANDARD_TEST_MATRIX.md`：S01-S08 标准化测试矩阵。
 - `INDUSTRY_STANDARD_AGNET_TEST_PLAN.md`：行业参考框架到本项目 Agent 质量标准的映射。
-- `JUEJIN_SWARM_ARTICLE_TEST_STANDARD.zh-CN.md`：掘金蜂群文章对应的测试标准说明。
-- `SWARM_BEHAVIOR_TEST_MATRIX.md`：蜂群行为测试矩阵。
-- `SWARM_TEST_INPUT_OUTPUT_REPORT.md`：英文输入输出测试报告。
-- `SWARM_TEST_INPUT_OUTPUT_REPORT.zh-CN.md`：中文输入输出测试报告。
 
 ## 交付计划
 

docs/STANDARD_TEST_MATRIX.md

@@ -1,38 +0,0 @@
-# Standard Test Matrix
-
-This file defines the stricter acceptance shape for the standalone minimal swarm.
-It separates deterministic checks from live Azure/NewAPI checks so a passing
-result means more than "one long run completed".
-
-## Layers
-
-| Layer | Purpose | Command |
-| --- | --- | --- |
-| Static | Python syntax and importability | `./.venv/bin/python -B -m py_compile swarm_minimal/*.py examples/*.py tests/*.py` |
-| Unit | Existing closed-loop and NewAPI mock behavior | `./.venv/bin/python -B -m unittest discover -s tests` |
-| Deterministic scenarios | No-network Given/When/Then cases for continuity, policy, final scoring, and failure injection | `./.venv/bin/python -B -m unittest tests.test_standard_scenarios` |
-| Academic Markov fit | No-network checks for Markov-style transition behavior and non-MDP limitations | `./.venv/bin/python -B -m unittest tests.test_markov_process_properties` |
-| Live integration | Real NewAPI + Azure PostgreSQL/Redis/Blob continuous reasoning chain against external GitHub code | `./.venv/bin/python -u -B examples/run_continuous_reasoning_acceptance.py` |
-| Report audit | Human-auditable model I/O report with scenario, input, output, handoff and secret-safety evidence | `./.venv/bin/python -B -m unittest tests.test_model_io_report_audit` |
-
-## Required Scenarios
-
-| ID | Scenario | Given | When | Then |
-| --- | --- | --- | --- | --- |
-| S01 | Syntax/import sanity | All prototype Python files | Compile every module | No syntax/import compile errors |
-| S02 | Unit regression | Mock NewAPI and in-memory store | Run all unit tests | All tests pass |
-| S03 | Chain continuity | Seven deterministic step outputs | Remove one previous-step link | Continuity checker fails the broken chain |
-| S04 | Dependency boundary | Text mentions NATS/Cosmos | Use negative and positive dependency wording | Negative wording passes; required dependency wording fails |
-| S05 | Final convergence scoring | STEP-06 and STEP-07 both produce rich outputs | Score both outputs | STEP-07 is strictly selected as final convergence |
-| S06 | Failure injection | One deterministic worker raises | Coordinator runs to convergence | Failed task is marked failed, pheromone is negative, completed task still converges |
-| S07 | External GitHub code reasoning | Real Azure/NewAPI env is loaded and target is `fastapi/fastapi` at a pinned commit | Run seven-step code reasoning against external GitHub files | PostgreSQL, Redis, Blob, stream events, model discovery, external file references, and final artifact all pass |
-| S08 | Model I/O report audit | Generated model/Agnet I/O report | Audit scenario coverage, task input/output, handoff evidence, and obvious secret patterns | Report is human-auditable and contains no obvious secret values |
-| M01 | Markov-style claim transition | Same current tasks and pheromone values but different prior paths | Claim next task | The same high-pheromone task is claimed |
-| M02 | Markov-style score transition | Same current task, agent, output, and score but different prior paths | Complete task | Task status, output, score, and pheromone update match |
-| M03 | Formal MDP limitation | Project metadata is evaluated | Assess Markov fit | Prototype is classified as Markov-style state machine, not a formal Markov process or MDP |
-
-## Stop Rule
-
-The standard acceptance result is PASS only if every scenario passes. Any FAIL
-must include the scenario ID, the failed assertion, and enough evidence to
-reproduce it without printing secrets.

