{
  "id": 18798,
  "url": "https://arxiv.org/abs/2608.11623v1",
  "title": "FM-LLM: A frequency-enhanced mixture-of-experts framework for adapting LLMs to time series forecasting",
  "summary": "Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual prompts for modality alignment-introducing nontrivial computational overhead and failing to leverage the rich spectral dynamics inherent in time-series data. To enable prompt-free, frequency-aware adaptation of frozen LLMs, we propose FM-LLM (Frequency-Enhanced Mixture-of-Experts for adapting LLMs to Time Series Forecasting), an autoreg",
  "authors": "Rentao Gu, Yihang Ding, Junjie Li, Yi Ding, Weijing Sang, Xiaoli Huo et al.",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T04:09:52.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18798",
  "original_url": "https://arxiv.org/abs/2608.11623v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}