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.
By Rahimeh Neamatian Monemi, Shahin Gelareh, Lubin Cui, Nelson Maculan
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.
arXiv:2608. 15016v1 Announce Type: cross Abstract: Network incident response remains slow and labor-intensive as the defender must infer multi-stage attacks from partial observations and translate recovery decisions into reliable system commands.
By Yiran Gao, Juntao Chen, Tao Li
arXiv:2608. 05790v1 Announce Type: new Abstract: General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockchain environments.
By Jiacheng Wei, Zhaoxin Fan, Xin Wen, Yuqin Lan, Dongrun Li, Wenjun Wu, Faguo Wu, Xiao Zhang
arXiv:2602. 15983v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations---a feasibility--correctness gap reaching 90 percentage points on compositional problems.
By Junbo Jacob Lian, Yujun Sun, Huiling Chen, Chaoyu Zhang, Hanzhang Qin, Chung-Piaw Teo
arXiv:2607. 16632v1 Announce Type: cross Abstract: Hardware engineering exposes coding agents to a form of long-horizon work that is difficult to capture with pass-at-k: progress is continuous, tool feedback is delayed and heterogeneous, and a backend failure may require revising RTL rather than tuning another physical-design parameter.
By Peilong Zhou, Zhirong Chen, Cangyuan Li, Haoyu Gao, Kaiyan Chang, Ziming Qu, Ying Wang
Large language model (LLM)-assisted energy-management tools can translate natural-language context into structured grid commands, but syntactic validity does not imply physical admissibility. This pap...
The paper proposes adding a verification layer to large language model agents, using a V-model design where each step of the agent’s plan has a dedicated verifier. In a pilot study on the MuSiQue dataset, verified configurations outperformed unverified ones, with deterministic gates correcting most errors at zero cost. The authors plan to extend the architecture to full integration and evaluate it over the next year.
By Ali Afoud, Jie JW Wu
arXiv:2608. 15391v1 Announce Type: new Abstract: Large language model (LLM)-assisted energy-management tools can translate natural-language context into structured grid commands, but syntactic validity does not imply physical admissibility.
By Md Fazley Rafy
arXiv:2607. 00041v1 Announce Type: cross Abstract: Multi-agent LLM systems can decompose software-engineering work into planning, generation, validation, and repair, but a narrower systems problem remains: before any governed shared mutation is applied, a system must decide which concurrently formed write intents may proceed in parallel, which require deterministic composition or serialization, and which must take a fail-closed path.
By Eagl Huang
DNative‑Twin is a graph‑native digital twin that records an AI agent’s committed decision as a typed trajectory, linking observed state, decision path, and authority. It re‑executes the decision mechanism under declared conditions, synchronizing and replaying the process in isolation to compare outcomes under controlled changes. Experiments on enterprise decision logs show that adding replay‑contract state and verification evidence improves unresolved‑divergence recall from 0 to 1.0, while end‑to‑end processing time rises from 0.794 to 8.889 seconds across 500–5,000 cases.
By Junjie Pang, Zhenzhen Xie, Haoke Han, Ying He, Jing Wang, Gang Liu
arXiv:2601.19747v2 Announce Type: replace-cross
Abstract: In the rapidly evolving field of Electronic Design Automation (EDA), the deployment of Large Language Models (LLMs) for Register-Transfer Lev...
By Jiale Liu, Taiyu Zhou, Tianqi Jiang