MicroMilo · MicroMilo · Aug 21, 2026 · Aug 21, 2026
@@ -34,6 +34,47 @@ body:
         - Historical OpenAI Codex model
     validations:
       required: true
+  - type: dropdown
+    id: conference
+    attributes:
+      label: Conference or source
+      description: Choose the accepted conference series. Use arXiv when no listed acceptance is verified.
+      options:
+        - arXiv
+        - AAAI
+        - ASE
+        - FSE
+        - ICLR
+        - ICML
+        - ICSE
+        - ISSTA
+        - NeurIPS
+        - Other
+    validations:
+      required: true
+  - type: input
+    id: venue
+    attributes:
+      label: Exact venue or track
+      description: For example, ISSTA Research Papers, ICLR, or arXiv.
+    validations:
+      required: true
+  - type: dropdown
+    id: domains
+    attributes:
+      label: Research domains
+      multiple: true
+      options:
+        - Software engineering
+        - Security
+        - Systems and performance
+        - Machine learning
+        - Scientific computing
+        - Formal methods
+        - Web and UI
+        - Documents
+    validations:
+      required: true
   - type: dropdown
     id: classification
     attributes:

@@ -31,17 +31,20 @@ same_budget: unknown
 1. Add one entry to `data/papers.yaml`.
 2. Use a stable identifier such as `short-title-year`.
 3. Record authors, identifiers, system name, artifact status, and publication date from primary sources.
-4. Choose the narrowest correct evidence class and comparison scope.
-5. Record where the supporting result appears in the paper.
-6. Run `make build` to regenerate README stats, both JSON exports, views, and paper dossiers.
-7. Run `make check` and `make site-check` before opening a pull request.
+4. Assign one standardized `conference`, preserve the exact `venue`, and select every applicable `domain`.
+5. Choose the narrowest correct evidence class and comparison scope.
+6. Record where the supporting result appears in the paper.
+7. Run `make build` to regenerate README stats, both JSON exports, views, and paper dossiers.
+8. Run `make check` and `make site-check` before opening a pull request.
 
-Do not manually edit content between the `CATALOG:STATS:START` and `CATALOG:STATS:END` markers in either README. Files under `papers/` and `views/`, plus `data/papers.json` and `website/data/catalog.json`, are also generated.
+Do not manually edit content between either pair of `CATALOG:STATS` or `CATALOG:COVERAGE` markers in the READMEs. Files under `papers/` and `views/`, plus `data/papers.json` and `website/data/catalog.json`, are also generated.
 
 ## Required source checks
 
 - Confirm the paper title and authors against an official paper page or PDF.
 - Distinguish arXiv publication dates from final venue publication.
+- Use the standardized conference series only after acceptance is verified; otherwise use `arXiv` and keep the exact source in `venue`.
+- Select domains from `docs/taxonomy.md`; multiple domains are encouraged when the evidence genuinely crosses areas.
 - Confirm that an artifact repository is author-maintained before marking it `official`.
 - Record the exact CLI version and model when the paper or artifact reports them.
 - Identify whether the result is product-level, component-level, a configuration ablation, benchmark-only, or historical-model evidence.
@@ -70,6 +73,7 @@ Future products must be added to both `data/schema.json` and `docs/taxonomy.md`
 - [ ] All quantitative claims match the cited primary source.
 - [ ] Model, version, budget, and tool details are recorded or marked unknown.
 - [ ] Author, identifier, publication, and artifact fields come from primary sources.
+- [ ] Conference, exact venue, and domains follow the catalog taxonomy.
 - [ ] The evidence location and comparison scope are recorded.
 - [ ] Comparison caveats are explicit.
 - [ ] `make check` passes.

@@ -18,37 +18,49 @@
 
 <p align="center">
   <a href="https://github.com/MicroMilo/awesome-claude-code-codex-papers/actions/workflows/validate.yml"><img alt="Validate catalog" src="https://github.com/MicroMilo/awesome-claude-code-codex-papers/actions/workflows/validate.yml/badge.svg"></a>
-  <a href="LICENSE"><img alt="MIT License" src="https://img.shields.io/badge/license-MIT-22c55e"></a>
-  <a href="CONTRIBUTING.md"><img alt="PRs welcome" src="https://img.shields.io/badge/PRs-welcome-brightgreen"></a>
+  <a href="LICENSE"><img alt="MIT License" src="https://img.shields.io/badge/license-MIT-4bcbd5"></a>
+  <a href="CONTRIBUTING.md"><img alt="PRs welcome" src="https://img.shields.io/badge/PRs-welcome-f16f51"></a>
   <a href="data/papers.yaml"><img alt="Data YAML" src="https://img.shields.io/badge/catalog-YAML%20%2B%20JSON-2563eb"></a>
 </p>
 