docs/SWARM_BEHAVIOR_TEST_MATRIX.md

@@ -1,32 +0,0 @@
-# Swarm Behavior Test Matrix
-
-This matrix tests swarm behavior itself, not security compliance. Its primary
-basis is the Juejin article "Agent 蜂群模式（Swarm）": decentralized behavior,
-self-organization, emergence, robustness, scalability, implicit collaboration,
-and the initialize/perceive/decide/interact/converge loop. LangGraph Swarm is
-used only as a handoff reference, not as the main swarm standard.
-
-## Core Swarm Claims
-
-| Claim | Why It Matters | Observable Evidence |
-| --- | --- | --- |
-| Fault isolation | A swarm should not lose the whole result when one Agnet fails. | A failed agent produces a failed observation while other agents still complete and convergence is written. |
-| Stigmergy / pheromone coordination | Agents should coordinate indirectly through the shared environment, not only through a central planner. | Pheromone score changes claim order and later convergence preference. |
-| Emergent consensus | Group-level output should be stronger than any single local signal in selected scenarios. | Multiple local weak signals aggregate into the accepted result even though no single weak signal is the strongest individual observation. |
-| Handoff continuity | LangGraph-style swarm handoff must preserve control target and context across active agents. | `transfer_to_<agent>` naming, active-agent state changes, and payload continuity are checked step by step. |
-
-## Scenarios
-
-| ID | Scenario | Given | When | Then | Different From |
-| --- | --- | --- | --- | --- | --- |
-| B01 | Single Agnet failure isolation | Three competing route tasks; one Agnet always raises | The swarm coordinator runs to convergence | The failed task is recorded, two alternative tasks finish, and the accepted output comes from a healthy Agnet | Failure handling, not normal success |
-| B02 | Emergent consensus from local evidence | Several local evidence tasks, where no single weak signal beats the strongest individual alternative | Agents update shared state with local candidate evidence | The accumulated candidate wins through shared-state aggregation | Emergence, not simple highest single answer |
-| B03 | Pheromone-biased task selection | Multiple pending tasks with different pheromone values | Agents claim tasks through the shared task pool | The highest pheromone task is claimed first and positive feedback is recorded | Indirect coordination, not explicit handoff |
-| B04 | LangGraph-style handoff continuity | Collector, analyst, and reporter agents with shared active-agent state | Each stage writes a `transfer_to_<agent>` handoff and payload | The next agent observes the previous handoff, continues context, and final convergence uses the reporter output | Control transfer, not parallel competition |
-
-## Passing Bar
-
-The behavior acceptance result is PASS only if all B01-B04 scenarios pass. A
-passing run proves the current prototype has minimal swarm behavior under
-deterministic conditions. It does not prove large-scale production performance,
-Byzantine-agent resistance, or certified LangGraph package compatibility.

docs/SWARM_TEST_INPUT_OUTPUT_REPORT.md

@@ -1,363 +0,0 @@
-# Swarm Test Input / Output Report
-
-This report shows the actual deterministic test content for the minimal swarm
-prototype. It separates the swarm-behavior tests from the traditional-vs-swarm
-comparison benchmark.
-
-## Commands Executed
-
-```bash
-./.venv/bin/python -u -B examples/run_swarm_behavior_acceptance.py
-./.venv/bin/python -u -B examples/run_swarm_vs_traditional_benchmark.py
-```
-
-Both commands returned `status: PASS`.
-
-## Behavior Test Content
-
-### B01: Single Agnet Failure Isolation
-
-Purpose: prove one failed Agnet does not collapse the swarm result.
