让不同模型用统一语义锚点高效对话，跨架构通信几乎零损失。
Improving Relative Representations with Learned Anchors and Whitened Inner Products
- 用可学习的语义原型替代随机锚点，更贴合模型真实分布。
- 引入几何感知相似度，保留特征重要性且抗线性变换干扰。
- 小模型间零样本通信稳定，跨视觉语言任务表现一致提升。
独立训练的神经网络通常收敛到互不兼容的隐空间表征，构成高度模块化AI系统的根本障碍。相对表征（RR）通过将绝对坐标映射到共享空间（以共同锚点相似性定义）来解决此问题，但传统方法依赖随机采样锚点和余弦相似度，难以捕捉Transformer等现代架构的各向异性几何结构。本文提出一种鲁棒的跨模型通信框架，包含两项改进：学习具有鲁棒性的语义原型作为锚点，并采用几何感知的相似度度量，该度量保留区分性幅度信息且对仿射变换不变。实验表明，该方法在视觉与语言任务中均实现显著性能提升与一致性增强，尤其能实现近乎无损的信息传递及在高度异构架构（如不同规模的小型语言模型）间的稳定零样本通信。
原文摘要 · Abstract (English)
Independently trained neural models typically converge to incompatible latent representations, creating a fundamental barrier to highly modular AI systems. While Relative Representations (RR) address this by mapping absolute coordinates to a shared space defined by similarities to common anchor points, traditional implementations rely on randomly sampled anchors and cosine similarity, which frequently fail to capture the anisotropic geometries of modern architectures like Transformers. In this work, we propose a robust framework for cross-model communication based on two improvements. We learn anchors as robust semantic prototypes and utilize a geometry-aware similarity metric which preserves discriminative magnitude information and is invariant to affine shifts. Our approach demonstrates significant gains in performance and consistency across vision and language tasks. Notably, it enables nearly lossless information transfer and stable zero-shot communication even between highly heterogeneous architectures, such as small language models of varying scales.
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