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Reputation-weighted preprint discovery layer
A platform that sits on top of arXiv and other preprint servers, providing reputation-weighted quality signals through community endorsements, citation analysis, and researcher credentials. Researchers get a personalized feed of high-quality preprints filtered by trusted endorsers rather than facing the unfiltered firehose. Institutions get quality-weighted analytics for hiring and evaluation.
이것이 중요한 이유
You are an active researcher who checks arXiv daily, but finding genuinely valuable work is becoming impossible. Submission volume has quadrupled in eight years, and a large fraction is low-quality or AI-generated material. You waste hours skimming irrelevant preprints. Conference reviews are too slow and arbitrary to serve as a reliable filter, and Google Scholar counts everything equally. You have no way to see which papers trusted colleagues in your subfield have endorsed. When you sit on a hiring committee, you are handed crude paper counts that you know are gameable, with no quality-weighted alternative. You need a reputation layer that helps you discover and evaluate research without relying on broken volume-based metrics.
- · Active academic researchers in CS, math, and physics who regularly read arXiv preprints and need to filter signal from noise; university hiring committees and grant reviewers who need quality-weighted evaluation tools beyond raw paper counts을(를) 위해 제작되었습니다.
- · 가장 유력한 수익화 모델: Freemium with premium discovery features for individuals; institutional SaaS subscriptions for hiring/evaluation analytics.
고충 · 내러티브
You are an active researcher who checks arXiv daily, but finding genuinely valuable work is becoming impossible. Submission volume has quadrupled in eight years, and a large fraction is low-quality or AI-generated material. You waste hours skimming irrelevant preprints. Conference reviews are too slow and arbitrary to serve as a reliable filter, and Google Scholar counts everything equally. You have no way to see which papers trusted colleagues in your subfield have endorsed. When you sit on a hiring committee, you are handed crude paper counts that you know are gameable, with no quality-weighted alternative. You need a reputation layer that helps you discover and evaluate research without relying on broken volume-based metrics.
점수 세부
시장 신호
시장 진출 전략
Postdocs and junior faculty in theoretical CS and mathematics who read arXiv daily and are overwhelmed by submission volume in their subfields
~50,000 active daily arXiv readers in CS and math who would benefit immediately
Academic Twitter and Hacker News launch targeting CS/AI researchers, followed by direct outreach to math and physics communities
Free for individuals with endorsement features; $29/month for premium analytics; $2,000/month for institutional evaluation dashboards
500 registered users and 50 active endorsers within 30 days of launch, with at least 5 institutional inquiries
MVP 범위 · 1~2주
- Build arXiv API ingestion pipeline to fetch daily submissions in target categories (cs.*, math.*)
- Create basic researcher profile system using ORCID integration for identity verification
- Implement simple upvote/endorse mechanism for registered users on individual papers
- Design and build a personalized feed UI showing recent papers sorted by endorsement count
- Deploy minimal web app and seed with 20-30 endorsements from friendly researchers in your network
- Add Semantic Scholar API integration to display citation counts and reference networks
- Implement researcher reputation score based on endorsement activity and citation history
- Add topic-based filtering so users can subscribe to specific arXiv categories
- Create a public leaderboard showing most-endorsed papers per week to drive engagement
- Reach out to 10 established researchers for feedback and ask them to endorse 2-3 papers each
차별화
실패 가능 요인
자가 반박 — 가장 중요한 신뢰 신호
- 1arXiv itself could implement reputation features internally, leveraging their existing user base and data access to make a third-party layer redundant
- 2Academic culture strongly resists explicit reputation hierarchies, and the community may reject any system that appears to codify prestige-based gatekeeping, viewing it as antithetical to scientific egalitarianism
- 3The chicken-and-egg problem of endorsement systems may be insurmountable without significant initial seeding, and academics have low tolerance for tools without immediate value
근거 요약
AI가 이 인사이트를 합성한 방법 — 직접 인용 없음
Approximately 8 commenters independently proposed reputation, karma, or endorsement systems as the logical solution to preprint quality filtering. Multiple users discussed weighting by institutional prestige, citation counts, and community endorsements. The discussion explicitly compared arXiv to GitHub and Substack models where filtering is a separate layer. Several commenters noted that conference review is broken and slow, leaving a gap for a faster quality signal. The pain of unfiltered discovery was acknowledged even by those supporting rate limits.
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헤드라인
Reputation-weighted preprint discovery layer
서브 헤드라인
A platform that sits on top of arXiv and other preprint servers, providing reputation-weighted quality signals through community endorsements, citation analysis, and researcher credentials. Researchers get a personalized feed of high-quality preprints filtered by trusted endorsers rather than facing the unfiltered firehose. Institutions get quality-weighted analytics for hiring and evaluation.
대상 사용자
대상: Active academic researchers in CS, math, and physics who regularly read arXiv preprints and need to filter signal from noise; university hiring committees and grant reviewers who need quality-weighted evaluation tools beyond raw paper counts
기능 목록
✓ Reputation-weighted preprint feed using endorsement and citation signals ✓ Researcher profile with quality-weighted publication metrics beyond h-index ✓ Endorsement system where established researchers can vouch for papers ✓ Personalized discovery based on followed researchers and topics ✓ Institutional dashboard for hiring committees with quality-adjusted analytics
어디서 검증할까요
r/HN · front_page에 랜딩 페이지 링크를 공유하세요 — 바로 이 고통이 발견된 곳입니다.
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