-
-Input:
-
-```json
-{
-  "run_id": "behavior-fault-isolation",
-  "goal": "fault isolation with redundant routes",
-  "tasks": [
-    {"kind": "route", "input": "fragile-route"},
-    {"kind": "route", "input": "robust-route-a"},
-    {"kind": "route", "input": "robust-route-b"}
-  ],
-  "agents": [
-    {"id": "crashing-agnet", "capability": "route", "behavior": "raise RuntimeError('single agnet crashed')"},
-    {"id": "backup-agnet-a", "capability": "route", "behavior": "return healthy result, score 0.91"},
-    {"id": "backup-agnet-b", "capability": "route", "behavior": "return alternative result, score 0.86"}
-  ]
-}
-```
-
-Expected output:
-
-```json
-{
-  "failed_tasks": 1,
-  "done_tasks": 2,
-  "run_status": "converged",
-  "accepted_output_contains": "healthy result",
-  "observations_include": ["route:failed", "route:done"]
-}
-```
-
-Actual output:
-
-```json
-{
-  "passed": true,
-  "evidence": "Ran 1 test ... OK"
-}
-```
-
-Concrete assertions are in
-`tests/test_swarm_behavior_academic.py::test_single_agnet_failure_isolated_by_redundant_convergence`.
-
-### B02: Emergent Consensus From Local Evidence
-
-Purpose: prove group-level aggregation can beat the best single local signal.
-
-Input:
-
-```json
-{
-  "run_id": "behavior-emergent-consensus",
-  "goal": "local evidence should create group consensus",
-  "tasks": [
-    {"kind": "evidence", "input": "alpha:0.31"},
-    {"kind": "evidence", "input": "beta:0.33"},
-    {"kind": "evidence", "input": "beta:0.34"},
-    {"kind": "evidence", "input": "gamma:0.45"}
-  ],
-  "rule": "each agent adds its local value into shared_state['candidate:<name>:score']"
-}
-```
-
-Traditional baseline output:
-
-```json
-{
-  "best_single_candidate": "gamma",
-  "best_single_score": 0.45
-}
-```
-
-Expected swarm output:
-
-```json
-{
-  "accepted_candidate": "beta",
-  "aggregated_beta_score": 0.67,
-  "accepted_score_greater_than_best_single_weak_signal": true,
-  "run_status": "converged"
-}
-```
-
-Actual output:
-
-```json
-{
-  "passed": true,
-  "evidence": "Ran 1 test ... OK"
-}
-```
-
-Concrete assertions are in
-`tests/test_swarm_behavior_academic.py::test_emergent_consensus_accumulates_local_evidence`.
-
-### B03: Pheromone / Stigmergy Selection
-
-Purpose: prove the shared pheromone environment changes task selection order.
-
-Input:
-
-```json
-{
-  "run_id": "behavior-pheromone",
-  "goal": "pheromone should bias task selection",
-  "tasks": [
-    {"kind": "probe", "input": "low-signal", "initial_pheromone": 0.1, "completion_score": 0.31},
-    {"kind": "probe", "input": "high-signal", "initial_pheromone": 0.9, "completion_score": 0.82},
-    {"kind": "probe", "input": "medium-signal", "initial_pheromone": 0.4, "completion_score": 0.62}
-  ],
-  "claim_rule": "claim_next sorts pending tasks by pheromone descending"
-}
-```
-
-Expected output:
-
-```json
-{
-  "first_claimed_task": "high-signal",
-  "accepted_output_contains": "high-signal",
-  "final_pheromone_order": ["high-signal", "medium-signal", "low-signal"]
-}
-```
-
-Actual output:
-
-```json
-{
-  "passed": true,
-  "evidence": "Ran 1 test ... OK"
-}
-```
-
-Concrete assertions are in
-`tests/test_swarm_behavior_academic.py::test_pheromone_biases_claim_order_and_records_positive_feedback`.
-
-### B04: LangGraph-Style Handoff Continuity
-
-Purpose: prove handoff preserves active agent and payload continuity.
