ZTL Judge

io.github.inventor1975v1.0.0更新于 Sep 29, 2026

Zero-trust logic judge: your AI writes a claim as a ZFL table, the ZTL core judges it.

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概览

AI 生成的概览

让助手把自然语言主张转换为形式化的 ZFL 表格，再由确定性的 ZTL 核心进行裁决，给出结论、保证等级和通行证。

功能
助手帮助把主张形式化为 ZFL，即由若干行加一条主张组成的表格，每行包含状态（T 已核验、F 已驳斥、Z 未核验）、依据以及用文字说明的含义。随后由确定性的 ZTL 核心进行裁决，返回二值结论、保证等级、未核验输入的通行证以及完备性表。对于自指系统，它会报告有根据的部分、隔离集合，以及每个组件的通行证，例如 PARADOX、UNDERDETERMINED、INPUT 或 DOWNSTREAM。确定性的回读会复述核心实际读到的内容，可选的 AI 解释只复述结论而不重新裁决。
适用场景
当你希望助手帮助精确表述某个主张或悖论，再由确定性的非 LLM 组件进行裁决，而不是依赖模型自身的推理时，适合使用。它适用于逻辑、形式化和一致性检查类工作，这类工作要求把翻译步骤与裁决步骤分开。对于普通问答或无法归约为形式化表格的任务，它不太适用。
运行要求
远程端点 streamable HTTP；清单声明无需认证、环境变量或请求头。README 描述了一个本地 Python 工作室（python3 ztlstudio.py），仅用标准库，ZTL 核心已内置。AI 翻译是可选的：没有密钥时以 pro 模式手动填表；有密钥时可使用 Groq、Anthropic、OpenAI、OpenRouter、DeepSeek、Gemini、xAI 或 NVIDIA 等提供商。
安装前请注意
可选的 AI 翻译步骤会把你的主张文本发送给第三方模型提供商；API 密钥来自设置字段、环境变量（如 GROQ_API_KEY、ANTHROPIC_API_KEY）或本地 . _key 文件。README 称密钥保留在本机且被 gitignore，但能访问该机器或文件的人仍可读取。AI 解释被标注为未经核验，不能当作裁决结果。

安装

在 SourceWeft 中

  1. 打开 控制台中的 ZTL Judge，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "ztl-judge": {
      "type": "http",
      "url": "https://api.vitalyreznik.com/mcp"
    }
  }
}

README

ZTLStudio

The AI translates; the measured core judges — truth is never granted on credit, not even to the translator.

A local studio for judging claims and paradoxes. You state one in natural language (any language); an LLM only translates it into ZFL, the formal table language — it never judges. A deterministic, measured ZTL core does the judging: verdicts with warranties, quarantine passports for self-referential systems, and a deterministic back-reading that verbalizes exactly what the core read from your table.

The pipeline embodies the logic it serves: the LLM's output is an unverified input (the mark Z), and the core is the customs house — truth is never granted on credit, not even to the translator.

human ──meta-chat──► the AI fills a ZFL table (rows + a claim), you sign off                         │                         ▼ validator ──► the deterministic core judges                         ▼ back-reading (no AI — the second auditor)                         ▼                   verdict · warranty · passport · stipulations

Run

python3 ztlstudio.py        # → http://localhost:8190

Python stdlib only; the ZTL core is vendored in ztlcore/, so a clone is self-contained (no submodules, no dependencies). The AI is optional: with no key the studio runs in pro mode — fill the ZFL table by hand. To enable AI translation, open ⚙ Model, pick a provider + model + key, or set the env var, or drop a key into a local .<provider>_key file (all gitignored — no keys ship).

What you hand it: one table, no genre to declare

ZFL v2 is a single table of rows plus a claim. Each row states a fact, its status (T verified / F refuted / Z unverified — the zero-trust default), its ground, and — importantly — what it means in words (the polarity auditor: it lets the back-reading catch an encoding that says the opposite of what you intended). You never declare whether this is a "statement" or a "paradox": the genre is computed, and whichever instruments apply fire — a verdict + warranty for a claim, a passport for a self-referential system.

The studio ships 41 worked examples — open one to see the exact shape of the table, then edit it. The back-reading verbalizes what the core actually read, so your translation is audited by a component that cannot hallucinate.

The workflow

  1. Meta-chat — describe the claim in your language; the AI fills the table's rows and asks only when formalization is genuinely blocked. It knows its boundary: arithmetic, quantities and numeric wordplay get an honest "does not formalize into propositional ZTL", never an invented encoding.
  2. The table — a grid of rows, the grounds bar, and the claim line, all hand-editable (pros skip the chat entirely). Run validates and judges; validator issues are machine-readable and can be fed back to the AI to repair.
  3. The report — the core's verdict, its warranty grade (hereditary / sound / until-verification), the passport of unverified inputs, and the completion table — followed by the deterministic back-reading and an optional AI explanation that retells the verdict and is forbidden to re-judge (labeled unverified by definition: the pipeline applies its own logic to itself).

What the core reports

  • Claims — the verdict (T/F — verdicts are always two-valued; Z is a mark on an input, never a verdict), the warranty grade, the passport of unverified inputs, and the completion table showing how the verdict behaves under every reading of the unverified rows.
  • Self-referential systems — the grounded part (identical in every fixed point), the quarantine set, and a passport per component: PARADOX (no classical solution — permanent refusal, with the oscillation period), UNDERDETERMINED (refusal until stipulation), INPUT (until verification), DOWNSTREAM (inherited).

Providers

Keys stay on this machine, read in order: the Settings field, the env var (GROQ_API_KEY, ANTHROPIC_API_KEY, …), then a local .<provider>_key file. Supported: Groq, Anthropic (Claude), OpenAI, OpenRouter, DeepSeek, Gemini, xAI, NVIDIA. A stronger model formalizes cleaner; the core judges the same regardless of who translated.

Related

  • ZTL — the logic itself: the kernel, the papers, and the ZFL language.
  • introspect — the same zero-trust core applied to code: a taint analyzer for seven languages.

AI disclosure

Built by Claude (Anthropic) as architect and implementer, with Vitaly Reznik as human curator and decision-maker, under a strict honesty discipline: mark boundaries honestly, measure — don't guess, and never claim more than was verified.

License

Dual-licensed under MIT and Apache-2.0 (see LICENSE-MIT, LICENSE-APACHE).

来源：README.md，提交 c7e6f19

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版本历史

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  1. v1.0.0最新Sep 29, 2026