Yanlun Tu, Huacan Wang, Jin Xie, Ziyue Zhou, Jie Zhou, Ningyan Zhu, Ge Chen, Wangyi Chen 等 14 位作者
In industrial practice, AI-generated programmable logic controller (PLC) code must integrate with existing projects and implement the required behavior across scan cycles. Compilation alone does not establish runtime behavior, and subsequent edits can invalidate earlier verification results. We present SemaPLC, a project-grounded, verification-gated agent harness for PLC code generation. The harness combines project context and reusable PLC skills with specification review, compilation, and live-runtime validation. Its completion gate requires recorded checks to meet acceptance criteria for the current implementation. Edits invalidate previous verdicts, and failed checks guide bounded repair. We evaluate SemaPLC on 117 independent-POU tasks and 65 project-context tasks across seven language-model backbones. SemaPLC outperforms the evaluated baselines on both metrics for every backbone. Its mean verified-pass rate and dynamic score reach 72.6% and 52.2, exceeding the strongest baseline means by 8.7% points and 20.8 points, respectively. Verification-layer and failure analyses reveal how build recovery and output observability affect end-to-end results and identify behavioral errors that remain after compilation. We apply the harness in KUKA's iiQworks product environment for PLC development and discuss engineering lessons for its industrial integration and deployment. The industrial use indicates the agent assistance substantially improves PLC development efficiency by 45%.
Every perfect $r$-error-correcting Lee code in $\mathbb{Z}^n$ induces a finite configuration around each codeword: a family of pairwise disjoint radius-$r$ Lee spheres, all disjoint from the central sphere, that covers every lattice point at Lee distance $r+1$ from the codeword. We call such a configuration a complete covering. Under projection onto a suitable Cartesian product of cycles, a complete covering corresponds to an optimal local structure in the sense of Špacapan. The existence of a complete covering is necessary but, in general, not sufficient for the existence of a perfect Lee code, as our construction for $(n,r)=(3,2)$ shows. We prove that no complete covering exists whenever $n\ge 8$ and $r^2+\lfloor 25r/34\rfloor-3\ge 2n$. For the corresponding Cartesian products of cycles, this strengthens Špacapan's nonexistence theorem by replacing the linear radius condition $r\ge n$ with a threshold of order $\sqrt{2n}$. We also obtain an improved large-radius nonexistence bound for perfect Lee codes, strengthening the bound of Horak and Kim. Furthermore, we show that, for every $\varepsilon>0$, no complete covering exists when $n\ge 3$, $r$ is sufficiently large, and $r^2+(2/\mathrm{e}-\varepsilon)r\ge 2n$. On the positive side, we use near-optimal ternary constant-weight codes to construct almost complete coverings whose pairwise disjoint shadows cover a $1-o(1)$ proportion of the first outer Lee shell as the dimension tends to infinity, for every fixed radius.
Jiayi Tuo, Cheng Tang, Zihan Wang University of Science and Technology of China, Chenyue Zhou Nanjing University of Aeronautics and Astronautics, Yao Li, Yanbiao Ma Renmin University of China, Chao Wang, Wei Dai Tsinghua University 等 11 位作者
Intelligent education systems often collect exam sheets as in-the-wild photos. These photos often suffer from distortions and noise caused by handwriting and occlusions, collectively referred to as Real-World Degraded Exam Images (RDEI). Structure-preserving reconstruction is key to converting RDEI into structured assets for downstream educational applications. Existing Multimodal Large Language Models (MLLMs) often fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations. To tackle these challenges, we propose MessToClean, a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components. By grounding extraction in pixel-aligned evidence and enforcing post-hoc consistency auditing on recovered structures, MessToClean mitigates unsupported hallucinations and enhances both controllability and structural fidelity in question-level reconstruction. We curate RDEI-Exam from our educational platforms and evaluate across 12 state-of-the-art MLLM backbones. Across these, MessToClean improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types.
尚无 AI 解读，展示原文摘要
cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表