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ScholarCatalyst

A Benchmark for Retrieving Papers that Inspire New Research

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This is 1 of 154 launches in machine learning and search infrastructure — see how it stacks up on momentum and crowding →

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Details

External ID
1394224747
Source
GITHUB
Company
—
Product
ScholarCatalyst
Website domain
github.io
Launched
Sept. 29, 2026
Cohort
—
Upvotes
17
Upvotes percentile
0.5389042081285218
Tags
benchmark, retrieval, scientific-discovery
Fetched at
Oct. 3, 2026, 1:02 a.m.
Updated at
Oct. 3, 2026, 1:02 a.m.

Enrichment

Niche
machine learning and search infrastructure
Vertical
Education
Function
Search & retrieval
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
paper retrieval benchmark for research
Manually corrected
False

Could you build this?

Partial The website showcasing the benchmark is a basic static site, but the core product is an academic research benchmark requiring careful curation, citation network extraction, and rigorous evaluation methodologies. Vibe coding cannot automate the scientific domain expertise or data collection needed to create a recognized academic benchmark.

What it would actually take: Creating the benchmark requires scraping and parsing millions of academic papers (arXiv, Semantic Scholar APIs), building dense retrieval evaluation pipelines (BEIR, TREC style), and establishing human or citation-grounded ground truth pairs. The hard parts are noise filtering, preventing data leakage, and designing robust ranking metrics (nDCG@k, MRR). Requires an NLP/IR researcher with expertise in information retrieval and benchmark construction.

Competitors

Other products that read as similar to this one — 718 launches clear the similarity bar, closest 8 shown.

Attention rank: #303 of 719 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 334 days after the earliest competitor.

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