-
-Input:
-
-```json
-{
-  "run_id": "behavior-handoff",
-  "goal": "handoff continuity",
-  "initial_active_agent": "collector",
-  "tasks": [
-    {"kind": "collector", "input": "collect code facts"},
-    {"kind": "analyst", "input": "analyze code facts"},
-    {"kind": "reporter", "input": "write final report"}
-  ],
-  "handoffs": [
-    {"from": "collector", "to": "analyst", "tool_name": "transfer_to_analyst"},
-    {"from": "analyst", "to": "reporter", "tool_name": "transfer_to_reporter"}
-  ],
-  "payload": "facts: task_pool pheromone shared_state convergence; analysis: ready"
-}
-```
-
-Expected output:
-
-```json
-{
-  "handoff_collector_to_analyst": "transfer_to_analyst",
-  "handoff_analyst_to_reporter": "transfer_to_reporter",
-  "final_output_contains": "reporter final result",
-  "completed_tasks": 3
-}
-```
-
-Actual output:
-
-```json
-{
-  "passed": true,
-  "evidence": "Ran 1 test ... OK"
-}
-```
-
-Concrete assertions are in
-`tests/test_swarm_behavior_academic.py::test_langgraph_style_handoff_preserves_active_agent_and_payload`.
-
-## Traditional vs Swarm Benchmark Content
-
-### Baseline Definitions
-
-Traditional Agnet:
-
-```json
-[
-  "single agent fails closed when its one route fails",
-  "best-of local answers without shared-state aggregation",
-  "FIFO task selection without pheromone feedback",
-  "stateless handoff without active-agent/payload continuity"
-]
-```
-
-Swarm Agnet:
-
-```json
-[
-  "redundant agents share task pool and converge despite a failed individual",
-  "local observations accumulate through shared_state",
-  "pheromone scores bias claim order and final selection",
-  "handoff records active agent, transfer target, and payload continuity"
-]
-```
-
-### C01: Fault Isolation Comparison
-
-Input:
-
-```json
-{
-  "traditional": {
-    "tasks": [{"kind": "route", "input": "fragile-route"}],
-    "agents": [{"id": "single-agnet", "behavior": "raise RuntimeError('single agnet crashed')"}]
-  },
-  "swarm": {
-    "tasks": ["fragile-route", "robust-route-a", "robust-route-b"],
-    "agents": ["crashing-agnet", "backup-agnet-a", "backup-agnet-b"]
-  }
-}
-```
-
-Actual output:
-
-```json
-{
-  "traditional": 0.0,
-  "swarm": 1.0,
-  "traditional_result": "failed before convergence",
-  "swarm_completed_tasks": 2,
-  "swarm_failed_tasks": 1
-}
-```
-
-### C02: Emergent Consensus Comparison
-
-Input:
-
-```json
-{
-  "local_values": {
-    "alpha": [0.31],
-    "beta": [0.33, 0.34],
-    "gamma": [0.45]
-  }
-}
-```
-
-Actual output:
-
-```json
-{
-  "traditional_best_single": {"candidate": "gamma", "score": 0.45},
-  "swarm_aggregated_best": {"candidate": "beta", "score": 0.67},
-  "relative_gain_percent": 48.9
-}
-```
-
-### C03: Pheromone Efficiency Comparison
-
-Input:
-
-```json
-{
-  "traditional_order": ["low-signal", "medium-signal", "high-signal"],
-  "swarm_pheromone_order": ["high-signal", "medium-signal", "low-signal"],
-  "quality": {
-    "low-signal": 0.31,
-    "medium-signal": 0.62,
-    "high-signal": 0.82
-  }
-}
-```
-
-Actual output:
-
-```json
-{
-  "traditional_steps_to_best": 3,
-  "swarm_steps_to_best": 1,
-  "best_route_efficiency_traditional": 0.3333,
-  "best_route_efficiency_swarm": 1.0,
-  "relative_gain_percent": 200.0,
-  "first_claim_quality_gain_percent": 164.5,
-  "steps_to_best_reduction_percent": 66.7
-}
-```
-
-### C04: Handoff Context Retention Comparison
-
-Input:
-
-```json
-{
-  "required_context": [
-    "task_pool",
-    "pheromone",
-    "shared_state",
-    "convergence",
-    "analysis: ready"
-  ],
-  "traditional_payload": "final report",
-  "swarm_payload": "facts: task_pool pheromone shared_state convergence; analysis: ready"
-}
-```
-
-Actual output:
-
-```json
-{
-  "traditional_retained_ratio": 0.0,
-  "swarm_retained_ratio": 1.0
-}
-```
-
-## Full Benchmark Output
-
-```json
-{
-  "standard": "swarm-vs-traditional-deterministic-benchmark-v1",
-  "status": "PASS",
-  "overall_normalized_score": {
-    "traditional": 0.1958,
-    "swarm": 0.9175,
-    "relative_gain_percent": 368.5,
-    "ratio": 4.69,
-    "note": "This aggregate is a deterministic academic benchmark over four selected swarm properties, not a universal production claim."
