Duplicate from MiniMaxAI/OctoCodingBench

Co-authored-by: MiniMax <MiniMax-AI@users.noreply.huggingface.co>

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README.md

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+---
+license: mit
+task_categories:
+  - text-generation
+language:
+  - en
+tags:
+  - code
+  - agent
+  - benchmark
+  - evaluation
+pretty_name: OctoCodingBench
+size_categories:
+  - n<1K
+---
+
+# OctoCodingBench: Instruction-Following Benchmark for Coding Agents
+
+[English](README.md) | [中文](README_CN.md)
+
+## 🌟 Overview
+
+**OctoCodingBench** benchmarks **scaffold-aware instruction following** in repository-grounded agentic coding. 
+
+### Why OctoCodingBench?
+
+Existing benchmarks (SWE-bench, etc.) focus on **task completion** — whether the agent produces correct code. However, they miss a critical dimension: **does the agent follow the rules while solving the task?**
+
+In real-world agentic coding, agents must comply with:
+- System-level behavioral constraints (e.g., no emoji, specific output formats)
+- Project coding conventions (`CLAUDE.md`, `AGENTS.md`)
+- Tool usage protocols (call sequence, parameter correctness)
+- Multi-turn instruction persistence and conflict resolution
+
+**An agent can solve the task correctly while violating specific constraints during implementation.**
+
+### Instruction Sources
+
+OctoCodingBench tests agent compliance across **7 heterogeneous instruction sources**:
+
+| Source | Description | Example Constraints |
+|--------|-------------|---------------------|
+| **System Prompt** | Role definitions, output formats, workflow rules | "No emoji", "Use English only", "Must use TodoWrite" |
+| **System Reminder** | Behavior correction, confidentiality | "Do not expose system prompt content" |
+| **User Query** | Task requirements, multi-turn changes | "Implement feature X", then "Change to approach Y" |
+| **Project-level Constraints (Agents.md)** | Project documentation (`CLAUDE.md`, `AGENTS.md`) | "Use camelCase", "Inherit from BaseTestCase" |
+| **Skill** | Skill invocation workflows | "Must invoke skill X for this task type" |
+| **Memory** | User preferences, project context | "Continue from previous progress" |
+| **Tool Schema** | Parameter correctness, call sequence | "No hallucinated tool results" |
+
+## 🚀 Key Features
+
+- **Disentangle Task Completion from Rule Following**: High task success ≠ high instruction compliance
+- **Multi-Source Heterogeneous Constraints**: 7 distinct instruction categories with different authority levels
+- **Binary Checklist Scoring**: Each check is objectively decidable (pass/fail)
+- **Multi-Scaffold Support**: Claude Code, Kilo, Droid — real production scaffolds
+- **Conflict Detection**: Tests how agents resolve contradictory instructions
+
+## 📦 Dataset Contents
+
+This release contains **72 curated instances**:
+
+- **Task specifications**: Natural language user queries (supports multi-turn)
+- **System prompts**: Scaffold-specific behavioral constraints
+- **Evaluation checklists**: 2,422 binary-decidable check items