 <!-- CATALOG:STATS:START -->
 <p align="center">
-  <img alt="papers: 19" src="https://img.shields.io/badge/papers-19-0f766e">
-  <img alt="direct comparisons: 13" src="https://img.shields.io/badge/direct%20comparisons-13-dc6b46">
-  <img alt="official artifacts: 11" src="https://img.shields.io/badge/official%20artifacts-11-2563eb">
-  <img alt="venues: 7" src="https://img.shields.io/badge/venues-7-7c3aed">
+  <img alt="papers: 32" src="https://img.shields.io/badge/papers-32-16616a">
+  <img alt="direct comparisons: 19" src="https://img.shields.io/badge/direct%20comparisons-19-dc6b46">
+  <img alt="official artifacts: 18" src="https://img.shields.io/badge/official%20artifacts-18-2563eb">
+  <img alt="domains: 8" src="https://img.shields.io/badge/domains-8-4bcbd5">
+  <img alt="conference groups: 8" src="https://img.shields.io/badge/conference%20groups-8-7c3aed">
   <img alt="reviewed: 2026-08-21" src="https://img.shields.io/badge/reviewed-2026--08--21-475569">
 </p>
 <!-- CATALOG:STATS:END -->
 
+## Coverage at a glance
+
+<!-- CATALOG:COVERAGE:START -->
+<p align="center">
+  <a href="views/by-domain.md"><strong>Research domains</strong></a><br>
+  <code>Software Engineering · 21</code> · <code>Security · 5</code> · <code>Systems & Performance · 5</code> · <code>Machine Learning · 1</code> · <code>Scientific Computing · 2</code> · <code>Formal Methods · 2</code> · <code>Web & UI · 3</code> · <code>Documents · 1</code><br><br>
+  <a href="views/by-conference.md"><strong>Conferences / sources</strong></a><br>
+  <code>ASE · 1</code> · <code>FSE · 1</code> · <code>ICLR · 4</code> · <code>ICML · 1</code> · <code>ICSE · 2</code> · <code>ISSTA · 3</code> · <code>NeurIPS · 2</code> · <code>arXiv · 18</code>
+</p>
+<!-- CATALOG:COVERAGE:END -->
+
 This repository is the open data and maintenance layer behind the **web-first research catalog**. It covers papers that evaluate, analyze, or outperform **Claude Code** and **Codex CLI** as complete products—not papers that merely use a Claude or GPT-family model.
 
 ## Start with the website
 
 The [interactive catalog](https://micromilo.github.io/awesome-claude-code-codex-papers/) is the fastest way to:
 
 - search systems, tasks, methods, authors, and reported results;
-- filter by product, evidence class, venue, method, and comparison fairness;
+- choose a research domain or conference, then refine by product, evidence class, method, and comparison fairness;
 - inspect baseline models, versions, budgets, evidence locations, and caveats;
 - switch between English and Chinese without reading giant Markdown tables.
 
 ## Evidence standard
 
 This is not a leaderboard. Product versions, backbone models, budgets, tools, and task domains often differ. Every entry separates the paper's reported result from our comparison controls and caveats. Missing details stay explicitly `unknown`.
 
-For raw or generated research material, use the [paper dossiers](papers/README.md), [method view](views/by-method.md), [fair-comparison view](views/fair-comparisons.md), or [machine-readable JSON](data/papers.json).
+For raw or generated research material, use the [paper dossiers](papers/README.md), [domain view](views/by-domain.md), [conference view](views/by-conference.md), [fair-comparison view](views/fair-comparisons.md), or [machine-readable JSON](data/papers.json).
 
