{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreictfwzzu2odnfcpnk4okarxtloayie2gvs7fsk26cafhd4tlshonq",
    "uri": "at://did:plc:pgryn3ephfd2xgft23qokfzt/app.bsky.feed.post/3memsz2u2kwh2"
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  "path": "/t/wave-coherence-scoring-phase-aware-alternative-to-cosine-similarity/173375#post_1",
  "publishedAt": "2026-02-12T00:29:00.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "GitHub - atech-hub/Wave-Coherence-as-a-Computational-Primitive: Harmonic coherence as a universal relationship detection operator"
  ],
  "textContent": "I’m not a developer or mathematician — I’m a systems administrator. I had an intuition about how meaning might organise itself through phase relationships and wave mechanics rather than vector distances. I collaborated with AI (Claude) to formalise and test those ideas, and the result aligned with published research by Listopad (2025) — Wave-Based Semantic Memory with Resonance-Based Retrieval (arXiv:2509.09691).\nThe core idea: semantic relationships encoded as harmonic waveforms on circular embeddings, with retrieval through constructive interference rather than cosine similarity.\nRepo: GitHub - atech-hub/Wave-Coherence-as-a-Computational-Primitive: Harmonic coherence as a universal relationship detection operator\nSharing it here in case anyone working in embedding spaces or retrieval finds it interesting or wants to take it further.",
  "title": "Wave-Coherence Scoring: Phase-Aware Alternative to Cosine Similarity"
}