Contextual Subspace Manifold Projection for Structural Refinement of Large Language Model Representations

TL;DR

This paper proposes a structured refinement method called Contextual Subspace Manifold Projection that improves internal representations of large language models by enhancing feature distribution stability and interpretability without sacrificing performance.

Contribution

It introduces a novel structured projection technique that refines token embeddings, reducing anisotropy and improving feature separability in transformer models.

Findings

Reduced anisotropy in embeddings

Enhanced feature separability and clustering

Maintained model performance and efficiency

Abstract

Internal representations within deep neural architectures encode high-dimensional abstractions of linguistic structures, yet they often exhibit inefficiencies in feature distribution, limiting expressiveness and adaptability. Contextual Subspace Manifold Projection introduces a structured refinement technique that selectively reconfigures token embeddings through controlled subspace constraints, ensuring more stable and geometrically well-defined feature distributions. Empirical evaluations demonstrated that the structured intervention reduced anisotropy, leading to improved representation compactness while preserving semantic fidelity across transformer layers. Clustering analyses indicated that token embeddings exhibited greater feature separability, reinforcing the hypothesis that structured projection techniques enhance internal representation organization without sacrificing linguistic coherence. Gradient magnitude distributions suggested that the method introduced a smoother optimization trajectory, potentially contributing to more stable parameter updates throughout training. Computational overhead associated with the projection operations remained minimal, ensuring that the refinements did not introduce significant trade-offs in model efficiency or inference speed. Comparisons with standard embedding refinement techniques highlighted that structured manifold constraints provided a direct mechanism for improving representation quality without requiring additional gradient-based optimization. Perplexity evaluations confirmed that the adjustments did not negatively impact sequence coherence, further validating the effectiveness of the proposed approach.