{
  "abstract": "Vehicle-to-Everything (V2X) cooperative perception improves 3-D detection by sharing intermediate features, but dense remote features may repeat context that the ego agent can infer locally. Most communication-efficient designs optimize masks or codes empirically, leaving a more basic question open: which remote evidence is indispensable given the receiver's own observation? We introduce a closure-fidelity perspective on ego conditioned remote perception. Under a finite deductive abstraction and explicit conditions, its rate--distortion function decomposes over an irredundant core, and the exact zero-distortion rate becomes $P_A H(π_A)$. This analysis suggests a concrete design principle: transmit compact evidence and recover derivable context with bounded receiver-side inference. Guided by this principle, SemRD-V2X is an operational neural proxy that combines exact-budget BEV support selection, pointwise channel compression, and masked shared-weight reconstruction before standard fusion. Experiments on simulated V2XSet and real-world DAIR-V2X validate the resulting design. In a controlled five-run V2XSet comparison against a locally reproduced V2X-ViT-v1 baseline on one Tesla V100, SemRD-V2X reduces the analytical feature payload by $26.6\\times$ while improving AP@0.5/AP@0.7 by 4.13/8.57 points, with 3.81\\% additional mean compute latency. These results position closure fidelity as both an analytical lens and an actionable design principle for communication-efficient cooperative perception.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.34353",
  "author_total": 5,
  "authors": [
    {
      "name": "Hu Xu"
    },
    {
      "name": "Chun Li"
    },
    {
      "name": "Siyuan Qiu"
    },
    {
      "name": "Zeyan Li"
    },
    {
      "name": "Jianfeng Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0amdpt6pfeyfw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.34353",
  "fulltext_url": null,
  "id": "0amdpt6pfeyfw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.34353",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ]
  ],
  "openalex": "W7214736265",
  "pdf_url": "https://arxiv.org/pdf/2609.34353",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0amdpt6pfeyfw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:17Z",
      "native_id": "2609.34353",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.34353"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "SemRD-V2X: Closure-Guided Communication with Bounded Inference for Cooperative Perception",
  "title_text": "SemRD-V2X: Closure-Guided Communication with Bounded Inference for Cooperative Perception",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:17Z",
  "url": "https://research.draconitic.com/paper/0amdpt6pfeyfw",
  "venue": "arXiv",
  "versions": []
}