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Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction — arXiv:2608.30050

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.30050
License (as archived): CC0-1.0
Archived: 2026-10-17T07:44:30.453Z
SHA-256 of the archived text: ee13295b02fb248f9ef56460515b0d710b93d20e90aeecc0891e22f4a88ee0c1
Stable URL of this page: https://metraipolis.com/documento/dc_cc415ce876da

How to cite

Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction — arXiv:2608.30050 — arXiv (en), https://arxiv.org/abs/2608.30050 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cc415ce876da (Metraipolis archive dc_cc415ce876da, sha256 ee13295b02fb248f9ef56460515b0d710b93d20e90aeecc0891e22f4a88ee0c1)

BibTeX

@misc{dccc415ce876da,
  title = {Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction — arXiv:2608.30050},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.30050},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cc415ce876da; sha256 ee13295b02fb248f9ef56460515b0d710b93d20e90aeecc0891e22f4a88ee0c1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.30050. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Building a blockchain digital twin largely requires translating domain knowledge and specific system descriptions into a simulator architecture, calibrating its parameters against behavioral evidence, and validating the constructed twin. These steps are commonly performed through application-specific modeling efforts that can be difficult to reuse across systems and downstream decision problems. We consider automating this process through Spec2Twin-Chain, a framework that formulates blockchain digital-twin construction as a bi-level optimization problem. At the upper level, a large language model proposes and revises structurally admissible architectures using system specifications, behavioral evidence, and feedback from evaluated designs. At the lower level, a simulation-based optimizer calibrates the architecture-conditioned parameters under explicit objectives and guardrail constraints. The two levels iterate. The evaluated candidates at lower levels are retained in a global archive and used to guide subsequent proposals at upper levels. We conduct controlled experiments involving twin calibration, feedback-driven recovery, stress analysis, downstream policy optimization, and policy updating. The results demonstrate that the framework can construct behaviorally accurate twins, improve initial designs through iterative feedback, and reuse calibrated twins to support downstream decisions.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.30050 — 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 2026-10-17T07:28:26.581Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).