发表机构
University of Cambridge; Durham University（剑桥大学; 杜伦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
R4Tun利用大语言模型结合记忆、状态和知识上下文，对SAM4Tun进行有界参数调整，在Seg2Tunnel点云分割中显著提升mIoU和OA，实现跨LLM的自适应机制。
AI 中文摘要
分段隧道衬砌的自动化检测需要从三维点云中进行自适应分割，然而，当隧道条件变化时，专家调优的流程往往会性能下降。本文提出了R4Tun，一种由大语言模型（LLM）驱动的自适应框架，它扩展了专家设计的流程（SAM4Tun），通过受结构化上下文（记忆（$m$）、状态（$s$）和知识（$k$））指导的有界参数调整来实现。在30个选定的Seg2Tunnel子集（13个常规，17个复杂）上，使用三种LLM进行评估，完整的$m+s+k$设计将平均交并比（mIoU）从0.18提高到0.43至0.48，总体精度（OA）从0.42提高到0.59至0.65，相对于静态的SAM4Tun基线，其中接近参考的常规（交错）子集在多种LLM下达到mIoU 0.784至0.796。在270次运行（30个隧道×3种不同LLM×3种上下文设置）中，LLM表现出相似的参数调整趋势（平均增益的95%置信区间重叠），并一致地调整了一组共享的关键参数。这些结果支持R4Tun作为在测试的SAM4Tun–Seg2Tunnel设置中一种受控、无标签、跨LLM的自适应机制，展示了持续的精度提升；我们将R4Tun定位为一种机制贡献，而非可部署的最终检测系统，其中每个有界参数变化都可通过记录的理由进行审计。
英文摘要
Automated inspection of segmental tunnel linings requires adaptive segmentation from 3D point clouds, yet expert-tuned pipelines often degrade when tunnel conditions vary. This paper presents R4Tun, a large language model (LLM)-driven adaptation framework that extends an expert-designed pipeline (SAM4Tun) with bounded parameter tuning informed by structured context: memory ($m$), state ($s$), and knowledge ($k$). Evaluated on 30 selected Seg2Tunnel subsets (13 regular, 17 complex) across three LLMs, the full $m+s+k$ design raised mean Intersection-over-Union (mIoU) from 0.18 to 0.43--0.48 and overall accuracy (OA) from 0.42 to 0.59--0.65 relative to the static SAM4Tun baseline, with the near-reference regular (staggered) subsets reaching mIoU 0.784--0.796 across LLMs. Across 270 (30 tunnels $\times$ 3 different LLMs $\times$ 3 context settings) runs, the LLMs showed similar parameter-adjustment trends (with overlapping 95\% CIs on mean gains) and consistently adjusted a shared set of critical parameters. These results support R4Tun as a controlled, label-free, cross-LLM adaptation mechanism in the tested SAM4Tun--Seg2Tunnel setting, demonstrating consistent accuracy gains; we position R4Tun as a mechanism contribution rather than a deployable final-inspection system, in which each bounded parameter change is auditable via logged rationales.
