Launch HN: Undermind (YC S24) – AI agent for discovering scientific papers

Hacker News Discussion ↗

Overall reception

  • Many commenters are impressed; several say it found papers they had missed with Google Scholar/arXiv and plan to keep using or subscribing.
  • Users from diverse fields (CS, medicine, marketing, animal advocacy, neuroscience, etc.) report relevant results, sometimes discovering genuinely new papers.
  • Some are “very impressed” by quality but emphasize it should complement, not replace, traditional systematic search methods.

Search quality and capabilities

  • Strengths:
    • Handles complex, detailed queries and refines them via follow-up questions.
    • Often surfaces niche and obscure but relevant work with fewer false positives than generic search.
    • Produces structured outputs: ranked lists, topic-match scores, brief notes, and an estimated coverage (“% discovered”).
  • Weaknesses:
    • Currently relies mainly on abstracts; missing full-text-only signals and some gray literature, theses, and key theoretical papers.
    • Ranking sometimes overweights topical similarity/recency and underweights perceived “importance” or seminal status.
    • False positives still present; some users report high precision, others note ~50% noise.

Architecture and design choices

  • Uses multi-stage, LLM-heavy retrieval with high-quality models, trading latency and compute cost for accuracy.
  • Citations are used to explore the graph but not primarily to rank final results, unless explicitly requested.
  • Core dataset is from a large academic aggregator; open-access full texts are planned, paywalled full text would require publisher deals.

Positioning vs. other tools

  • Compared to Elicit, Scite, Consensus, Semantic Scholar, Exa, etc., Undermind is framed as:
    • Slower but more accurate and suited for complex topic discovery.
    • Less focused on fast summarization and more on deep, agentic search.

Access, pricing, and UX feedback

  • Some frustration with institutional-email and signup requirements; a special HN link bypasses this.
  • Requests for:
    • Student or lower-cost tiers, pay-per-query, or rate-limited cheaper plans.
    • API access for integration into in-house tools and VS Code extensions.
    • Better follow-up questioning (e.g., multiple choice), “refine” as well as “extend” searches, importance-weighted ranking, richer citation formatting, and easy PDF/export options.
  • Concerns that long-term availability of results depends on startup survival; users want robust offline saving.