{
  "id": 11629,
  "url": "https://arxiv.org/abs/2607.14314v1",
  "title": "NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis",
  "summary": "Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling. While spatial-temporal graph neural networks (STGNNs) have advanced EEG brain network representation learning, the resulting graph structures suffer from low clinical plausibility and limited interpretability due to their purely data-driven nature. To this end, we introduce NeuroGRIP, a retrieval-augmented graph refinem",
  "authors": "Lincan Li, Zheng Chen, Yushun Dong",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-15T19:20:29.000Z",
  "fetched_at": "2026-07-18T05:10:55.931Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/11629",
  "original_url": "https://arxiv.org/abs/2607.14314v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}