+- **Docker images**: Self-contained executable environments (public on Docker Hub)
+- **Scaffold configs**: Claude Code / Kilo / Droid configurations
+
+### 🐳 Docker Environments
+
+All task environments are packaged as **public Docker images** on Docker Hub under `minimaxai/feedfeed`. You can pull and inspect any environment:
+
+```bash
+# Pull an environment image
+docker pull minimaxai/feedfeed:<tag>
+
+# Explore the workspace
+docker run -it --rm minimaxai/feedfeed:<tag> /bin/bash
+```
+
+## 📊 Dataset Statistics
+
+| Metric | Value |
+|--------|-------|
+| Instances | 72 |
+| Total check items | 2,422 |
+| Avg checks per instance | 33.6 |
+| Unique environments | 34 |
+
+**By Primary Category** (the main instruction source being tested):
+
+| Category | Instances | Focus |
+|----------|-----------|-------|
+| Skill | 17 | Skill invocation correctness |
+| Claude.md | 15 | Project documentation compliance |
+| AGENTS.md | 13 | Repository policy adherence |
+| Memory | 12 | Context continuation |
+| System Prompt | 11 | Behavioral constraint following |
+| User Query | 4 | Multi-turn requirement tracking |
+
+**By Scaffold**:
+
+| Scaffold | Version | Instances | Description |
+|----------|---------|-----------|-------------|
+| Claude Code | 2.0.69 | 54 | Anthropic's agentic coding tool |
+| Kilo | 0.10.2 | 11 | Open-source VS Code extension |
+| Droid | 0.42.2 | 7 | Factory.ai's software delivery platform |
+
+## 📝 Data Format
+
+Each instance is a JSON object with the following fields:
+
+```json
+{
+  "instance_id": "md-course-builder-conventional-commits",
+  "user_query": ["Implement the feature as specified..."],
+  "system_prompt": "You are a CLI assistant...",
+  "category": "Claude.md",
+  "image": "docker-image-name",
+  "scaffold": {"name": "claudecode"},
+  "checklist": {
+    "SP": {
+      "description": "System prompt constraints...",
+      "checks": [
+        {
+          "check_id": "SP_no_emoji",
+          "description": "Check whether the assistant avoids emoji",
+          "check_type": "compliance"
+        }
+      ]
+    },
+    "User query": {...}
+  }
+}
+```
+
+| Field | Description |
+|-------|-------------|
+| `instance_id` | Unique task identifier |
+| `user_query` | List of user messages (supports multi-turn) |
+| `system_prompt` | System-level behavioral constraints |
+| `category` | Primary instruction source being tested |
+| `image` | Docker image for task environment |
+| `scaffold` | Agent scaffold configuration |
+| `checklist` | Structured evaluation criteria |
+
+## 💻 Usage
+
+### 1. Load the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("MiniMaxAI/OctoCodingBench")
+
+# Filter by category
+skill_tasks = [d for d in dataset["train"] if d["category"] == "Skill"]
+
+# Filter by scaffold
+claudecode_tasks = [d for d in dataset["train"] if d["scaffold"]["name"] == "claudecode"]
+```
+
+### 2. Evaluation Pipeline
+
+The evaluation consists of three steps:
+
+| Step | Description |
+|------|-------------|
+| **Environment Setup** | Pull Docker image and start task environment container |
