Results for “paper-compilation”

学术论文正文写作、结构设计与语言润色总入口。完整起草、续写、扩写、补写或实质性修订各学科的中英文论文正文时，进入paper-write-zh或paper-write-en；只做提纲、结构诊断或重排方案时，进入paper-shape结构模式；只做忠实润色或不新增研究内容的中译英时，进入paper-shape润色模式。研究评价转/doubao-academic-evaluator；独立系统性文献调研转/doubao-literature-research。

Write academic papers with a 12-agent pipeline supporting full drafting, revision, citation checks, and LaTeX/DOCX output across multiple disciplines and paper types.

Provides nine skills for Nature-journal-family academic publishing, covering figure creation, prose polishing, manuscript writing, citation formatting, data availability statements, paper reading, reviewer responses, paper-to-PPT conversion, and academic search via an MCP server.

Generates a curated supplementary reading list from any course syllabus using Consensus academic search, producing a professionally formatted .docx with recent peer-reviewed papers per course section.

Reviews an academic paper using the NeurIPS review form with three reviewer personas, ensemble scoring, and reflection refinement. Extracts text from PDF, runs structured review, and outputs actionable feedback.

Provides reviewer-style audit and deep review for academic papers in LaTeX, Typst, and PDF formats, producing structured issue bundles, pass/fail gates, and revision roadmaps.

Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.

Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.

Simulates a NeurIPS/SC/ICSE-style peer review with five reviewer personas (HPC, ML, Stats, Reproducibility, Devil's Advocate) that verify every claim against actual result data before submission.

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a.