{
  "abstract": "Designing a massive multiple-input multiple-output (MIMO) detector that is tailored to each specific deployment site can improve detection performance, yet most existing detectors, including deep-unfolding methods, adapt mainly within human-prescribed algorithmic structures. This letter proposes SiteEvo, a site-adaptive agentic discovery framework that extends adaptation from parameter tuning to detector-algorithm discovery. Rather than selecting or recombining predefined architectures, the large language model (LLM) agent synthesizes complete executable detector programs under a common design contract by combining its pretrained communication knowledge, site-specific wireless evidence, and experience accumulated during the search. Each proposal defines its own processing structure and trainable parameterization, and the parameters are optimized by a communication-system backend. The resulting high-performance detector is further structurally refined to reduce block-level computational cost under a bit error rate (BER)-preservation constraint and is finally frozen for deployment without online LLM inference. Simulations across three sites demonstrate BER reductions by factors of 2.6 to 6.0 over the best-performing competing detector at each site at 6 dB, while structural refinement reduces block-level computational cost by 16.7-34.1% under a validation-BER constraint.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.15490",
  "author_total": 4,
  "authors": [
    {
      "name": "Jiajia Guo"
    },
    {
      "name": "Chao-Kai Wen"
    },
    {
      "name": "Shi Jin"
    },
    {
      "name": "Jun Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1st1gtmsbhhyw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.15490",
  "fulltext_url": null,
  "id": "1st1gtmsbhhyw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.15490",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "2208|Advanced Wireless Communication Techniques"
    ]
  ],
  "openalex": "W7221831418",
  "pdf_url": "https://arxiv.org/pdf/2610.15490",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1st1gtmsbhhyw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:15Z",
      "native_id": "2610.15490",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.15490"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "SiteEvo: An LLM Agent for Autonomous Discovery of Site-Adaptive Massive MIMO Detectors",
  "title_text": "SiteEvo: An LLM Agent for Autonomous Discovery of Site-Adaptive Massive MIMO Detectors",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:15Z",
  "url": "https://research.draconitic.com/paper/1st1gtmsbhhyw",
  "venue": "arXiv",
  "versions": []
}