-  }
-}
-```
-
-## Verification Boundary
-
-This report proves deterministic behavior for the four selected swarm properties:
-fault isolation, emergence, pheromone coordination, and handoff continuity. It
-does not prove all production-scale properties, large-cluster performance,
-malicious-agent resistance, or certified compatibility with the external
-LangGraph package.

docs/SWARM_TEST_INPUT_OUTPUT_REPORT.zh-CN.md

@@ -1,404 +0,0 @@
-# 蜂群测试输入 / 输出报告
-
-这份报告展示 `swarm-minimal` 最小蜂群原型的确定性测试内容。报告分成两部分：
-
-1. 蜂群行为本体测试：验证它是否真的具备蜂群特征。
-2. 传统 Agnet vs 蜂群 Agnet 对比测试：量化蜂群机制相对传统方式强多少。
-
-## 已执行命令
-
-```bash
-./.venv/bin/python -u -B examples/run_swarm_behavior_acceptance.py
-./.venv/bin/python -u -B examples/run_swarm_vs_traditional_benchmark.py
-```
-
-两个命令都返回：
-
-```json
-{
-  "status": "PASS"
-}
-```
-
-## 一、蜂群行为测试内容
-
-### B01：单个 Agnet 故障隔离
-
-测试目的：证明单个 Agnet 出错不会导致整个蜂群失败。
-
-输入：
-
-```json
-{
-  "run_id": "behavior-fault-isolation",
-  "目标": "使用冗余路线验证故障隔离",
-  "任务": [
-    {"类型": "route", "输入": "fragile-route"},
-    {"类型": "route", "输入": "robust-route-a"},
-    {"类型": "route", "输入": "robust-route-b"}
-  ],
-  "Agnet": [
-    {"id": "crashing-agnet", "能力": "route", "行为": "抛出 RuntimeError('single agnet crashed')"},
-    {"id": "backup-agnet-a", "能力": "route", "行为": "返回 healthy result，分数 0.91"},
-    {"id": "backup-agnet-b", "能力": "route", "行为": "返回 alternative result，分数 0.86"}
-  ]
-}
-```
-
-预期输出：
-
-```json
-{
-  "失败任务数": 1,
-  "完成任务数": 2,
-  "运行状态": "converged",
-  "最终采纳结果包含": "healthy result",
-  "观测记录包含": ["route:failed", "route:done"]
-}
-```
-
-实际输出：
-
-```json
-{
-  "passed": true,
-  "evidence": "Ran 1 test ... OK"
-}
-```
-
-对应测试代码：
-
-```text
-tests/test_swarm_behavior_academic.py::test_single_agnet_failure_isolated_by_redundant_convergence
-```
-
-结论：传统单点失败会中断；蜂群中一个 Agnet 失败后，其他 Agnet 仍然完成任务并收敛。
-
-### B02：群体涌现
-
-测试目的：证明多个局部弱信号通过共享状态聚合后，可以产生强于单个局部判断的整体结果。
-
-输入：
-
-```json
-{
-  "run_id": "behavior-emergent-consensus",
-  "目标": "局部证据形成群体共识",
-  "任务": [
-    {"类型": "evidence", "输入": "alpha:0.31"},
-    {"类型": "evidence", "输入": "beta:0.33"},
-    {"类型": "evidence", "输入": "beta:0.34"},
-    {"类型": "evidence", "输入": "gamma:0.45"}
-  ],
-  "规则": "每个 Agnet 把局部值累加到 shared_state['candidate:<name>:score']"
-}
-```
-
-传统 Agnet 输出：
-
-```json
-{
-  "最佳单点候选": "gamma",
-  "最佳单点分数": 0.45
-}
-```
-
-蜂群预期输出：
-
-```json
-{
-  "最终采纳候选": "beta",
-  "beta 聚合分数": 0.67,
-  "采纳分数高于最佳单点弱信号": true,
-  "运行状态": "converged"
-}
