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README.md

@@ -27,12 +27,12 @@ size_categories:
 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 (no emoji, specific output formats)
+- 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 silently violating higher-priority constraints.** OctoCodingBench explicitly disentangles *solving the task* from *following the rules*.
+**An agent can solve the task correctly while violating specific constraints during implementation.**
 
 ### Instruction Sources
 
@@ -40,10 +40,10 @@ OctoCodingBench tests agent compliance across **7 heterogeneous instruction sour
 
 | Source | Description | Example Constraints |
 |--------|-------------|---------------------|
-| **System Prompt (SP)** | Role definitions, output formats, workflow rules | "No emoji", "Use English only", "Must use TodoWrite" |
+| **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" |
-| **Agents.md** | Project documentation (`CLAUDE.md`, `AGENTS.md`) | "Use camelCase", "Inherit from BaseTestCase" |
+| **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" |
@@ -79,11 +79,6 @@ docker run -it --rm minimaxai/feedfeed:md_course_builder /bin/bash
 
 ```
 
-Each image contains:
-- **Source code repository** at `/workspace/<project>`
-- **Project documentation** (`CLAUDE.md`, `AGENTS.md`, etc.) with coding conventions
-- **Pre-installed dependencies** for running tests and builds
-
 ## 📊 Dataset Statistics
 
 | Metric | Value |
@@ -170,19 +165,21 @@ claudecode_tasks = [d for d in dataset["train"] if d["scaffold"]["name"] == "cla
 | 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** (Checklist Success Rate) | Passed checks / Total checks | Fine-grained compliance — what proportion of rules were followed |
+| **CSR** (Checkitem Success Rate) | Passed checks / Total checks | Fine-grained compliance — what proportion of rules were followed |
 
 
 ## 🏆 Leaderboard
 
-| Model | ISR (%) |
-|-------|---------|
-| Claude Opus 4.5 | 36.2 |
-| MiniMax-M2.1 | 26.1 |
-| DeepSeek V3.2 | 26.0 |
-| Gemini 3 Pro | 22.9 |
-| Claude Sonnet 4.5 | 22.8 |
-| MiniMax-M2 | 13.3 |
+| 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
 

README_CN.md

@@ -27,12 +27,12 @@ size_categories:
 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（禁止使用 emoji、特定输出格式）
+- 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，却悄悄违反了更高优先级的约束。** OctoCodingBench 明确区分*完成任务*和*遵循规则*。
+**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
 
@@ -40,13 +40,13 @@ 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 |
 |------|------|----------|
-| **系统提示 (SP)** | 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" |
-| **系统提醒** | 行为纠正、信息保密 | "不要暴露系统提示内容" |
-| **用户查询** | 任务需求、多轮变更 | "实现功能 X"，然后 "改用方案 Y" |
-| **项目文档 (Agents.md)** | 项目文档（`CLAUDE.md`、`AGENTS.md`） | "使用 camelCase"、"继承 BaseTestCase" |
+| **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" |
-| **工具模式** | 参数正确性、调用顺序 | "禁止幻觉工具结果" |
+| **Tool Schema** | 参数正确性、调用顺序 | "禁止幻觉工具结果" |
 
 ## 🚀 核心特性
 
@@ -165,19 +165,21 @@ claudecode_tasks = [d for d in dataset["train"] if d["scaffold"]["name"] == "cla
 | 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**（检查清单成功率） | 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 |
+| **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 |
 
 
 ## 🏆 排行榜
 
-| 模型 | ISR (%) |
-|------|---------|
-| Claude Opus 4.5 | 36.2 |
-| MiniMax-M2.1 | 26.1 |
-| DeepSeek V3.2 | 26.0 |
-| Gemini 3 Pro | 22.9 |
-| Claude Sonnet 4.5 | 22.8 |
-| MiniMax-M2 | 13.3 |
+| 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