forrestneo · Oct 14, 2026
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+{
+  "name": "jev-sim",
+  "version": "0.2.0",
+  "description": "Jev-style decision simulation on the session model: Choice/Noul/Score typed decisions with rotation debiasing, an explicit NONE outlet, three-state output, and temperature-scaling calibration",
+  "description_i18n": {
+    "en": "Jev-style decision simulation on the session model: Choice/Noul/Score typed decisions with rotation debiasing, an explicit NONE outlet, three-state output, and temperature-scaling calibration",
+    "zh-CN": "用会话内大模型模拟 Jev 决策模型：Choice/Noul/Score 类型化决策，轮转去偏差、NONE 出口、三态输出、温度缩放校准"
+  },
+  "author": { "name": "forrestneo" },
+  "skills": "./skills"
+}
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+# jev-sim: an in-session decision model
+
+Simulates [Jev](https://typesafe.ai)-style typed decisions with the LLM (GLM) already running in your ZCode session. Turns judgment questions into probabilistic, machine-readable decisions — no external decision-model API, no API key, no data leaves your machine.
+
+## Three primitives
+
+| Primitive | Input | Output | Typical use |
+|---|---|---|---|
+| Choice | state + question + options | probability per option + NONE outlet | classification, routing, branching |
+| Noul | state + proposition | P(true) in 0-1 | boolean judgment, risk screening |
+| Score | state + rubric | level distribution + expected value | ranking, quality rating |
+
+## How it works
+
+Jev scores each candidate via token probabilities from sentence completion. Session-model APIs expose no token probabilities (the GLM API has no logprobs), so the protocol compensates with mechanisms validated in the open decision-model community:
+
+1. **Letter readout** — each judge subagent outputs exactly one JSON line (options encoded A-Z)
+2. **Option rotation** (cyclic shift, AnyJev's L0 layer) — ask the same question K=3 times with rotated option order, removing position bias; vote disagreement is itself signal
+3. **Symmetric framings** (Noul) — positive / reversed / evidence-first views, removing framing bias
+4. **Frequency as probability** — K votes, smoothed and normalized into a raw distribution
+5. **Temperature-scaling calibration** — fit temperature T on labeled items, `p_cal ∝ p_raw^(1/T)`; instruction models are overconfident, and this step translates "vote unanimity" into "how often this much confidence was historically right"
+
+## Output contract
+
+```json
+{
+  "primitive": "choice",
+  "protocol": "jev-sim/0.2",
+  "status": "answer",
+  "question": "Which team should handle this email?",
+  "decision": { "type": "choice", "choice": "billing-support", "probability": 0.72 },
+  "probabilities": [
+    { "option": "billing-support", "probability": 0.72 },
+    { "option": "legal", "probability": 0.14 },
+    { "option": "sales", "probability": 0.14 }
+  ],
+  "none_probability": 0.02,
+  "confidence": 0.72,
+  "threshold": 0.6,
+  "abstained": false,
+  "calibrated": true
+}
+```
+
+`status` is one of: **answer** (normal decision), **none** (NONE votes won — no option holds, refuse to force a pick), **clarify** (top probability below threshold; ask for more context or escalate). Harness-level errors are reported as errors, never disguised as decisions.
+
+## Usage
+
+After installing, start a new task, select the jev-sim skill in the composer, and hand it a judgment question:
+
+> State: customer email — broken chair on arrival, no support response for three days, threatens a consumer-complaint hotline and a lawyer.
+> Question: which team should handle this? Options: marketing / billing-support / legal / sales
+
+**Use for**: classification, routing, boolean judgment, rubric scoring, risk screening.
+**Do not use for**: open-ended writing, exact symbol/string lookups, arithmetic, or anything expressible as a deterministic rule — just do those directly.
