最重要的首先的结果，记录实现了什么效果或者得出什么结论。创新性首先也是在结果上，做到之前人没做过的事情，然后才是评价做的好坏。

Structure of a Computer Science Paper (Top Conference Style)

  1. Abstract — A self-contained summary: problem, approach, key results, impact.
  2. Introduction — Hook → problem → why it’s hard → our idea → contributions → road-map.
  3. Method — Problem formulation + core technical contribution: architecture, algorithm, loss, inference. Preliminaries (if any) are briefly covered here.
  4. Experiments — Datasets, setup, baselines, main results, ablation studies, analysis. Implementation details (framework, hyperparameters, hardware) appear as a subsection.
  5. Related Work — Situate against prior work; highlight gap and how we differ. Often placed after Method so readers can better compare.
  6. Discussion & Limitations — Deeper analysis of failure cases, assumptions, scope. Can merge into Conclusion if short.
  7. Conclusion — Recap contributions, summary of findings, future work.
  8. References (unnumbered)

Common Academic Expressions for Computer Science Papers

1. Abstract
- Problem / Motivation: The growing prevalence of … has raised the need for effectively addressing …
- Limitation of prior work: Existing methods for … suffer from several critical limitations.
- Challenge statement: Despite considerable progress in …, it remains challenging to …
- Failure of prior work: Previous approaches often fail to handle … under complex scenarios.

2. Introduction
- Background / Hook: Recently, … has attracted significant attention in both academia and industry.
- Problem importance: Addressing … is crucial for …
- Gap identification: However, existing approaches are limited in that …
- Proposal: To fill this gap, we propose a … framework.
- Contribution list: Our main contributions are summarized as follows:
1. We design … to solve the problem of …
2. A new … mechanism is introduced to improve …
3. Extensive experiments demonstrate that our method outperforms existing approaches.
- Key insight: The key insight of this work is that …
- Road-map: The rest of this paper is organized as follows.

3. Related Work
- Overview of prior work: Numerous studies have investigated the task of …
- Historical progression: Early attempts focused on …, while modern works tend to …
- Critiquing drawbacks: A major drawback of the above methods is that …
- Contrast with ours: In contrast to our work, these approaches ignore …
- Positioning ours: Several recent works are most relevant to our research.
- Summary of gap: To the best of our knowledge, no prior work has addressed …

4. Method
- Problem formulation: We formally define the problem as follows.
- Architecture overview: The overall architecture of our model is illustrated in Figure 1.
- Component description: The system consists of three main components: …, … and …
- Module role: This module is responsible for …
- Backbone selection: We adopt … as the backbone network for feature extraction.
- Objective / Loss: Formally, we define the loss function as follows:
- Forward computation: Given an input \(x\), the output can be computed by:
- Efficiency optimization: To further reduce computational overhead, we optimize …

5. Experiments
- Setup — Dataset: We evaluate our method on the public … dataset.
- Setup — Baselines: We compare our method with several state-of-the-art (SOTA) approaches.
- Setup — Metrics: We use … as the evaluation metric.
- Main results: As shown in Table 1, our approach achieves the best performance on all benchmarks.
- Performance claim: It can be observed that our method outperforms baselines by a clear margin.
- Effectiveness claim: The improvement demonstrates the effectiveness of our module.
- Ablation study: We conduct ablation studies to validate the necessity of each component.
- Analysis — Visualization: The convergence curve is plotted in Figure 2 for intuitive comparison.
- Analysis — Statistical test: Statistical significance tests are applied to confirm the reliability of results.

**Implementation Details** (subsection)
	- **Framework:** All experiments are conducted using PyTorch/TensorFlow.
	- **Hyperparameters:** We use the Adam optimizer with a learning rate of …
	- **Hardware:** We run our experiments on a machine with an NVIDIA RTX GPU and Intel CPU.
	- **Fair comparison:** For fair comparison, we keep consistent experimental settings.

6. Discussion & Limitations
- General limitation: One potential limitation of our work is …
- Failure case: The performance degrades slightly when dealing with extreme cases.
- Possible explanation: Possible reasons for this phenomenon can be explained as follows.
- Scope discussion: Our method assumes …, which may not hold in …

7. Conclusion
- Summary of work: In this paper, we presented … for the task of …
- Summary of results: Experimental results demonstrate that our method is effective and efficient.
- Limitation recap: We also discussed several limitations of the current approach.
- Future direction (general): For future research directions, we plan to extend our model to …
- Future direction (specific): Another promising direction is to explore lightweight deployment on edge devices.

8. Transition & Linking Phrases
- Adding information: Furthermore, … / Moreover, … / In addition, …
- Emphasis: In particular, … / Specifically, … / Notably, …
- Result / Consequence: Consequently, … / As a result, … / Therefore, …
- Contrast: On the contrary, … / However, … / Nevertheless, …
- Exemplification: For instance, … / For example, … / Such as …

参考论文

bert
transformer