Adversarial Solver / 对抗式求解器

Dual-LLM adversarial content generation — Generator produces, Critic reviews, loop until PASS. 双 LLM 对抗式内容生成 — Generator 产出，Critic 审核，不通过则修订，循环直到 PASS。

Why? / 为什么？

Writing content is easy. Writing correct content is hard. 写内容容易，写"对"的内容很难。

Adversarial Solver makes two (or three) LLMs keep each other in check: 对抗式求解器让两个（或三个）LLM 互相制衡：

• Generator produces the content / 产出内容
• Critic reviews it against your rules / 按你的规则审核
• If it fails, the Generator revises / 不通过就修订
• Loop until the Critic (or Arbiter) says "PASS" / 循环直到通过

The result: content that has been through a quality-control pipeline before it reaches human eyes. 结果：内容在到达人眼之前，已经跑完一条质检流水线。

Key Features / 核心功能

• Dual-Model Adversarial Loop / 双模型对抗循环 — Generator → Critic → Revise → PASS
• Optional Arbiter / 可选仲裁者 — 3rd model breaks Generator-Critic deadlocks / 第三模型打破僵局
• Auto Mode Detection / 自动模式检测 — Short-context models (e.g. MiniMax M3 at 8K) auto-trigger segmented mode / 短上下文模型自动分段
• Hard Empty-Response Prevention / 空值硬约束 — Built-in retry + temperature escalation + automatic fallback model switching / 重试 + 升温 + 兜底模型切换
• Tone Checker / 调性检查器 — Hard-filter banned words with configurable YAML rules / 可配置违禁词硬过滤
• Segmented Mode / 分段模式 — Long-form content split into outline → sections → assembly / 长内容自动拆分为大纲→逐节→拼装
• Config-Driven / 配置驱动 — YAML departments and rules; no code changes needed / 不改代码，只改 YAML
• Multi-Provider / 多模型支持 — Any LiteLLM-supported model (OpenAI, Anthropic, DeepSeek, MiniMax, Gemini, local models)
• Full Audit Trail / 完整审计日志 — Every round logged; results archived as JSON / 每轮记录，归档为 JSON

Quick Start / 快速开始

# Install / 安装
pip install adversarial-solver

# Create config templates / 生成配置模板
adversarial-solver init

# Edit config/departments.yaml and config/rules.yaml / 编辑配置文件
# Add your API keys to config/.env / 填入 API Key

# Run your first adversarial solve (mode auto-detected) / 跑第一次对抗求解
adversarial-solver solve -t "Write a product description." -d marketing

How It Works / 工作原理

Task / 任务 → Generator (v1) → Critic reviews / 审核
                    ↑            ↓
                    │      PASS? ─── Yes → Output / 输出
                    │         │
                    └─ Revise / 修订 ← No
                    │         │
                    │   Deadlocked? / 僵局? → Arbiter (optional / 可选) → Final verdict / 最终裁决
                    │
               [Empty response? / 空值? → Retry / 重试 → Fallback model / 兜底模型]

Auto Mode Detection / 自动模式检测

The solver automatically detects whether to use standard or segmented mode based on model context windows: 求解器根据模型上下文窗口自动判断用 standard 还是 segmented 模式：

Scenario / 场景	Detection / 检测	Behavior / 行为
Both models 128K+ context / 两模型都 128K+	→ standard	Single-pass generation / 单次生成
One model ≤16K context (e.g. M3) / 一个模型 ≤16K	→ segmented	Auto-split into outline + sections / 自动分段
Manual override / 手动覆盖	-m standard or -m segmented	User choice respected / 尊重用户选择

Run adversarial-solver info -m MiniMax/M3 to check a model's context window. 运行 adversarial-solver info -m MiniMax/M3 查看模型上下文窗口。

Configuration / 配置

departments.yaml — Define your review teams / 定义审查部门

departments:
  marketing:
    name: "Marketing"                            # Department name / 部门名称
    primary_model: "openai/gpt-4.1"             # Generator / 生成器
    reviewer_model: "anthropic/claude-sonnet-4-6" # Critic / 审核者
    arbiter_model: ""                            # Optional 3rd model / 可选仲裁
    fallback_model: ""                           # Fallback on empty / 空值兜底
    tone_checker: true                           # Enable tone checker / 启用调性检查

rules.yaml — Define your banned words / 定义违禁词

banned_words:
  medical:                        # Medical claims / 医疗宣称
    - "cure"
    - "treat"
    - "heal"
  exaggeration:                   # Exaggerated language / 夸张用语
    - "premium"
    - "luxury"
    - "revolutionary"

constitution.md — Define your principles / 定义品牌原则

Write your brand guidelines, core principles, and hard rules. The Generator references this for every task. 写入你的品牌准则、核心原则和硬性规则。Generator 每次执行任务时都会参考。