 ## Contributing
 

@@ -18,37 +18,49 @@
 
 <p align="center">
   <a href="https://github.com/MicroMilo/awesome-claude-code-codex-papers/actions/workflows/validate.yml"><img alt="目录校验" src="https://github.com/MicroMilo/awesome-claude-code-codex-papers/actions/workflows/validate.yml/badge.svg"></a>
-  <a href="LICENSE"><img alt="MIT License" src="https://img.shields.io/badge/license-MIT-22c55e"></a>
-  <a href="CONTRIBUTING.md"><img alt="欢迎 PR" src="https://img.shields.io/badge/PRs-welcome-brightgreen"></a>
+  <a href="LICENSE"><img alt="MIT License" src="https://img.shields.io/badge/license-MIT-4bcbd5"></a>
+  <a href="CONTRIBUTING.md"><img alt="欢迎 PR" src="https://img.shields.io/badge/PRs-welcome-f16f51"></a>
   <a href="data/papers.yaml"><img alt="YAML 数据" src="https://img.shields.io/badge/catalog-YAML%20%2B%20JSON-2563eb"></a>
 </p>
 
 <!-- CATALOG:STATS:START -->
 <p align="center">
-  <img alt="papers: 19" src="https://img.shields.io/badge/papers-19-0f766e">
-  <img alt="direct comparisons: 13" src="https://img.shields.io/badge/direct%20comparisons-13-dc6b46">
-  <img alt="official artifacts: 11" src="https://img.shields.io/badge/official%20artifacts-11-2563eb">
-  <img alt="venues: 7" src="https://img.shields.io/badge/venues-7-7c3aed">
+  <img alt="papers: 32" src="https://img.shields.io/badge/papers-32-16616a">
+  <img alt="direct comparisons: 19" src="https://img.shields.io/badge/direct%20comparisons-19-dc6b46">
+  <img alt="official artifacts: 18" src="https://img.shields.io/badge/official%20artifacts-18-2563eb">
+  <img alt="domains: 8" src="https://img.shields.io/badge/domains-8-4bcbd5">
+  <img alt="conference groups: 8" src="https://img.shields.io/badge/conference%20groups-8-7c3aed">
   <img alt="reviewed: 2026-08-21" src="https://img.shields.io/badge/reviewed-2026--08--21-475569">
 </p>
 <!-- CATALOG:STATS:END -->
 
+## 收录范围一览
+
+<!-- CATALOG:COVERAGE:START -->
+<p align="center">
+  <a href="views/by-domain.md"><strong>研究领域</strong></a><br>
+  <code>软件工程 · 21</code> · <code>安全 · 5</code> · <code>系统与性能 · 5</code> · <code>机器学习 · 1</code> · <code>科学计算 · 2</code> · <code>形式化方法 · 2</code> · <code>Web 与 UI · 3</code> · <code>文档 · 1</code><br><br>
+  <a href="views/by-conference.md"><strong>会议 / 来源</strong></a><br>
+  <code>ASE · 1</code> · <code>FSE · 1</code> · <code>ICLR · 4</code> · <code>ICML · 1</code> · <code>ICSE · 2</code> · <code>ISSTA · 3</code> · <code>NeurIPS · 2</code> · <code>arXiv · 18</code>
+</p>
+<!-- CATALOG:COVERAGE:END -->
+
 这个仓库是**网页优先论文目录**背后的开放数据与维护层。只收录把 **Claude Code** 和 **Codex CLI** 当作完整产品进行评测、分析或超越的论文，而不是只要用了 Claude 或 GPT 系列模型就算。
 
 ## 先看网站
 
 [可交互论文目录](https://micromilo.github.io/awesome-claude-code-codex-papers/)适合直接：
 
 - 搜索系统、任务、方法、作者和论文结果；
-- 按产品、证据类型、会议、方法和实验公平性筛选；
+- 先选择研究领域或会议，再按产品、证据类型、方法和实验公平性细筛；
 - 查看 baseline 模型、版本、预算、证据位置和限制；
 - 在中英文之间切换，不再阅读超长 Markdown 表格。
 
 ## 证据标准
 
 这不是排行榜。产品版本、底层模型、预算、工具权限和任务领域经常不同。每条记录都会分开呈现论文报告的结果、实验控制和限制；论文没写清的内容会保留为 `unknown`。
 
-需要原始或生成资料时，可以查看[论文证据页](papers/README.md)、[按方法浏览](views/by-method.md)、[公平对比视图](views/fair-comparisons.md)或[机器可读 JSON](data/papers.json)。
+需要原始或生成资料时，可以查看[论文证据页](papers/README.md)、[按领域浏览](views/by-domain.md)、[按会议浏览](views/by-conference.md)、[公平对比视图](views/fair-comparisons.md)或[机器可读 JSON](data/papers.json)。
 
 ## 参与贡献