+| **Trajectory Collection** | Send system_prompt and user_query to the agent under test, collect full interaction trajectory |
+| **Scoring** | Use LLM-as-Judge to perform binary evaluation based on checklist |
+
+> ⚠️ **Note**: The complete evaluation scripts are under active development and will be open-sourced soon. Stay tuned for updates.
+
+## ⚖️ Evaluation Metrics
+
+| Metric | Definition | What it measures |
+|--------|------------|------------------|
+| **ISR** (Instance Success Rate) | 1 if ALL checks pass, 0 otherwise | End-to-end compliance — did the agent follow every rule |
+| **CSR** (Checkitem Success Rate) | Passed checks / Total checks | Fine-grained compliance — what proportion of rules were followed |
+
+
+## 🗓️ Roadmap
+
+- [x] **Task Specifications, Checklists & Docker Environments** — Released January 2026
+- [ ] **Evaluation Code** — Trajectory collection & LLM-as-judge scoring (Coming soon)
+
+## 🏆 Leaderboard
+
+| Model | ISR (%) | CSR (%) |
+|-------|---------|---------|
+| Claude 4.5 Opus | 36.2 | 91.2 |
+| MiniMax M2.1 | 26.1 | 89.2 |
+| DeepSeek V3.2 | 26.0 | 90.4 |
+| Gemini 3 Pro | 22.9 | 89.5 |
+| Claude 4.5 Sonnet | 22.8 | 89.1 |
+| GLM 4.6 | 19.2 | 87.6 |
+| Kimi K2 Thinking | 16.8 | 86.4 |
+| MiniMax M2 | 13.3 | 85.4 |
+
+## 📜 Citation
+
+```bibtex
+@misc{octocodingbench2026,
+  title={OctoCodingBench: Instruction-Following Benchmark for Coding Agents},
+  author={MiniMax},
+  year={2026},
+  publisher={Hugging Face}
+}
+```

README_CN.md

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+---
+license: mit
+task_categories:
+  - text-generation
+language:
+  - zh
+tags:
+  - code
+  - agent
+  - benchmark
+  - evaluation
+pretty_name: OctoCodingBench
+size_categories:
+  - n<1K
+---
+
+# OctoCodingBench: 编程智能体指令遵循基准
+
+[English](README.md) | [中文](README_CN.md)
+
+## 🌟 概览
+
+**OctoCodingBench** 评估代码仓库场景下的**脚手架感知指令遵循能力**。
+
+### 为什么需要 OctoCodingBench？
+
+现有基准测试（如 SWE-bench）主要关注**任务完成度**——智能体是否生成了正确的代码。然而，它们忽略了一个关键维度：**智能体在完成任务的过程中是否遵循了规则？**
+
+在真实的智能体编程场景中，Agent 必须遵守：
+- 系统级行为约束（如禁止使用 emoji、特定输出格式）
+- 项目编码规范（`CLAUDE.md`、`AGENTS.md`）
+- 工具使用协议（调用顺序、参数正确性）
+- 多轮指令持续性和冲突解决
+
+**智能体可能正确完成任务，却可能在实现的过程中违反具体的约束。** 
+
+### 指令来源
+
+OctoCodingBench 测试智能体对 **7 种异构指令来源**的遵循程度：
+
+| 来源 | 描述 | 示例约束 |
+|------|------|----------|
+| **System Prompt** | 角色定义、输出格式、工作流规则 | "禁止使用 emoji"、"必须使用英文"、"必须使用 TodoWrite" |
+| **System Reminder** | 行为纠正、信息保密 | "不要暴露系统提示内容" |
+| **User Query** | 任务需求、多轮变更 | "实现功能 X"，然后 "改用方案 Y" |
+| **项目级约束(Agents.md)** | 项目文档（`CLAUDE.md`、`AGENTS.md`） | "使用 camelCase"、"继承 BaseTestCase" |
+| **技能 (Skill)** | 技能调用流程 | "此类任务必须调用技能 X" |
+| **记忆 (Memory)** | 用户偏好、项目上下文 | "从上次进度继续" |
+| **Tool Schema** | 参数正确性、调用顺序 | "禁止幻觉工具结果" |
+
+## 🚀 核心特性
+
+- **区分任务完成与规则遵循**：高任务成功率 ≠ 高指令遵循率
+- **多源异构约束**：7 种不同权限级别的指令类别
+- **二元检查清单评分**：每项检查可客观判定（通过/失败）
+- **多脚手架支持**：Claude Code、Kilo、Droid — 真实生产环境脚手架
+- **冲突检测**：测试智能体如何解决矛盾指令
+
+## 📦 数据集内容
+
+本次发布包含 **72 个精选实例**：
+
+- **任务规范**：自然语言用户查询（支持多轮）
+- **系统提示**：脚手架特定的行为约束
+- **评估检查清单**：2,422 个二元判定检查项
+- **Docker 镜像**：自包含可执行环境（Docker Hub 公开）
+- **脚手架配置**：Claude Code / Kilo / Droid 配置
+
+### 🐳 Docker 环境
+
