Implementation:ThreeSR Awesome Inference Time Scaling Search Papers Function

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

Concrete tool for querying the Semantic Scholar Academic Graph API to discover papers by keyword, provided by the search_papers() function in fetch_semantic_info.py.

Description

The search_papers() function constructs a search URL against the Semantic Scholar Graph API v1 endpoint, sends an HTTP GET request, and returns a list of paper metadata dictionaries. It requests a specific set of fields (title, authors, venue, year, publicationDate, fieldsOfStudy, url) and sorts results by year. If the API returns a non-200 status code, the function prints an error message and returns an empty list.

The function relies on two module-level constants:

• BASE_URL = "https://api.semanticscholar.org/graph/v1/"
• FIELDS = "title,authors,venue,year,publicationDate,fieldsOfStudy,url"

Usage

Import or call this function when:

• You need to discover papers matching a search query (e.g., "Inference-Time Scaling")
• You are building or updating a curated paper list
• You want to retrieve structured metadata for a batch of recent papers from Semantic Scholar

Code Reference

Source Location: fetch_semantic_info.py, lines 22-29

Function Signature:

def search_papers(query: str, limit: int = 5) -> list[dict]

Import Statement:

from fetch_semantic_info import search_papers

Source Code:

def search_papers(query, limit=5):
    """Fetch relevant papers from the Semantic Scholar API"""
    url = f"{BASE_URL}paper/search?query={query}&fields={FIELDS}&limit={limit}&sort=year"
    response = requests.get(url)
    if response.status_code != 200:
        print("Error fetching data from Semantic Scholar API")
        return []
    return response.json().get("data", [])

Dependencies:

• requests (third-party HTTP library)

API Endpoint:

GET https://api.semanticscholar.org/graph/v1/paper/search?query={query}&fields={fields}&limit={limit}&sort=year

I/O Contract

Inputs:

Parameter	Type	Default	Description
query	str	(required)	The search query string (e.g., a paper topic or title)
limit	int	5	Maximum number of paper results to return

Outputs:

Field	Type	Description
title	str	The title of the paper
authors	list[dict]	List of author objects, each containing a name field
venue	str	The publication venue (journal, conference, or empty string)
year	int	The publication year
publicationDate	str	The publication date in YYYY-MM-DD format
fieldsOfStudy	list[str]	Academic fields the paper belongs to (e.g., "Computer Science")
url	str	URL to the paper on Semantic Scholar
paperId	str	Semantic Scholar unique paper identifier (always included by default)

The function returns a list[dict]. Each dictionary contains the fields listed above. On API failure, an empty list [] is returned.

Usage Examples

Example 1: Basic search with default limit

from fetch_semantic_info import search_papers

papers = search_papers("Inference-Time Scaling")
for paper in papers:
    print(f"{paper['title']} ({paper['year']})")
    authors = ", ".join(a["name"] for a in paper["authors"])
    print(f"  Authors: {authors}")
    print(f"  Venue: {paper['venue']}")
    print(f"  Date: {paper['publicationDate']}")
    print()

Example 2: Search with a custom limit

from fetch_semantic_info import search_papers

# Fetch the single most recent paper
papers = search_papers("Chain of Thought Reasoning", limit=1)
if papers:
    latest = papers[0]
    print(f"Latest paper: {latest['title']}")
    print(f"Paper ID: {latest['paperId']}")
else:
    print("No papers found or API error occurred.")

Example 3: Handling empty results

from fetch_semantic_info import search_papers

papers = search_papers("xyznonexistenttopic12345", limit=10)
if not papers:
    print("No papers found. Check your query or try again later.")
else:
    print(f"Found {len(papers)} papers.")

Example 4: Command-line integration

# The main script uses search_papers internally:
python fetch_semantic_info.py --paper_name "Reward Modeling"
# This calls search_papers("Reward Modeling", limit=1) and updates README.md

Related Pages

• Principle:ThreeSR_Awesome_Inference_Time_Scaling_Academic_Paper_Search -- The principle of Academic Paper Search that this function implements
• Environment:ThreeSR_Awesome_Inference_Time_Scaling_Python_Runtime_Environment
• Environment:ThreeSR_Awesome_Inference_Time_Scaling_Semantic_Scholar_API_Environment
• Heuristic:ThreeSR_Awesome_Inference_Time_Scaling_API_Rate_Limiting_Tip