+
+Calibration (recommended once before first use): `python scripts/calibrate.py --results eval/your-results.jsonl`. It writes `scripts/calibration.json`, applied automatically afterwards. `eval/choice-eval.jsonl` ships a 12-item seed set; see the "calibration workflow" section in SKILL.md.
+
+## Side effects and dependencies
+
+- **Network access: none.** All sampling happens inside the current ZCode session; no external API is called, no data is sent anywhere.
+- **Model dependency:** uses the session's built-in LLM (judge subagents via the Agent tool); no extra account or key.
+- **Command execution:** runs `python scripts/aggregate.py` and `scripts/calibrate.py` (Python stdlib only) for local aggregation and calibration.
+- **File writes:** normal use only prints results to the conversation. Only an explicitly run calibration writes `calibration.json` under the plugin's `scripts/`; the aggregator only reads the votes file you specify.
+- **No hooks, no MCP server, no background process, no credentials or user-data collection.**
+
+## Third-party sources and license
+
+- The Choice/Noul/Score primitive taxonomy references public Jev (System One, TypeSafe AI) concepts; this plugin is not affiliated with it.
+- The rotation-debiasing idea references the AnyJev paper (Nokia Applied Research, Apache-2.0); no code was copied.
+- All plugin code is original. MIT License.
+
+## Test results (2026-10-14, GLM-5.3 as judge)
+
+**Unit tests** (synthetic data, verifying the math): letter→option mapping, smoothing, and gray-zone abstention all correct; the calibrator reduced ECE from 0.125 to 0.042 on synthetic error data (T=1.45, correct softening direction); a golden-section boundary-update bug was found and fixed during testing. The 0.2.0 NONE outlet and status states passed a nine-path smoke test (majority-NONE correctly triggers `none`, vote splits trigger `clarify`, option renormalization correct).
+
+**Live-model evaluation** (12 easy + 8 trap items, 3 rotations each, 60 votes total): 60/60 correct, zero vote splits, self-reported judge confidence 72-100 (mean ~97). Position-bias check: across all three rotations judges answered by option content, not position. Consequence: 20/20 correct degenerates temperature fitting ("all-correct data proves nothing"), so **no calibration.json ships**; output honestly reports `calibrated: false` until you calibrate on your own labeled items.
+
+**Primitive demos**: Choice (email routing, 4 options × 3 rotations) 3/3 unanimous → 0.928; Noul (contract clause, three framings) 0.95/0.98/0.96 → p_true 0.963; Score (5-level rubric) 3/3 → level 4, expected value 3.88.
