{
  "apiVersion": "v1",
  "methodology": "https://sourcescore.org/methodology/",
  "canonical": "https://sourcescore.org/claims/f8d64457ba9fd35b/",
  "claim": {
    "vertical": "ai-ml",
    "subject": "Rotary Position Embedding (RoPE)",
    "predicate": "introduced_in_paper",
    "object": "RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021)",
    "confidence": 1,
    "sources": [
      {
        "url": "https://arxiv.org/abs/2104.09864",
        "title": "RoFormer: Enhanced Transformer with Rotary Position Embedding",
        "publisher": "arXiv (Su, Lu, Pan, Murtadha, Wen, Liu)",
        "publishedDate": "2021-04-20",
        "accessedDate": "2026-05-16",
        "type": "preprint",
        "excerpt": "In this paper, we first investigate various methods to integrate positional information into the learning process of transformer-based language models. Then, we propose a novel method named Rotary Position Embedding (RoPE) to effectively leverage the positional information."
      },
      {
        "url": "https://github.com/ZhuiyiTechnology/roformer",
        "title": "ZhuiyiTechnology/roformer — official implementation",
        "publisher": "Zhuiyi Technology",
        "publishedDate": "2021-04-20",
        "accessedDate": "2026-05-16",
        "type": "github-release"
      }
    ],
    "publishedAt": "2026-05-16T00:00:00Z",
    "lastVerified": "2026-05-16",
    "methodologyVersion": "veritas-v0.1",
    "tags": [
      "rope",
      "position-embedding",
      "transformer",
      "foundational",
      "2021"
    ],
    "id": "f8d64457ba9fd35b",
    "statement": "Rotary Position Embedding (RoPE) introduced in paper: RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021)."
  },
  "signature": {
    "algorithm": "HMAC-SHA256",
    "signedBy": "did:web:sourcescore.org",
    "signedAt": "2026-05-16T00:00:00.000Z",
    "signature": "726e8aeb6268bfd699ed0c7d95c72b2dca30749066d2100a4b3ee76d217a0897"
  },
  "citedAs": "Rotary Position Embedding (RoPE) introduced in paper: RoFormer: Enhanced Transformer with Rotary Position Embedding (Su et al., 2021). — SourceScore Claim f8d64457ba9fd35b (verified 2026-05-16, signed 726e8aeb…). https://sourcescore.org/claims/f8d64457ba9fd35b/"
}