Principle:ThreeSR Awesome Inference Time Scaling Academic Paper Search

Page Metadata
Knowledge Sources	Awesome-Inference-Time-Scaling
Domains	Academic Search, API Integration, Scholarly Literature Discovery
Last Updated	2026-02-14 00:00 GMT

Overview

Academic Paper Search is the principle of programmatically querying academic databases to discover scholarly papers matching a given keyword or topic, enabling automated literature curation.

Description

Academic Paper Search involves issuing structured queries against scholarly APIs -- such as the Semantic Scholar Academic Graph API -- to retrieve metadata about published research papers. Rather than manually browsing a database, the program constructs a URL with query parameters (search terms, field selectors, result limits, sort order) and parses the structured JSON response into a list of paper records.

This principle is foundational to any tool that automates the construction of reading lists, survey papers, or "awesome lists" of academic work. It transforms a manual, time-consuming activity into a repeatable, scriptable operation. The key design elements include:

• Query parameterization: The search term, number of results, and sort criteria are configurable inputs.
• Field selection: The API is asked to return only the fields needed (title, authors, venue, year, etc.), reducing bandwidth and simplifying downstream processing.
• Graceful degradation: If the API returns an error status, the function returns an empty list rather than crashing, allowing the caller to handle the absence of data.

In the context of this repository, Academic Paper Search is the first step in a pipeline that discovers new inference-time scaling papers, formats them, and inserts them into a curated README.

Usage

Use Academic Paper Search when:

• You need to discover papers on a topic without knowing specific titles or authors in advance
• You are building an automated pipeline to keep a curated list of papers up to date
• You want to retrieve structured metadata (title, authors, venue, year) for a batch of papers
• You need to sort results by recency to identify the latest publications

Theoretical Basis

The general algorithm for keyword-based academic paper search follows this pattern:

PROCEDURE SearchAcademicPapers(query, limit, sort_field):
    1. CONSTRUCT a search URL:
        a. Base URL = API endpoint for paper search
        b. Append query parameter with the search term
        c. Append fields parameter specifying desired metadata fields
        d. Append limit parameter to cap the number of results
        e. Append sort parameter (e.g., by year descending)
    2. SEND an HTTP GET request to the constructed URL
    3. IF the response status code indicates failure (not 200):
        a. LOG an error message
        b. RETURN an empty collection
    4. PARSE the JSON response body
    5. EXTRACT the "data" array from the response (default to empty if absent)
    6. RETURN the list of paper records

Key design considerations:

• Idempotency: Search requests are read-only GET operations with no side effects on the remote database.
• Rate limiting: Academic APIs typically enforce rate limits; production usage should include retry logic or delays between requests.
• Pagination: For large result sets, the API may support offset-based or token-based pagination. The basic search retrieves only the first page up to the specified limit.
• Field projection: Requesting only the needed fields reduces payload size and parsing overhead. In this repository, the fields requested are: title, authors, venue, year, publicationDate, fieldsOfStudy, and url.

Related Pages

• Implementation:ThreeSR_Awesome_Inference_Time_Scaling_Search_Papers_Function -- The concrete search_papers() function that implements this principle