-```
-
-实际输出：
-
-```json
-{
-  "passed": true,
-  "evidence": "Ran 1 test ... OK"
-}
-```
-
-对应测试代码：
-
-```text
-tests/test_swarm_behavior_academic.py::test_emergent_consensus_accumulates_local_evidence
-```
-
-结论：单看局部值，传统 Agnet 会选择 `gamma=0.45`；蜂群通过共享状态把两个 `beta` 局部信号聚合成 `0.67`，最终选择 `beta`。这就是最小形式的群体涌现。
-
-### B03：信息素 / 间接协作
-
-测试目的：证明任务选择不是由中央调度器硬编码，而是由共享环境中的信息素影响。
-
-输入：
-
-```json
-{
-  "run_id": "behavior-pheromone",
-  "目标": "信息素影响任务选择",
-  "任务": [
-    {"类型": "probe", "输入": "low-signal", "初始信息素": 0.1, "完成分数": 0.31},
-    {"类型": "probe", "输入": "high-signal", "初始信息素": 0.9, "完成分数": 0.82},
-    {"类型": "probe", "输入": "medium-signal", "初始信息素": 0.4, "完成分数": 0.62}
-  ],
-  "claim 规则": "claim_next 按信息素从高到低选择 pending 任务"
-}
-```
-
-预期输出：
-
-```json
-{
-  "第一个被 claim 的任务": "high-signal",
-  "最终采纳结果包含": "high-signal",
-  "最终信息素顺序": ["high-signal", "medium-signal", "low-signal"]
-}
-```
-
-实际输出：
-
-```json
-{
-  "passed": true,
-  "evidence": "Ran 1 test ... OK"
-}
-```
-
-对应测试代码：
-
-```text
-tests/test_swarm_behavior_academic.py::test_pheromone_biases_claim_order_and_records_positive_feedback
-```
-
-结论：信息素高的 `high-signal` 先被选择，并在完成后形成正反馈。这个场景验证的是蜂群的间接协作机制。
-
-### B04：LangGraph 风格 Handoff 连续性
-
-测试目的：证明 agent 交接时保留 active agent 状态、目标 agent 名称和上下文 payload。
-
-输入：
-
-```json
-{
-  "run_id": "behavior-handoff",
-  "目标": "handoff continuity",
-  "初始 active_agent": "collector",
-  "任务": [
-    {"类型": "collector", "输入": "collect code facts"},
-    {"类型": "analyst", "输入": "analyze code facts"},
-    {"类型": "reporter", "输入": "write final report"}
-  ],
-  "handoff": [
-    {"from": "collector", "to": "analyst", "tool_name": "transfer_to_analyst"},
-    {"from": "analyst", "to": "reporter", "tool_name": "transfer_to_reporter"}
-  ],
-  "payload": "facts: task_pool pheromone shared_state convergence; analysis: ready"
-}
-```
-
-预期输出：
-
-```json
-{
-  "collector 到 analyst 的 handoff": "transfer_to_analyst",
-  "analyst 到 reporter 的 handoff": "transfer_to_reporter",
-  "最终输出包含": "reporter final result",
-  "完成任务数": 3
-}
-```
-
-实际输出：
-
-```json
-{
-  "passed": true,
-  "evidence": "Ran 1 test ... OK"
-}
-```
-
-对应测试代码：
-
-```text
-tests/test_swarm_behavior_academic.py::test_langgraph_style_handoff_preserves_active_agent_and_payload
-```
-
-结论：这个测试对应 LangGraph Swarm 的核心语义：通过 `transfer_to_<agent>` 将控制权交给指定 agent，并保留上下文。
-
-## 二、传统 Agnet vs 蜂群 Agnet 对比测试
-
-### 基线定义
-
-传统 Agnet：
-
-```json
-[
-  "单个 agent 路线失败后整体失败",
