Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
GenOR‑Twin is a neuro‑symbolic middleware that translates unstructured operational logs into formal constraints for mathematical optimization. It uses Large Language Models as semantic translators, not direct solvers, preserving the feasibility guarantees of exact combinatorial methods. The system dynamically injects constraints in real time, couples operational observations with a virtual model, and adapts its decision policy between schedule repair and full re‑optimization, demonstrating applicability across six optimization domains.
General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockchain environments. On-chain execution is stateful, adversarial, and economically irreversible, exposing three fundamental gaps: Reactivity, Irreversibility, and Observability.
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