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ResearchPilot 科研助手[阶段 G.8]：中英翻译（仅中文版）
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Based on SOC occupation classification
ResearchPilot Research Assistant [Phase G.0]: Paper planning
ResearchPilot 科研助手[阶段 G.0]：论文规划
Global-installable, project-level academic rebuttal strategy skill for AI/ML/CV/NLP/Robotics papers. Use when authors need a workspace-local .awesome-rebuttal state folder, paper/code/review/venue-rule intake, JSON memory, snapshots, LaTeX/template handling for one-page rebuttals, reviewer stance analysis, strategy planning, experiment triage, safe author response drafting, or AC summaries under confirmed venue rules.
Use when the user has a research question and needs a complete experiment package — design document, runnable code, results (measured or simulated with honest provenance), publication-grade figures, structured report. Single-stage, no Python runtime.
Use when the user wants a paper audited for integrity issues — image misuse, numerical anomalies, logical gaps — and needs a reviewable evidence report. Works on external papers (PDF / DOI / arXiv) and on outputs from a local paper-writer run. Single-stage skill.
Use when the user wants a comprehensive literature survey on a specific research topic. Outputs a complete PDF survey (6–20 pages, 60+ real citations, 100+ recommended) with LaTeX source, taxonomy figures, and a classified literature table. Single-stage, no Python runtime.
| name | research[G.8]-translate |
| description | ResearchPilot 科研助手[阶段 G.8]：中英翻译（仅中文版） |
| version | 2.0.0 |
| license | LICENSE |
仅中文版 skill 包含本阶段。英文版 skill 无此阶段。
/research[G.8]-translate
命令后可跟可选的自然语言指令，AI 会将其作为本次调用的额外约束或补充说明优先处理。
前置条件：G.7 审阅通过，中文手稿基本定稿。
当前论文为中文稿，是否需要生成英文 LaTeX 版本？
→ 需要：继续步骤 2
→ 不需要：G 阶段完成
请选择英文版 LaTeX 模版：
① 使用 G.0 中已有的模版（docs/manuscripts/templates/ 下现有文件）
请告知模版文件名
② 上传新模版
请将模版文件放入 docs/manuscripts/templates/ 目录，完成后告知我
docs/manuscripts/paper-en.tex（模版原文件不动）-zh 后缀：
paper.md → 确认存为 paper-zh.mdpaper.tex → 确认存为 paper-zh.texdocs/manuscripts/references.bib（无需复制）按章节顺序翻译，每章完成后展示给用户确认，再进行下一章。
翻译格式规范：
% 局部注意力机制能够有效捕捉短时间窗内的特征变化。
Local attention effectively captures feature variations within short temporal windows.
% 与标准 Transformer 相比，我们的方法将计算复杂度从 O(n²) 降低至 O(nW)。
Compared to standard Transformers, our method reduces computational complexity from $\mathcal{O}(n^2)$ to $\mathcal{O}(nW)$.
% === WRITING PLAN: Method ===
% 3.1 Overview: introduce overall pipeline with Fig.1
% 3.2 Local Temporal Attention (LTA): design → motivation → advantage
% ...
翻译约束：
翻译完成后在 paper-en.tex 文件头追加：
% 修改时间：{mm-dd_hh-mm}
% 修改内容：从中文稿翻译生成英文版
% 对应中文稿：paper-zh.{md/tex}（或 paper-zh_{mm-dd_hh-mm}.{md/tex}）
% 上一备份：（首次生成，无备份）
G.8 完成。英文版已生成：docs/manuscripts/paper-en.tex
文件说明：
- docs/manuscripts/paper-zh.{md/tex}：中文稿
- docs/manuscripts/paper-en.tex：英文稿
- docs/manuscripts/references.bib：共用参考文献
→ 论文全流程完成。
references/template-flexibility.md