# R4Tun: LLM-guided adaptive segmental tunnel lining segmentation in point clouds

arXiv: https://arxiv.org/abs/2609.11360 · HTML: https://mlsanity.com/paper/2609.11360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 51 references that are in the map.
- First posted on arXiv 10 Sep 2026.

## Builds on (most cited first, 20 of 20)

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## Abstract (arXiv)

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 × 3 different LLMs × 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.