Parallel Latent Reasoning for Sequential Recommendation
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.
695dc084c03d6d81e4399e46
Jiakai Tang
claimed_verified
2026-01-07T09:26:09.165Z
65acfb3a14e6582c30b4ce76
https://cdn-avatars.huggingface.co/v1/production/uploads/65acfb3a14e6582c30b4ce76/RhEhePggBtyM0RIIqXQen.jpeg
TangJiakai
TangJiakai5704
user
TangJiakai5704
695dc084c03d6d81e4399e47
Xu Chen
695dc084c03d6d81e4399e48
Wen Chen
695dc084c03d6d81e4399e49
Jian Wu
695dc084c03d6d81e4399e4a
Yuning Jiang
695dc084c03d6d81e4399e4b
Bo Zheng
Parallel Latent Reasoning for Sequential Recommendation
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.
Parallel Latent Reasoning framework improves sequential recommendation by exploring multiple diverse reasoning trajectories simultaneously through learnable trigger tokens and adaptive aggregation.
latent reasoning sequential recommendation multi-step reasoning depth-level scaling width-level computational scaling reasoning trajectories learnable trigger tokens global reasoning regularization mixture-of-reasoning-streams aggregation