# Genexxis Research Paper Detail

> 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.

## Facts

- Endpoint: GET https://research.genexxis.com/paper?utm_source=zero.xyz
- Price: $0.003/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-30
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/genexxis-research-paper-detail-4114bb21
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_JfWdRvlwSsBYHNQB-AYQg

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability genexxis-research-paper-detail-4114bb21
```

Example prompt: Can you pull up the full details for the paper 'Attention Is All You Need' — I need the authors with their institutions, the abstract, citation count, and a link to the open-access PDF? The arXiv ID is 1706.03762.

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method"
   ],
   "properties": {
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "GET"
     ],
     "type": "string"
    },
    "queryParams": {
     "type": "object",
     "properties": {
      "doi": {
       "type": "string",
       "description": "DOI, e.g. 10.48550/arXiv.1706.03762"
      },
      "arxiv": {
       "type": "string",
       "description": "arXiv id, e.g. 1706.03762"
      },
      "openalex": {
       "type": "string",
       "description": "OpenAlex work id, e.g. W2626778328"
      }
     }
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object"
    }
   }
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "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)"
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/genexxis-research-paper-detail-4114bb21/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from research.genexxis.com](https://www.zero.xyz/host/research.genexxis.com/llms.txt)