{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/18c337fa-6ca7-412b-959e-ecee6dfbe1c9","identifier":"18c337fa-6ca7-412b-959e-ecee6dfbe1c9","url":"https://froggit.ai/public/capsules/18c337fa-6ca7-412b-959e-ecee6dfbe1c9","name":"LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis","text":"# LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis\n\nSource-backed public reference for arXiv:2604.28178.\n\n**Authors:** Lincan Li, Zheng Chen, Yushun Dong\n**Primary source:** https://arxiv.org/abs/2604.28178\n**Published:** 2026-04-30T17:57:12Z\n**Updated:** 2026-04-30T17:57:12Z\n**Categories:** cs.AI\n\n## Abstract Summary\nElectroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging. Existing graph construction methods, whether correlation-based or learning-based, often generate redundant or irrelevant edges due to the noisy nature of EEG data. This significantly impairs the quality of graph representation and limits downstream task performance. Motivated by the remarkable reasoning and contextual understanding capabilities of large language models (LLMs), we explore the idea of using LLMs as graph edge refiners. Specifically, we propose a two-stage framework: we first verify that LLM-based edge refinement can effectively identify and remove redundant connections, leading to significant improvements in seizure detection accuracy and more meaningful graph structures. Building on this insight, we further develop a robust solution where the initial graph is constructed using a Transformer-based edge predictor and multilayer perceptron, assigning probability scores to potential edges and applying a threshold to determine their existence. The LLM then acts as an edge set refiner, making informed decisions based on both textual and statistical features of node pairs to validate the remaining connections. Extensive...\n\n## Public Use Notes\n- This capsule summarizes the paper's arXiv metadata and abstract; it is not an independent replication or endorsement of the paper's claims.\n- Use it as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, or deployment-sensitive topics, treat the paper as re","keywords":["cs.AI"],"about":[],"citation":["https://arxiv.org/abs/2604.28178"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-05-01T06:00:02.979000Z","dateModified":"2026-06-19T03:07:28Z","isBasedOn":"https://arxiv.org/abs/2604.28178","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":100},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"}]}