Principle:ThreeSR Awesome Inference Time Scaling Paper Detail Retrieval

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

Overview

Paper Detail Retrieval is the principle of fetching extended metadata for a specific paper using its unique identifier, enriching basic search results with additional fields not available in bulk search.

Description

Paper Detail Retrieval addresses a common limitation of search APIs: bulk search endpoints return only a subset of available metadata fields. To obtain richer information -- such as the abstract, arXiv identifier, citation counts, or reference lists -- a second, targeted request must be made using the paper's unique identifier (e.g., a Semantic Scholar paper ID).

This two-phase pattern is widespread in API design:

1. Discovery phase: A search query returns a list of lightweight records with basic fields.
2. Enrichment phase: For each record of interest, a detail endpoint is queried using the unique ID to retrieve the full record with extended fields.

The enrichment phase trades additional HTTP requests for richer data. In the context of this repository, the discovery phase returns title, authors, venue, year, and publication date, while the enrichment phase adds the arxivId (needed to construct arXiv PDF links) and the abstract (displayed in the curated README).

Usage

Use Paper Detail Retrieval when:

• Bulk search results lack fields that are essential for your use case (e.g., abstracts, arXiv IDs)
• You need to construct external links (like arXiv PDF URLs) that require identifiers not in the search response
• You are enriching a list of discovered papers before formatting them for display
• The detail endpoint provides a different or more complete schema than the search endpoint

Theoretical Basis

The general algorithm for paper detail retrieval follows this pattern:

PROCEDURE RetrievePaperDetails(paper_id):
    1. CONSTRUCT a detail URL:
        a. Base URL = API endpoint for individual paper lookup
        b. Append the paper_id as a path parameter
    2. SEND an HTTP GET request to the constructed URL
    3. PARSE the JSON response body into a dictionary
    4. RETURN the dictionary containing extended paper metadata

PROCEDURE EnrichSearchResults(papers):
    1. FOR EACH paper in the search results:
        a. EXTRACT the paper_id from the search result
        b. CALL RetrievePaperDetails(paper_id)
        c. MERGE the extended fields (arxivId, abstract) into the paper record
    2. RETURN the enriched list of papers

Key design considerations:

• API version differences: The detail endpoint may use a different API version than the search endpoint. In this repository, the search uses graph/v1 while the detail retrieval uses the older v1 endpoint, which returns different field names and structures.
• N+1 request pattern: Retrieving details for N papers requires N additional HTTP requests. For large result sets, consider batching (if the API supports it) or caching to reduce overhead.
• Rate limiting: Each detail request counts against the API's rate limit. Production code should include delays or exponential backoff between requests.
• Error handling: If a detail request fails, the caller should decide whether to skip that paper or retry. The current implementation assumes success, which is acceptable for small batches but should be hardened for production use.
• Field availability: Not all papers have an arXiv ID or abstract. The calling code should handle missing fields gracefully with default values (e.g., "N/A" or "No abstract available.").

Related Pages

• Implementation:ThreeSR_Awesome_Inference_Time_Scaling_Get_Paper_Info_Function -- The concrete get_paper_info() function that implements this principle