Parallel Latent Reasoning for Sequential Recommendation

2026-01-06

arXiv.org

AI Summary

This work addresses the challenge of modeling complex user preferences in sequential recommendation, where sparse interaction data and reliance on single behavioral trajectories limit the performance of existing deep reasoning approaches. To overcome this bottleneck, the authors propose a parallel implicit reasoning framework that constructs multiple diverse reasoning paths within a continuous latent space to enhance both representational capacity and generalization. Key innovations include learnable trigger tokens that initiate parallel reasoning streams, global regularization to preserve path diversity, and a hybrid aggregation strategy for adaptive fusion of reasoning flows. Extensive experiments on three real-world datasets demonstrate that the proposed framework significantly outperforms state-of-the-art methods while maintaining real-time inference efficiency. Theoretical analysis further corroborates the effectiveness of parallel reasoning in improving model generalization.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Knowledge Representation and Reasoning: Preferences
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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 \textbf{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.

Problem

• sequential recommendation
• user preferences
• sparse behavioral sequences
• latent reasoning
• reasoning capacity

Innovation

• Parallel Latent Reasoning
• Sequential Recommendation
• Multi-stream Reasoning
• Latent Space Diversity
• Width-level Scaling