# Parallel Latent Reasoning for Sequential Recommendation

arXiv: https://arxiv.org/abs/2601.03153 · HTML: https://mlsanity.com/paper/2601.03153/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2026.

## Built on by (most cited first, 5 of 5)

- [The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook](https://mlsanity.com/paper/2604.02029/) (2026)
- [Verifiable Reasoning for LLM-based Generative Recommendation](https://mlsanity.com/paper/2603.07725/) (2026)
- [DisenReason: Behavior Disentanglement and Latent Reasoning for Shared-Account Sequential Recommendation](https://mlsanity.com/paper/2603.03782/) (2026)
- [LaRec: Unleashing LLM-based Latent Reasoning for Generative Recommendation](https://mlsanity.com/paper/2607.24617/) (2026)
- [Hierarchical Latent Reasoning for LLM-based Recommendation](https://mlsanity.com/paper/2607.27760/) (2026)

## Abstract (arXiv)

Capturing complex user preferences from sparse behavioral sequences remains a fundamental challenge in sequential recommendation. Recent latent reasoning methods have shown promise by extending test-time computation through multi-step reasoning, yet they exclusively rely on depth-level scaling along a single trajectory, suffering from diminishing returns as reasoning depth increases. To address this limitation, we propose Parallel Latent Reasoning (PLR), a novel framework that pioneers width-level computational scaling by exploring multiple diverse reasoning trajectories simultaneously. PLR constructs parallel reasoning streams through learnable trigger tokens in continuous latent space, preserves diversity across streams via global reasoning regularization, and adaptively synthesizes multi-stream outputs through mixture-of-reasoning-streams aggregation. Extensive experiments on three real-world datasets demonstrate that PLR substantially outperforms state-of-the-art baselines while maintaining real-time inference efficiency. Theoretical analysis further validates the effectiveness of parallel reasoning in improving generalization capability. Our work opens new avenues for enhancing reasoning capacity in sequential recommendation beyond existing depth scaling.