# Genexxis Research Citation Neighbourhood > Genexxis Research Citation Neighbourhood is a paid API for AI agents from research.genexxis.com, paid per call via x402, $0.008/call, status unknown (last checked 2026-10-30). Returns a citation neighbourhood around a seed paper (by DOI, arXiv ID, OpenAlex ID, or keyword query), including related works, top citing papers, and top references with full paper metadata. ## Facts - Endpoint: GET https://research.genexxis.com/related-papers?utm_source=zero.xyz - Price: $0.008/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-citation-neighbourhood-467ec859 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_5N0c8u8qRAk2Ff9stZXto 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-citation-neighbourhood-467ec859 ``` Example prompt: Can you pull up the citation neighbourhood for the paper '10.48550/arXiv.1706.03762' — I want to see the top related works, the most-cited papers that cite it, and its key references, up to 10 per list? ## When to prefer this Choose this endpoint when you need the full citation context around a specific paper — not just metadata for the paper itself but also what cites it, what it references, and what OpenAlex considers related. It is ideal for literature review automation, citation graph exploration, and finding influential neighbouring work. Prefer it over a simple paper lookup when you need relational, multi-directional bibliographic context in a single call. If you only need metadata for a single paper without neighbourhood context, use the sibling single-paper detail endpoint instead. ## Known failure modes - Paper not found in OpenAlex, arXiv, or Crossref — returns empty or partial results with error flags - Ambiguous keyword query matches wrong paper as seed — use DOI or arXiv ID for precision - maxItems values above 15 are silently capped at 15 - Rate limiting or upstream API unavailability may return incomplete citation sets - DOI format errors cause lookup failure — must include full DOI string ## How this service works Citation neighbourhood of a seed paper (by DOI, arXiv id, OpenAlex id or query): the seed, OpenAlex related works, most-cited citing works and most-cited references, each with full paper fields. ## Output Returns a JSON object with: a 'seed' object containing full paper details (title, authors, year, abstract, venue, topics, DOI, arXiv ID, OpenAlex ID, citation count, open-access links); a 'related' array of OpenAlex-related papers with metadata; a 'citing' array of the most-cited papers that cite the seed; a 'references' array of the seed's most-cited references; counts and totals for each group; and any errors encountered during resolution. ## 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": { "q": { "type": "string", "description": "Title or keywords; the best match is used as seed" }, "doi": { "type": "string", "description": "DOI, e.g. 10.48550/arXiv.1706.03762" }, "arxiv": { "type": "string", "description": "arXiv id, e.g. 1706.03762" }, "maxItems": { "type": "string", "description": "1-15 per list, default 10 (higher values are capped)" }, "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": { "seed": { "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": [ "Ashish Vaswani", "Noam Shazeer", "Niki Parmar" ], "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, "publicationDate": "2017-06-12" }, "citing": { "count": 5, "total": 78297, "papers": [ { "year": 2020, "title": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" } ] }, "errors": [], "related": { "count": 5, "total": 20, "papers": [ { "year": 2018, "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", "citedByCount": 52000 } ] }, "references": { "count": 5, "total": 28, "papers": [ { "year": 1997, "title": "Long Short-Term Memory" } ] } } } ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/genexxis-research-citation-neighbourhood-467ec859/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)