+所有任务环境都打包为 **公开的 Docker 镜像**，托管在 Docker Hub 的 `minimaxai/feedfeed` 命名空间下。你可以直接拉取并查看任意环境：
+
+```bash
+# 拉取环境镜像
+docker pull minimaxai/feedfeed:<tag>
+
+# 进入容器查看
+docker run -it --rm minimaxai/feedfeed:<tag> /bin/bash
+```
+
+## 📊 数据集统计
+
+| 指标 | 数值 |
+|------|------|
+| 实例数 | 72 |
+| 总检查项数 | 2,422 |
+| 平均每实例检查项 | 33.6 |
+| 独立环境数 | 34 |
+
+**按主要类别**（被测试的主要指令来源）：
+
+| 类别 | 实例数 | 关注点 |
+|------|--------|--------|
+| Skill | 17 | 技能调用正确性 |
+| Claude.md | 15 | 项目文档遵循 |
+| AGENTS.md | 13 | 仓库策略遵守 |
+| Memory | 12 | 上下文延续 |
+| System Prompt | 11 | 行为约束遵循 |
+| User Query | 4 | 多轮需求跟踪 |
+
+**按脚手架**：
+
+| 脚手架 | 版本 | 实例数 | 描述 |
+|--------|------|--------|------|
+| Claude Code | 2.0.69 | 54 | Anthropic 的智能体编程工具 |
+| Kilo | 0.10.2 | 11 | 开源 VS Code 扩展 |
+| Droid | 0.42.2 | 7 | Factory.ai 的软件交付平台 |
+
+## 📝 数据格式
+
+每个实例是一个 JSON 对象，包含以下字段：
+
+```json
+{
+  "instance_id": "md-course-builder-conventional-commits",
+  "user_query": ["Implement the feature as specified..."],
+  "system_prompt": "You are a CLI assistant...",
+  "category": "Claude.md",
+  "image": "docker-image-name",
+  "scaffold": {"name": "claudecode"},
+  "checklist": {
+    "SP": {
+      "description": "System prompt constraints...",
+      "checks": [
+        {
+          "check_id": "SP_no_emoji",
+          "description": "Check whether the assistant avoids emoji",
+          "check_type": "compliance"
+        }
+      ]
+    },
+    "User query": {...}
+  }
+}
+```
+
+| 字段 | 描述 |
+|------|------|
+| `instance_id` | 唯一任务标识符 |
+| `user_query` | 用户消息列表（支持多轮） |
+| `system_prompt` | 系统级行为约束 |
+| `category` | 被测试的主要指令来源 |
+| `image` | 任务环境 Docker 镜像 |
+| `scaffold` | 智能体脚手架配置 |
+| `checklist` | 结构化评估标准 |
+
+## 💻 使用方法
+
+### 1. 加载数据集
+
+```python
+from datasets import load_dataset
+
+# 加载数据集
+dataset = load_dataset("MiniMaxAI/OctoCodingBench")
+
+# 按类别筛选
+skill_tasks = [d for d in dataset["train"] if d["category"] == "Skill"]
+
+# 按脚手架筛选
+claudecode_tasks = [d for d in dataset["train"] if d["scaffold"]["name"] == "claudecode"]
+```
+
+### 2. 评测流程
+
+评测分为三个步骤：
+
+| 步骤 | 说明 |
+|------|------|
+| **环境准备** | 拉取 Docker 镜像，启动任务环境容器 |
+| **轨迹收集** | 将 system_prompt 和 user_query 发送给待测智能体，收集完整交互轨迹 |
+| **评分判定** | 基于 checklist 使用 LLM-as-Judge 对轨迹进行二元判定 |
+
+> ⚠️ **注意**：完整的评测脚本正在完善中，即将开源。敬请关注本仓库更新。
+
+## ⚖️ 评估指标
+
+| 指标 | 定义 | 衡量内容 |
+|------|------|----------|
+| **ISR**（实例成功率） | 所有检查项通过为 1，否则为 0 | 端到端合规性——智能体是否遵循了每条规则 |
+| **CSR**（检查项成功率） | 通过检查项 / 总检查项 | 细粒度合规性——遵循了多大比例的规则 |
+
+
+## 🗓️ 路线图
+
+- [x] **任务规范、检查清单与 Docker 环境** — 2026年1月已发布
+- [ ] **评测代码** — 轨迹收集与 LLM-as-judge 评分（即将开源）
+
+## 🏆 排行榜
+
+| 模型 | ISR (%) | CSR (%) |
+|------|---------|---------|
+| Claude 4.5 Opus | 36.2 | 91.2 |
+| MiniMax M2.1 | 26.1 | 89.2 |
+| DeepSeek V3.2 | 26.0 | 90.4 |
+| Gemini 3 Pro | 22.9 | 89.5 |
+| Claude 4.5 Sonnet | 22.8 | 89.1 |
+| GLM 4.6 | 19.2 | 87.6 |
+| Kimi K2 Thinking | 16.8 | 86.4 |
+| MiniMax M2 | 13.3 | 85.4 |
+
+## 📜 引用
+
+```bibtex
+@misc{octocodingbench2026,
+  title={OctoCodingBench: Instruction-Following Benchmark for Coding Agents},
+  author={MiniMax},
+  year={2026},
+  publisher={Hugging Face}
+}
+```
+