{
  "id": 16923,
  "url": "https://arxiv.org/abs/2608.05030v1",
  "title": "From Score Matrices to Football-Aware Match-State Simulation: An Auditable LLM Harness for Exact-Score Reranking",
  "summary": "Football score forecasting combines a strong statistical core with a difficult contextual edge. Dynamic Poisson-family models estimate team strength, expected goals, and coherent score probabilities, but do not directly understand roles, tactical matchups, motivation, or how a first goal changes behaviour. Large language models (LLMs) can reason about such concepts, yet are not calibrated probability engines. We combine both components through an auditable information harness. This paper documen",
  "authors": "Shaopeng Liang",
  "category": "research",
  "topics": "transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T16:34:53.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16923",
  "original_url": "https://arxiv.org/abs/2608.05030v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}