Add CAPruner and QuatRoPE

[fz-zsl]

Dear authors,

We would like to contribute to the repository by adding our work QuatRoPE and CAPruner. Here is a brief summary of the papers:

• [CVPR'26 Main QuatRoPE] This paper proposes QuatRoPE, a linear-scalable 3D positional embedding method that computes pairwise object spatial relations via quaternion rotations in Transformer attention layers, and the Isolated Gated RoPE Extension (IGRE) to minimize its interference with LLMs’ original language RoPE, while also introducing the ASR benchmark for pure 3D spatial reasoning evaluation; extensive experiments show that the proposed methods consistently boost the 3D spatial reasoning performance of LLMs on multiple 3D VL benchmarks and the ASR benchmark, outperforming strong baselines and validating their effectiveness.
• [ACL'26 Main CAPruner] Existing scene graph pruning for 3D vision-language tasks often discards task-critical relations, harming spatial reasoning. To address this issue, we propose CAPruner, which combines semantic relevance and spatial proximity to estimate relation importance under specific task context, trained without expensive relation-level annotations. Experiments show it preserves key spatial relations and significantly boosts LLM performance on 3D-VL tasks.

Thank you for your time and consideration.