+
+**Honest conclusion**: GLM-5.3 as judge is highly consistent on well-posed items; frequency probabilities often hit 1.0 (~0.93 after smoothing); abstention and NONE only fire when judges genuinely split. To profile real failure rates, recalibrate on items your business actually gets wrong.
+
+## Known limitations
+
+- Each decision spawns K judge subagents — slower and more session-token-hungry than calling a dedicated decision-model API (you save API cost and privacy)
+- Probabilities come from sampling frequency, not token logits; resolution is capped by K (at K=3, non-NONE votes only give 0/⅓/⅔/1)
+- Letter readout supports ≤26 options; more requires grouped elimination (see SKILL.md)
+- Until calibrated, output reports `calibrated: false` and probabilities skew confident
+
+## Layout
+
+```
+.zcode-plugin/plugin.json   plugin manifest
+skills/jev-sim/SKILL.md     decision protocol (core)
+scripts/aggregate.py        vote aggregation + temperature scaling + NONE/status
+scripts/calibrate.py        ECE computation + temperature fitting
+eval/choice-eval.jsonl      12-item seed evaluation set
+```
+
+## Versions
+
+- 0.2.0 — explicit NONE outlet and three-state `status`; option-cap note with grouped sampling; threshold statement (0.6 default is a starting point); audit fields (protocol/threshold/n_samples/none_probability); EN/CN READMEs
+- 0.1.0 — Choice/Noul/Score primitives, rotation sampling, temperature-scaling calibration, 20-item evaluation (60/60 votes correct)
@@ -0,0 +1,103 @@
+# jev-sim：会话内决策模型
+
+用当前 ZCode 会话里的大模型（GLM）模拟 [Jev](https://typesafe.ai) 式决策模型。把"判断题"变成带概率的类型化决策——不调外部决策模型 API、不需要 API key、数据不出本机。
+
+## 三个原语
+
+| 原语 | 输入 | 输出 | 典型用途 |
+|---|---|---|---|
+| Choice | 背景 + 问题 + 选项 | 每个选项的概率 + NONE 出口 | 分类、路由、分支 |
+| Noul | 背景 + 命题 | 命题为真的概率 0-1 | 布尔判断、风险筛查 |
+| Score | 背景 + 量规 | 等级分布 + 期望分值 | 排序、质量评估 |
+
+## 工作原理
+
+Jev 的底层是把决策变成句子填空，用 token 概率给每个候选打分。会话模型拿不到 token 概率（GLM API 不开放 logprobs），所以用替代机制，全部来自开源决策模型社区的已验证做法：
+
+1. **字母作答**（letter readout）——判官子代理只回一行 JSON（选项编成 A-Z）
+2. **选项轮转**（cyclic shift，AnyJev 的 L0 层）——同一问题换选项顺序问 K=3 遍，消除位置偏差，选票分歧本身就是信息
+3. **对称问句**（Noul 专用）——正问、反问、证据式三视角，消除框架偏差
+4. **频率即概率**——K 张选票平滑归一化成原始概率分布
+5. **温度缩放校准**——拿带标准答案的题拟合温度 T，`p_cal ∝ p_raw^(1/T)`；指示模型普遍过自信，这步把"投票一致性"翻译成"历史上这种把握实际对多少"
+
+## 输出契约
+
+```json
+{
+  "primitive": "choice",
+  "protocol": "jev-sim/0.2",
+  "status": "answer",
+  "question": "这封邮件路由给谁？",
+  "decision": { "type": "choice", "choice": "客服-退款", "probability": 0.72 },
+  "probabilities": [
