research-paper-writing

End-to-end academic paper writing skill covering proposal, structure, writing, formatting, and quality review. Targets Nature, Science, IEEE, NeurIPS, ICML, ICLR, AAAI, ACL, COLM. Emphasizes narrative-driven writing, citation discipline, and iterative refinement.

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最近来源活动: 2026年5月21日 13:38
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name	research-paper-writing
title	Research Paper Writing Pipeline
description	End-to-end academic paper writing skill covering proposal, structure, writing, formatting, and quality review. Targets Nature, Science, IEEE, NeurIPS, ICML, ICLR, AAAI, ACL, COLM. Emphasizes narrative-driven writing, citation discipline, and iterative refinement.
version	2.0.0
author	Orchestra Research / Scholar's Tea
license	MIT
dependencies	["requests"]
platforms	["linux","macos"]
metadata	{"hermes":{"tags":["Research","Paper Writing","Academic Writing","LaTeX","Citations","ML","AI"],"category":"research","related_skills":["arxiv","ml-paper-writing","plan"],"requires_toolsets":["terminal","files"]}}

Research Paper Writing Skill

Academic paper writing skill targeting Nature, Science, IEEE, NeurIPS, ICML, ICLR, ACL, AAAI, COLM and general STEM venues.

Core Principle: A paper is a story with one clear contribution supported by evidence. Every section must serve that narrative.

When To Use This Skill

Generating a research proposal (选题立项) with gap analysis and innovation points
Designing paper structure (架构规划) with IMRAD or domain-appropriate organization
Writing sections (正文写作) — Introduction, Methods, Results, Discussion, Abstract
Formatting (排版交付) — converting drafts to LaTeX, Markdown, or plain text
Quality review (质量自检) — simulating reviewer critique before submission

Core Philosophy

Be proactive. Deliver complete drafts, not questions. Produce something concrete the user can react to, then iterate.
Never hallucinate citations. Mark unverifiable citations as [CITATION NEEDED].
Paper is a story, not a collection of experiments. One clear contribution in a single sentence.
Experiments serve claims. Every result must explicitly state which claim it supports.
Use reference materials wisely. When provided with uploaded PDFs or reference documents, deeply analyze their core ideas, methods, and conclusions. Do not copy verbatim. Rephrase, synthesize, critique, and extend. Direct quotation is acceptable only when clearly marked and necessary.

Proactivity Matrix

Confidence	Action
High (clear topic, good references)	Write full draft, deliver, iterate on feedback
Medium (some ambiguity)	Write draft with flagged uncertainties
Low (major unknowns)	Ask 1-2 targeted questions, then draft

The Narrative Principle

Single most critical insight: Your paper is a story with one clear contribution supported by evidence.

Three Pillars (must be crystal clear by end of introduction):

Abstract: 5-sentence formula (Context→Gap→Method→Result→Implication), 150-250 words
1. Introduction (~1500 words)
   1.1 Research background
   1.2 Related work (grouped methodologically, not paper-by-paper)
   1.3 Limitations of existing methods
   1.4 Contributions (3 bullets)
   1.5 Paper organization

2. Preliminaries (~800 words, optional)
   2.1 Problem definition
   2.2 Notation table

3. Methodology (~2500 words)
   3.1 Overall framework
   3.2 Core modules (each: motivation → definition → explanation)
   3.3 Optimization objective

4. Experiments (~2500 words)
   4.1 Datasets & metrics
   4.2 Implementation details
   4.3 Main results (table + analysis)
   4.4 Ablations
   4.5 Case studies
   4.6 Comparison with SOTA

5. Discussion (~800 words)
   5.1 Result interpretation
   5.2 Limitations
   5.3 Future work

6. Conclusion (~300 words)

1. What you achieved: "We introduce...", "We prove...", "We demonstrate..."
2. Why this is hard and important
3. How you do it (with specialist keywords)
4. What evidence you have
5. Your most remarkable number/result

Overall Score: X/10

Strengths:
- ...

Issues to Address (prioritized):
1. [Issue description] → [Specific fix suggestion]
2. ...

Priority:
- P0 (Must fix): ...
- P1 (Strongly recommended): ...
- P2 (Optional): ...

Principle	Rule
Subject-verb proximity	Keep subject and verb close
Stress position	Place emphasis at sentence ends
Topic position	Put context first, new info after
Old before new	Familiar info → unfamiliar info
One unit, one function	Each paragraph makes one point
Action in verb	Use verbs, not nominalizations
Context before new	Set stage before presenting

Issue	Solution
Abstract too generic	Delete first sentence if it could prepend any paper. Start with your specific contribution.
Introduction exceeds 1.5 pages	Split background into Related Work. Front-load contribution bullets.
Experiments lack explicit claims	Add: "This experiment tests whether [specific claim]..." before each one.
Reviewers find paper hard to follow	Add signposting, use consistent terminology, make figure captions self-contained.
Missing statistical significance	Add error bars, number of runs, statistical tests, confidence intervals.
Scope creep in experiments	Every experiment must map to a specific claim. Cut experiments that don't.
Theory paper lacks intuition	Add proof sketches with plain-language explanations before formal proofs.
Results are negative/null	Frame as analysis. Honest reporting of failures strengthens the paper.

