{
  "abstract": "Spatio-temporal forecasting is a cornerstone of logistics, urban planning, and intelligent transportation systems. However, constrained by deployment costs and maintenance resources, sensor networks often lack comprehensive spatial coverage, rendering Forecast Unobserved Node States (FUNS) a critical yet formidable challenge. Conventional models rely on historical observations and typically falter when encountering nodes without prior records. To address this, we redefine the problem as a conditional generation task on spatio-temporal graphs and propose GenST, a framework that introduces Large Language Models (LLMs) as a semantic bridge, leveraging a pre-trained LLM fine-tuned to extract rich semantic features from node descriptions, such as functional zones and road network structures, to compensate for missing spatio-temporal signals. Specifically, we design a two-stage generative architecture: a Spatio-Temporal VAE first compresses spatio-temporal dynamics into a latent space, followed by a Generative Transformer (GenT) that reconstructs the future states of unobserved nodes from noise, guided by multi-modal conditions including semantics, geographic coordinates, and neighborhood contexts. Experiments on six traffic and two non-traffic datasets show GenST significantly outperforms existing baselines in zero-shot prediction tasks, demonstrating the practical potential of semantic-guided generation for mitigating spatio-temporal data sparsity.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.08818",
  "author_total": 7,
  "authors": [
    {
      "name": "Shuhao Li"
    },
    {
      "name": "Weidong Yang"
    },
    {
      "name": "Changan Liu"
    },
    {
      "name": "Wei Zhuo"
    },
    {
      "name": "Yingbo Zhou"
    },
    {
      "name": "Fan Zhang"
    },
    {
      "name": "Siqiang Luo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b6w8jnpp2k6vr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.08818",
  "fulltext_url": null,
  "id": "b6w8jnpp2k6vr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.08818",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.CL"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2215|Traffic Prediction and Management Techniques"
    ]
  ],
  "openalex": "W7220954050",
  "pdf_url": "https://arxiv.org/pdf/2610.08818",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b6w8jnpp2k6vr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:03Z",
      "native_id": "2610.08818",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.08818"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Just for FUNS: LLM-Guided Spatio-Temporal Graph Node Generation for Forecasting Unobserved Node States",
  "title_text": "Just for FUNS: LLM-Guided Spatio-Temporal Graph Node Generation for Forecasting Unobserved Node States",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:03Z",
  "url": "https://research.draconitic.com/paper/b6w8jnpp2k6vr",
  "venue": "arXiv",
  "versions": []
}