+    { "option": "客服-退款", "probability": 0.72 },
+    { "option": "法务", "probability": 0.14 },
+    { "option": "销售", "probability": 0.14 }
+  ],
+  "none_probability": 0.02,
+  "confidence": 0.72,
+  "threshold": 0.6,
+  "abstained": false,
+  "calibrated": true
+}
+```
+
+`status` 三态：**answer**（正常决策）、**none**（NONE 票占多数——没有选项成立，不硬选）、**clarify**（最高概率低于阈值，建议补背景重问或人工处理）。采样/脚本层面的错误按错误原样上报，不伪装成决策。
+
+## 使用
+
+安装后新建任务，在 composer 里选中 jev-sim 技能，直接给判断题：
+
+> 背景：发件人李某，正文：你们平台 2 号下单的椅子到货就是断腿的，联系客服三天没人理，今天不退款就投诉……
+> 问题：这封邮件路由给哪个团队？选项：市场 / 客服-退款 / 法务 / 销售
+
+**适用**：分类、路由、布尔判断、按量规评分、风险筛查。
+**不适用**：开放式写作、精确符号/字符串查找、算术、能写成确定性规则的判断——这些直接做，不过决策协议。
+
+校准（建议首次使用前跑一次）：`python scripts/calibrate.py --results eval/your-results.jsonl`，生成 `scripts/calibration.json` 后所有决策自动套用。自带 `eval/choice-eval.jsonl` 是 12 题种子评测集，方法见 SKILL.md"校准流程"。
+
+## 副作用与依赖声明
+
+- **网络访问：无。** 全部采样发生在当前 ZCode 会话内，不请求任何外部 API，不发送任何数据。
+- **模型依赖：** 使用会话内置大模型（通过 Agent 工具派出判官子代理）；无需额外账号或密钥。
+- **命令执行：** 运行 `python scripts/aggregate.py`、`scripts/calibrate.py`（仅 Python 标准库）做本地聚合与校准。
+- **文件写入：** 常规使用只把结果输出到对话。仅当主动运行校准时会在插件 `scripts/` 下写 `calibration.json`；聚合脚本只读指定的 votes 文件。
+- **无 hooks、无 MCP server、无后台进程，不收集凭据或用户数据。**
+
+## 第三方来源与许可
+
+- Choice/Noul/Score 原语分类参考 Jev（System One，TypeSafe AI）的公开概念；本插件与其无关联。
+- 选项轮转去偏差思路参考 AnyJev 论文（Nokia Applied Research，Apache-2.0）的 cyclic shift，未复制其代码。
+- 本插件全部代码原创，MIT License。
+
+## 测试结果（2026-10-14，GLM-5.3 判官）
+
+**单元测试**（合成数据，验证脚本数学）：Choice 字母→选项映射、平滑、灰区弃答全部正确；校准器在合成误差数据上把 ECE 从 0.125 降到 0.042（T=1.45，正确软化方向）；期间发现并修复了黄金分割搜索的边界更新 bug。0.2.0 新增的 NONE 出口与 status 三态通过九条路径冒烟测试（多数 NONE 正确触发 none、平票触发 clarify、选项概率重归一正确）。
+
+**真模型评测**（12 道易题 + 8 道陷阱题，各 3 轮转，共 60 张选票）：60/60 全对，零选票分裂，判官自报置信度 72-100（均值约 97）。位置偏差检验：三个轮转下判官始终按选项内容作答，未观察到位置偏差。后果：20 题全对使温度拟合退化（"全对数据证明不了什么"），**本插件不预置 calibration.json**，输出如实标注 `calibrated: false`，直到用自己的标注题跑过校准。
+
+**三原语功能演示**：Choice（邮件路由，4 选项 3 轮转）3/3 一致 → 0.928；Noul（合同条款，三视角）0.95/0.98/0.96 → p_true 0.963；Score（5 级量规）3/3 一致 → 4=满意，期望分值 3.88。
+
+**诚实结论**：GLM-5.3 当判官在明确题上高度一致，频率概率经常是 1.0（平滑后约 0.93）；弃答与 NONE 机制只在判官分裂（真分歧/真难题）时触发。要刻画真实失败率，需要用你自己业务里"会错"的题重新跑校准。
+
+## 已知边界
+
+- 每次决策要派 K 个判官子代理，比直接调专用决策模型 API 慢、费会话 token（省的是 API 费和隐私）
+- 概率来自采样频率而非 token logit，分辨率受轮次 K 限制（K=3 时非 NONE 票只有 0/⅓/⅔/1 四档）
+- 字母作答支持 ≤26 个选项，更多需分组初筛（见 SKILL.md）
+- 校准没跑过之前，输出标 `calibrated: false`，概率偏自信
+
+## 目录
+
+```
+.zcode-plugin/plugin.json   插件清单
+skills/jev-sim/SKILL.md     决策协议（核心）
+scripts/aggregate.py        选票聚合 + 温度缩放 + NONE/status
+scripts/calibrate.py        ECE 计算 + 温度拟合
+eval/choice-eval.jsonl      12 题种子评测集
+```
+
+## 版本
+
