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AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC — arXiv:2609.39964

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.39964
License (as archived): CC0-1.0
Archived: 2026-10-06T07:47:30.178Z
SHA-256 of the archived text: aebd18d19a9d0590979d685f7dd951ffa0a7f639837383e4ab3c53546bc5b880
Stable URL of this page: https://metraipolis.com/documento/dc_98644bf4e71e

How to cite

AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC — arXiv:2609.39964 — arXiv (en), https://arxiv.org/abs/2609.39964 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_98644bf4e71e (Metraipolis archive dc_98644bf4e71e, sha256 aebd18d19a9d0590979d685f7dd951ffa0a7f639837383e4ab3c53546bc5b880)

BibTeX

@misc{dc98644bf4e71e,
  title = {AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC — arXiv:2609.39964},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.39964},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_98644bf4e71e; sha256 aebd18d19a9d0590979d685f7dd951ffa0a7f639837383e4ab3c53546bc5b880},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.39964. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-modal integrated sensing and communication (ISAC) enables environmental perception and reliable connectivity for intelligent wireless networks. Data-driven multi-modal ISAC models depend heavily on annotated real-world data to learn relationships across sensing and wireless observations, thereby constraining scalable deployment. Although synthetic data generation reduces the burden, adapting existing simulation pipelines to a target deployment requires consistent scene, sensing, wireless, and learning configurations, while mismatches among these coupled components impair sim-to-real transferability. To address the challenge, we propose an agentic artificial intelligence (AI) framework for sim-to-real multi-modal ISAC, named AIMS. Given a natural-language deployment request specifying the target task, deployment conditions, and real-data budget, AIMS derives a deployment-specific sim-to-real configuration and coordinates its execution to produce a deployment-specific task model. A two-agent architecture coordinates scene construction with task learning. A scene construction agent generates geographically grounded, synchronized sensing and wireless records from shared physical states, while a scene understanding agent configures task-relevant modalities and mixture-of-experts (MoE) learning for zero-shot inference or few-shot adaptation. Structured domain knowledge guides dependency-aware planning, while validation evidence supports feedback-driven revision of affected decisions. Experiments on the real-world DeepSense 6G dataset demonstrate improved vehicle detection and beam prediction over the considered simulation and fusion baselines. A separate orchestration benchmark evaluates task interpretation, dependency reasoning, and feedback-driven replanning across diverse deployment requests, showing improved plan correctness with structured domain knowledge and validation feedback.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-05 in cs.MA).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Multiagent Systems (cs.MA); Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2609.39964 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T07:47:29.319Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-10-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).