{
  "id": 19068,
  "url": "https://arxiv.org/abs/2608.11656v1",
  "title": "Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models",
  "summary": "Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subjects, recording environments, and datasets. However, dominant pretraining paradigms face key challenges: masked autoencoding tends to prioritize low-level signal reconstruction over task-relevant semantics, while autoregressive modeling creates a mismatch between continuous neural dynamics and discrete token spaces. To address these challenges, ne",
  "authors": "Myeong-Ju Cho, Hye-Bin Shin, Seo-Hyun Lee, Seong-Whan Lee",
  "category": "research",
  "topics": "safety-alignment,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T04:54:43.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "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/19068",
  "original_url": "https://arxiv.org/abs/2608.11656v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}