+- 0.2.0 —— NONE 显式出口与 `status` 三态输出；选项上限与分组采样说明；阈值声明（默认 0.6 仅为起点）；审计字段（protocol/threshold/n_samples/none_probability）；中英 README
+- 0.1.0 —— Choice/Noul/Score 三原语、轮转采样、温度缩放校准、20 题评测（60/60 票正确）
@@ -0,0 +1,12 @@
+{"id": "ev01", "question": "这封邮件应该路由给哪个团队？", "state": "发件人：李某。正文：你们平台2号下单的椅子到货就是断腿的，联系客服三天没人理。今天不给我退款，我就去12315和黑猫投诉，并且找律师发函。订单号SF88231。", "options": ["客服-退款", "法务", "销售", "市场"], "correct": "客服-退款"}
+{"id": "ev02", "question": "这段代码是什么语言？", "state": "def flatten(xs):\n    out = []\n    for x in xs:\n        if isinstance(x, list):\n            out.extend(flatten(x))\n        else:\n            out.append(x)\n    return out", "options": ["Python", "JavaScript", "Go", "Ruby"], "correct": "Python"}
+{"id": "ev03", "question": "这条用户评论的情感倾向是？", "state": "评论原文：真是'太棒了'，买来第二天屏幕就绿了，客服还教我重启大法，学到了学到了。下次一定还来（不会）。", "options": ["正面", "负面", "中性", "无法判断"], "correct": "负面"}
+{"id": "ev04", "question": "这个故障该定什么优先级？", "state": "监控告警：23:47 主数据库实例无响应，订单服务全量报错，支付成功率跌到0，持续12分钟，影响所有线上交易。", "options": ["P0-立即响应", "P1-高优处理", "P2-工作时间内处理", "P3-排期处理"], "correct": "P0-立即响应"}
+{"id": "ev05", "question": "这段文字是什么语言？", "state": "Trotz der Herausforderungen in den vergangenen Monaten hat das Team eine bemerkenswerte Leistung erbracht, die Anerkennung verdient.", "options": ["德语", "荷兰语", "瑞典语", "丹麦语"], "correct": "德语"}
+{"id": "ev06", "question": "这份简历的技术背景最贴近哪个岗位方向？", "state": "技能：Docker、Kubernetes、Terraform、Prometheus、Grafana、Shell、Go 基础。项目：搭建过 200 节点 K8s 集群，落地 CI/CD 流水线，负责服务网格治理。", "options": ["DevOps工程师", "前端工程师", "算法工程师", "数据分析师"], "correct": "DevOps工程师"}
+{"id": "ev07", "question": "这段新闻属于哪个频道？", "state": "摘要：某研究团队宣布在超导量子比特的纠错码距离上取得新纪录，逻辑错误率随码距增加实现了指数级下降，相关论文发表于 Nature。", "options": ["科技", "财经", "体育", "娱乐"], "correct": "科技"}
+{"id": "ev08", "question": "这段代码的主要缺陷类型是？", "state": "function total(items) {\n  let sum = 0;\n  for (let i = 0; i <= items.length; i++) {\n    sum += items[i].price;\n  }\n  return sum;\n}", "options": ["边界错误off-by-one", "空指针风险", "死循环", "类型错误"], "correct": "边界错误off-by-one"}
+{"id": "ev09", "question": "这封邮件的核心意图是什么？", "state": "发件人：某公司采购部王某。正文：看到贵司展会的工业相机方案，我们要采购30台用于产线质检，请报含税单价、货期和批量折扣，发到这个邮箱即可。", "options": ["销售-询价", "客服-售后", "人事-招聘", "合作-商务"], "correct": "销售-询价"}
+{"id": "ev10", "question": "下列哪种动物是哺乳动物？", "state": "（常识判断）", "options": ["海豚", "鲨鱼", "企鹅", "鳄鱼"], "correct": "海豚"}
+{"id": "ev11", "question": "这条消息应该路由给哪个处理组？", "state": "用户App内消息：上周买的落地灯罩运损裂了一条缝，灯体没事。不用退货吧？能不能只补发一个灯罩，或者我留着用你们补我差价也行。", "options": ["售后-补发换货", "客服-退款", "物流-理赔", "销售-续购"], "correct": "售后-补发换货"}
+{"id": "ev12", "question": "光年是什么类型的单位？", "state": "（常识判断）", "options": ["距离单位", "时间单位", "速度单位", "亮度单位"], "correct": "距离单位"}