Genexxis Research Paper Detail

x402

Genexxis Research Paper Detail is a paid API for AI agents from research.genexxis.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-30).

Retrieves full scholarly paper metadata by DOI, arXiv ID, or OpenAlex ID, including all authors with institutions and ORCIDs, abstract, venue, open-access links, topics, citation counts, and references.

Price / call$0.003

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How this service works

One paper in full detail by DOI, arXiv id or OpenAlex id: all authors with institutions and ORCID, abstract, venue, open-access links, topics with fields, citation count and citations per year, references count.

Output

A JSON object containing the paper's DOI, arXiv ID, OpenAlex ID, title, publication year, type, venue, abstract, full author list (each with name, ORCID, position, and institutions with country), topics with fields, open-access status and PDF URL, total citation count, year-by-year citation breakdown, reference count, and the data sources it was found in.

Example use cases

Verify paper authorship and affiliations

“I need to confirm the authors and their institutional affiliations for this paper — can you look it up by DOI 10.1038/s41586-021-03819-2 and give me the full author list with ORCIDs?”

Check citation impact of a paper

“How many times has the paper with arXiv ID 2303.08774 been cited, and can you show me the citation breakdown by year?”

Find open-access PDF for a paper

“Can you find a free PDF link for the paper with DOI 10.48550/arXiv.1706.03762? I want to read the full text without hitting a paywall.”

Technical details

Endpoint

GEThttps://research.genexxis.com/paper?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need comprehensive, structured metadata for a single known paper and already have its DOI, arXiv ID, or OpenAlex ID. It is ideal for citation analysis, author disambiguation, open-access discovery, or enriching a bibliography. Prefer it over a search endpoint when the paper identifier is already known, and over a citation-neighbourhood endpoint when you need paper-level details rather than network traversal.

Side effects

Invalid or malformed DOI returns an error or empty result
arXiv ID not found in OpenAlex returns partial or no data
Paper exists only behind a paywall with no open-access version, so pdfUrl is null
Very recent papers may have zero citations or incomplete metadata
OpenAlex coverage gaps may return detailLevel of 'partial' rather than 'full'
No query parameters provided results in a missing-parameter error

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "paper": {
      "doi": "10.48550/arxiv.1706.03762",
      "type": "preprint",
      "year": 2017,
      "links": {
        "doi": "https://doi.org/10.48550/arxiv.1706.03762",
        "arxiv": "https://arxiv.org/abs/1706.03762",
        "openalex": "https://openalex.org/W2626778328"
      },
      "title": "Attention Is All You Need",
      "venue": "arXiv",
      "topics": [
        "Natural Language Processing Techniques",
        "Topic Modeling",
        "Speech Recognition and Synthesis"
      ],
      "arxivId": "1706.03762",
      "authors": [
        {
          "name": "Ashish Vaswani",
          "orcid": "0000-0002-7794-2085",
          "position": "first",
          "institutions": [
            {
              "name": "Google (United States)",
              "country": "US"
            }
          ]
        }
      ],
      "foundIn": [
        "openalex",
        "arxiv"
      ],
      "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...",
      "openAccess": {
        "isOa": true,
        "pdfUrl": "https://arxiv.org/pdf/1706.03762",
        "status": "green",
        "landingUrl": "https://arxiv.org/abs/1706.03762"
      },
      "openalexId": "W2626778328",
      "authorsTotal": 8,
      "citedByCount": 78297,
      "countsByYear": [
        {
          "year": 2025,
          "citedBy": 10763
        },
        {
          "year": 2024,
          "citedBy": 14261
        }
      ],
      "publicationDate": "2017-06-12",
      "referencedWorksCount": 28
    },
    "errors": [],
    "detailLevel": "full (OpenAlex)"
  }
}

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How this service works

Output

Example use cases

Verify paper authorship and affiliations

“I need to confirm the authors and their institutional affiliations for this paper — can you look it up by DOI 10.1038/s41586-021-03819-2 and give me the full author list with ORCIDs?”

Check citation impact of a paper

“How many times has the paper with arXiv ID 2303.08774 been cited, and can you show me the citation breakdown by year?”

Find open-access PDF for a paper

“Can you find a free PDF link for the paper with DOI 10.48550/arXiv.1706.03762? I want to read the full text without hitting a paywall.”

Technical details

Endpoint

GEThttps://research.genexxis.com/paper?utm_source=zero.xyz

When to prefer

Side effects

Invalid or malformed DOI returns an error or empty result
arXiv ID not found in OpenAlex returns partial or no data
Paper exists only behind a paywall with no open-access version, so pdfUrl is null
Very recent papers may have zero citations or incomplete metadata
OpenAlex coverage gaps may return detailLevel of 'partial' rather than 'full'
No query parameters provided results in a missing-parameter error

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "paper": {
      "doi": "10.48550/arxiv.1706.03762",
      "type": "preprint",
      "year": 2017,
      "links": {
        "doi": "https://doi.org/10.48550/arxiv.1706.03762",
        "arxiv": "https://arxiv.org/abs/1706.03762",
        "openalex": "https://openalex.org/W2626778328"
      },
      "title": "Attention Is All You Need",
      "venue": "arXiv",
      "topics": [
        "Natural Language Processing Techniques",
        "Topic Modeling",
        "Speech Recognition and Synthesis"
      ],
      "arxivId": "1706.03762",
      "authors": [
        {
          "name": "Ashish Vaswani",
          "orcid": "0000-0002-7794-2085",
          "position": "first",
          "institutions": [
            {
              "name": "Google (United States)",
              "country": "US"
            }
          ]
        }
      ],
      "foundIn": [
        "openalex",
        "arxiv"
      ],
      "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...",
      "openAccess": {
        "isOa": true,
        "pdfUrl": "https://arxiv.org/pdf/1706.03762",
        "status": "green",
        "landingUrl": "https://arxiv.org/abs/1706.03762"
      },
      "openalexId": "W2626778328",
      "authorsTotal": 8,
      "citedByCount": 78297,
      "countsByYear": [
        {
          "year": 2025,
          "citedBy": 10763
        },
        {
          "year": 2024,
          "citedBy": 14261
        }
      ],
      "publicationDate": "2017-06-12",
      "referencedWorksCount": 28
    },
    "errors": [],
    "detailLevel": "full (OpenAlex)"
  }
}