-  "只选择最佳单点答案，不做共享状态聚合",
-  "按 FIFO 选择任务，不使用信息素反馈",
-  "无状态 handoff，不保留 active_agent 和 payload 连续性"
-]
-```
-
-蜂群 Agnet：
-
-```json
-[
-  "多个 agent 共享任务池，单个个体失败后仍可收敛",
-  "局部观察通过 shared_state 聚合",
-  "信息素分数影响任务 claim 顺序和最终选择",
-  "handoff 记录 active_agent、目标 agent 和上下文 payload"
-]
-```
-
-### C01：故障隔离对比
-
-输入：
-
-```json
-{
-  "传统 Agnet": {
-    "任务": [{"类型": "route", "输入": "fragile-route"}],
-    "Agnet": [{"id": "single-agnet", "行为": "抛出 RuntimeError('single agnet crashed')"}]
-  },
-  "蜂群 Agnet": {
-    "任务": ["fragile-route", "robust-route-a", "robust-route-b"],
-    "Agnet": ["crashing-agnet", "backup-agnet-a", "backup-agnet-b"]
-  }
-}
-```
-
-实际输出：
-
-```json
-{
-  "传统 Agnet": 0.0,
-  "蜂群 Agnet": 1.0,
-  "传统结果": "收敛前失败",
-  "蜂群完成任务数": 2,
-  "蜂群失败任务数": 1
-}
-```
-
-### C02：群体涌现对比
-
-输入：
-
-```json
-{
-  "局部值": {
-    "alpha": [0.31],
-    "beta": [0.33, 0.34],
-    "gamma": [0.45]
-  }
-}
-```
-
-实际输出：
-
-```json
-{
-  "传统最佳单点": {"候选": "gamma", "分数": 0.45},
-  "蜂群聚合最佳": {"候选": "beta", "分数": 0.67},
-  "提升比例": "48.9%"
-}
-```
-
-### C03：信息素效率对比
-
-输入：
-
-```json
-{
-  "传统顺序": ["low-signal", "medium-signal", "high-signal"],
-  "蜂群信息素顺序": ["high-signal", "medium-signal", "low-signal"],
-  "质量分": {
-    "low-signal": 0.31,
-    "medium-signal": 0.62,
-    "high-signal": 0.82
-  }
-}
-```
-
-实际输出：
-
-```json
-{
-  "传统找到最优所需步数": 3,
-  "蜂群找到最优所需步数": 1,
-  "传统最优路线效率": 0.3333,
-  "蜂群最优路线效率": 1.0,
-  "相对提升": "200.0%",
-  "首次 claim 质量提升": "164.5%",
-  "找到最优步数减少": "66.7%"
-}
-```
-
-### C04：Handoff 上下文保留对比
-
-输入：
-
-```json
-{
-  "必须保留的上下文": [
-    "task_pool",
-    "pheromone",
-    "shared_state",
-    "convergence",
-    "analysis: ready"
-  ],
-  "传统 payload": "final report",
-  "蜂群 payload": "facts: task_pool pheromone shared_state convergence; analysis: ready"
-}
-```
-
-实际输出：
-
-```json
-{
-  "传统上下文保留率": 0.0,
-  "蜂群上下文保留率": 1.0
-}
-```
-
-## 三、完整对比结果
-
-```json
-{
-  "standard": "swarm-vs-traditional-deterministic-benchmark-v1",
-  "status": "PASS",
-  "overall_normalized_score": {
-    "传统 Agnet": 0.1958,
-    "蜂群 Agnet": 0.9175,
-    "相对提升": "368.5%",
-    "倍率": "4.69x",
-    "说明": "这是四个蜂群核心性质下的确定性学术 benchmark，不是所有生产任务的通用结论。"
-  }
-}
-```
-
-## 四、验证边界
-
-本报告证明的是这四个蜂群核心性质：
-
-1. 单个 Agnet 故障隔离。
-2. 群体涌现。
-3. 信息素间接协作。
-4. Handoff 状态连续性。
-
-它没有证明：
-
-1. 大规模生产集群性能。
-2. 恶意 / 拜占庭 Agnet 对抗能力。
-3. 所有业务任务都能提升 4.69 倍。
-4. 官方 LangGraph 包兼容性认证。
-
-所以更严谨的结论是：
-
-```text
-在当前最小蜂群原型的四个核心蜂群机制场景下，蜂群 Agnet 相比传统 Agnet 的综合归一化得分约为 4.69x。
-```