# Rethinking Semantic Alignment in LLM-Enhanced Collaborative Filtering: A Spectral Decoupling Approach

arXiv: https://arxiv.org/abs/2608.24363 · HTML: https://mlsanity.com/paper/2608.24363/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Builds on (most cited first, 5 of 5)

- [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [Is ChatGPT a Good Recommender? A Preliminary Study](https://mlsanity.com/paper/2304.10149/) (2023)
- [Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models](https://mlsanity.com/paper/2306.10933/) (2023)
- [ASPIRE: Make Spectral Graph Collaborative Filtering Great Again via Adaptive Filter Learning](https://mlsanity.com/paper/2604.22549/) (2026)

## Abstract (arXiv)

Recent advances in LLM-enhanced recommendation commonly align semantic representations with collaborative embeddings in a shared space, yet how alignment affects LLM-encoded information remains unclear. In this work, we revisit LLM-enhanced recommendation from a spectral perspective and show that collaborative and semantic signals benefit from different spectral parts. While collaborative representations are dominated by smooth low-frequency components due to user-item homophily, semantic embeddings contain useful non-principal singular components. Through component-wise evaluation and training-dynamics analysis, we find that alignment increasingly concentrates learned representations in dominant collaborative and principal semantic subspaces, reducing overlap with non-principal semantic components. Controlled comparisons show that non-principal components provide inconsistent gains under alignment but consistently improve performance through component-level decoupling, while full prediction-level decoupling achieves the best overall performance. These results indicate that alignment fails to effectively exploit complementary non-principal semantic information. Motivated by these findings, we propose UniSpecRec (Unifying Spectral Signals for Recommendation), which applies signal-specific spectral filtering while preserving collaborative and semantic representations in their respective spaces. UniSpecRec combines their predictions without cross-space alignment or additional trainable parameters. Extensive experiments demonstrate its effectiveness, efficiency, and generalizability.