Document	Contents
`writing-guide.md`	Gopen & Swan principles, word choice, figure design
`citation-workflow.md`	Citation APIs, BibTeX management
`checklists.md`	NeurIPS, ICML, ICLR, ACL requirements
`reviewer-guidelines.md`	Evaluation criteria, scoring, rebuttal template
`experiment-patterns.md`	Experiment design patterns, evaluation protocols
`paper-types.md`	Theory papers, survey papers, benchmark papers, position papers

1. Research Background & Significance:
   Medical imaging is central to clinical decision-making, yet single-modality images (CT, MRI, PET) often fail to capture complete lesion characteristics. Multi-modal fusion has shown great potential for improving diagnostic accuracy [REF-1]. However, existing methods rely on simple feature concatenation or weighted fusion, failing to exploit complementary inter-modal information [REF-2]. This study proposes a contrastive learning framework to learn shared representations and complementary features across modalities, building a more robust diagnostic model.

2. Core Scientific Questions:
   Q1: How to design a contrastive learning strategy that captures both inter-modal consistency and complementarity?
   Q2: Does the learned representation maintain diagnostic performance under missing-modality scenarios?

3. Innovation Points:
   • Cross-modal contrastive loss function explicitly modeling the consistency-complementarity tradeoff
   • Modality-adaptive attention mechanism dynamically adjusting contribution weights
   • Validation on 3 public datasets with average AUC improvement of 4.2%

Federated Learning (FL) enables collaborative model training without sharing raw data, providing an effective solution for privacy-sensitive machine learning scenarios [REF-1]. However, gradient exchange mechanisms still pose privacy leakage risks: studies show malicious participants can reconstruct original training data from shared gradients via gradient inversion attacks [REF-2]. Although Differential Privacy (DP) is widely used to mitigate such risks, existing methods face a severe tradeoff between privacy budget and model utility [REF-3].

Specifically, excessive noise injection guarantees privacy but significantly degrades model accuracy, while lower noise levels fail to provide adequate privacy protection [REF-4]. Recent work attempts to optimize this tradeoff through adaptive noise allocation strategies, but primarily focuses on homogeneous data distributions, leaving the adaptation to Non-IID federated learning understudied [REF-5].

This paper proposes an adaptive differential privacy mechanism for heterogeneous federated learning, dynamically adjusting privacy budget allocation based on each client's data distribution characteristics. Compared to existing methods, our scheme improves model convergence accuracy by X% under equivalent privacy guarantees (experimentally verified).

Sentence 1 (Context): Transformer architectures have achieved breakthroughs in NLP, but their quadratic computational complexity limits application in long-sequence tasks.
Sentence 2 (Gap): Existing linear attention mechanisms reduce computational overhead but suffer from significant feature expression degradation.
Sentence 3 (Method): This paper proposes a kernel-based linear attention variant that preserves feature expressiveness through learnable kernel mapping functions.
Sentence 4 (Result): On Wikitext-103, PG-19, and Long-Range Arena benchmarks, the method reduces perplexity by 8.3% while maintaining linear complexity.
Sentence 5 (Implication): This work provides a practical solution for long-text modeling that balances efficiency and effectiveness.

3.1 Problem Formalization
Let D = {(x_i, y_i)}_{i=1}^n be the training dataset, where x_i ∈ ℝ^d are input features and y_i ∈ {0,1} are labels. Our goal is to learn a classifier f_θ: ℝ^d → ℝ that minimizes expected risk on the held-out set.

3.2 Core Algorithm
[Algorithm description follows: Input → Output → Steps (numbered) → Complexity analysis]
Input: Training data D, hyperparameters α, β
Output: Trained model parameters θ*
Step 1: Initialize model parameters θ_0
Step 2: For each epoch t = 1, ..., T:
  Step 2.1: Randomly sample mini-batch B from D
  Step 2.2: Compute forward loss L_t = (1/|B|) Σ_{(x,y)∈B} L(f_θ(x), y)
  Step 2.3: Update parameters θ_t = θ_{t-1} - η ∇_θ L_t
Step 3: Return θ_T
Time complexity: O(T · |D| · d), Space complexity: O(d).

Criterion	What They Check
Quality	Technical soundness, well-supported claims, fair baselines
Clarity	Clear writing, reproducible by experts, consistent notation
Significance	Community impact, advances understanding
Originality	New insights (doesn't require entirely new method)