CLI Reference / 命令行参考

# Single task (mode auto-detected) / 单任务（自动检测模式）
adversarial-solver solve -t "Write a landing page headline." -d marketing

# Force segmented mode / 强制分段模式
adversarial-solver solve -t "Generate a 50-page report." -d editorial -m segmented

# Tone check on existing text / 对已有文本做调性检查
adversarial-solver check-tone -t "This product cures everything."

# Check model context window / 查看模型上下文窗口
adversarial-solver info -m MiniMax/M3

# Create config templates / 创建配置模板
adversarial-solver init -p my_project/config

Python API

from adversarial_solver import adversarial_solve

result = adversarial_solve(
    task="Write a product description.",       # Task / 任务
    dept="marketing",                          # Department / 部门
    max_rounds=3,                              # Max review rounds / 最大审核轮次
    config_path="my_config",                   # optional / 可选
    mode=None,                                 # None = auto-detect / 自动检测
)

print(result["final"])          # The approved output / 最终通过的输出
print(result["status"])         # "PASS" or "PENDING_REVIEW" / 通过或待人工审核
print(result["total_rounds"])   # How many review rounds / 审核轮次
print(result.get("tone_check")) # Tone checker results / 调性检查结果

Batch Processing / 批量处理

from adversarial_solver import batch_solve

results = batch_solve([
    {"task": "Write a homepage headline.", "dept": "marketing"},
    {"task": "Write a privacy policy summary.", "dept": "legal"},
])
print(f"{results['passed']}/{results['total']} passed")

Real-World Use Cases / 实际应用场景

Use Case / 场景	Department / 部门	Typical Mode / 模式
Brand copy & ads / 品牌文案·广告	marketing	standard
Legal/regulatory docs / 法律合规文档	legal	standard
Code review / 代码审查	engineering	standard
Blog posts / 博客文章	editorial	standard
Competitive analysis (50+ pages) / 竞品分析	editorial	auto-segmented
Strategy documents / 策略文档	marketing	auto-segmented

FAQ

Q: Do I need two different models? / 必须用两个不同的模型吗？ A: No. Same model works — the Critic gets a different system prompt that makes it behave as a reviewer. / 不需要。同一个模型也可以——Critic 用不同的 system prompt 就会以审核者身份工作。

Q: When do I need the Arbiter (3rd model)? / 什么时候需要第三个模型？ A: When Generator and Critic disagree for multiple rounds and you want an automatic tie-breaker. Most cases, 2 models are enough. / Generator 和 Critic 多轮僵持不下时，Arbiter 自动裁决。大多数情况两个模型够了。

Q: What happens if a model returns empty? / 模型返回空值怎么办？ A: The solver retries with temperature escalation. If still empty, it switches to fallback_model (if configured). If fallback also fails, the task is archived for human review. / 先重试 + 升温。还空就切 fallback_model。都失败则归档待人工介入。

Q: How does auto mode detection work? / 自动模式检测怎么工作？ A: The solver checks both models' context windows (via built-in lookup table). If either model has ≤16K context, it auto-switches to segmented mode. / 检查两个模型的上下文窗口，任一 ≤16K 就自动切分段模式。

Q: What if my model isn't in the context window lookup table? / 模型不在查表里？ A: Unknown models default to 128K (safe for most modern LLMs). You can submit a PR to add your model. / 未知模型默认 128K。欢迎提 PR 添加。

Q: Can I use local models? / 能用本地模型吗？ A: Yes — via LiteLLM's support for Ollama, vLLM, and other local providers. / 可以，通过 LiteLLM 对接 Ollama/vLLM 等。

Q: Does the tone checker work with non-English text? / 调性检查支持非英文吗？ A: Yes. Banned words are case-insensitive substring matches. Add any language words to rules.yaml. / 支持。违禁词是大小写不敏感的子串匹配，任何语言都行。

Requirements / 环境要求

• Python 3.10+
• API key for at least one LLM provider / 至少一个 LLM 服务商的 API Key

Contributing / 贡献

See / 参见 CONTRIBUTING.md.

License / 许可证

MIT — see / 参见 LICENSE.