Implementation:ThreeSR Awesome Inference Time Scaling Paper Entry Template

Metadata
Knowledge Sources	Awesome Inference Time Scaling
Domains	Open_Source, Version_Control, Curation
Last Updated	2026-02-14 00:00 GMT

Overview

Concrete pattern documentation for the paper entry markdown template that contributors must follow when adding new papers to the Awesome Inference Time Scaling curated list.

Description

This implementation defines the exact markdown template format used for every paper entry in the repository. The template is specified in README.md:L39-51 and uses a consistent structure with emoji markers, markdown links, and a collapsible HTML block for the abstract. Contributors must produce an entry that conforms to this template regardless of whether they compose it manually or generate it via the fetch_semantic_info.py script.

The template serves as the canonical format contract for the repository. Every entry in the curated list follows this identical structure, which enables visual consistency when browsing the list on GitHub.

Usage

Use this pattern when:

• Manually composing a new paper entry for the curated list.
• Reviewing or editing the output of the fetch_semantic_info.py script.
• Verifying that an existing entry conforms to the expected format.
• Contributing a batch of papers and need a reference for the expected structure.

Interface Specification

Template Structure

The complete template for a single paper entry is:

🔹 [Title](https://arxiv.org/abs/{arxivId})
- 🔗 **arXiv PDF Link:** [Paper Link](https://arxiv.org/pdf/{arxivId})
- 👤 **Authors:** Author1, Author2
- 🗓️ **Date:** YYYY-MM-DD
- 📑 **Publisher:** Venue or arXiv.org
- 📝 **Abstract:**
    <details>
    <summary>Expand</summary>
    Abstract text here.
    </details>

Field Specification

Field	Emoji	Format	Example
Title	🔹	Markdown link to arXiv abstract page	🔹 [Chain-of-Thought Prompting](https://arxiv.org/abs/2201.11903)
arXiv PDF Link	🔗	Bold label with markdown link to PDF	🔗 **arXiv PDF Link:** [Paper Link](https://arxiv.org/pdf/2201.11903)
Authors	👤	Bold label followed by comma-separated names	👤 **Authors:** Jason Wei, Xuezhi Wang, Dale Schuurmans
Date	🗓️	Bold label followed by ISO date	🗓️ **Date:** 2022-01-28
Publisher	📑	Bold label followed by venue name	📑 **Publisher:** NeurIPS 2022
Abstract	📝	Bold label followed by collapsible <details> block	See template above

Collapsible Abstract Block

The abstract must be wrapped in an HTML collapsible block:

<details>
<summary>Expand</summary>
The full abstract text goes here. It can span multiple lines
and should contain the complete abstract from the paper.
</details>

This keeps the main list compact while allowing readers to expand individual abstracts on demand.

I/O Contract

Inputs

Input	Type	Required	Description
Paper title	String	Yes	The full title of the paper
arXiv ID	String	Yes	The arXiv identifier (e.g., 2201.11903)
Authors	List of strings	Yes	All author names
Date	Date string	Yes	Publication date in YYYY-MM-DD format
Publisher/Venue	String	Yes	Conference name, journal, or arXiv.org
Abstract	String	Yes	Full abstract text

Outputs

Output	Type	Description
Formatted entry	Markdown block	A complete paper entry conforming to the template, ready to be inserted into README.md

Usage Examples

Example 1: Manually composing an entry

🔹 [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://arxiv.org/abs/2408.03314)
- 🔗 **arXiv PDF Link:** [Paper Link](https://arxiv.org/pdf/2408.03314)
- 👤 **Authors:** Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar
- 🗓️ **Date:** 2024-08-06
- 📑 **Publisher:** arXiv.org
- 📝 **Abstract:**
    <details>
    <summary>Expand</summary>
    Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study the scaling of inference-time computation in LLMs, with a focus on answering the question: if an LLM is allowed to use a fixed but non-trivial amount of inference-time compute, how much can it improve its performance on a challenging prompt?
    </details>

Example 2: Using the automated script

# Generate entry metadata automatically
python fetch_semantic_info.py --paper_name "Scaling LLM Test-Time Compute"

# Review the generated output and paste it into README.md
# Verify it matches the template format before committing

Related Pages

• Principle:ThreeSR_Awesome_Inference_Time_Scaling_Paper_Entry_Addition -- The principle of adding a new paper to the curated list using a consistent template.